Zhongbao Wei（魏中宝）

Electrochemical impedance spectroscopy (EIS) is a promising technique for the diagnostic of lithium-ion battery (LIB). However, its onboard application is still challenging considering the limitation of in-vehicle environment. Motivated by this, this paper proposes an equivalent sampling-enabled module-level battery impedance measurement method, which shows a strong fidelity for lithium plating diagnostic. A module-level perturbation topology is designed allowing for the generation of high-precision perturbation current with reasonable space occupation. A simplified equivalent sampling method is exploited for the first time with the proposed topology, enabling the EIS sampling with low hardware cost and full compatibility with the commercially-ready battery monitoring chips. With the proposed prototype, an EIS feature-based nondestructive method is further proposed for the in-situ lithium plating diagnostic of LIB …

The control technology of the air supply system is key to the performance and reliability of the fuel cell system (FCS). However, due to the low oxygen pressure in high altitude environment, the power consumption of the air compressor is large, and the output power of FCS is seriously degraded. To effectively increase the output power of the FCS under changeable altitude conditions, this article introduces a real-time power optimization strategy based on the particle swarm optimization (PSO) feedforward and perturbation and observation (P&O) algorithm. Considering the change of total entropy and loss of centrifugal air compressor with altitude, the working characteristics of centrifugal compressors at different altitudes are studied. Aiming at the long optimization time of the traditional P&O method and the misjudgment problem when the external conditions change drastically, the PSO feedforward-based P&O method …

The efficiency of the proton exchange membrane fuel cell (PEMFC) system drops remarkably with the changed ambient pressure and temperature under variable altitudes. To enhance the adaptability of PEMFC, this article proposes a hierarchical optimal control strategy (HOCS) that guarantees the efficient operation of the PEMFC system during changes in altitude. In particular, the sparrow search algorithm (SSA) is exploited to optimize the air supply strategy under different operating conditions. To support the HOCS, a variable altitude model of PEMFC is established, which integrates the environmental impacts on components. A sliding mode controller (SMC) is employed to achieve precise and fast control of the air supply system across various situations. Comparative results validate the superiority of the proposed method in terms of the efficiency of the air compressor and the net power output. In a typical driving …

Thermal management is crucial for the mass transport and water balance of proton exchange membrane fuel cell (PEMFC). Inspired by this, a hierarchical thermal management strategy (TMS) is proposed for fuel cell hybrid electric vehicle (FCHEV). In particular, the transient TMS demands are determined by a well-designed energy management strategy (EMS) taking health and thermal safety into consideration. Furthermore, along with the high-efficiency heat dissipation, a hydrogen consumption minimization strategy (HCMS) is proposed via optimal temperature tracking, which investigates the desirable trace offline. These parallel strategies are incorporated through the deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) algorithm. With the help of its self-adaptive ability, DDPG deals with the complicated TRS problem in multidimensional coupled cooling system, through a …

The escalating level of vehicle electrification and intelligence makes higher requirements for the energy management strategy (EMS) of fuel cell vehicles. Environmental and road conditions can significantly influence the power demand of the load, thereby affecting the lifespan and efficiency of vehicular energy systems. To ensure that the vehicle is always in optimal working condition, this study innovatively proposes a health-conscious EMS framework based on twin delayed deep deterministic policy gradient (TD3) algorithm for fuel cell hybrid electric bus (FCHEB). First, the environment and look-ahead road information obtained through vehicle sensors, GPS and Geographic Information System is used to establish the energy management problem formulation. Secondly, a TD3-based data-driven EMS is developed with the objective of optimizing hydrogen fuel economy, fuel cell durability and battery thermal health …

A two-step approach for state-of-health (SOH) estimation of a lithium-ion (Li-ion) battery is developed. In the first step, state-of-charge (SOC) estimation is performed by a constrained extended Kalman filter (EKF) based on the so-called equivalent-hydraulic model. The latter model allows to characterize the internal battery state and main physical parameters while being suitable for on-line computation. The internal battery states are further exploited in the second step of the approach to obtain parameter-based SOH indicators that characterize the long term evolution of the diffusion and charge transfer processes associated to aging. Capacity and power fade indicators are determined by using notably an instrumental variable method in order to obtain unbiased parameter estimates in the presence of heteroscedastic colored noise. The methodology is validated on both simulation and experimental data for a lithium …

The accurate aging evaluation is one of the important factors to find the optimal operation mode of the regional wind-hydrogen integrated energy system (RWHIES). In this article, a wind turbine (WT) power prediction method is proposed based on wind physical information and topography characteristics (WPITC). By making full use of the modified numerical weather data, the wind power can be predicted by combining wind conditions and terrain information. Under the guidance of the WT power prediction model, the aging state is evaluated and a RWHIES model is proposed, which allows striving to maximizing the production capacity with the minimal cost. An improved multi-objective dragonfly algorithm (IMODA) is proposed to address the optimization problem. Finally, three different cases are conducted to analyze the relationship between cost and capacity, which indicates that the proposed approach can improve …

Proton exchange membrane fuel cell (PEMFC) is a highly promising renewable energy conversion technology. However, durability issues have hindered their large-scale commercialization process. Performance degradation prediction is an essential component of PEMFC prognostics and health management and is critical for extending the service life of fuel cell. Given that, this paper proposes a novel data-driven prediction model that fuses multi-head self-attention (MHSA) mechanism and bi-directional long short-term memory (BiLSTM). This model can effectively capture different types of dependencies from large-scale high-dimensional data and achieve global information modeling. Specifically, the preprocessed historical voltage data and PEMFC system operating parameters are fed into the proposed prediction model. Where BiLSTM understands the contextual information and temporal dependencies in …

Cold-start is a challenging issue for proton exchange membrane (PEM) fuel cell engine due to the high-probability of freeze-out. Apart from the clog inside the channel, the freeze among components is equally critical especially in the system-level engine. Motivated by this, a precheck and fault diagnosis method is proposed, for the first time, to ensure the functional reliability of PEM fuel cell engine in cold climate. Based on a 40-kW stack, the proposed method includes three stages of precheck, preheat and self start-up, covering eight susceptible components within both air and hydrogen subsystems. Through the speed-and pressure-related detections, the alarm signals alert when not passing the threshold judgements. Experimental results validate the identifiability among multiple component-level faults and the fidelity of diagnostic in application. Moreover, during several months of investigations, the significance of …

Advanced energy management strategy (EMS) can ensure healthy, stable, and efficient operation of the on-board energy systems. Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) are two powerful control methods that have been extensively researched in the field of vehicle energy management. However, there are some problems with both approaches. On the one hand, MPC is difficult to cope with the complex systems and the excessive computational load caused by the non-linear solving over long prediction horizon, on the other hand, DRL lacks adaptability to different driving conditions and is poorly interpretable. Therefore, this paper innovatively proposes a learning-based model predictive (L-MPC) EMS for fuel cell hybrid electric bus (FCHEB) with health-aware control. This method effectively merges the advantages of control theory and machine learning. Specifically, firstly, the …

The predictive energy management strategy (PEMS) offers potential advantages in enhancing the driving economy of electrified vehicles using vehicle speed prediction. However, realizing accurate predictions in practical contexts remains a challenge. Departing from conventional PEMS that rely on historical speed or static traffic data, we introduce a real-time traffic-aware PEMS for improved performance. To better understand the interplay between the host vehicle and its surrounding traffic, we use a Transformer network as the predictor that employs the speeds and relative distances of the surrounding six vehicles to forecast future speed sequences for the host vehicle. To augment this data-driven approach, we develop a dual-predictor strategy based on the deep ensemble technique. This strategy measures the Transformer’s output uncertainty to gauge prediction reliability and introduce an automated threshold …

Fast charging of lithium-ion batteries (LIBs) is critical for the further popularity of electric vehicles (EVs). However, overlooking physical limits of LIBs may cause quick health degradation or even catastrophic safety issues. Motivated by this, this paper proposes a hybrid multi-physics-constrained charging strategy for LIBs combining an active set method (ASM)-based model predictive control (MPC) and a rule-based method. A general form of the optimal charging problem is constructed as a constrained quadratic program (QP) to balance the charging rapidity, thermal safety, and battery degradation. Enabled by this formulation, the cost-efficient ASM is proposed to solve the optimization problem, which virtually gives a safety-and health-aware fast charging strategy. Comparative results suggest that the proposed strategy outperforms the traditional MPC solutions remarkably in terms of the computational tractability …

Battery packs inconsistency is inevitable in the use of series-connected cells. For packs equalization in confined environments, direct electrical contact is unpermitted. To address this issue, a novel double-layer equalization topology is proposed. It combines a wireless equalizer for inter-group equalization with a wired active equalizer for intra-group equalization. Firstly, current and voltage of intra-group equalization and inter-group equalization are introduced. Then, the Thevenin equivalent model is developed and parameter identification of the model is performed using the least squares method with a forgetting factor. In addition, the intra-group equalization parameters and inter-group equalization parameters are designed to improve the equalization performance. For different currents corresponding to different cell resistances, the duty cycle of the buck circuit is analyzed to achieve a constant equalization current …

Accurate state of health (SOH) estimation is crucial for Lithium-ion battery in electric vehicles (EVs). This work proposes a battery SOH estimation method based on voltage reconstruction and fusion models. Firstly, a voltage curve reconstruction method based on importance sampling is proposed to solve the V–Q curve. Then, feature factors related to SOH are extracted and their correlation with SOH is analyzed. Furthermore, a SOH estimation fusion model is established based on improved Support Vector Regression (SVR) and Convolutional Neural Network (CNN). Finally, the accuracy of the proposed algorithm is verified in 20% and 30% small sample scenarios, respectively. The experimental results show that the numerical evaluation indicators of the proposed method are superior to Gauss Process Regression (GPR), CNN, whale optimization algorithm-SVR (WOA-SVR) and Long Short Term Memory (LSTM …

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today's high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online …

Complex environments and variable working conditions lead to irreversible attenuation of battery pack capacity in electric vehicles (EVs). Online capacity estimation is of great significance for battery pack management and maintenance. This work proposes a state-of-health (SOH) attenuation model considering driving mileage and seasonal temperature for battery health estimation. Firstly, a variable forgetting factor recursive least square (VFFRLS) algorithm is proposed for battery model parameter identification. It adaptively adjusts the forgetting factor according to current fluctuations. Then, an extended Kalman-particle filter (EPF) algorithm is proposed for online capacity estimation. In addition, a battery pack SOH attenuation model is constructed considering seasonal temperature and driving mileage. Finally, the performance of the proposed model and algorithm is verified with nine months of actual vehicle data …

Accurate battery states estimation is critical to the safe and stable operation of Li-ion batteries, and it is one of the fundamental functions of a battery management system (BMS). This paper proposes a novel deep learning framework called multi-timescale dual feature-based state estimation network (MFN) using extremely limited charging data to estimate three critical states simultaneously end-to-end: maximum capacity, the capacity at the beginning of charging, and the capacity at the end of charging. The core of the framework is the dual feature extraction module (DFM) and the multi-timescale information extraction module (MTM). First, DFM extracts independent and coupling features of external signals (voltage, current, temperature) in the time dimension, respectively, and then merges them. Based on this, MTM explores the health features of the external signals on different time scales and merges them. Finally …

A battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence, and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multilevel perspective. The widely explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multidimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for …

The grouping and large-scale of battery energy storage systems lead to the problem of inconsistency. Practical consistency evaluation is significant for the management, equalization and maintenance of the battery system. Various evaluation methods have been developed over the past decades to better assess battery pack consistency. In these research efforts, the accuracy of the assessment results is often of paramount importance. In this work, a battery pack consistency evaluation approach is proposed based on multi-feature information fusion. Ohmic resistance, polarization resistance and open circuit voltage are identified as feature parameters from electric vehicle operation data. An adaptive forgetting factor recursive least squares (AFFRLS) algorithm is developed using fuzzy logic to modify the forgetting factor for parameter identification. Grey correlation analysis is applied to calculate the dispersion of …

This paper presents a model of the fuel cell (FC) cathode air supply system that considers altitude variation. Additionally, an algorithm that optimizes power by using perturbation and observation (P&O) is proposed to ensure that the FC outputs the maximum net power under high altitude conditions. To analyze the sensitivity of the algorithm parameters, power optimization results with varying iteration cycle times, voltage perturbation steps, and power change thresholds are compared to optimize the algorithm parameters. The proposed algorithm's power optimization performance and altitude adaptation capability are demonstrated through a comparative analysis with the offline optimization method at two different altitudes. The simulation results show that the proposed P&O algorithm has the same optimization capability as the offline optimization algorithm and can adapt to the altitude change.

The porous electrode of vanadium redox flow batteries (VRBs) is subject to deformation due to mechanical stress during stack assembling. The forces compress the electrode fiber into the flow channel and thus alter the electrode porosity ratio. Due to the complex mechanisms, the effects of resulting electrode morphological changes on VRB performance were usually ignored in existing studies. This paper proposes a three-dimensional VRB model considering the uneven electrode deformation to investigate the cell performance under different electrode compression ratios with three flow-field designs. Compression ratio (CR) and the intrusive part of the electrode are obtained under various mechanical stress by adjusting gasket thickness in the experiment. The proposed electrochemical model is established based on the comprehensive description of conservation laws and analyzed using the COMSOL platform …

Fast charging of the lithium-ion battery (LIB) is an enabling technology for the popularity of electric vehicles. However, high-rate charging regardless of the physical limits can induce irreversible degradation or even hazardous safety issues to the LIB system. Motivated by this, this paper proposes a machine learning-based fast charging strategy with multi-physical awareness within a battery-to-cloud framework. In particular, a reduced-order electrochemical-thermal model is built in the cloud to perceive the microscopic states of LIB, leveraging which the soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm is exploited for the first time to train a fast charging strategy. Hardware-in-Loop tests and experiments with practical LIBs are carried out for validation. Results suggest that the battery-to-cloud architecture can mitigate the risk of a heavy computing burden in the real-time controller. The proposed …

The air-conditioning system (ACS), as a high-power component on the fuel cell hybrid electric bus (FCHEB), has a significant impact on the whole vehicle economy while maintaining comfortable temperature. Achieving cabin comfort in a way that reduces the whole vehicle operating cost is a great challenge. This task requires excellent coordination between the ACS and the on-board energy sources. Given that, this paper proposes an energy management strategy (EMS) with on-board energy sources health awareness considering air-conditioning control. Specifically, firstly, cabin comfort is combined with fuel cell/battery durability control to minimize total vehicle operating cost while satisfying cabin comfort. Secondly, the state-of-the-art twin delayed deep deterministic policy gradient algorithm is adopted to improve the training efficiency and optimization capability of the EMS to achieve the best power allocation …

The safety, life expectancy and operating cost of fuel cell hybrid electric vehicle (FCHEV) are highly dependent on the power allocation among the onboard power sources. Motivated by this, this paper proposes a machine learning-based multi-physical-constrained energy management strategy to improve the driving economy, thermal safety, and durability of FCHEV. In particular, the fully-continues deep deterministic policy gradient (DDPG) algorithm is exploited to optimize the power distribution of FCHEV in a real-time fashion. Within the proposed framework, the thermal and aging behaviors of the hybrid power sources are scrutinized and optimized, for the first time, to enhance the safety and life performance of FCHEV. The proposed strategy is tested under typical road missions for validation. The unexpected temperature build-up of lithium-ion battery (LIB) and the degradation of hybrid system can be well …

Guest Editorial: Smart operation and control of energy system for low-carbon applications guest Swinburne Research Bank Swinburne Research Bank Help Swinburne Research Bank > Guest Editorial: Smart operation an… Link to this page: http://hdl.handle.net/1959.3/471828 Guest Editorial: Smart operation and control of energy system for low-carbon applications Authors Liu, Kailong ; Wang, Yujie; Shen, Weixiang; Wei, Zhongbao; Zhao, Chunhui; Fang, Huazhen (Search for a current Swinburne researcher) Publisher's website https://doi.org/10.1016/j.conengprac.2023.105512 Publication year 2023 Publication type Journal article Source Control Engineering Practice, Vol. 135 (Jun 2023), 105512 ISSN 0967-0661 Publisher Elsevier BV Copyright Copyright © 2023 Details Collection: Swinburne Research Bank Version: 1 (show all) Status: Live Views: 73 Home Log in Search Browse by Publication Year Browse by …

Battery-related faults have become the most intractable problem hindering the further prosperity of fields like electric vehicle and grid energy storage. This paper is devoted to constructing a novel diagnostic framework for the faults in series battery packs, resorting to signal imaging and convolutional neural network (CNN) techniques. First, the voltage synchronicity between adjacent cells in a pack is quantified using the recursive correlation coefficient which can percept system anomalies sensitively. Then, reliant on the Gramian Angular Field (GAF) and Markov Transition Field (MTF) transformations, the correlation coefficient series is converted into pseudo images, the textures of which are full of informative details regarding system state. Finally, CNN models are employed to analyze the images for fault symptoms, thereby detecting fault occurrence, inferring fault type and evaluating fault grade. To obtain realistic …

State of health (SOH) estimation is essential to the health diagnostic of lithium-ion battery. The data-driven approach with charging feature extraction is promising for online SOH estimation and has been widely explored over years. However, their deployment can be barriered by the lack of complete charging data in real-world applications. Motivated by this, this paper proposes an artificial intelligence-based SOH estimator using the transient phase between constant current (CC) and constant voltage (CV) charging, which is easily obtained in real-world charging scenarios. Specifically, a convolutional neural network (CNN) model is proposed to explain the relationship between the charging data and the SOH. Following this endeavor, the transfer learning is exploited for model mitigation and SOH estimation on different battery types, relying on much reduced amount of data for efficient CNN model re-training. The …

Lithium-ion batteries commonly used in electric vehicles are an indispensable part of the development process of decarbonization, electrification, and intelligence in transportation. From intelligent designing, manufacturing to controlling, an intelligent battery management system plays a crucial role in the long life, high efficiency, and safe operation of lithium-ion batteries. As a first-principle model, the electrochemical parameters of the electrochemical model have physical meanings and reflect the internal state of the lithium-ion batteries. The application of electrochemical models in an advanced intelligent battery management system is a future trend that promises to mitigate battery life degradation and prevent safety incidents. The reduced-order electrochemical model is expected to alleviate the requirements of advanced battery management systems for high accuracy and fast computing of lithium-ion battery …

Vanadium redox flow battery (VRB) is one of the most promising batteries at present. In order to enhance the stability and anti-interference ability of VRB in microgrids, a novel learning-based data-driven H∞ control approach is proposed for the VRB, which uses a new integral reinforcement learning algorithm to produce excellent steady-state and dynamic responses only by measurements. Compared to the model-based control methods, it is insensitive to model parameter variations. Furthermore, compared to most of the existing artificial intelligent control approaches that require large amounts of experimental data for offline neural network (NN) training, the proposed control strategy contributes to eliminate the offline training process and therefore, does not need the costly and tedious training data acquisition process. More importantly, the proposed control offers guaranteed closed-loop control stability, which …

Hybrid Electric Vehicles have great potential to be discovered in terms of energy saving and emission reduction, and ecological driving provides theoretical guidance for giving full play to their advantages in real traffic scenarios. In order to implement ecological driving strategy with the lowest cost throughout life cycle in car-following scenario, the safety and comfort, fuel economy and battery health need to be considered, which is a complex nonlinear and multi-objective coupled optimization task. Therefore, a novel multi-agent deep deterministic policy gradient (MADDPG) based framework with two heterogeneous agents to optimize adaptive cruise control and energy management strategy respectively is proposed, thereby decoupling optimization objectives of different domains. Due to the asynchronous of multi agents, different learning rate schedules are analyzed to coordinate learning process to optimize training …

Accurate remaining useful life (RUL) prediction is of great importance to the battery management second-life utilization. This paper proposes a novel hybrid data-driven RUL prediction method based on Gaussian process regression (GPR) and long-short term memory neural network (LSTM). An initial prediction of RUL through LSTM is employed as the mean function of GPR instead of simply assuming it to be zero or a linear form. The aging data of four batteries from NASA data repository is used for model verification and comparison. The results show that the proposed LSTM-GPR approach has higher prediction accuracy than the traditional LSTM and GPR approaches with less training data.

Fast charging of lithium-ion battery (LIB) is critical for the further popularity of electric vehicles. However, the partial pursuit of high-power charging can violate the physical limits of LIB, and further cause unexpected side effects or even catastrophic safety issues. Motivated by this, this paper proposes a multi-state-constrained fast charging strategy for LIB, enabled by a novel deep reinforcement learning (DRL) technique. In particular, the SAC-Lagrange DRL is developed, for the first time, to train the fast-charging strategy assisted by an electro-thermal model for unmeasurable state perceiving. The proposed strategy is further performed within a cyber-physical system-based management framework, where the complicated training is carried out in the cloud, while the trained low-complexity policy is executed in the onboard controller to mitigate the risk of high computing burden. Hardware-in-Loop tests and practical …

Data-driven intelligent energy management strategy (EMS) helps to further improve the performance and efficiency of fuel cell hybrid electric bus (FCHEB). However, most deep reinforcement learning (DRL) algorithms suffer from disadvantages such as overestimation and poor training stability, which limit the optimization effectiveness of the strategy. In addition, DRL-based EMSs tend to achieve good control only for the set optimization objectives and cannot be generalized to optimization objectives beyond the reward function. To solve the above problems, a novel health-aware DRL energy management for FCHEB is proposed in this paper. Firstly, based on the actual collected city bus driving cycles, a large amount of high-quality learning experience containing health-aware information is obtained through an advanced model predictive control strategy. Secondly, the state-of-the-art Twin Delayed Deep …

With the development of fuel cells, multi-stack fuel cell system (MFCS) for high power application has shown tremendous development potential owing to their obvious advantages including high efficiency, durability, reliability, and pollution-free. Accordingly, the state-of-the-art of MFCS is summarized and analyzed to advance its research. Firstly, the MFCS applications are presented in high-power scenarios, especially in transportation applications. Then, to further investigate the MFCS, MFCS including hydrogen and air subsystem, thermal and water subsystem, multi-stack architecture, and prognostics and health monitoring are reviewed. It is noted that prognostics and health monitoring are investigated rarely in MFCS compared with previous research. In addition, the efficiency and durability of MFCS are not only related to the application field and design principle but also the energy management strategy (EMS …

This paper proposes a novel learning-based linear quadratic regulator (LQR) to overcome the long-lasting problem of model dependency in the existing vanadium redox flow battery (VRB) control approaches. Compared to the conventional model-dependent control methods, such as PI control and model predictive control (MPC), the proposed method automatically updates the optimal control policy through the online learning mechanism without any knowledge of the VRB system dynamics. The ability of the proposed method to handle uncertainties is verified by simulations under various scenarios.

The potential electrochemical and thermos-dynamic processes of fuel cell (FC) systems lead to a slow response, which is often combined with direct current-direct current (DC-DC) boost converters (DBC) to ensure the stability of the output voltage when the load changes. This paper aims to coordinate the dynamic characteristics between the FC system and the DBC, a DBC control considering the optimal gas response characteristics of the FC system is proposed. The variable load experiment of the FC and DBC system is carried out to verify the validity of the gas-electric coupling model, and the dynamic process of the stack voltage is obtained under different loads and operating parameters by the model. The voltage undershoots and net power are analyzed to determine the optimal operating parameters under the different step currents. Combined with the pre-supply gas scheme, the optimal response law of the FC …

Recently, with the deterioration of global climate and the shortage of traditional fossil energy, electric vehicles have been got more attention at present. However, due to the lack of charging piles, the range anxiety regarding electric vehicles become an important pain point, which affects the development of electric vehicles. Based on this, this paper proposes one DC-DC converter using virtual power based model predictive control (VP-MPC), which can provide the energy mutual aid function between two electric vehicles. Firstly, the bidirectional full bridge series resonant DC-DC converter (BDB-SRC) is applied to satisfy the demand high voltage gain and high power density. Meanwhile, this structure also has portable advantages. Furthermore, the different types of electric vehicles have variable parameters, such as load parameters during charging. To solve this problem, VP-MPC is proposed for DC-DC converter …

The charging of lithium-ion batteries (LIBs) is remarkably slowed down at sub-zero environment due to the declined activity of electrochemical processes. This has been a major barrier of the further popularity of electric vehicles especially at cold regions. Motivated by this, this paper proposes a heating-cooperative charging strategy for lithium-ion batteries at low-temperature scenarios. An experimental-validated electro-thermal model is built to explain the coupling mechanism of heating and charging at low-temperatures conditions. On this premise, a multi-objective optimization problem is formulated regarding charging rapidity and energy loss. The dynamic programming (DP)-based solution virtually gives rise to a heating-cooperative charging strategy. Results show that the proposed strategy can adaptively regulate the heating/charging switching mode and the associated current magnitude to ensure the high …

Maximizing the utilization of lithium-ion battery capacity is an important means to alleviate the range anxiety of electric vehicles. Battery pack inconsistency is the main limiting factor for improving battery pack capacity utilization, and poses major safety hazards to energy storage systems. To solve this problem, a maximum capacity utilization scheme based on a path planning algorithm is proposed. Specifically, the reconfigurable topology proposed is highly flexible and fault-tolerant, enabling battery pack consistency through alternating cell discharge and reducing the increased risk of short circuits due to relay error. The Dijkstra algorithm is used to find the optimal energy path, which can effectively remove faulty cells and find the current path with the best consistency of the battery pack and the lowest relay loss. Finally, the effectiveness of the scheme is verified by hardware-in-the-loop experiments, and the …

锂离子电池在快速充电过程中极易触发内部过热, 并加速寿命衰退, 因此在确保快速充电的同时主动约束锂离子电池重要中间物理状态具有重要意义. 因此, 提出一种基于多物理过程变量约束的电池快速充电方法. 建立电-热-老化综合模型, 并在典型充电场景下进行电热模拟精度验证; 在此基础上, 设计基于模型的荷电状态与内部温度估计方法, 兼顾充电速度, 温度约束与寿命衰退抑制, 设计基于模型预测控制的快速充电策略. 试验验证结果表明, 所提出的充电策略能主动限制电池内部温度始终低于预定阈值, 在相似的充电速度前提下, 所提出的充电策略相比优选的恒流恒压充电法具有更低的寿命衰减速率, 两者 200 次快充-放电循环的容量衰减分别为 2.12% 和 4.88%. 所提出的快速充电策略基于模型预测控制方法实现了电池内部状态的有效约束, 综合提升了锂离子电池充电过程的快速性, 安全性和耐久性.

State of Health (SOH) is pivotal to the health diagnostics of lithium-ion battery (LIB). However, the SOH estimation of large-format batteries commonly-used in energy storage power stations, especially at uncertain environmental conditions and aging modes, has been less explored. This paper proposes a SOH estimation method applicable to large-format batteries, by combining the multi-feature extraction and artificial intelligence approach. Especially, different sets of health indicators (HIs) exhibiting the morphological incremental capacity (IC) characteristic are extracted from the charging curve of LIBs. Following this exertion, artificial neural network-based HI fusion is proposed to estimate the SOH accurately. The proposed method is validated with long-term degradation experiments on the LFP cells. Results suggest that the proposed method manifests itself with high estimation accuracy, high reliability and …

In the field of future transportation, hydrogen fuel cell hybrid electric vehicles (FCHEVs) are regarded as the most potential renewable energy vehicles, but improper use of the Lithium-ion battery (LIB) system and the proton exchange membrane fuel cell system (PEMFCS), during vehicle operation, can significantly increase the maintenance costs of the vehicle. In order to fully utilize the economic potential of FCHEVs, a novel cost-minimization energy management strategy (EMS) is proposed in this paper. Specifically, for the first time, thermal safety and degradation awareness for on-board LIB system are integrated into the optimization framework with fuel cell aging suppression to trade-off energy sources durability and hydrogen mass consumption. In addition, an enhanced online self-learning stochastic Markov predictor is proposed in the speed prediction stage to improve the prediction accuracy for future driving …

Accurate modeling and estimation of the internal temperature distribution is of great significance to the thermal management of lithium-ion batteries (LIBs). Existing control-oriented models generally assume a uniform temperature distribution along the axial direction of LIB. The ignorance of thermal inhomogeneity however challenges the refined thermal monitoring of LIB. To remedy this deficiency, this paper proposes for the first time a novel distributed thermal model for LIB, by hybridizing the thermal transfer law and the artificial intelligence approach. Relying on the spatial temperatures of LIB obtained by a distributed sensing technique, a lumped-parameter thermal network model is developed to capture the general thermal behavior of LIB. In a cascaded manner, the long short-term memory (LSTM) neural network is proposed to compensate for the thermal inhomogeneities that cannot be explained. The proposed …

Deep reinforcement learning-based energy management strategy (EMS) is essential for fuel cell hybrid electric vehicles to reduce hydrogen consumption, improve health performance and maintain charge. This is a complex nonlinear constrained optimization problem. In order to solve the problem of high bias caused by the inconsistency between the infinite support of stochastic policy and the bounded physics constraints of application scenarios, this paper proposes the Beta policy to improve standard soft actor critic (SAC) algorithm. This work takes hydrogen consumption, health degradation of both fuel cell system and power battery, and charge margin into consideration to design an EMS based on the improved SAC algorithm. Specifically, an appropriate tradeoff between money cost during driving and charge margin is firstly determined. Then, optimization performance differences between the Beta policy and …

Battery failures have become the most intractable obstacles undermining the market confidence in applications like electric vehicle and power grid energy storage. This article aims to fashion a generic diagnosis scheme against the faults in large-scale battery systems. First, a voltmeter array-based anomaly perception mechanism against the electrical behaviors of battery packs is developed. Then, system information on spatial arrangement and temporal dynamics is organically fused and drawn as a kind of pseudo 2-D images (P2Is). Afterward, by analyzing the resultant P2Is with the 2-D variational mode decomposition (2-D-VMD) and gray level co-occurrence matrix (GLCM), some statistical quantities concerning multi-scale texture features, extracted and refined by the principal component analysis (PCA), are found to have strong indicative associations with battery fault type and fault grade. Finally, relying on the …

Editorial for Special Issue called Interdisciplinary approaches and innovations in transport-grid interfacing systems — Queen's University Belfast Skip to main navigation Skip to search Skip to main content Queen's University Belfast Home Queen's University Belfast Logo Help & FAQ Home Profiles Organisations Research output Projects Impact Datasets Activities Prizes Press/Media Student theses Facilities Search by expertise, name or affiliation Editorial for Special Issue called Interdisciplinary approaches and innovations in transport-grid interfacing systems Aoife M. Foley * , Kailong Liu, Clive Roberts, Zhongbao Wei, John Paul Clarke, Sidun Fang * Corresponding author for this work School of Mechanical and Aerospace Engineering Queen's University Belfast Research output: Contribution to journal › Editorial Overview Original language English Article number 123393 Journal Energy Volume 246 Early online …

Low activity of Lithium-ion batteries (LIBs) at low temperatures is an essential barrier for the future popularity of electric vehicles. Low-temperature charging of LIB consumes much longer time than that at normal temperature, and even induces irreversible damage to the LIB. Motivated by this, an electrical-thermal model-based synergized charging method is proposed in this paper. In particular, a coupled electrical-thermal model is built and validated for accurately reproducing the battery dynamics within a wide temperature range. On this premise, the dynamic programming (DP) algorithm is employed to synergize the heating and charging of LIB optimally regarding the fast charging at low temperatures. Results suggest that the charging and heating processes can promote each other to achieve a fast refueling of the battery.

This study aims to solve the key issue for electric buses on how to improve the accuracy and reliability of battery fault diagnosis with the emerging intelligence technology on battery management. The battery fault diagnosis method needs to fuse both the physic and cyber systems, reflecting the real-time dynamic battery system in the physical-layer, as well as taking full advantage of battery historical data and outside information in the cyber-layer. Given that, this paper proposes a fault diagnosis method based on the physical-layer model updated by cyber-layer deep learning algorithms. Long short-term memory network (LSTMN) and back propagation neural network (BPNN) are used in the proposed framework to anticipate present aging conditions and update physical-layer model parameters. As a result, the battery model may self-update in response to the environment and aging circumstances, increasing the …

Heat generation rate is a significant safety indicator for lithium-ion battery thermal management which need to be monitored in real time. A distributed fiber optic sensor embedded smart battery configuration is proposed in this paper to acquire the multi-point temperature measurements inside and outside the battery. Hence, a machine learning-based heat generation rate estimation and diagnosis method for Lithium-ion batteries is proposed in this paper to estimate the heat generation rate leveraging the multi-point temperature measurements and detect the abnormal heat generation in real time. The proposed heat generation rate estimation method and smart configuration are experimentally validated to be effective and accurate, and the proposed abnormal heat generation diagnosis method is verified by simulation.

The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integration of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from …

State of health (SOH) is critical to the management of lithium-ion batteries (LIBs) due to its deep insight into health diagnostic and protection. However, the lack of complete charging data is common in practice, which poses a challenge for the charging-based SOH estimators. This article proposes a multistage SOH estimation method with a broad scope of applications, including the unfavorable but practical scenarios of heavily partial charging. In particular, different sets of health indicators (HIs), covering both the morphological incremental capacity features and the voltage entropy information, are extracted from the partial constant-current charging data with different initial charging voltages to characterize the aging status. Following this endeavor, artificial neural network based HI fusion is proposed to estimate the SOH of LIB precisely in real time. The proposed method is evaluated with long-term aging experiments …

摘 要 锂离子电池作为新一代可充电电源 ꎬ 具有能量密度大, 安全性能高等优点 ꎬ 显示出了广阔的市场前景 ꎮ 但锂离子电池在运行过程中会发生各种内, 外部故障 ꎬ 所以锂离子电池安全问题一直备受关注 ꎮ 锂离子电池的传感器正常运行是保证对电池系统实时监测的关键 ꎬ 但是传感器故障微小且不易察觉 ꎬ 并且故障具有关联性, 并发性的特征 ꎬ 可能引起多故障的发生 ꎬ 进而触发热失控的风险 ꎮ 所以如何保证传感器精确, 快速地进行锂离子电池故障检测与诊断是确保安全稳定运行的关键 ꎮ 首先从锂离子电池结构出发总结了锂离子故障的类型及成因 ꎬ 并详细分析了传感器故障和多故障产生的机理 ꎮ 然后 ꎬ 对锂离子电池从单体电池到电池包所涉及的传感器故障和多故障诊断策略进行全面的阐述 ꎬ 并且分析了可能成为未来发展趋势的传感器多故障协同诊断策略和电池新模式下的故障诊断方式 (如气体检测等) ꎮ 最后 ꎬ 以全文锂离子电池的传感器多故障研究的重难点 ꎬ 提出了传感器多故障诊断未来可能的研究方向 ꎮ

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health …

Solid oxide fuel cell (SOFC) becomes increasingly popular in dc microgrid applications. Controlling SOFC is challenging because the dynamics of SOFC are difficult to maintain under complex internal reactions and changing operating conditions. To solve these problems, this article proposes a novel adaptive model predictive control (AMPC) algorithm, which adopts a parameter estimator to update the system parameters online. The robustness of the proposed AMPC is investigated under different microgrid scenarios, including the overload, underload, short-circuit, and significant dc bus voltage drop situations. The proposed AMPC algorithm produces superior SOFC control performance over the conventional model predictive control (MPC), Wiener MPC, and PI and fuzzy PI controllers. Furthermore, it significantly reduces the system model dependence that is shared by nearly all the model-based SOFC control …

The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants. Besides, the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane. For maintaining the relative stability of anode pressure, this study proposes a decentralized model predictive controller (DMPC) to control the anodic supply system composed of a feeding and returning ejector assembly. Considering the important influence of load current on the system, the piecewise linearization approach and state space with current-induced disturbance compensation are comparatively analyzed. Then, an innovative switching strategy is proposed to prevent frequent switching of the sub-model-based controllers and to ensure the most appropriate predictive model is applied. Finally, simulation results demonstrate the better stability and robustness of the proposed control schemes …

A model-based battery power capability prediction method is reported to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints such as lithium plating and thermal runaway, can be readily taken into account. The online prediction of maximum power is accomplished by formulating and successively solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, an accurate and computationally efficient scheme based on nonlinear model predictive control is designed.

The accelerating market penetration of electric vehicles (EVs) raises important questions for both industry and academia: how to deal with potentially millions of retired batteries (RBs) from EVs and how to extend the potential value of these batteries after they are retired. It is therefore critical to deepen our understanding of the comprehensive performance of RBs in appropriate applications, such as stationary energy storage with less demanding on power capacity. The following literature review evaluates the opportunity of the emerging RB market in detail. Meanwhile, various specifically technical issues and solutions for battery reuse are compiled, including aging knee, life predicting, and inconsistency controlling. Furthermore, the risks and benefits of battery reuse are highlighted referring to transportation electrification and entire industrial chain. Also, current policy shortcomings and uncertainties are outlined, and …

State of health estimation of lithium-ion battery is the basis of lithium-ion battery life assessment and health management. A practical multi-stage state of health estimation method was proposed to deal with different charging stages, including the scene of serious lack of charging data. According to the voltage, the constant current-constant voltage charging process was divided into three stages and their target state of health estimation methods were proposed respectively. Especially for the constant current-constant voltage transition stage, being a lack of constant current data and constant voltage data heavily, the relationship between raw voltage/current data and battery state of health was directly established taking the strong data mining capability of convolutional neural network. The proposed method was evaluated by long-term aging experiments on lithium-ion battery. The results show that this method possesses the advantages of high estimation accuracy, strong ability to deal with serious data loss, and strong robustness to battery inconsistency.

Available capacity of lithium-ion batteries is directly linked to the mileage of the electric vehicle. The cell imbalance is recognized as a significant concern hindering the full utilization of pack capacity. Following the emerging concept of battery reconfiguration, this article proposes a dual-scale hierarchical equalization scheme enabled by a novel four-switch reconfigurable topology. In particular, a four-switch reconfigurable topology is proposed, for the first time, which enjoys the benefits of flexible reconfigurability, moderate complexity, and high fault tolerance. Relying on the new topology, a hierarchical equalization strategy is proposed incorporating the intramodule time-sharing intervention and inter-module splitting recombination. This endeavor contributes to achieving all-cell flexibility, which further promises the all-cell equalization and maximum capacity utilization. Hardware-in-the-loop results validate that the …

Developing new energy vehicles has been a worldwide consensus, and developing new energy vehicles characterized by pure electric drive has been China's national strategy. After more than 20 years of high-quality development of China's electric vehicles (EVs), a technological R & D layout of “Three Verticals and Three Horizontals” has been created, and technological advantages have been accumulated. As a result, China's new energy vehicle market has ranked first in the world since 2015. To systematically solve the key problems of battery electric vehicles (BEVs) such as “driving range anxiety, long battery charging time, and driving safety hazards”, China took the lead in putting forward a “system engineering-based technology system architecture for BEVs” and clarifying its connotation. This paper analyzes the research status and progress of the three core components of this architecture, namely, “BEV …

Fuel cell based combined heat and power (FC-CHP) system that has high energy efficiency while no carbon emissions is a promising distributed energy solution in south of China where is no central heating in the winter but very high power demand. Dealing with the coupling heat and power generation, a decoupling strategy that could satisfy both the thermal/electricity demand meanwhile realize system energy economy is essential. The single-demand-led strategy that either heat-led mode or power-led mode is generally used to decouple the heat/power generations but may loss part of the high efficiency working range of the CHP system. Moreover, the key part of the FC-CHP that the fuel cell may suffer significate degradation in aforementioned methods as ignoring CHP system switching flexibility between heat and power. To address this deficiency, first, a dynamic heat/power switching strategy is developed …

Accurate detection of capacity degradation is critical to the safe and efficient utilization of battery systems. Many data-driven capacity estimators were proposed based on emerging intelligent algorithms, but their accuracy depends on the data of complete charged/discharged process and complex algorithm structures. This article developed a computer vision (CV)-based method, constructing battery multidimensional aging features as the key image to estimate capacity using specific charging data segment. Specifically, the designed image-aging recognition method is used to extract multidimensional aging features from the partial charging current sequence and then establish map inputs for a computer vision model that recognizes the constructed feature maps. Consequently, the mapping relationship between the charging information and capacity degradation can be obtained as the 2-D grayscale images that …

锂离子电池的健康状态估计是锂离子电池寿命评估和健康管理的基础. 文中针对实际应用场景中充电数据的缺失, 提出一种实用的多阶段电池的健康状态估计方法. 研究中根据电压大小, 将充电过程划分为 3 个阶段, 分别提出了具有针对性的电池的健康状态估计方法. 特别是对于恒流电压过渡阶段, 在恒流数据和电压数据都严重缺失地情况下, 利用卷积神经网络的数据挖掘能力, 直接建立了电压电流数据与电池的健康状态的关系, 在锂离子电池的长期老化实验数据研究基础上对所提出的方法进行了验证. 结果表明, 该方法具有估计精度高, 应对严重数据缺失的能力强, 对电池不一致性鲁棒性强等优点.Abstract: State of health estimation of lithium-ion battery is the basis of lithium-ion battery life assessment and health management. A practical multi-stage state of health estimation method was proposed to deal with different charging stages, including the scene of serious lack of charging data. According to the voltage, the constant current …

Accurate monitoring of internal temperature distribution is critical to the safety of lithium-ion batteries (LIBs). However, both the radial and the axial thermal inhomogeneities are remarkable in practical LIB utilizations, which challenges the control-oriented thermal modeling. Motivated by this, a novel smart battery implanting internally the distributed fibre optical sensor is designed to perceive the inhomogeneity of temperature distribution of LIB. Enabled by this, a hybrid lumped-thermal-neural-network (LTNN) model is proposed, for the first time, by combining the mechanism-driven distributed lumped thermal model and the machine learning-based axial thermal gradient compensation. A hybrid LTNN-based close-loop observer is further proposed to estimate the internal multi-point temperature of LIB in a real-time fashion. Experimental results suggest that the proposed hybrid LTNN model captures the complicated …

Accurate monitoring of the internal statuses is highly valuable for the management of the lithium-ion battery (LIB). This article proposes a thermal-model-based method for multistate joint observation, enabled by a novel smart battery design with an embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to coestimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB …

The crossover rate of vanadium ions through the membrane and oxygen transport determines the capacity of vanadium/air redox flow battery due to the ion diffusion and the side reactions and the electro-migration and convection. The battery's high reaction temperature also impacts the diffusion coefficient. In this paper, the electrolyte concentration change is predicted, and battery performances with different membranes, including Nafion 115, CMS, and CMX, in different working conditions are compared using the developed dynamic model: Mass balance for each reaction ions and reaction temperature are studied by Fick's Law and Arrhenius Equation to predict; the voltage change of the battery can be calculated by the Nernst Equation. The partial differential equations are applied to predict the performance of the battery. It is observed that with a low crossover rate, the capacity of the battery using CMS is almost …

Compared with passenger cars, the fuel cell bus (FCB) driving cycles have obvious periodicity. Therefore, based on the driving cycles’ periodicity characteristics and the traditional velocity prediction energy management strategy (EMS), this article proposes an economic driving EMS (EDEMS) for the FCB. In EDEMS, two scenarios are designed for the bus line condition: when there is no front bus in the bus lane (main scenario), the FCB can follow a trapezoidal programming curve (TPC)-based speed planning from the bus station out/in, which reduce the intersection stop condition, and the speed planning as the input applied to the model predictive control (MPC)-based EMS; otherwise, traditional velocity prediction is used in the MPC-based EMS (backup scenario). Moreover, the busload change at the bus station is added for the EDEMS cost function, which can accurately calculate the energy consumption and …

Dear editor, State of health (SOH) estimation is critical for the management of lithium-ion batteries (LIBs). Data-driven estimation methods are appealing with the availability of real-world battery data. However, time- and data-costly training for batteries with different chemistries and models barriers their efficient deployment. Motivated by this, a novel deep transfer ensemble learning method is proposed to estimate the SOH with limited sampling data. Specifically, the convolutional neural network (CNN) is employed for model training based on available data. With the new batteries, the trained CNN model is adapted using only a small proportion of samples with the model selection and parameter-sharing transfer learning (TL). The weighted average ensemble learning (EL) is further incorporated to enhance the estimation performance, giving rise to a novel CNN-EL-TL model. Experimental results suggest that the …

Low-temperature charging can induce irreversible damage to the lithium-ion batteries (LIBs) due to the low activity of key composites and physical processes. This has been recognized as a major challenge for the popularity of electric vehicles. Motivated by this, this article proposes a novel heating-charging synergized strategy which coordinates the heating and charging mode smartly for enhanced cold-charging performance. This endeavor is enabled by formulating and solving a multi-objective optimization problem, which considers comprehensively the charging rapidity, energy loss, and the consciousness to LIB physical limitations. The coupling effect and optimized synergy between charging and heating are suggested, for the first time, to provide an improved low-temperature charging solution. The proposed synergized strategy is compared with commonly used decoupled “preheating-charging” strategy by …

The increasing penetration of the renewables and integration of the power electronic devices leads to lower system inertia, which is changeling the system stability of a multiterminal HVdc (MTdc) system. This article presents an improved adaptive predictive control with the multiobjective targets coordinating the key parameters that the dc voltage, ac frequency and power-sharing among the terminals in MTdc system. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, the proposed method is based on minimal information exchange by only considering neighboring terminals. Second, the adaptive control is achieved by setting a weighted fitness function to adaptively tune the weights with the effective integration of the trust-region and particle swarm optimization. A four-terminal HVdc system built within the IEEE 30-bus ac system is …

Accurate current measurement is indispensable for the management of lithium-ion battery (LIB), especially for the state-of-charge (SOC) estimation. However, accurate current sensing is challenging in electric vehicles (EVs) due to the electromagnetic interference. Moreover, the currents across the parallel branches of battery pack are even unmeasurable due to the absence of current sensor. Motivated by this, this paper proposes a hierarchical soft measurement framework for the load current and SOC addressing different degrees of current sensor uncertainty. Rooted from a common least squares (LS)-based state optimization problem, a total least square (TLS)-based modification is proposed and solved to compensate for the measurement disturbances, and in accordance to estimate the SOC more accurately. One step further, an input-free optimization method is proposed to co-estimate the SOC and load current …

Auxiliary power sources such as batteries and supercapacitors are commonly used in fuel cell buses to meet complex power requirements and extend the life of the fuel cell. However, different power supplies have different sensitivity to the actual driving conditions which may cause degradation imbalance, reducing the service lifetime of the whole hybrid system. To solve the problem, a lifetime game optimization management strategy for fuel cell triple-source hybrid bus buses is developed in this paper, which takes into account the competitive relationship between fuel cells and batteries. To begin, a method for predicting driving cycles based on learning vector quantization (LVQ) and back-propagation (BP) neural networks is presented as to improve forecast accuracy. Second, the relational expression of hydrogen efficiency decreasing with the fuel cell state-of-health (SOH) is further derived, forming a new fuel cell …

This article proposes a multiobjective sizing method of the retired battery integrating with the photovoltaic solar energy used for the electric vehicle charging station (EVCS) against the charging demand uncertainty. The proposed size optimization approach employs non-dominated sorting genetic algorithm II (NSGA-II) to minimize the renewable energy waste, energy purchased from the external grid, as well as the cost characterized by the net present value produced in 20 years. Especially for the remaining life prediction of retired batteries, this article leverages the calendar-life degradation model by integrating the battery cycle-life counting method. Also, in this article, the charging demand uncertainty is built as different charging patterns for various EVCS scenarios with different combinations of fast- and slow-charging demand. Furthermore, the technoeconomic attractions of retired batteries are verified by a …

Fast charging of lithium-ion battery (LIB) is an enabling technique for the popularity of electric vehicles (EVs). However, utmost pursuit of the charging rapidity can violate the physical limits of LIB, and induces irreversible degradation or even hazardous safety issues. Motivated by this, this paper proposes an electrochemical-aware constant overpotential fast charging strategy to mitigate the lithium plating in LIB during high-rate charging. In particular, an electrochemical model is built to keep awareness of the inner physical statues of LIB. Following this endeavour, a state-of-the-art twin delayed deep deterministic policy gradient (TD3) algorithm is exploited to determine the fast charging strategy, which can accelerate the charging while constrain the side reaction overpotential within a safe range. Results reveal that the proposed strategy outperforms the traditional constant-current-constant-voltage (CCCV) charging …

Aiming at the scenario where the energy storage system participates in the grid enhanced frequency response auxiliary service, this research initially constructs a frequency response model to provide power requirements for the energy storage system (ESS). A two-layer optimization approach is proposed to solve the size optimization problem. A mixed-integer linear programming technique is researched on the bottom layer to optimize the power allocation of the hybrid energy storage system (HESS). On the top layer, a size optimization framework is proposed for optimising the configuration of the energy storage system. The size optimization results show that compared with the battery energy storage system (BESS), the capacity of the HESS was reduced by 64%, the battery aging cost was reduced by 52%, and the total cost was reduced by 35%. The results also show that the proposed optimal HESS design could …

Modern autonomous emergency braking (AEB) system is a typical safety-critical cyber-physical system (CPS) synthesizing the vehicular communications, control, and proception technologies. However, the control performance of braking can be easily deteriorated by the road adhesion saturation in physical environment and the multi-hop communication network-induced delays in cyber systems. Motivated by this, a new multi-hop loop delay analysis method and its associated upper-bound expression is proposed to scrutinize the system uncertainties, within the scope of CPS. Following this endeavor, a hierarchical cyber-physical control scheme for the AEB system is proposed to mitigate the adverse effects of road adhesion saturation and multi-hop communication network-induced delays. At the upper layer, a μ-adaptive time-to-collision (TTC) planning strategy is adopted to generate the desired acceleration for …

Lithium-ion batteries (LIBs) has been widely used in Electric vehicles (EVs) benefiting from their high-power density and long cycle life. Fast charging technology becomes a critical factor for EVs large-scale penetration in automotive market. This paper proposed an online stress-limited fast charging strategy based on close-loop control. A simplified single particle electrochemical model is established, based on which the computational complexity of stress model is greatly reduced. Proportional-integral (PI) observer is used for stress estimation, while proportional-integral-derivative (PID) controller is devised for stress limitation. Comparation results exhibit that the proposed fast charging strategy possesses a greater ability on stress constrain than the widely used multi-stage constant current charging protocol. Simulation results validated the applicability of the proposed strategy for arbitrary conditions.

The National Grid in the U.K. proposed the firm frequency response (FFR) documents on recent developments regarding tendering options for active balancing mechanism units, specifically for batteries. However, frequent bipolar converting and instantaneous high power demand challenge battery lifetime and operation cost in the FFR. Combining a battery with a supercapacitor (SC) has several advantages, but the system cost may rise. Targeting the FFR service, this article presents a new variable voltage control within a semiactive battery SC hybrid scheme. In the proposed hybrid energy storage system, the dc bus voltage is controllable to improve the SC use ratio while reducing the converter's cost. A power management strategy integrating fuzzy logic with dynamic filtering method is proposed benefiting in broadening the filtering flexibility while reducing battery degradation. In addition, both the long-term …

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This article proposes a knowledge-based, multiphysics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multiobjective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to tradeoff smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the …

This paper proposes a knowledge-based, thermal-conscious strategy for the energy management of hybrid electric bus (HEB). The deep deterministic policy gradient (DDPG) algorithm with priority experience replay (PER) is exploited to distribute the power smartly among energy components. The fully-continuous separate speed- and torque-control mechanism is further devised to excavate the upper optimization potential of PER-DDPG strategy. Moreover, in the PER-DDPG framework, the penalties to over-temperature are embedded for thermal safety enforcement. Comparative results also disclose the superiority of the proposed strategy in terms of the over-temperature protection and overall optimization performance in the energy management of HEB.

Accurate state of health (SOH) estimation of the lithium-ion battery plays an important role in ensuring the reliability and safety of the battery management system (BMS). The data-driven method based on the selection of degradation features can be effectively applied to SOH estimation. In practice, lithium batteries often work in complex discharge conditions, but they are charged under constant current (CC) conditions. Therefore, the suitable degradation features of the battery are extracted in this work for accurate SOH estimation. First, the degradation features are summarized and extracted from the CC charging data. Second, the Pearson correlation coefficient is utilized to quantify the relationship between the extracted degradation features and the battery SOH, thus determining the most influential degradation feature. Finally, the long short term memory (LSTM) is used for model training and SOH estimation based on …

This paper proposes a hierarchical sizing method and a power distribution strategy of a hybrid energy storage system for plug-in hybrid electric vehicles (PHEVs), aiming to reduce both the energy consumption and battery degradation cost. As the optimal size matching is significant to multi-energy systems like PHEV with both battery and supercapacitor (SC), this hybrid system is adopted herein. First, the hierarchical optimization is conducted, when the optimal power of the internal combustion engine is calculated based on dynamic programming, and a wavelet transformer is introduced to distribute the power between the battery and the SC. Then, the fuel economy and battery degradation are evaluated to return feedback value to each sizing point within the hybrid energy storage system sizing space, obtaining the optimal sizes for the battery and the SC by comparing all the values in the whole sizing space …

In this paper, we propose an energy management strategy based on deep reinforcement learning for a hybrid battery system in electric vehicles consisting of a high-energy and a high-power battery pack. The energy management strategy of the hybrid battery system was developed based on the electrical and thermal characterization of the battery cells, aiming at minimizing the energy loss and increasing both the electrical and thermal safety level of the whole system. Primarily, we designed a novel reward term to explore the optimal operating range of the high-power pack without imposing a rigid constraint of state of charge. Furthermore, various load profiles were randomly combined to train the deep Q-learning model, which avoided the overfitting problem. The training and validation results showed both the effectiveness and reliability of the proposed strategy in loss reduction and safety enhancement. The …

State of health (SOH) estimation is insightful for the lithium-ion battery (LIB) health management. This paper proposes a new set of health indicators (HIs) based on early-stage constant-voltage (CV) charging, which are easily available in practical vehicle applications. Particularly, a thorough analysis is performed over different CV-based HIs to obtain the informative ones with strong correlation against the SOH. A gaussian process regression (GPR) model is further employed to fusion the extracted HIs and to estimate the battery SOH. The proposed method is validated based on cycling experiments performed on the LiNiCoAlO2 cells. Results suggest that the proposed method promises multifold benefits, including the high estimation accuracy, low requirement on the charging integrity, and the high robustness to cell inconsistency.

An accurate estimate of the battery state of charge and state of health is critical to ensure the lithium‐ion battery's efficiency and safety. The equivalent circuit model‐based methods and data‐driven models show the potential for robust estimation. However, the state of charge and state of health estimation system's performance with a parallel comparison has been rarely investigated. In this study, the performances of state of charge and state of health with equivalent circuit model‐based methods and data‐driven estimations are analyzed by different aged and capacity batteries through methods including extended Kalman filters, fully connected deep network with drop methods, and the combination (extended Kalman filters—fully connected deep network with drop methods). Besides the battery state of the voltage and current, the relationship between inner resistance, temperature, and capacity are also considered …

With the merits of high energy density and long lifespan, lithium-ion batteries (LIBs) have emerged as the dominant power source for a wide range of industrial applications. However, the safety hazards of LIBs hinder its further promotion and need to be solved urgently. Internal short circuit (ISC) is one of the most critical causes for the dangerous thermal runaway of lithium-ion battery (LIB); thus, the accurate early-stage detection of the ISC failure is critical to improving the safety of LIBs. Motivated by this, this paper proposes a multi-state fusion-based ISC diagnostic method to diagnose the ISC fault quantitatively, with high robustness to the capacity fading. Particularly, a model-switching framework is established to model the electrical characteristic of the LIBs. Within this framework, the battery states are estimated using onboard measured load current and voltage. Then, the estimated multiple states of LIB are fused …

Lithium-ion batteries (LIBs) has seen widespread applications in a variety of fields like the renewable penetration, electrified transportation, and portable electronics. A reliable battery management system (BMS) is critical to fulfill the expectations on the reliability, efficiency and longevity of LIB systems. Recent research progresses have witnessed the emerging technique of smart battery and the associated management system, which can potentially overcome the deficiencies met by traditional BMSs. Motivated by this, this paper reviews the research progresses on the smart cell and smart battery system from multiple aspects, including the system design, sensing techniques, and the potential innovation of system integration. The transition from conventional LIB system towards higher smartness and the incurred advantages/challenges are overviewed. Special focuses are given to the existing and emerging cell-level …

In this article, a computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical–thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive …

State of health (SOH) estimation has deep insights into the lithium-ion battery (LIB) life diagnostic and protection. A machine learning-based SOH estimator is established, utilizing a new set of health indicators (His) extracted from the regional constant-voltage (CV) charging. First, a thorough analysis is performed over different CV-based His to obtain the informative ones with strong correlation against the SOH. Second, an artificial neural network model is employed to construct the nonlinear mapping from the selected His to the battery SOH. The proposed SOH estimator is validated with long-term degradation experiments performed on LiNiCoAlO2 (NCA) cells. Results imply the proposed method manifests itself with high estimation accuracy, low charging integrity requirements, and a high robustness to cell inconsistency.

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. However, the pursuit of utmost charging speed risks the violation of critical physical limits companied by the unexpected thermal/stress buildup and side reactions. Motivated by this, this paper proposes a multi-physics constrained fast charging strategy of the lithium-ion battery (LIB). In particular, a novel stress-coupled electrochemical model is established to describe the mechanical-electro-thermal dynamics of the lithium-ion battery (LIB). Based on this, A stress-and thermal-limited fast charging method is proposed based on the model predictive control (MPC) method. Comparative results show that the proposed charging strategy can improve the charging speed of LIB while fulfill the thermal and mechanical tolerance, which promise a favorable thermal safety and longevity.

The installation of current sensors on lithium-ion batteries (LIBs) can be challenging due to practical constraints in specific applications like portable electronics and smart batteries. Motivated by this, our letter proposes a method for online load current and state-of-charge (SOC) coestimation, which mitigates the need of installing the current sensor for LIB management. The essence is to transform the state observation into a constrained optimization problem, which is solved numerically in a moving horizon framework to allow the online coestimation of SOC and input current. Experimental results suggest that the proposed method can coestimate the load current and SOC of LIB precisely even if the current sensor is absent. The encouraging results are insightful for reducing the structural complexity and cost of future LIB utilization.

Battery state estimation is the key function of the battery management system which relies heavily on accurate current measurements. However, for the newly designed intelligent batteries with a lot of sensors and controllers integrated, the current sensors are expensive and easily disturbed. To this end, this paper proposed a moving horizon estimation (MHE)-based unknown input state observer (UIO) for the lithium-ion batteries to estimate the states of the battery. First, a first order RC battery model is built in Simulink, based on which the state-space function is derived. Then, the parameters of the model are identified offline and the MHE-based UIO is designed. The proposed method is validated for the superiority in SOC estimation without knowledge of the load current.

In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery system with a high-energy and a high-power battery pack can be implemented as the energy source. This paper explores a cloud-based multi-objective energy management strategy for the hybrid architecture with a deep deterministic policy gradient, which increases the electrical and thermal safety, and meanwhile minimizes the system’s energy loss and aging cost. In order to simulate the electro-thermal dynamics and aging behaviors of the batteries, models are built for both high-energy and high-power cells based on the characterization and aging tests. A cloud-based training approach is proposed for energy management with real-world vehicle data collected from various road conditions. Results show the improvement of electrical and thermal safety, as well as the reduction of energy loss and aging cost of the whole …

An accurate estimation of the internal states of lithium-ion batteries is critical to improving the reliability and durability of battery systems. Data-driven methods have exhibited enormous potential for precisely capturing electric and thermal cell dynamics with a low computational cost. However, challenges remain regarding accurate and low-cost data acquisition as electrode-level states are unmeasurable with conventional sensors. This paper presents a hybrid state estimation method for lithium-ion batteries integrating physics-based and machine learning models to leverage their respective strengths. An electrochemical-thermal model is developed and experimentally verified, which is employed to generate a large quantity of data, i.e., voltage, current, temperature and internal electrochemical states, under a comprehensive operating condition matrix including various load profiles and temperatures. These data are …

Battery electric vehicles (BEVs) are advocated due to their environmental benign characteristic. However, the long charging time and the degradation caused by fast charging impede their further popularization. Extensive research has been carried out to optimize the charging process, such as minimizing charging time and aging, of lithium-ion batteries (LIBs). Motivated by this, a comprehensive review of existing charging optimization (ChgOp) techniques is provided in this article. First, the operation and models for LIBs are explained. Then, unexpected side effects, especially for the aging mechanism (AM) of LIB associated with unregulated fast charging, are scrutinized. This provides a solid theoretical foundation and forms the optimization problem. Following this endeavor, the general framework with critical concerns for ChgOp system design is overviewed. Within this horizon, the state-of-the-art ChgOp …

With the advent of sustainable and clean energy, lithium-ion batteries have been widely utilised in cleaner productions such as energy storage systems and electrical vehicles, but the management of their electrode production chain has a direct and crucial impact on the battery performance and production efficiency. To achieve a cleaner production chain of battery electrode involving strongly-coupled intermediate parameters and control parameters, a reliable approach to quantify the feature importance and select the key feature variables for predicting battery intermediate products is urgently required. In this paper, a Gaussian process regression-based machine learning framework, which incorporates powerful automatic relevance determination kernels, is proposed for directly quantifying the importance of four intermediate production feature variables and analysing their influences on the prediction of battery …

This article proposes a synergistic approach that traverses the battery optimal size simultaneously against the optimal power management based on deep reinforcement learning (DRL). A fuel cell hybrid electric vehicle (FC-HEV) with the FC/battery hybrid powertrain is used as the study case. The battery plays a key role in current transportation electrification, and the optimal sizing of the battery is critical for both system technical performances and economical revenues, especially in the hybrid design. The optimal battery design should coordinate the static sizing study against the dynamic power distribution for a given system, but few works provided the synergistic consideration of the two parts. In this study, the interaction happens in each sizing point with the optimal power sharing between the battery and the FC, aiming at minimizing the summation of hydrogen consumption, FC degradation, and battery …

State of health (SOH) estimation is critical for the lithium-ion battery health management. In this paper, a new set of health indicators (HIs) based on partial constant-current charging are extracted by scrutinizing the correlation against the SOH and the robustness to uncertainty of practical disturbance. A multiple linear regression model with an activation function is further established to construct the relationship between the extracted HIs and the battery capacity. Results give sufficient evidence for the low requirement on the charging integrity, the sufficient adaptability to the practical disturbances and the high robustness to cell inconsistency of the proposed method.

Accurate state-of-charge (SOC) estimation and lifetime prognosis of lithium-ion batteries are of great significance for reliable operations of energy storage systems. This paper proposes a novel two-layer hierarchical approach for online SOC estimation and remaining-useful-life (RUL) prediction based on a robust observer and Gaussian-process-regression (GPR). At the bottom layer, an equivalent-circuit model is first developed to describe battery dynamics. Second, a combination method of a recursive least square method and a finite time H- observer is designed to estimate battery open-circuit-voltage (OCV) and SOC through stability and robustness analysis. Next, the estimated OCV and SOC are fed into the top layer to generate the incremental-capacity-analysis-based aging feature, through which a robust signature associated with battery aging is identified. The feature is further employed for RUL prediction …

Current sensor fault diagnostic is critical to the safety of lithium-ion batteries (LIBs) to prevent over-charging and over-discharging. Motivated by this, this article proposes a novel residual statistics-based diagnostic method to detect two typical types of sensor faults, leveraging only the 50 current–voltage samples at the startup phase of the LIB system. In particular, the load current is estimated by using particle swarm optimization (PSO)-based model matching with measurable initial system states. The estimation residuals are analyzed statistically with Monte–Carlo simulation, from which an empirical residual threshold is generated and used for accurate current sensor fault diagnostic. The residual evaluation process is well proved with high robustness to the measurement noises and modeling uncertainties. The proposed method is validated experimentally to be effective in current sensor fault diagnosis with low miss …