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• Impact loading can cause the short-circuit of lithium-ion batteries in an earlier displacement than quasi-static loading. • A simulation model of a cylindrical battery is developed to illustrate the loading-rate dependent short-circuit mechanisms. • A short-circuit criterion is established based on the strain-rate dependent fracture behaviour of the separator. • The results can provide a design guide to enhance crashworthiness of cylindrical batteries for electric vehicles. Lithium-ion battery failures, particularly in the case of high-speed collisions in electric vehicles, have become a growing concern. This study investigates the failure mechanism of an 18650 cylindrical battery which is indicated by the occurrence of an inner short circuit at various loading rate. The voltage drop due to an internal short circuit typically occurs shortly before the maximum force is reached in quasi-static loading cases. Whereas, under dynamic loading conditions, the battery exhibits a loading-rate effect, which causes a voltage drop due to short circuits occurring at an earlier displacement. The loading-rate hardening mechanism is primarily attributed to electrolyte flux. A finite element model of an 18650 cylindrical battery is established and calibrated with the in-situ tests results. The failure location inside the jellyroll cross-section is identified with the maximum equivalent plastic strain. Under the dynamic loading, the maximum stress corresponding to the short circuiting is higher than the quasi-static counterpart. The finite element model is used to illustrate the inner short-circuit mechanisms of the batteries under different loading rates, providing a design guide for enhancing the crashworthiness of the battery components. [ABSTRACT FROM AUTHOR]

Significant variance exists in the nominal capacity of lithium ion (Li-ion) pouch cells used for commercial electric vehicle battery packs. Accurate estimation of heat generation in such cells is critical for designing battery thermal management system. However, multi-physics models describing thermal behaviour of these cells are too complex whereas other numerical models discount the effect of cell capacity on heat generation. This paper proposes a new computational model based on artificial neural network (ANN) for estimating battery heat generation rate with cell nominal capacity as one of its key inputs along with ambient temperature, discharge rate and depth of discharge. A custom-designed calorimeter is utilised for experimentally generating the training dataset for the ANN. Problem of data scarcity is addressed analytically and virtual samples are produced via enthalpy formulation for battery heat generation. Subsequently, the model is trained using Levenberg–Marquardt algorithm. Results disclose that a three-layered feedforward ANN with one hidden layer having six neurons is optimum for this application. The architecture of the trained ANN for accurately simulating thermal behaviour of LiFePO 4 pouch cells of the nominal capacities from 8 to 20 Ah under varied conditions is exemplified. [ABSTRACT FROM AUTHOR]

A lithium iron phosphate battery (LiFePO 4 ) pack is one of the main power resources for electric vehicles and the non-uniformity of cells in the battery pack has become the bottleneck to improve the pack capacity. An active balancing method based on chargeable and dischargeable capacities, derived from the dynamically estimated state of charge (SOC) and capacity in the pack, is proposed to tackle this problem in both the charging and discharging processes. To determine the current of each cell in balancing operation, one extra current sensor is added with a chosen flyback balancing circuit. The balancing simulation of a LiFePO 4 battery pack has been conducted in the moderate and severe capacity imbalance scenarios. The simulation results show that the proposed battery balancing method has better performance than the other balancing methods based on voltage or SOC in increasing the charged and discharged pack capacity in the charging and discharging process. [ABSTRACT FROM AUTHOR]

• Battery SOC and SOP are co-estimated by combining the fractional-order calculus and the model predictive control theory. • A fractional-order modified moving horizon estimation algorithm is proposed for online SOC estimation. • A fractional-order model predictive control algorithm is devised to optimize current sequences for online SOP estimation. • Different battery current–voltage behaviors in the prediction horizon are researched over a battery operating range. Accurate battery modelling is the cornerstone to state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries in electric vehicles. Due to strong battery nonlinearity over a broad frequency range, traditional integer-order models are incapable of capturing complex battery dynamics for SOC and SOP co-estimation. This paper proposes a fractional-order modified moving horizon estimation (FO-mMHE) algorithm and a fractional-order model predictive control (FO-MPC) algorithm. Firstly, a second-order FOM is constructed by performing a series of hybrid pulse tests at different SOC regions, and its model parameters are identified through a particle swarm optimization-genetic algorithm method. Secondly, online SOC estimation is converted into a constrained optimization problem in a past moving horizon and then solved by the FO-mMHE algorithm, which enables fast convergence speed and proactive smoothing of estimation outcomes. Thirdly, the FO-MPC algorithm is devised to manipulate the current sequence in a prediction horizon for maximizing discharge/charge power accumulation and determining battery SOP in real time. Moreover, different battery current–voltage behaviors are comprehensively researched in the prediction horizon over a whole battery operating range. The proposed co-estimation method is validated under different dynamic load profiles. The experimental results demonstrate a SOC estimation error reduction of up to 1.2 % compared with the commonly used fractional-order extended Kalman filter while the SOP estimation error could be limited below 0.35 W. [ABSTRACT FROM AUTHOR]

In an electric vehicle (EV), thermal runaway, vibration or vehicle impact can lead to a potential failure of lithium-ion (Li-ion) battery packs due to their high sensitivity to ambient temperature, pressure and dynamic mechanical loads. Amongst several factors, safety and reliability of battery packs present the highest challenges to large scale electrification of public and private transportation sectors. This paper reviews mechanical design features that can address these issues. More than 75 sources including scientific and technical literature and particularly 43 US Patents are studied. The study illustrates through examples that simple mechanical features can be integrated into battery packaging design to minimise the probability of failure and mitigate the aforementioned safety risks. Furthermore, the key components of a robust battery pack have been closely studied and the materials have been identified to design these components and to meet their functional requirements. Strategic battery pack placement technique is also discussed using an example of Nissan LEAF battery packaging design. Finally, the disclosed design solutions described in this paper are compared with the Chevrolet Volt battery pack design to reveal the basic mechanical design requirements for a robust and reliable battery packaging system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data-model fusion method (DMFM) for online state of power (SOP) estimation of lithium-ion batteries at high discharge rates in electric vehicles. First, battery polarisation characteristics responding for high discharge rates are experimentally investigated through a series of decremental pulse tests. Battery polarisation voltage is observed with diverse growing patterns over a whole battery operation range, and its underlying correlations with state of charge (SOC), discharge rate and pulse runtime are recognised. Second, a feed-forward neural network (FFNN) with SOC, discharge rate and pulse runtime as inputs, is constructed to characterise battery polarisation voltage through modelling the current excited polarisation resistance. Third, a DMFM is proposed to combine the data-driven method and equivalent-circuit model based method for accurate online SOP estimation in a lengthy prediction window ranging from 30 s to 120 s. Moreover, an unscented Kalman filter is devised to filter the estimation outcomes of the DMFM for noise suppression. The experimental results validate the effectiveness of the constructed FFNN in reproducing the nonlinearity of battery polarisation characteristics at high discharge rates and show the significant improvement in SOP estimation accuracy. • A novel data-model fusion method is proposed for online SOP estimation. • Battery polarisation characteristics at high discharge rates are investigated. • A feed-forward neural network is constructed for battery polarisation modelling. • Experimental validations are conducted under the dynamic load profile. [ABSTRACT FROM AUTHOR]

Highlights: [•] State equations are derived from the combined battery equivalent circuit model. [•] An adaptive gain sliding mode observer for state of charge estimation is purposed. [•] The new observer minimises the chattering and improves the estimation accuracy. [•] The experiments of a lithium-ion battery verify the effectiveness of the observer. [ABSTRACT FROM AUTHOR]

The desire to reduce costs and increase productivity is driving mining industries to deploy underground mining electric vehicles (UMEVs). Due to the complex, working environments in underground mines, safer and more stable control strategies of UMEVs are required than the conventional control approaches used for electric vehicles (EVs). In this paper, first a UMEV model with bounded system uncertainties is derived. Next, a fuzzy parameter tuning (FPT) technique is used to adjust parameters in the sliding mode controller (SMC) against unexpected external disturbances and unbounded system uncertainties, which significantly improves system stability and robustness. Simulation and experimental results demonstrate that the proposed fuzzy parameter tuning sliding mode controller (FPTSMC) achieves better performance than SMCs with parametric uncertainties. [ABSTRACT FROM AUTHOR]

To enhance the heat transfer, an innovative origami channel heat exchanger is proposed and its performance is studied experimentally and numerically. The experimental results show the maximum temperature on the origami channels' pipe wall is 2.8 °C lower than that on the flat channel with the same the heat flux of 9500 W/m2. Additionally, the temperature difference across the pipe wall of the origami channel shows a reduction of 5.7 °C compared with that across the flat channel. The simulation results show the mean heat transfer coefficient of the origami channel increases by up to 331% compared to the flat channel with the 0.012 kg/s mass flow rate. Nevertheless, the origami channel leads to an extra energy consumption penalty. To evaluate overall performances of the origami channel, this paper introduces three dimensionless efficiency factors to quantify relevant costs in terms of energy, volume and mass. It is found that the origami channel achieves much higher dimensionless mass efficiency and volume efficiency but less dimensionless energy efficiency than the flat channel. The comprehensive evaluation, which considers all the three factors, reveals that the combined efficiency factor of origami channels with folding angles ranging from 65° to 75° improves by 80% to 95% compared to the flat channel. [ABSTRACT FROM AUTHOR]

Accurate and rapid fault detection is essential for the safe operation of lithium-ion batteries in electric vehicles. However, conventional fault detection methods dependent on constant thresholds may have false alarms or missing alarms due to the inevitable disturbances resulted from the battery system modeling errors and measurement noises. In this paper, we design a multi-objective nonlinear fault detection observer for lithium-ion batteries, which is robust against disturbances but sensitive to battery multi-fault. We then perform formal stability and L ∞ / H _ performance analysis for the resultant estimation error system. Furthermore, tractable design procedures for the observer gain parameter and an adaptive threshold are derived. Then, via adaptive thresholding, a delicate three-step multi-fault detection scheme is developed to detect the occurrence of battery various faults, including short-circuit faults, current and voltage sensor faults. Finally, the efficacy of the proposed scheme is validated under several experimental case studies involving a variety of faults with their different levels of severity and erroneous SOC initialization. • A general battery model that incorporates both multi-faults and disturbances. • A multi-objective nonlinear observer with formal stability and performance analysis. • A three-step multi-fault detection scheme with an adaptive threshold. • Extensive experimental studies involving a variety of faults. [ABSTRACT FROM AUTHOR]

Ultracapacitors are common power sources for new energy vehicles with the advantage of providing high current and power. Modelling and state of charge (SOC) estimation of ultracapacitors are still a research focus. In this study, a fractional order model (FOM), which is derived from the analysis of electrochemical impedance spectroscopy, is established for ultracapacitors and validated in frequency and time domains. After analyzing characteristics of an ultracapacitor and its open circuit voltage, the established FOM is combined with a fractional order Unscented Kalman filter to realize SOC estimation for ultracapacitors. Results show that the proposed method can estimate SOC more accurately than traditional SOC methods. Furthermore, an online joint estimation method for SOC and FOM parameters is proposed and validated by a hardware-in-the-loop platform, indicating that the FOM can be used for onboard applications. • A fractional order model is proposed using distribution of relaxation times. • The proposed fractional order model is validated in frequency and time domains. • A joint parameter and SOC estimation method is proposed for fractional order model. • A hardware-in-the-loop platform is established to validate the model and method. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to design a robust adaptive backstepping excitation controller for multimachine power systems in order to reject external disturbances. The parameters which significantly affect the stability of power systems (also called stability sensitive parameters) are considered as unknown and the external disturbances are incorporated into the power system model. The proposed excitation controller is designed in such a way that it is adaptive to the unknown parameters and robust to external disturbances. The stability sensitive parameters are estimated through the adaptation laws and the convergences of these adaptation laws are obtained through the negative semi-definiteness of control Lyapunov functions (CLFs). The proposed controller not only provides robustness property against external disturbances but also overcomes the over-parameterization problem of stability sensitive parameters which usually appears in some conventional adaptive methods. Finally, the performance of the proposed controller is tested on a two-area four machine 11-bus power system by considering external disturbances under different scenarios and is compared to that of an existing nonlinear adaptive backstepping controller. Simulation results illustrate the robustness of the proposed controller over an existing one in terms of rejecting external disturbances. [ABSTRACT FROM AUTHOR]

Alternating current (AC) preheating strategy for lithium ion batteries (LiBs) at high state of charge (SOC) is prone to exceeding their voltage limit and risking their health. To address these problems, DC-AC hybrid rapid heating method is proposed to preheat LiBs at high SOC operated from low temperatures. In the proposed method, a fractional order circuit model is adopted to derive a total impedance of a LiB which is used to calculate the optimal excitation parameters of a DC-AC preheating at different conditions. Experimental results demonstrate that the proposed strategy can preheat a LiB from −20 °C to 10.02 °C within 443s and a series-connected LiB pack from −19.26 °C to 10.97 °C within 395s at an average heat generation rate of 4.07 °C/min and 4.6 °C/min, respectively. No appreciable capacity fade for the LiB is observed after the proposed method is used to preheat LiBs after 210 cycles. • A DC-AC hybrid rapid heating method for LiBs at high SOC. • A good trade-off between rapid temperature-rise and safe heating. • No appreciable capacity loss after 210 preheating cycles. [ABSTRACT FROM AUTHOR]

With the increasing installation of battery energy storage systems, the safety of high-energy-density battery systems has become a growing concern. Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional information of voltage, current and temperature have gradually encountered bottlenecks. It is necessary to adopt a proactive approach by using mulitidimensional information to advance fault diagnosis techniques. This involves integrating advanced sensing technologies, collecting multidimensional data and uncovering subtle changes in battery behavior. This paper delves into the mechanisms and evolutionary paths of battery faults, with a specific focus on the multidimensional observable signals associated with different faults for enhanced safety strategy. Furthermore, the paper provides a comprehensive overview of potential applications of different sensors for multidimensional measurement in battery fault diagnosis. It also explores the future trends and research directions of the next generation of battery fault diagnosis techniques driven by multidimensional data collection and artificial intelligence algorithms. [Display omitted] • Enhanced safety through proactive, multidimensional fault diagnosis techniques. • Integration of advanced sensing tech for precise multidimensional data collection. • Uncovering subtle battery behavior changes for improved fault detection. • Specific focus on multidimensional signals to enhance safety strategies. • Future trends in battery fault diagnosis driven by AI and multidimensional data. [ABSTRACT FROM AUTHOR]

• A deep learning approach is proposed to estimate the SOC of LiFePO 4 batteries. • Accurate SOC estimation can be ensured even in the case of voltage plateaus. • A closed-loop framework is developed for improving robustness. • The proposed method can quickly adapt to new scenarios via transfer learning. State of charge (SOC) estimation constitutes a critical task of battery management systems. Conventional SOC estimation methods designed for dynamic profiles have difficulties in estimating SOC for LiFePO 4 batteries due to their flat open circuit voltage characteristics in the middle range of SOC. In this study, a deep neural network (DNN) based method is proposed to estimate SOC with only 10-min charging voltage and current data as the input. This method enables fast and accurate SOC estimation with an error of less than 2.03% over the entire battery SOC range. Thus, it can be used to calibrate the SOC estimation for the Ampere-hour counting method. We also demonstrate that by incorporating the DNN into a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved. In the case of significant disturbances, the method still maintains a root mean square error of 0.385%. Moreover, the trained DNN can quickly adapt to various scenarios, including different ageing states and battery types charged at different rates, thanks to the transfer learning nature. Compared with developing a new DNN, transfer learning can provide more accurate estimation results at less training costs. By only fine-tuning one layer of the pre-trained DNN, the root mean square error can be less than 3.146% and 2.315% for aged batteries and different battery types, respectively. When more layers are fine-tuned, superior performance can be achieved. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries decay every time as it is used. Aging-induced degradation is unlikely to be eliminated. The aging mechanisms of lithium-ion batteries are manifold and complicated which are strongly linked to many interactive factors, such as battery types, electrochemical reaction stages, and operating conditions. In this paper, we systematically summarize mechanisms and diagnosis of lithium-ion battery aging. Regarding the aging mechanism, effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode, and other battery structures. The influence of different external factors on the aging mechanism is explained, in which temperature can exert the greatest impact compared to other external factors. As for aging diagnosis, three widely-used methods are discussed: disassembly-based post-mortem analysis, curve-based analysis, and model-based analysis. Generally, the post-mortem analysis is employed for cross-validation while the curve-based analysis and the model-based analysis provide quantitative analysis. The challenges in the use of quantitative diagnosis and on-board diagnosis on battery aging are also discussed, based on which insights are provided for developing online battery aging diagnosis and battery health management in the next generation of intelligent battery management systems (BMSs). • Basic aging reactions inside battery during storage and cycling were described. • Detailed classification and comparison of aging diagnosis methods were presented. • Progress and challenges of aging diagnosis in quantitative analysis and on-board applications were provided. • Evolution of dominant aging mechanism under different external factors was discussed. [ABSTRACT FROM AUTHOR]

Fractional order models have been successfully applied to estimate states and diagnose faults for lithium ion batteries. However, their order has not been identified online, which restricts their applications in battery management systems due to the intuitive nonlinearity of fractional order identification. In this study, a novel online method is proposed to identify the parameters and order of a fractional order model for lithium ion batteries using least squares and a gradient-based method, respectively. This online method is validated against both simulation and experimental results. Compared with the fixed-order method under different operation conditions, the proposed method has achieved better model accuracy and robustness of identified model parameters. Furthermore, a hardware-in-the-loop test is also used to verify the efficacy of the proposed method. Based on the analysis of the online identification results, the limitations of existing fractional order models are also pointed out, and the directions to further improve the existing models are discussed. • The order and parameters of a fractional order battery model are identified online. • The proposed method is validated by experiments and hardware-in-the-loop tests. • The proposed method is more accurate and robust than the fixed order method. [ABSTRACT FROM AUTHOR]

The stiffness of Li-ion batteries is defined as the derivative of force with respect to displacement. The existing constitutive models of the jellyroll of Li-ion batteries reveal that such stiffness keeps increasing as displacement increases. In this study, quasi-static mechanical abusive tests are performed on 18650 cylindrical Li-ion batteries at different state of charge. The experimental results indicate that three distinct stages are identified in the stiffness curve corresponding to densification stage, microscopic damage stage and macroscopic failure stage, and the stiffness only increases in the first stage and decreases in the latter two stages. Therefore, this paper proposes the improved constitutive model of the jellyroll of Li-ion batteries to describe their kinematics considering microscopic damage. An explicit finite element model of a Li-ion battery is established to validate the improved constitutive model. The voltages and temperatures of Li-ion batteries are also recorded to reveal their responses at different stages. It is found that Li-ion batteries at the fully charged state initiate internal short circuit before the end of the stage 2 (microscopic damage stage) whereas Li-ion batteries at low state of charge will only initiate internal short circuit at the stage 3 (macroscopic failure stage). • A Li-ion battery's deformation process can be divided into three stages. • Improved constitutive model of the jellyroll is proposed with considering microscopic damage. • A Li-ion battery at fully charged state initiates internal short circuit at microscopic damage stage. [ABSTRACT FROM AUTHOR]

Accurate state-of-charge (SoC) estimation is remarkably difficult due to nonlinear characteristics of batteries and complex application environment in electric vehicles (EVs), particularly low temperature and low SoC. In this paper, an improved battery model is first built using a feedforward neural network (FFNN) by introducing newly defined inputs. Based on the FFNN model and the extended Kalman filter algorithm, a FFNN-based SoC estimation method is designed, and its robustness is verified and discussed using the experimental data obtained at different temperatures. Finally, a hardware-in-loop test bench is built to further evaluate the real-time and generalization of the designed FFNN model. The results show that the SoC estimation can converge to the reference value at erroneous settings of an initial SoC error and an initial capacity error, and the SoC estimation errors can be stabilized within 2% after convergence, which applies to all the cases discussed in this paper, including low temperature and low SoC. This indicates that the FFNN-based method is an effective method to estimate SoC accurately in complex EV application environment. • Battery model is built using a feedforward neural network with newly defined inputs. • The SoC estimation method performs well even at low SoC and low temperature. • The proposed method can result in a good accuracy even using an inaccurate capacity. • The effectiveness of the method is verified by hardware-in-loop test. [ABSTRACT FROM AUTHOR]

Accurate estimating the state of charge (SOC) can improve battery reliability, safety, and extend battery service life. The existing battery models used for SOC estimation inadequately capture the dynamic characteristics of a battery in a wide temperature over the full SOC range, leading to significant inaccuracies in SOC estimation, especially in low temperature and low SOC. A novel SOC estimation approach is developed based on a fusion of neural network model and equivalent circuit model. Firstly, the weight-SOC-temperature relationship is established by obtaining the weights of the equivalent circuit model and the neural network model offline using the standard deviation weight assignment method. Following that, an online adaptive weight correction approach is implemented to update the weight-SOC-temperature relationship. Finally, a novel multi-algorithm fusion technique is utilized to achieve SOC estimation accuracy within 1%. The results clearly demonstrate that the developed approach achieves twice the accuracy of the existing approach, highlighting its superior effectiveness. • A method to integrate a NN model and an ECM is developed to obtain a fusion model. • An online adaptive correction method for updating the model weight is developed to build the fusion model. • A method for SOC fusion estimation is proposed in a wide temperature over the full SOC range. • The robustness of the method is verified at various temperatures and operating conditions. [ABSTRACT FROM AUTHOR]

The control system of pseudo-OCV construction. [Display omitted] • Equation between SOC error with voltage error and OCV slope is established. • Novel pseudo open circuit voltage is proposed to estimate SOC of LiFePO 4 batteries. • Accurate SOC estimation can achieved even during the plateau period. • Inter-partition correction is proposed to improve the robustness and convergency. LiFePO 4 batteries are widely used in electric vehicles and energy storage due to their high safety. However, their flat voltage characteristics in the middle state of charge (SOC) range make it difficult to correct SOC estimation errors with a feedback mechanism. This study proposes a pseudo-open circuit voltage (OCV) modeling method to improve the performance of a closed-loop feedback correction. First, the relationship between the derivative of OCV and SOC or the slope of OCV, SOC range, and estimation error is analyzed to select the SOC interval for OCV curve construction. Second, a pseudo-OCV curve is established. An optimization algorithm is developed to identify the parameters of the OCV model based on the selected SOC interval and OCV slope, which establishes a pseudo-OCV curve. Third, the pseudo-OCV is compared with the real OCV to adjust the selected interval and further determine the optimal construction interval, then the curves of the pseudo-OCV and real OCV are stitched together to obtain the optimal OCV curve for global SOC estimation. Finally, SOC estimation is carried out using the constructed full pseudo-OCV and compared with the reference SOC obtained from experiments. The results show that the SOC estimation error at the full SOC range is less than 3 %. [ABSTRACT FROM AUTHOR]

Automotive electrification is a main source of demand for lithium ion batteries. Performances of battery charging directly affect consumers' recognition and acceptability of electric vehicles. Study on optimized charging methods is vital for future development of a smarter battery management system and an intelligent electric vehicle. This paper starts from introducing the working principles and existing problems of simple charging methods and then elaborating various optimized charging methods along with their characteristics and applications. It demonstrates that the optimized charging methods can reduce charging time, improve charging performance and extend battery life cycle comparing with conventional charging methods. At the end, this paper also provides a four-step pathway towards the design of an optimal charging method of Li-ion batteries: determine optimization objectives, establish optimization scheme, develop matching design and implement and promote the optimal charging method. • Optimized charging methods are reviewed. • A new viewpoint on optimized charging methods is presented. • A four-step pathway towards the design of an optimal charging method is provided. • The prospects and development direction of charging methods are expounded. [ABSTRACT FROM AUTHOR]

Abstract A wavelet transform (WT)-based energy management strategy (EMS) is developed to reduce the damages caused by transient and peak power demands on batteries in plug-in hybrid electric vehicles. A hybrid energy storage system (HESS) consisting of a battery pack, an ultracapacitor pack and two DC/DC converters is established based on MATLAB/Simulink. The WT-based EMS with different decomposition levels is evaluated by using simulation under the New European Driving Cycle (NEDC). Comparison results show that the 3-decomposition-level based EMS is the optimal selection. The developed EMS is further evaluated by using simulation under three typical driving cycles including HWFET, WVUBUS and MANHATTAN. To validate the feasibility of the developed EMS, a hardware-in-the loop (HIL) test bench is constructed to simulate the EMS. The results indicate that the developed WT-based EMS with 3 decomposition levels achieves better accuracy performances. Highlights • A wavelet transform-based energy management strategy is developed in details. • The developed EMS with different decomposition levels is evaluated with NEDC cycles. • The developed EMS is further evaluated by different driving cycles. • A-hardware-in-the-loop test bench is constructed to verify the simulation results. [ABSTRACT FROM AUTHOR]

With the continuous concern on the safety of battery systems, accurate and rapid assessment of battery degradation is essential for practical applications. In this paper, a transferable attention network model based on deep learning is developed to evaluate battery degradation, which can simultaneously predict state of health (SOH) and remaining useful life (RUL) for lithium-ion batteries. First, degradation patterns of the cells are identified by K-means clustering based on the difference of the cells at their early cycles. Secondly, the attention mechanisms are designed to suppress noises in extracted feature maps and trace the interaction between long- and short-term degradation modes. Thirdly, the common knowledge represented by the reference cells and the unique degradation features of the target cell are fused by transfer learning, then SOH and RUL prediction are realized through multi-task learning. Finally, a large-scale battery dataset containing different cycle conditions is used to verify the accuracy and generalization of the developed method. The results show that the developed method achieves accurate SOH and RUL prediction with the average root mean square error within 0.14% and six cycles, respectively. • A lifespan classifier is developed to identify the cells with different degradation patterns. • A transferable attention neural network model is developed to simultaneously predict battery SOH and RUL. • The attention mechanisms are designed to reduce the effects of raw data noises and degradation rate changes. • The developed model with transfer learning strategy is validated on three battery datasets. • The superiority of the developed model is verified through comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A model for SOH estimation and degradation diagnosis is present. • Proposing a method to select appropriate segments of charging data as model input. • This model development process does not rely on aging data. • The method is validated on real aging data. Lithium-ion batteries (LiBs) have been widely used in electric vehicles and portable electronics. However, the performance and safety of these applications are highly dependent on degradation of LiBs. In this paper, three contributions have been made to achieve reliable degradation diagnosis and State-of-Health (SOH) estimation: (1) Open-circuit voltage is reconstructed to diagnose degradation modes of LiBs by performing scaling and translation transformations on open-circuit potential curves. (2) A degradation diagnosis model is developed to quantify aging characteristics of LiBs. In this model, a segment of charging data is taken to estimate SOH and the degradation modes in a degradation path. (3) An appropriate voltage range of the charging data is selected to improve model estimation accuracy. Experimental results show that the proposed method can achieve reliable degradation diagnosis and accurate SOH estimation with the maximum error of 1.44%. [Display omitted] [ABSTRACT FROM AUTHOR]

Electrochemical impedance spectroscopy (EIS) is a versatile tool to characterise lithium-ion batteries. However, EIS measurement is challenging in practice as it needs costly hardware and stringent test requirements. In this study, we propose a data-driven solution to predict battery impedance spectra at different states. An encoder-decoder deep neural network is developed to achieve simultaneous predictions of both impedance spectra and state of charge (SOC) only using short-term pulse data sampled at 1 Hz, thereby precluding the need for specific hardware and alleviating test requirements. A large dataset covering over 2700 impedance spectra over the frequency range of 100 mHz to 10 kHz is established to validate the proposed method at different SOCs, temperatures and ageing states. From the validation results, the proposed method enables accurate predictions at different temperatures and ageing levels while the associated errors of impedance spectra and SOC can be restricted within 1.5 mΩ and 1.26%, respectively. We further demonstrate that the predicted impedance spectra can provide detailed physical insight into battery kinetics as it offers accurate extractions of critical parameters of an impedance model. Our method makes EIS measurement more accessible to evaluate battery characteristics and highlights the potential of deep learning in battery research. [ABSTRACT FROM AUTHOR]

A bidirectional (Bi) DC/DC converter is one of the key components in a hybrid energy storage system for electric vehicles and plug-in electric vehicles. Based on the detailed analysis of the losses in the converter, this paper firstly develops a model to theoretically calculate the efficiency of the converter. Then, the influences of temperature, switching frequency, duty cycle and material of switching device on the converter’s efficiency are experimentally investigated. The analysis of the experimental results has shown that (1) The efficiency at the switching frequency of 15 kHz is about 2% higher than that of 25 kHz. (2) The efficiency at 25 °C is similar to that at 85 °C for the MOSFET SiC while the efficiency at 25 °C is 2% higher than that at 85 °C for the IGBT Si for both buck and boost modes. (3) In buck mode, when the duty cycles are decreasing from 66.7%, 50% to 33.33%, the peak efficiencies are also decreasing from 97.6%, 94.5% to 90.3%, respectively. In boost mode, when the duty cycle is increasing from 33.33%, 50% to 75%, the peak efficiency is decreasing from 96.9%, 96.5% to 92.4%, respectively. (4) The developed model can calculate the converter’s efficiency accurately [ABSTRACT FROM AUTHOR]

Experimental platform of proposed self-heating system and heating speed. [Display omitted] • A novel extremely fast self-heating and control system for cold batteries is proposed. • A thermal model and capacity fading model for batteries under the rapid heating process are proposed. • The battery optimal heating strategy taking into account the heating speed and aging factors are proposed. • A low-temperature self-heating battery system performance test and verification platform are developed. Lithium-ion batteries (LIBs) need to be heated before use at low temperatures to avoid poor electric vehicle performance. In this study, a self-heating method for LIBs at low temperatures is proposed, where the influence of various heating parameters on heating performance is explored experimentally. To make the balance between heating speed and capacity degradation while achieving efficient preheating, a lumped parameter thermal model and an empirical capacity fade model are established to determine appropriate duty ratio and external resistance, which can predict the corresponding time required for LIBs to be heated to the target temperature and reveal the capacity loss of LIBs quantitatively after repeated heating. A multi-objective optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain the Pareto optimal front between heating speed and capacity degradation, which leads to the selection of the optimal electrical parameters with the help of K-means clustering algorithm and three newly defined heating performance indicators. Finally, the duty ratio and external resistance are preferably 80% and 203.98 mΩ through the NSGA-II method, respectively. The experimental results verify the optimal heating strategy which can heat the LIB quickly from – 20.56 °C to 0 °C within 70 s. This optimal heating strategy is applied to heat the LIB for 200 times, the battery capacity degradation is only about 7.72%. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and complex time-variant property of batteries, the existing state of charge (SOC) estimation approaches based on a single equivalent circuit model (ECM) cannot provide the accurate SOC for the entire discharging period. This paper aims to present a novel SOC estimation approach based on a multiple ECMs fusion method for improving the practical application performance. In the proposed approach, three battery ECMs, namely the Thevenin model, the double polarization model and the 3rd order RC model, are selected to describe the dynamic voltage of lithium-ion batteries and the genetic algorithm is then used to determine the model parameters. The linear matrix inequality-based H-infinity technique is employed to estimate the SOC from the three models and the Bayes theorem-based probability method is employed to determine the optimal weights for synthesizing the SOCs estimated from the three models. Two types of lithium-ion batteries are used to verify the feasibility and robustness of the proposed approach. The results indicate that the proposed approach can improve the accuracy and reliability of the SOC estimation against uncertain battery materials and inaccurate initial states. [ABSTRACT FROM AUTHOR]

• Solid-phase diffusion equation based surface SOC is proposed. • New enhanced ECM to describe low SOC behavior more precisely was proposed. • Battery test platform was built to conduct battery tests for model validation. • Proposed model's voltage RMSE at low SOC has been reduced to 8 mV. Battery model is the basis of battery efficient and safe management. The widely used equivalent circuit model (ECM) generally shows poor behavior in predicting battery terminal voltage at low sate of charge (SOC), increasing the risk in the urgent use of a battery at low voltage greatly. To model strong nonlinearity of battery open circuit voltage (OCV), a solid-phase diffusion equation based surface SOC is proposed to characterize OCV behavior and establish the new structure of the enhanced ECM to describe low SOC behavior more precisely. Finally, a battery test platform was built to conduct battery tests for model validation. The results show that the root mean square error (RMSE) of the battery terminal voltage obtained from the proposed model at low SOC has been reduced to 8 mV compared with the RMSE of 17 mV from the traditional ECM model. It is expected that the proposed model can be employed in battery management systems to effectively improve the reliability and safety of emergency use of a battery at low SOC. [ABSTRACT FROM AUTHOR]

• An improved model with the voltage as input and current as output (VICO) is proposed. • The established VICO model is extended to an n -order VICO model. • The fault diagnosis method of current sensor is realized with the first-order VICO model. • The adaptability under different operating conditions and merit in detecting time are verified. Battery management systems (BMSs) are very important to ensure the safety of electric vehicles. The normal operation of BMSs is highly dependent on the accuracy of battery sensors. The present fault diagnosis efficiency of current sensors is much lower than that of voltage sensors due to model limitations in conventional methods. In this paper, a fault diagnosis method based on an improved model with voltage as input and current as output (VICO) is proposed to detect current sensor faults, where the least squares method combined with the unscented Kalman filter is used to estimate the fault current of current sensor. By comparing the estimated fault current with the diagnosis threshold, the fast fault diagnosis of current sensor is realized. The proposed method is verified under different operating conditions and compared with the methods based on state of charge and open-circuit voltage residuals. To highlight the importance of the proposed method, the influence and possible causes of minor faults and temperature on diagnosis are analyzed. The experimental results show that the method can detect the fault of the current sensor more accurately and quickly compared with the conventional methods, and has the ability to detect minor faults and adaptability under different operating conditions and temperatures. [ABSTRACT FROM AUTHOR]

• ESC behaviors at various temperatures are investigated experimentally. • ESC-induced heat generation and its impacts on electrical behaviors is modeled. • Distribution and anisotropy diffusion of ESC-caused heat generation is delineated. • An electro-thermal coupling model is proposed for batteries under ESCs. • Effectiveness of the proposed model is verified by experimental data. External short circuit (ESC) fault, which can cause large current and high temperature, is one of the main reasons for battery failure. Its analysis and diagnosis remains a challenging task due to complex electro-thermal characteristics of batteries under ESCs. In this paper, ESC experiments at various temperatures are conducted to investigate the impact of temperature on battery electro-thermal behaviors. Based on the analysis of the experimental data, heat generation inside a battery caused by ESC-induced high current and side reactions is modeled. The heat distribution and diffusion are also modeled by considering battery's internal jellyroll structure. The combination of the heat generation, distribution and diffusion models forms a novel electro-thermal coupling model, which is used to predict the complex thermal and electrical properties of a battery under ESCs. The presented model is simulated and verified by the test data. The maximum root mean square error of ESC current prediction is less than 1.73A and the maximum errors of the internal temperatures and the surface temperatures are only 1.771% and 3.915%, respectively. These results verify the effectivceness of the presented model. It is expected that the presented model is useful for safety analysis, temperature prediction and fault diagnosis applications of the lithium-ion batteries under ESC. [ABSTRACT FROM AUTHOR]

Understanding of mechanical property of lithium-ion batteries is the key to unlock complicated and coupled behaviors of thermal runaway, which is triggered during electric vehicle collision. In this study, mechanical behaviors of cylindrical lithium-ion batteries under dynamic loadings are investigated. Two types of 18650 lithium-ion batteries, namely LiNiCoAlO 2 and LiNiCoMnO 2, are chosen to perform compression tests at various dynamic loadings. Experimental results indicate that these two types of 18650 lithium-ion batteries exhibit strain rate hardening behaviors, namely their resistances to deformation enhance as loading rate increases. LiNiCoMnO 2 batteries show obvious strain rate hardening behaviors at low loading rates while LiNiCoAlO 2 batteries can only show strain rate hardening behaviors until the loading rate increases to a certain value. The constitutive model of the jellyroll of lithium-ion batteries is proposed to describe these mechanical behaviors under dynamic loadings and it is validated by a finite element model of lithium-ion batteries. The proposed constitutive model can be utilized to evaluate the crashworthiness of lithium-ion batteries in the case of impact accidents and provide valuable guidance for the structure design of battery packs in electric vehicles. • Two types of 18650 Li-ion batteries are performed dynamic compression tests. • Two types 18650 Li-ion batteries both exhibit strain rate hardening behaviors. • Establishing a finite element model of cylindrical Li-ion batteries suitable for dynamic loadings. [ABSTRACT FROM AUTHOR]