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In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future. [ABSTRACT FROM AUTHOR]

Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles. Short circuit, overcharge, and overheat are three common field failures of LiBs. In this paper, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe the ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5 s after their occurrences with the terminal voltage error less than 25 mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48 and 60 mV, respectively. [ABSTRACT FROM AUTHOR]

In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme. [ABSTRACT FROM AUTHOR]

• 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]

Battery models are the cornerstone to battery state of charge (SOC) estimation and battery management systems in electric vehicles. This paper proposes a novel fractional-order model for a battery, which considers both Butler–Volmer equation and fractional calculus of constant phase element. The structure characteristics of the proposed model are then analyzed, and a novel identification method, which combines least squares and nonlinear optimization algorithm, is proposed. The method is proven to be efficient and accurate. Based on the proposed model, a fractional-order unscented Kalman filter is developed to estimate SOC, while singular value decomposition is applied to tackle the nonlinearity of Butler–Volmer equation and fractional calculus of constant phase element. The systematic comparison between the proposed model and traditional fractional order model is carried out on two LiNiMnCo lithium-ion batteries at different temperatures, ageing levels, and electric vehicle current profiles. The comparison results show that the proposed model has higher estimation accuracy in battery terminal voltage and SOC than the traditional model over wide range of temperature and ageing level under electric vehicle operation conditions. Furthermore, the hardware-in-the-loop test validates that the proposed SOC estimation method is suitable for SOC estimation in electric vehicles. [ABSTRACT FROM AUTHOR]

An accurate battery capacity and state estimation method is one of the most significant and difficult techniques to ensure efficient and safe operation of the batteries for electric vehicles (EVs). Since capacity and state of charge (SoC) are strongly correlated, the SoC is hardly to be accurately estimated without knowing accurate battery capacity. Thus, a multiscale dual H infinity filter (HIF) has been proposed to estimate battery SoC and capacity in real time with dual timescales in response to slow-varying battery parameters and fast-varying battery state. The proposed method is first evaluated and verified using off-line experimental data and then compared with the single/multiscale dual Kalman filters (KFs). The results show that the proposed multiscale dual HIFs has better robustness and higher estimation accuracy than the single/multiscale dual KFs. To further validate the feasibility of the proposed method for EV applications, a lithium-ion battery-in-the-loop approach is applied to verify the stability and accuracy of the SoC estimation, and it is found that the SoC estimated from the proposed method can converge to the reference value gradually and be stabilized within 2%. [ABSTRACT FROM PUBLISHER]

Accurate estimation of state-of-charge (SoC) is vital to safe operation and efficient management of lithium-ion batteries. Currently, the existing SoC estimation methods can accurately estimate the SoC in a certain operation condition, but in uncertain operating environments, such as unforeseen road conditions and aging related effects, they may be unreliable or even divergent. This is due to the fact that the characteristics of lithium-ion batteries vary under different operation conditions and the adoption of constant parameters in battery model, which are identified offline, will affect the SoC estimation accuracy. In this paper, the joint SoC estimation method is proposed, where battery model parameters are estimated online using the H-infinity filter, while the SoC are estimated using the unscented Kalman filter. Then, the proposed method is compared with the SoC estimation methods with constant battery model parameters under different dynamic load profiles and operation temperatures. It shows that the proposed joint SoC estimation method possesses high accuracy, fast convergence, excellent robustness and adaptability. [ABSTRACT FROM PUBLISHER]

An adaptive H infinity filter approach is proposed to estimate the multistates including state of charge (SOC) and state of energy (SOE) for a lithium-ion battery pack. In the proposed approach, the covariance matching technique is used to adaptively update the covariance of system and observation noises and the recursive least square method is used to identify the battery model parameters in real time. The hardware-in-the-loop (HIL) platform for battery charge/discharge is set up to evaluate the accuracy and robustness of the SOC and the SOE estimation and compare the proposed approach with the multistate estimators using an extended Kalman filter and an H infinity filter. The experimental results indicate that the adaptive H infinity filter-based estimator is able to estimate the battery states in real time with the highest accuracy among the three filters. [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]

The simulation plays an important role in understanding of electrochemical behavior and internal process of lithium ion batteries. The existing finite difference method (FDM) to conduct the simulation of electrochemical process is time-consuming and computationally expensive. In this paper, a novel numerical method is proposed to accelerate the solution of the electrochemical model for a lithium ion battery. It is implemented in three steps. In the first step, physical analogy of electrochemical process to an electric circuit is used to solve charge conservation equations. In the second and third step, control volume method is used to solve species conservation equations. The simulation results show that the proposed method is much faster than the FDM by 2.2 times while maintain high accuracy which is verified by simulation and experimental data as well. [ABSTRACT FROM AUTHOR]

This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation. [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]

A theoretical electrochemical thermal model combined with a thermal resistive network is proposed to investigate thermal behaviours of a battery pack. The combined model is used to study heat generation and heat dissipation as well as their influences on the temperatures of the battery pack with and without a fan under constant current discharge and variable current discharge based on electric vehicle (EV) driving cycles. The comparison results indicate that the proposed model improves the accuracy in the temperature predication of the battery pack by 2.6 times. Furthermore, a large battery pack with four of the investigated battery packs in series is simulated in the presence of different ambient temperatures. The simulation results show that the temperature of the large battery pack at the end of EV driving cycles can reach to 50 °C or 60 °C in high ambient temperatures. Therefore, thermal management system in EVs is required to maintain the battery pack within the safe temperature range. [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]

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]

Current studies on the mechanical abuse of lithium-ion batteries usually focus on the mechanical damage process of batteries inside a jelly roll. In contrast, this paper investigates the internal short circuits inside batteries. Experimental results of voltage and temperature responses of lithium-ion batteries showed that battery internal short circuits evolve from a soft internal short circuit to a hard internal short circuit, as battery deformation continues. We utilized an improved coupled electrochemical-electric-thermal model to further analyze the battery thermal responses under different conditions of internal short circuit. Experimental and simulation results indicated that the state of charge of Li-ion batteries is a critical factor in determining the intensities of the soft short-circuit response and hard short-circuit response, especially when the resistance of the internal short circuit decreases to a substantially low level. Simulation results further revealed that the material properties of the short circuit object have a significant impact on the thermal responses and that an appropriate increase in the adhesion strength between the aluminum current collector and the positive electrode can improve battery safety under mechanical abusive conditions. [ABSTRACT FROM AUTHOR]

Parasitic load, which describes electrical energy consumed by battery thermal management system (TMS), is an important design criterion for battery packs. Passive TMSs using phase change materials (PCMs) are thus generating much interest. However, PCMs suffer from low thermal conductivities. Most current thermal conductivity enhancement techniques involve addition of foreign particles to PCMs. Adding foreign particles increases effective thermal conductivity of PCM-systems but at expense of their latent heat capacity. This paper presents an alternate approach for improving thermal performance of PCM-based TMSs. The introduced technique involves placing battery cells in a vertically inverted position within the battery-pack. It is demonstrated through experiments that inverted cell-layout facilitates build-up of convection current in the pack, which in turn minimises thermal variations within the PCM matrix by enabling PCM mass transfer between the top and the bottom regions of the battery pack. The proposed system is found capable of maintaining tight control over battery cell temperature even during abusive usage, defined as high-rate repetitive cycling with minimal rest periods. In addition, this novel TMS can recover waste heat from PCM-matrix through thermoelectric devices, thereby resulting in a negative parasitic load for TMS. [ABSTRACT FROM AUTHOR]

In this paper, a new charging strategy of lithium-polymer batteries (LiPBs) has been proposed based on the integration of Taguchi method (TM) and state of charge estimation. The TM is applied to search an optimal charging current pattern. An adaptive switching gain sliding mode observer (ASGSMO) is adopted to estimate the SOC which controls and terminates the charging process. The experimental results demonstrate that the proposed charging strategy can successfully charge the same types of LiPBs with different capacities and cycle life. The proposed charging strategy also provides much shorter charging time, narrower temperature variation and slightly higher energy efficiency than the equivalent constant current constant voltage charging method. [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]

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]

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]

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]

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]

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]

• A novel approach to reconstruct OCV for SOC estimation is proposed. • A parameter backtracking strategy is proposed for online parameter identification. • The OCV-SOC function is rewritten to locally reconstruct the OCV curve. • The constraint boundary of OCV are obtained from historical experimental data. • The OCV-SOC curve is reconstructed from the accumulated online data. Open circuit voltage (OCV) has a considerable influence on the accuracy of battery state of charge (SOC) estimation. Three efforts have been made to reconstruct OCV for SOC estimation of lithium ion batteries in this study: (1) A new parameter backtracking strategy is proposed for online parameter identification using the recursive least square (RLS) algorithm to obtain stable OCV, which significantly reduces the jitters occurring in OCV identification results. (2) Historical experimental data of lithium ion batteries are used to derive baseline OCV curve and determine constraint boundaries, then an extended Kalman filter (EKF) is employed as a state observer to estimate the SOC for the same types of the batteries that have not been tested. (3) The OCV-SOC curve is reconstructed based on the accumulated online parameter identification and SOC estimation results. The OCV curve can be locally reconstructed even when the accumulated data only cover a partial range of SOC, which is suitable for electric vehicle (EV) operation conditions. Once the OCV curve is reconstructed, the response surface model of OCV-SOC-Capacity is applied to update battery capacity. In this way, the OCV curve can be gradually reconstructed from high SOC to low SOC during battery discharging process. The use of the reconstructed OCV curve to estimate SOC significantly improves the SOC estimation accuracy with the maximum error less than 3% for EV operation conditions. [ABSTRACT FROM AUTHOR]