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This paper proposes a battery health-aware and deep reinforcement learning (DRL)-based energy management framework for power-split hybrid electric vehicles in a naturalistic driving scenario. First, based on the data collected from the actual traffic flow, a data-driven method is used to establish driving scenarios that reflect different driving patterns and behaviors. Second, the expert knowledge is embedded into the deep deterministic policy gradient (DDPG) to achieve faster convergence with the guaranteed vehicle performance. Third, the superiority of the control strategy is achieved by optimizing the trade-off among fuel consumption, battery aging cost and SOC sustainability penalty under different weight coefficients, and verified by comparison with the existing state-of-the-art strategies including the deep Q-network (DQN) and dynamic programming (DP). The results show that the proposed strategy can slow down battery aging by lowering the operating severity factor with minimal fuel economy penalty while remaining accelerated iterative convergence compared with DQN. The benefits of proposed strategy become very evident when the vehicle is driving under the high power demand and it has good stability to cope with the change of operating conditions.

Large battery packs are used in electric vehicles. Heat is generated when the battery pack is being used. Therefore, it is necessary to predict battery heat generation. An enhanced electro-thermal model is developed to describe the temperature distribution inside a battery pack. It combines the dynamic resistance model and the current distribution model. The resistance model is affected by the thermal and electrical parameters, while the current distribution model considers the interaction between cell status and current variation in the parallel branch. The proposed model can accurately predict the temperature change of cells in the pack under static and dynamic current conditions. Experiments are conducted to validate the prediction accuracy. Most of the average absolute errors (AEave) between the predicted value and test value displayed on the experimental device do not exceed 0.4 °C under static current conditions, and all of them are below 0.1 °C under dynamic current conditions. The two existing models, namely the state-of-charge (SOC)-dependent resistance model [R(SOC)] and SOC-T-dependent resistance model [R(SOC, T)], have AEave values of 1.6 and 0.54 °C when the pack is discharged at 0.5 C. In contrast, the AEave value achieved by our proposed model is 0.4 °C. Under dynamic current conditions, the maximum AEave s are 0.42 °C for the R(SOC) model, 0.26 °C for the R(SOC, T) model, and 0.16 °C for the proposed model. These results demonstrate that the proposed model provides more accurate predictions of the temperature rise inside the pack than the popular existing models.

Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.

A novel design optimization method is proposed to optimize the air passageway for an air-cooled battery pack with a 3P4S configuration (three strings in parallel and four cells in each string). This method includes the electrothermal model for the air-cooled pack and the optimization algorithm. Unlike other thermal models for battery packs, the model established in this article considers the interaction between the state of charge (SOC), current, heat generation, and temperature at the cell level and the impact of uneven cooling on the current distribution in the parallel branches at the pack level. Experiments are conducted to verify the prediction accuracy of the electrothermal model. The results show that the proposed model can accurately predict the electrical and thermal parameters under different conditions. For example, the root-mean-square error (RMSE) of temperature is less than 0.5 °C under all test conditions. As for the optimization algorithm, the particle swarm optimization (PSO) algorithm is used. In order to increase the optimization searching speed and accuracy of PSO, the inertia factor is added to the velocity formula, and the spatial neighborhood method is used. The design optimization method is used to optimize the air passageway of an air-cooling pack. It is found that the optimized pack not only has a lower maximum cell temperature and a smaller temperature variation among cells than the original pack but also has a smaller difference of branch current and a longer lifespan.

All-solid-state batteries (ASSBs) are considered to be the next generation of lithium-ion batteries. Physics-based models (PBMs) can effectively simulate the internal electrochemical reactions and provide critical internal states for battery management. In order to promote the onboard applications of PBMs for ASSBs, in this article, the parameter sensitivity of a typical PBM is analyzed, and a joint estimation method for states and parameters based on sigma-point Kalman filtering (SPKF) is proposed. First, to obtain accurate sensitivity analysis results, approaches from different principles, including local sensitivity, elementary effect test, and variance-based methods, are applied. Then, for the battery model based on partial differential equations, a nonlinear state-space model is constructed by using the finite-difference discretization method. Finally, the SPKF algorithm is employed to conduct the joint estimation of model parameters and lithium-ion concentrations. The results from constant current and dynamic cycles show that two parameters, namely maximum lithium-ion concentration and minimum lithium-ion concentration, have the most influence on the model results. The joint estimation method is validated in three different cases, and the mean absolute errors of the estimated voltage and state of charge (SOC) are below 2.1 mV and 1.5%, respectively.

Enhanced single-particle models (eSPMs) have been extensively studied in the development of advanced battery management systems for their accuracy and capability of tracking physical quantities, as well as for the reduced computational load. This article proposes an optimal discretization approach to model reduction for the eSPM using a particle swarm optimization algorithm. The battery diffusion dynamics were solved using different finite difference approaches, that is, an even discretization approach (baseline model) and an uneven discretization approach (optimized model). Because of the structure of the eSPM, internal nodes locations of the solid phase and the electrolyte phase are separately optimized. For the solid phase, a weighted multiobjective cost function is considered for achieving accurate surface and bulk concentration, aiming for accurate terminal-voltage and state-of-charge prediction. For the electrolyte phase, the optimization aims for accurate concentration prediction at the boundary of the electrolyte. The optimally reduced uneven discretization model can predict the battery dynamics accurately and with an improved computational cost: 1) the maximum voltage and SOC prediction errors demonstrated under dynamic current profiles are less than 2.73 mV and 0.37%, respectively, and 2) the number of states reduces by at least 11 times, leading to about a 64% reduction in the computation time.

State of health (SOH) is essential for battery management, timely maintenance, and safety incident avoidance. For specific applications, a variety of SOH estimation methods have been proposed. However, it is often difficult to apply these methods to other applications. In this article, a novel feature extraction method is proposed to extract health indicators (HIs) from general discharging conditions. A voltage partition strategy is used to obtain the discharge capacity differences of two cycles [△ Q ( V )] from nonmonotonic or pulse discharge voltage curve, and a filtering strategy is employed to obtain smooth voltage curves under dynamic discharging conditions. The standard deviations of the discharge capacity curve and △ Q ( V ) are selected as HIs and are verified to have strong correlations to battery capacity under different datasets for three types of batteries. By using these HIs as input features, typical data-driven methods, including linear regression, support vector machine, relevance vector machine, and Gaussian process regression (GPR), are constructed to predict battery SOH. The estimation results of these methods are compared under different operating conditions for the three types of batteries. Good estimation accuracy is achieved for all these methods. Among them, the GPR has the best performance, and its maximum absolute error and root-mean-square error are lower than 1% and 1.3%, respectively.

An accurate control-oriented electrothermal model is of great importance for onboard temperature monitoring and efficient performance management of lithium (Li)-ion batteries in automobile applications. This article presents a control-oriented electrothermal model for pouch-type electric vehicle batteries. This model uses the Chebyshev–Galerkin (CG) approximation method and captures the heat generation of positive and negative tabs, the heat flow between the tabs and the body, and the uneven heat generation inside the battery. This model consists of two lumped-mass submodels for positive and negative tabs and a 2-D CG submodel for the main battery body. The heat generation in the 2-D CG model is strongly dependent on the electrical parameters that are conversely functions of battery temperature. The lumped-mass models are decoupled from the 2-D CG model and parameterized separately by the particle swarm optimization algorithm and validated against the temperature measurements (covering three test scenarios) of a 20-Ah pouch-type Li-ion iron phosphate battery. The results demonstrate that the coupled model accurately predicts the temperatures of the tabs and the temperature distribution inside the battery. Besides, the computational complexity of the coupled model is also evaluated, and the result shows that the model has great potential for real-time temperature monitoring and efficient thermal management.

Detecting thermal faults is critical to the safety of lithium-ion batteries. This article, therefore, proposes a neural network-based approach. The approach relies on the long short-term memory neural network, in conjunction with an alteration to the walk-forward technique, to accurately estimate the surface temperature of the cell. It also relies on a residual monitor to detect the faults in real time. This data-driven method is introduced to expand the available options in thermal fault detection. It offers an easy-to-implement option that does not require expert understanding in battery physics, complex mathematical modeling, and tedious parameter tuning processes. The experimental results demonstrate that this approach can detect thermal faults accurately. It is adaptive to different battery chemistries and form factors, and thanks to its online training capability, it can also automatically retrain itself to capture changes in the battery over time.

Significant progress has been made in transportation electrification in recent years. As the main energy storage device, lithium-ion batteries are one of the key components that need to be properly managed. The remaining useful life, which represents battery health, has attracted increasing attention. Because accurate and robust predictions provide important information for predictive maintenance and cascade utilization. This paper proposes a novel method to predict remaining useful life based on the optimized health indicators and online model correction with transfer learning. Gaussian process regression is used to optimize the threshold for health indicators to determine the end of life, and a usefulness evaluation strategy is proposed to assess the health indicators. Then, a combination of transfer learning and gated recurrent neural network is designed to predict the remaining useful life based on the optimized health indicators directly, which can promote online applications. The prediction model initially trained based on a relevant battery is further fine-tuned according to the early degradation cycling data of the test battery to provide accurate predictions. Moreover, a self-correction strategy is proposed to retrain the regression models so that the models can gradually reach the optimal prediction performance during the operating cycles, which could not be achieved by traditional methods. The recommended input sequence lengths for potential applications are discussed. The method is verified by experiments of a batch of batteries under fast charging conditions, and the results show that, after fine-tuning, the proposed method predicts remaining useful life with an error of fewer than 5 cycles.

Accurate, reliable, and robust prognosis of the state of health (SOH) and remaining useful life (RUL) plays a significant role in battery pack management for electric vehicles. However, there still exist challenges in computational cost, storage requirement, health indicators extraction, and algorithm design. This paper proposes a novel dual Gaussian process regression model for the SOH and RUL prognosis of battery packs. The multi-stage constant current charging method is used for aging tests. Health indicators are extracted from partial charging curves, in which capacity loss, resistance increase, and inconsistency variation are examined. A dual Gaussian process regression model is designed to predict SOH over the entire cycle life and RUL near the end of life. Experimental results show that the predictions of SOH and RUL are accurate, reliable, and robust. The maximum absolute errors and root mean square errors of SOH predictions are less than 1.3% and 0.5%, respectively, and the maximum absolute errors and root mean square errors of RUL predictions are 2 cycles and 1 cycle, respectively. The computation time for the entire training and testing process is less than 5 seconds. This article shows the prospect of health prognosis using multiple health indicators in automotive applications.

In order to keep a lithium-ion battery within optimal temperature range for excellent performance and long lifespan, it is necessary to have an effective control strategy for a battery thermal management system (BTMS) consisting of electric pump, cooling plate and radiator. In this paper, a control-oriented model for BTMS is established, and an intelligent model predictive control (IMPC) strategy is developed by integrating a neural network-based vehicle speed predictor and a target battery temperature adaptor based on Pareto boundaries. The strategy is applied to plug-in electric vehicles operating in electric vehicle mode. Results show its superiority in terms of battery temperature control, battery lifespan extension and energy saving. Under the new European driving cycle, average difference between the real-time battery temperature under the novel IMPC and its target temperature is 0.26 °C, and maximum temperature difference among modules is 1.03 °C. Moreover, compared with the on-off controller, model predictive control (MPC), and MPC with VSP, state of health under IMPC at the end of the driving cycle is 0.016%, 0.012%, and 0.008% higher, respectively. At this moment, the energy consumption of IMPC is 24.5% and 14.1% lower than that of the on-off controller and traditional MPC, respectively.

Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

Lithium-ion (Li-ion) batteries have been extensively used in electric vehicles, portable electronics, cell phones, and laptops. The charging protocol, as one of the most critical technologies for Li-ion battery systems, has a significant impact on battery performance. Charging current affects battery degradation and charging time, and therefore, it needs to be carefully optimized. To this end, a novel charging protocol using a series of constant charging currents has been developed, which considers the charging time and the battery capacity fade simultaneously. These two conflicting charging objectives are traded off by solving a multiobjective optimization problem based on battery electrothermal-aging behavior. Particle swarm optimization has been applied to obtain the optimal charging current profile. Three optimal charging strategies for minimum charging time, minimum battery aging, and balanced charging performance are obtained by changing the weight factor. The proposed balanced charging is capable of reducing the charging time significantly with a negligible increase in capacity degradation compared with the 0.5 C constant-current constant-voltage (CC-CV) strategy recommended by the manufacturer.

Accurate and computationally efficient series-connected battery pack models (PMs) in new energy vehicles are extremely important for battery management. Based on a system of indexes of accuracy, adaptability, and computational complexity, this article presents a practical and comprehensive evaluation method for series-connected battery PMs, which is crucial for model selection and model-based algorithm development. Seventeen battery PMs, based on four series-connected battery pack structure models and three battery cell models, are introduced and discussed in detail. Experiments are designed and carried out to collect realistic battery test data for parameter identification and model comparisons. The estimation accuracy and computational complexity of different battery PMs are compared. Both the merits and demerits of each battery PM are thoroughly analyzed and discussed. The practical comprehensive evaluation results provide useful insights that will enable industry and academia to design more advanced battery management systems for battery packs.

Summary Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.

Current battery management systems (BMSs) in automotive applications monitor and control batteries in a relatively simple, conservative manner, with limited capabilities of sensing, estimation, proactive controls, and fault diagnosis. With ever-increasing computing power onboard and/or in the cloud, enhanced environmental perception and vehicular communications, emerging electrified vehicles and smart grids provide unprecedented opportunities for designing and developing next-generation smart BMSs. However, three entrenched technical challenges need to be addressed, including 1) limited knowledge of battery internal states and parameters; 2) poor adaptability to extreme operating conditions; and 3) lack of efficient predictive maintenance, resulting in great concern for battery safety and economy. This paper aims to present some critical insights into possible solutions to the three challenges. First, the multi-physics coupled battery modeling concept is introduced to emphasize that looking at mechanical-electrochemical-thermal-aging dynamics is critically important for devising revolutionary BMS algorithms. Second, electrothermal modeling, advanced optimization routines, and predictive control with vehicular autonomy and connectivity facilitate innovative designs in dynamically hysteresis-aware thermal management, heat transfer under extreme fast charging, and preheating in a cold climate. Third, battery models and machine learning are complementary and can be very useful for improving battery remaining useful life prediction and fault diagnosis, achieving high-efficiency predictive maintenance.

This paper studies the state estimation of a solid-state battery modeled as a partial differential equation system. Three assumptions simplifying the battery model underlie the study of state estimation: (1) neglecting the generation/recombination of Li-ions in the solid electrolyte; (2) assuming the charge transfer number of 0.5; and (3) the uniform electrolyte concentration. Results of a sensitivity study show the validity of the approaches to model simplification for the voltage prediction. Especially, two simplified models focused on the diffusion dynamics at cathode only and at both cathode and electrolyte are used for state estimation by applying an extended Kalman filter (EKF). Simulation results show that the state-of-charge of the battery can be reasonably well estimated by the EKFs. However, the inaccuracy of the state estimation in low SOC range ( 0.1 SOC 0.4 ) due to weak observability cannot be addressed without including the diffusion dynamics in the electrolyte. This inclusion leads in a 40% reduction in state-of-charge estimation error, from 9% to 5% error for the considered case.

This paper uses a physics-based battery model to develop a generic framework to solve optimal charging strategies. The study will also provide insight into the interplay between optimized charging strategies and the battery internal electrochemical kinetics. With a physics-based battery model, a multi-objective optimal control problem is proposed to investigate the charging strategies that optimally trade off the temperature rise, charging time, and loss. First, a fast-charging strategy (minimum time) with the sole purpose of reducing charging time is presented and experimentally validated. The fast-charging strategy can significantly reduce the charging time but causes a high-temperature rise and charging loss. Next, the interplays between temperature rise and charging time, charging loss, and charging time are investigated, respectively. It is found that, in order to reduce the battery temperature during charging, high-current charging at the initial stage should be avoided. Finally, a balanced charging strategy, which considers temperature rise, charging time, and charging loss simultaneously, is developed and analyzed. Experimental results show that the balanced charging strategy has a similar temperature rise as 4C CC/CV charging, but the charging time is reduced by 24.8% and the charging loss is reduced by 56.4%.

In the interest of improving the safety and reliability of lithium-ion (Li-ion) batteries on a cell and pack level, this study introduces a data-driven approach for detecting voltage faults. Due to the severity of short circuits or current leaks in Li-ion batteries, the proposed method uses a residual threshold to identify faults in the fast-acting voltage signal. Its data-driven element bypasses the need for an in-depth understanding of battery physics which makes it easier to implement. The approach uses the long short-term memory (LSTM) neural network (NN) together with the walk-forward testing technique to estimate voltage signals in non-faulty conditions. Known faulty data is then evaluated in a normalized receiver operating characteristic (NROC) optimizer to carefully select a residual threshold which is used to flag voltage faults that could result in safety and reliability issues. The experimental results demonstrate the ability of the NN to attain high accuracy in non-faulty voltage estimations. The residual monitor also shows great performance in its ability to detect voltage faults quickly and accurately as they occur. The method is further shown to be adaptive due to its ability to be trained online as the battery ages.

Lithium-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles and smart grids. However, various faults in a lithium-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This paper provides a comprehensive review of fault mechanisms, fault features, and fault diagnosis of various faults in LIBS, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

Inconsistencies between the cells in a battery pack can greatly limit the pack's cycle life and performance. This is why an integrated equalization management system (EMS) is necessary to limit these inconsistencies. This paper presents a novel multivariable fusion equalization strategy utilizing a fuzzy logic controller (FLC). This strategy begins by firstly analyzing the voltage inconsistencies within the battery pack and by proposing different equalization objectives. Secondly, a simplified model is created, which consists of a battery model and an equalization circuit model, which is then used as a predictive model. Thirdly, a top-bottom method and a predictive control method are proposed to the EMS based on the battery voltage and its state of charge (SOC). Lastly, the FLC is used to fuse these two strategies depending on the ratio of the current over the capacity (RCC). A battery pack setup is built to acquire experimental data, and a series of tests are used to verify the proposed method. The proposed multivariable fusion equalization achieves a 14.9% increase in computational efficiency, and the battery performance increases by 4.9% over a traditional equalization strategy.

Prognostic and health management for battery packs depend greatly on the accurate and efficient state of health prognosis. This paper proposed a strategy for generating universal health indicators and model fusion method. Several health indicators that reflect the integral characteristic and information distribution are generated and proved with high correlation with capacities. The generation method can be adapted for various battery packs regardless of the battery types, connected cell numbers, and aging statuses. Then the generation method is extended to dynamic working conditions by combining the mean plus difference model and recurrent least-square online parameter identification. Thanks to the uniform feature input and state of health output, a universal prognostic model is constructed with deep learning frameworks. The prognostic performance is further improved by the model migration and fusion, which extend the application area to predict the state of health for different battery packs and/or under different working conditions. Experimental results show that the prognostic model can be implemented among various battery packs with satisfactory accuracy and reliability. The mean absolute errors and root mean square errors are less than 2.5% and 3.1%, respectively, under various application occasions.

Battery fast charging is one of the most significant and difficult techniques that affects the acceptance of the electric vehicles. Due to the complex electrochemical reactions, fast recharge could affect the battery functionality and accelerate its aging processes. The accelerated aging process during fast charging can dramatically reduce cell lifetime, impact cell safety, and must be avoided. In this paper, we propose a health conscious fast charging framework with the aim of simultaneously reducing the charge duration and the battery degradation. This paper presents an electrolyte enhanced single particle model with degradation mechanisms. A multi-objective optimal control problem is formulated. Dynamic programming (DP) technique is employed to find the optimal charging strategies. Charging time and battery degradation are traded off and optimized. Strategies for fast charging (minimum time) and health conscious fast charging are examined and compared. Multiple experiments are carried out to compare the charge time and capacity fade between fast charging strategy, traditional CC/CV protocol, and health conscious fast charging strategy. The results demonstrate that the health conscious fast charging strategy is able to significantly reduce the charging time without sacrificing battery health.

Battery fault diagnosis is essential to ensure the safe and reliable operation of electric vehicles. Early detection of battery faults can reduce battery incidents and property losses. However, early warning of battery thermal runaway is still a challenging task. This paper proposes a novel data-driven method for lithium-ion battery pack fault diagnosis and thermal runaway warning based on state representation methodology. The normalized battery voltages are used to achieve accurate identification of battery early faults. The proposed method calculates the real-time state of each cell to characterize the internal characteristics of the battery cell, and the state changes are recorded to achieve battery fault diagnosis. The fault detection time is compared with the alarm time of real vehicles to verify the effectiveness of the proposed method. The real-world operation data of four electric vehicles with different specifications are used to verify the feasibility, robustness, and reliability of the proposed method. The results show that the method can achieve not only the accurate identification of the faulty cells and accurate determination of the voltage fault type but also the early detection of faults and early warning of thermal runaway.

The success of electric vehicles depends largely on energy storage systems. Lithium-ion batteries have many important properties to meet a wide range of requirements, especially for the development of electric mobility. However, there are still many issues facing lithium-ion batteries. One of the issues is the deposition of metallic lithium on the anode graphite surface under fast charging or low-temperature conditions. Lithium plating reduces the battery life drastically and limits the fast-charging capability. In severe cases, lithium plating forms lithium dendrite, which penetrates the separator and causes internal short. Significant research efforts have been made over the last two decades to understand the lithium plating mechanisms. However, the lithium plating mechanisms have not yet been fully elucidated. Meanwhile, another challenge in the development of fast charging technologies is to identify degradation mechanisms in real-time. This includes real-time detection of lithium plating while the battery is being charged. Accurate detection and prediction of lithium plating are critical for fast charging technologies. Many approaches have been proposed to mitigate lithium plating, such as adopting advanced material components and introducing hybrid and optimized charging protocols. Nevertheless, most detection techniques and mitigation strategies are only used for fundamental research with limited possibilities in large-scale applications. To date, there is still a lack of a comprehensive review of lithium plating, reflecting state of the art and elucidating potential future research directions. Therefore, in this article, we provide a snapshot of recent advances in lithium plating research in terms of mechanism, detection, and mitigation to fill this gap and incentivize more innovative thoughts and techniques. In the present study, the mechanisms of lithium plating and approaches used to characterize and detect it in different applications are carefully reviewed. This review also provides a summary of recent advances in model-based approaches to predict lithium plating. Based on the gathered information, the advantages and drawbacks of each model are compared. The mitigation strategies for suppressing lithium plating at different levels are studied. Finally, we highlighted some of the remaining technical challenges and potential solutions for future advancement.

This paper studies the state estimation of a solid-state battery. Partial differential equations based on a nonporous insertion model are presented to model the solid state battery. Two assumptions simplifying the battery model under-lie the study of state estimation: that the Li-ion concentration in the solid electrolyte is uniform and the charge transfer coefficient at the positive electrode is 0.5. The assumptions made resolve the issue of very weak observability: the diffusion dynamics in the electrolyte and positive electrode are connected via Butler-Volmer equation and the contribution of the charge transfer overpotential is found to be insignificant. For state estimation, an extended Kalman filter (EKF) is applied based on the simplified battery model using measurements of current and voltage of the cell. Simulation results show that the state-of-charge (SOC) of the battery can be accurately estimated by the EKF in specific SOC ranges, i.e., SOC 0.4.

This paper aims to design thermal dummy cells (TDCs) that can be used in the development of lithium-ion battery thermal management systems. Based on physical property and geometry of real 18,650 cylindrical cells, a three-dimensional model of TDCs was designed, and it is used to numerically simulate the thermal performance of TDCs. Simulations show that the TDC can mimic the temperature change on the surface of a real cell both at static and dynamic current load. Experimental results show that the rate of heating resistance of TDC is less than 0.43% for temperatures between 27.5 °C and 90.5 °C. Powered by a two-step voltage source of 12 V, the temperature difference of TDCs is 1 °C and 1.6 °C along the circumference and the axial directions, respectively. Powered by a constant voltage source of 6 V, the temperature rising rates on the surface and in the core are higher than 1.9 °C/min. Afterwards, the proposed TDC was used to simulate a real cell for investigating its thermal performance under the New European Driving Cycle (NEDC), and the same tests were conducted using real cells. The test indicates that the TDC surface temperature matches well with that of the real battery during the NEDC test, while the temperature rise of TDC exceeds that of the real battery during the suburban cycle. This paper demonstrates the feasibility of using TDCs to replace real cells, which can greatly improve safety and efficiency for the development of lithium-ion battery thermal management systems.

Lithium-ion batteries are promising energy storage devices for electric vehicles and renewable energy systems. However, due to complex electrochemical processes, potential safety issues, and inherent poor durability of lithium-ion batteries, it is essential to monitor and manage batteries safely and efficiently. This study reviews the development of battery management systems during the past periods and introduces a multilayer design architecture for advanced battery management, which consists of three progressive layers. The foundation layer focuses on the system physical basis and theoretical principle, the algorithm layer aims at providing a comprehensive understanding of battery, and the application layer ensures a safe and efficient battery system through sufficient management. A comprehensive overview of each layer is presented from both academic and engineering perspectives. Future trends in research and development of next-generation battery management are discussed. Based on data and intelligence, the next-generation battery management will achieve better safety, performance, and interconnectivity.

This study proposed a dynamic resistance-based thermal model to predict the temperature evolution of a prismatic lithium-ion battery in fast and regular charging strategies. The effects of battery temperature, state of charge (SOC), and charging current on resistance were investigated to form a dynamic heat generation model. This model included both the heat generation of battery body and the heat generation of electrodes. Then the charging resistance-based thermal model was used to predict the temperature distribution and temperature evolution of a 50 Ah prismatic battery under different charging strategies and ambient temperatures. The charging strategies included a multi-stage constant current-constant voltage (MSCC-CV) for fast charging and a constant current-constant voltage (CC-CV) for regular charging. Experiments verified the prediction accuracy of the proposed thermal model. The results showed that the dynamic thermal model accurately predicted the temperature distribution and its evolution under different charging strategies and ambient temperatures. With regard to temperature distribution prediction, the average root mean square errors (RMSEs) of temperature among the five test points were 0.43 °C for the MSCC-CV charging strategy and 0.3 °C for the CC-CV charging strategy. For the temperature prediction under various ambient temperatures, the average RMSEs at different ambient temperatures were 0.34 °C for the MSCC-CV strategy and 0.25 °C for the CC-CV strategy. Finally, the effect of the charging and discharging resistance on the temperature prediction under the MSCC-CV charging condition was studied. Although the discharging resistance-based thermal model could describe the temperature change with charging time, it showed higher predicted temperatures than the tested values and a larger estimation error than the charging resistance-based thermal model.

Lithium-ion battery degradation is one of the most important issues in hybrid electric vehicles. In order to minimize degradation and its equivalent battery life cost, the degradation of Li-ion batteries needs to be considered in energy management strategies. The existing methods mainly consider the battery aging in energy management strategies while ignoring its thermal dynamics. This paper proposes a battery aging- and temperature-aware predictive energy management strategy for parallel hybrid electric vehicles. This method is developed based on model predictive control (MPC) and evaluated in the scenario of urban bus transportation. First, due to the stochastic nature of speed transitions under actual driving conditions, a stochastic speed predictor is built based on the Markov chain model. Then, an optimal control framework for energy management strategy (EMS) is developed based on MPC, and the battery electrical-thermal-aging dynamics are considered in this control framework. The optimization is performed on the receding horizon using Pontryagin's minimization principle (PMP). The newly developed PMP-based MPC method is compared with the rule-based method and the global optimization method. The comparisons show that the PMP method is superior to dynamic programming when the battery electrical-thermal-aging dynamics are considered in the EMS development, and also show that the battery temperature-aware EMS can lower the total energy consumption.

For plug-in hybrid electric vehicles, deeper battery discharge provides more electrical energy at a lower cost than fossil fuel, which reduces the overall energy consumption cost. However, it also accelerates battery degradation and increases the equivalent cost of battery life loss. Therefore, the battery depth of discharge (DOD) needs to be optimized to minimize the overall cost. For connected plug-in hybrid electric vehicles, the longitudinal velocity planning determines the energy demands, which directly affects the charging or discharging current to the battery and therefore affects DOD, aging, and fuel consumption as well. For connected plug-in hybrid electric buses running on fixed routes, in order to coordinate the velocity planning and battery health protection, this paper proposes a real-time energy management strategy aimed at achieving the minimum overall cost by optimizing the DOD and velocity planning. The proposed method is evaluated in an urban traffic scenario, and the goal of achieving optimal DOD is divided into a co-optimization problem over each moving horizon, where the velocity planning and energy management are traded off by minimizing the sum of driving safety cost, energy consumption cost, and equivalent cost of battery life loss. The results show that the proposed far-sighted economy-oriented methodology is superior to a short-sighted velocity planning and energy management method, and has an obvious advantage in the total cost compared with other conventional methods using a preset DOD. Moreover, the impacts of possible communication delays and prediction horizon lengths on the optimization performance and computational cost are investigated. The proposed method provides a promising management strategy for future connected and autonomous mobility design, which can mitigate battery capacity degradation and improve the fuel economy.

Inconsistencies among battery cells and measurement errors have significant impacts on the accuracy and reliability of state of charge (SOC) estimations for series-connected battery packs. The aim of this paper is to propose a novel SOC estimation method for series-connected battery packs based on the fuzzy adaptive federated filtering. The mean-plus-difference model is employed to characterize the inconsistencies among battery cells. The fuzzy system is designed to improve the accuracy and adaptability of SOC estimation under cell inconsistencies. The SOC estimation value from a cell mean model and the standard deviation of SOC estimation are combined with a fuzzy system to determine their fusion weights. The master filter adaptively adjusts the information distribution coefficient according to the local filter estimation accuracy to improve reliability. Through simulation and experimentation on series-connected battery packs with different SOC distributions, the estimation accuracy of the proposed method is compared between the proposed method and the conventional methods. The SOC estimation accuracy of each battery cell is evaluated. The results show that, over the full SOC range, the root-mean-square error (RMSE) of the battery pack SOC estimation is less than 0.6% and 1.5% using online and offline parameters, respectively. The SOC estimation RMSEs of the battery cells using online and offline parameters are less than 0.4% and 1%, respectively. The fault tolerance is verified by artificially adding measurement errors. These accurate and reliable results show a strong prospect for the design and optimization of future clean and sustainable mobility.

Plug-in hybrid electric vehicles (PHEVs) have a large battery pack, and the depth of discharge (DOD) significantly affects the battery longevity. In this paper, the battery degradation is considered in the co-optimization of battery size and energy management for PHEVs using convex programming. The impact of DOD on battery degradation and energy management is also investigated. The cost function consists of fuel consumption, electrical energy consumption, and equivalent battery life loss. A real-world speed profile collected from the urban city bus route up to about 70 km is used as an input to evaluate the proposed method. The results suggest that, for both cases with and without battery degradation, the total cost curve with respect to the preset final state of charge (SOC) is an upward parabola, where the optimal DOD can be identified, and the optimal battery size and energy management can be determined. The results also show that, with an initial SOC of 0.9, the proposed method can reduce the total cost by 3.6 CNY compared to other existing studies with the fixed final SOC. Moreover, a sensitivity analysis is conducted to explore the effect of battery price and initial SOC on the optimal DOD and total cost.

The formation of a SEI layer and its growth cause internal resistance increase and capacity loss, leading to performance degradation of lithium-ion batteries. In order to comprehensively investigate the effects of SEI growth on battery performance, a one-dimensional thermal-electrochemical model was developed. This model is equipped with a growth mechanism of the SEI layer coupled with thermal evolution, based on the diffusional process of the solvent through the SEI layer and the kinetic process at the interface between the solid and liquid phases. The model is able to reveal the effects of diffusivity, reaction kinetics and temperature on SEI layer growth and cell capacity fade. We show that depending on the SEI thickness, the growth can be kinetics-limited or diffusion-limited. With the layer becoming thicker, its growth rate slows down gradually due to increased diffusion resistance. The SEI layer grows faster during charge than discharge due to the difference in the electron flux through the SEI layer and the temperature change during cycling. Temperature rise due to reaction and joule heating accelerates the SEI layer growth, leading to more capacity loss. Our model can provide insights on position-dependent SEI growth rate and be used to guide the strategic monitoring location.