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Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system's feasibility. [ABSTRACT FROM AUTHOR]

Summary: In order to solve the problem that the unsatisfactory accuracy of SOH estimation method, which seeks the relationship between battery life and external characteristics through experiments, is restricted by battery consistency in a large number of battery applications, this paper proposes an SOH estimation framework which can automatically correct the errors caused by the battery consistency problem online. The SOH framework realizes the automatic online fast correction of SOH estimation error through the designed closed‐loop feedback framework. Another advantage of this framework is that it can achieve accurate estimation for the batteries state of health (SOH) during the irregular charging and discharging process of electric vehicles. And in this framework, a new equivalent circuit based on the autoregressive (AR) model is proposed to reduce the complexity of the battery method while ensuring the accuracy of the estimation, which has better robustness in practical applications. Finally, it is proved that the online estimation of lithium‐ion batteries SOH during discharge proposed in this paper has better practicability and higher estimation accuracy by comparing with the traditional SOH method of external feature relationship. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system's modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery's efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation. [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single‐variable battery states are established to analyze the influence of OCV‐SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%. [ABSTRACT FROM AUTHOR]

Summary: The latest breakthroughs in artificial intelligence resulted in their adoption in the electric vehicle's battery management systems. Simultaneously, the remarkable increase in the number of embedded systems in electric vehicles (EVs) has led to the search for new optimized state of charge (SOC) estimation strategies suitable for implementation on small and limited MCUs. In this regard, this paper provides a design and quantization processes of the 1D convolutional neural network (1D CNN) and the GRU‐recurrent neural network (GRU‐RNN), devoted for SOC estimation. Both the NN algorithms are designed and trained using data collected from an 18650 Li‐ion battery. Furthermore, to account for the limited computational resources of the EV MCUs, the pre‐trained models are then converted into highly optimized C‐codes using the latest model quantification techniques. In this regard, this paper investigates, the performances of the STM32Cube.AI and the TensorFlow Lite for microcontroller (TFLite Micro), in quantizing the activation functions and weights of the proposed NN models. This is achieved by converting these latter from 32‐bits floating‐point to 8‐bit integer precision. The obtained experimental results, under different profiles, indicate that the 1D CNN is more accurate than the GRU‐based model with root mean squared error (RMSE) of 2.33% and mean absolute error (MAE) of 1.62%. Furthermore, the impact of the quantization using STM32Cube.AI is compared with that using the TFLite Micro. The obtained results, demonstrated the superiority of the 1D CNN model quantized using the STM32Cube.AI. This latter reduces the flash memory size of the 1D CNN model from 10.82 to 2.89 KB and the RAM size from 2.49 to 1.04 KB, compared to the TFLite Micro, which reduces the RAM size from 7.0107 to 4.23 KB, and the flash from 43.361 to 15.88 KB. [ABSTRACT FROM AUTHOR]

Summary: The battery pack of electric vehicles (EV) is generally composed of multiple cells in series. Due to the inconsistency between the cells in the production process and use stage, the capacity and state‐of‐charge (SOC) of the cells will be different. We propose an online estimation method based on the charging curve similarity principle in this paper. The proposed method uses a series of charging time differences (CTD) during the charge. By analyzing the CTD curve, the capacity and SOC difference can be achieved. The first‐order resistance circuit model is used for the series charging curve simulation. Further experimental verification is conducted using two groups of four cells in series. In simulations and experiments, the error of the proposed capacity estimation method and the initial SOC error are less than 1%. Finally, the robustness of the proposed method is verified using EV cloud data. The results demonstrate that the proposed method has good robustness at the level of EV cloud data. [ABSTRACT FROM AUTHOR]

Summary: The equivalent consumption minimization strategy is widely used in real‐time energy optimization for fuel cell electric vehicles due to its approximate optimality and good real‐time performance. However, equivalent factors have always been difficult to select in the research of this algorithm, and the existing methods have insufficient adaptability and robustness. In this paper, an equivalent factor predictor based on a neural network is proposed, which can predict the equivalent factor in real time by considering various operating conditions and vehicle states. In addition, we improve the real‐time performance of the equivalent consumption minimum strategy. A new method to quickly solve the continuous optimal solution of the algorithm by transforming the objective function into the quadratic function of the control variable is studied. Simulation and experimental results show that the designed equivalent consumption minimization strategy can ensure state of charge of the battery in a better range, and the energy saving effect is approximately 3% more than that of dynamic programming and 8% to 10% less than that of the traditional rule algorithm. Finally, the proposed method saves 2 to 3 orders of magnitude in computation time compared with the traditional equivalent consumption minimum strategy. [ABSTRACT FROM AUTHOR]

The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system's ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS's efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management. [ABSTRACT FROM AUTHOR]

The battery pack performance and expected lifespan are crucial in electric vehicle applications. Balancing the charge on a battery pack connected in series and parallel is crucial due to manufacturing discrepancies and distinct performance of each cell in a standard battery pack. In this paper, a switched-resistor passive balancing-based method is proposed for balancing cells in a battery management system (BMS). The value of the available voltage at the battery cell terminals is balanced using resistors in an electrical circuit, and the excess voltage is eliminated. The cell balancing outcome demonstrates that the electrical circuit can maintain an even voltage across each cell. The procedure of balancing involves individually adjusting each cell's level of charge. Passive balancing releases energy as heat by draining charge from cells that have too much charge. A passive cell balancer is a cost-effective solution and easy to install, but due to thermal loss from a resistor, it has a low energy efficiency for cell balancing and necessitates a lengthy balancing process. This passive cell balancer is an effective and reliable method for low-power devices and portable applications such as electrical vehicles. The power limits during charging and discharging are estimated using the bisection method. [ABSTRACT FROM AUTHOR]

Summary: To accurately evaluate the state of charge (SOC) and state of health (SOH) of Li‐ion battery, the second‐order RC equivalent‐circuit model is used to characterize the battery performance, a novel dual adaptive Kalman filtering algorithm is presented by adding double cycles and noise adaptive steps to realize the joint estimation of the SOC and internal resistance. The state variables can be corrected with each other as go through the cycle under three operating conditions. The accuracy of the SOC estimation method proposed in this paper is significantly improved compared with the extended Kalman filtering and the unscented Kalman filtering algorithm. Under three operating conditions, the average error and the maximum error decreased obviously. An equation for calculating the SOH in terms of internal resistance increase was built. The estimation result of the SOH effectively simulated the actual situation, compared with the actual result, the maximum error under the three operating conditions are within a lower level than the improved unscented Kalman filtering algorithm. The convergence effect of the algorithm has obvious advantages over that of the algorithm used for comparison, which could effectively track the state change of the battery. [ABSTRACT FROM AUTHOR]

Summary: Battery inconsistency in electric vehicles is an important factor causing battery capacity degradation and safety problems. Therefore, battery equalization technology plays an important role in improving the performance and safety of battery packs. Among the existing equalization technologies, passive equalization is inefficient and active equalization is expensive and complex. Most of the active equalization algorithms only consider the balance of the cells within the battery module, but the balance between modules are not involved. However, battery packs for electric vehicles often consist of multiple modules, cooperative equalization between modules are essentially required to improve the balance efficiency. A novel cooperative equalization system for multi‐modules in the battery pack is proposed in this paper. The system combines active and passive equalization, and also includes a fast discharge function for balancing modules by a power resistor. An equalization algorithm aiming at the optimal equalization time is studied. Finally, the equalization system is verified in simulation and experiment. The experimental results show that the proposed equalizer demonstrates good performance in different consistency cases using the proposed control strategy and optimization equalization algorithm. [ABSTRACT FROM AUTHOR]

Summary: Extension of driving range and battery run time optimization are necessary key points in the modeling of Electric Vehicle (EV). In this view, Battery Management System (BMS) plays a major role to ensure a safe and trustworthy battery operation, especially when using Lithium‐ion (Li‐ion) batteries in an electric vehicle. Key function of BMS is State of Charge (SoC) estimation. A well‐parameterized battery model is required for accurate state estimation. Consequently, the major factors to be considered in battery modeling are the SoC estimation and charging methodology of an effective BMS development. By focusing on these features, in this paper, the well‐known battery models such as the electrochemical model, equivalent circuit model, and data‐driven model are comprehensively reviewed along with their strengths and weaknesses. Further, the SoC estimation of a battery is also discussed by using standard methodologies such as direct estimation methods and model‐based estimation methods. The comparisons of the three most distinct battery models and the classification of SoC estimation techniques to develop a proper BMS for EV with the focus on accuracy, configuration effort, computational complexity, ease of implementation, and real‐time applications are systematically reviewed. In addition to this, convenient battery charging approaches with the consideration of some constraints such as charging time, charging efficiency, state of charge, state of health, charging voltage threshold, capacity fade, power fade, aging effect, capacity utilization, impedance rise, and temperature rise of the battery in EV are presented. Finally, the perspectives of the existing work and the recommended future research work of BMS are summarized. [ABSTRACT FROM AUTHOR]

Summary: Series‐connected lithium battery packs are widely adopted in industries such as electrical vehicles and large‐scale energy storage systems. It is necessary to configure an equalization system for them to reduce the inconsistency of single cells, to ensure the battery pack cycle capacity. Although many novel active converters have been proposed for equalization, there still lacks systematic analyses on variables, topologies, and modular methods. Therefore, this paper provides a systematic review on the above aspects. Initially, an overall summary is present on current variables for equalization control. Second, the dominant structures are divided into cell‐to‐cell, cell‐to‐pack, and pack‐to‐pack topologies, and their characteristics are pointed out for comparison. And then, the modular methods, including single‐layer‐based, multi‐layer‐based, and hybrid integrations are evaluated via graph theory. Some key issues and potential research directions are given, which are expected to offer assistance on the selection of appropriate variables and topologies for equalization systems in the future. [ABSTRACT FROM AUTHOR]

Summary: For lithium‐ion batteries (LIBs) used in electric vehicles (EVs), their performance is greatly affected by temperature. To ensure the safety and reliability of EVs, a reliable and efficient battery thermal management system (BTMS) is essential. This paper mainly analyzes air cooling BTMS. First, we propose an air‐cooling heat dissipation method for battery modules with herringbone fins and long sleeves, and prove the effectiveness of the program by comparing it with the finless and sleeveless schemes. Second, parametric modeling of the scheme is carried out, the response surface methodology (RSM), radial basis function (RBF), and Kriging surrogate model of the scheme are constructed through design experiments. Then, the Kriging model is determined by the model evaluation index to be well adapted to the problem. We build a multi‐objective optimization model (MOOM) and optimize it by multi‐objective genetic algorithm (MOGA). Finally, it is found that the maximum temperature (Tmax) and the maximum temperature difference (ΔTmax) decrease by 1.5 K and 36.79% without considering the cooling cost. When considering the cooling cost, Tmax and ΔTmax dropped by 0.69 K and 17.92%, respectively. The optimization result improves the heat dissipation effect of the battery module and controls the cooling cost within the required range. Besides, optimization analysis can be carried out according to different actual cost requirements, which provide corresponding guidance for large‐scale air‐cooling heat dissipation analysis of LIBs. [ABSTRACT FROM AUTHOR]

SUMMARY: Volume deformation of lithium‐ion batteries is inevitable during operation, affecting battery cycle life, and even safety performance. Accurate prediction of volume deformation of lithium‐ion batteries is critical for cell development and battery pack design. In this paper, a practical approach is proposed to predict the volume deformation of lithium‐ion batteries. In the proposed method, the deformation of a full cell is determined as the superposition of the thickness changes of cathodes and anodes, which are induced by the expansion/contraction of crystal structure during lithiation/de‐lithiation. The electrodes' crystal volume changes are calculated based on the evolution of lithium stoichiometry in cathode and anode, which can be identified through the reconstruction of the battery charging voltage profile. Finally, the thickness changes of cathode and anode as well as the deformation of the full cell are predicted. The proposed method can achieve accurate predictions of the deformations of lithium‐ion batteries with various chemistries, with the predicted errors less than 10%. Moreover, the proposed method can help to interpret the differences in deformation profiles of batteries with different chemistries, and provide useful guidance for cell development. [ABSTRACT FROM AUTHOR]

Summary: This paper studies LiFePO4 (LFP) battery capacity fading diversity among different cells with same type and specification under same working states during their whole life cycle; and with consideration of this phenomenon, a novel battery state of health (SOH) estimation method with adaptability to capacity fading diversity is proposed. In order to cope with this capacity fading diversity, a machine learning structure involving a sparse auto‐encoder (SAE) and a backward propagation neural network (BPNN) is designed for battery SOH estimation. In this strategy, battery terminal voltage during the later stage of charging process is used as input of SAE; through the reconstruction of input signal, compressive feature of battery voltage is abstracted by SAE; then this compressive feature is used as the input signal of BPNN, and through nonlinear mapping of the neural network, battery SOH can be finally obtained. In this way, a relationship between the battery voltage information at its later charging stage and its SOH can be established. Verification tests show that this SAE‐BPNN based SOH estimation strategy possesses a good accuracy with adaptability to the capacity fading diversity and voltage differences among different battery cells, the SOH estimation error can be restrained within the range of ±5%, and it is also very convenient to adopt this method in real online battery management system (BMS). [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion battery accidents occur frequently, and thermal runaway accidents are common. Lithium plating may occur in batteries at different temperatures, state of charge (SOCs), charging rates, etc., and lithium plating at the anode is one of the important incentives for thermal runaway of batteries. The plating of lithium metal is closely related to the anode potential of the battery. In this paper, based on the anode potential sensor, the anode potential of the battery under various working conditions is detected in real‐time, and the direct anode equivalent circuit model (DAECM) and the indirect anode equivalent circuit model (IDAECM) are proposed. The DAECM directly estimates the anode potential based on identifying anode parameters. Therefore, the IDAECM is proposed to estimate the cathode potential directly based on cathode parameter identification, to estimate the anode potential indirectly. First, the high‐precision P2D model is used to simulate the cathode, anode, and full cell potentials under various working conditions. According to the 1/50C discharge rate test, the open circuit voltage (OCV)‐SOC curve of the cell is obtained. The NEDC conditions are used to identify the parameters of the model, and the values of each parameter of the two models under different SOCs are obtained. According to the parameter identification results above, the anode potential under multirate constant current charging (MRCC) test conditions is estimated. Based on the method above, the IDAECM model is suitable for estimating the anode potential in the early stage of fast charging with large rate constant current, while the DAECM model is suitable for the final stage of fast charging with small rate constant current. [ABSTRACT FROM AUTHOR]

Summary: The gradual rise in global warming owing to fossil fuels usage is causing massive environmental problems and global temperature rise and countries are focusing on the negative emissions technologies (NETs) to restrict the average global temperature rise to 1.5°C by 2050. As Italy instigates its stable recovery from the coronavirus emergency, the Italian government is offering Italian people financial support of up to €500 to buy a personal bicycle or e‐scooter, to reduce dependence on private cars and global warming issues and people inItaly have greeted this opportunity for new personal e‐scooters. This paper aims to employ a life cycle assessment (LCA) on the personally owned electric scooters (PEOS) that are circulating in Turin. The results are reported for five impact categories: global warming potential (GWP), acidification potential, eutrophication potential, ozone layer depletion, and human toxicity potential. We found that the environmental load associated with the usage and charging of e‐scooters is less compared with the materials and manufacturing burdens of e‐scooters. Regarding the GWP, the results of Analysis methodology of the use of the PEOS generate 21 g of CO2‐eq. per passenger‐kilometer dominated by around 50% from materials processing, 17.5% from Li‐ion battery production, 1.6% from transportation, and 30.9% from usage and charging of e‐scooter. Four scenarios are compared with the base case situation, which are the substitution of alternate materials, use of 50% recycled aluminum, transportation of e‐scooters via plan, and charging with solar power. Results from these scenarios are proved to be highly sensitive to baseline scenarios. Results also revealed that PEOS has higher (21 g CO2 eq./km) environmental consequences on the global warming potential as compared to bicycles (8 g CO2 eq./km) and lower (21 g CO2 eq./km) environmental impacts as compared with electric bicycles (40 g CO2 eq./km) and battery electric vehicles (80 g CO2 eq./km). [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new energy management strategy (EMS) for electric vehicles (EVs) with battery/supercapacitor hybrid energy storage systems (HESS). Firstly, the battery/supercapacitor HESS configuration and obtaining the load current from the driving cycle are comprehensively explained. Secondly, fixed and adaptive frequency‐based (AFB) EMS are discussed in detail. In the proposed method, the current demanded by the load is separated into low and high‐frequency components with the help of an adaptive low‐pass filter (A‐LPF). In addition, adaptive battery current (ABC) is generated according to the supercapacitor (SC) state of charge (SoC), and a double adaptive power allocation strategy is performed. The proposed method is compared with the AFB‐EMS for load currents, load powers, DC link voltages, filter cut‐off frequencies, battery, and SC SoCs on the UDDS driving cycle. When the obtained results were evaluated together, it was observed that the proposed method completed the driving cycle with a higher battery SoC and realized the adaptive cut‐off frequency in a narrower band. [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion batteries are widely used in the market, and are continuously improving, given their numerous benefits. Choosing the best materials for the cathode is fundamental for optimal battery pack projects. Lithium batteries using nickel cobalt aluminum and nickel manganese cobalt have technology that is already well consolidated within the market. However, some state‐of‐the‐art research describes important technological advances in lithium‐ion stores with lithium iron phosphate oxide and lithium titanate oxide. These have numerous advantages that can improve electric vehicle performance. Different battery technologies were analyzed in this paper, providing a guideline for lithium‐ion battery manufacturers. Previous evaluations on niobium batteries are also presented, analyzing comparative perspectives with lithium‐ion batteries. Advances in cutting edge knowledge show that niobium is a promising metal for use in lithium‐ion batteries. Niobium‐doped batteries have shown good conductivity at low temperatures and high energy density compared with other lithium‐ion storage systems. This study encourages researchers to further develop research on lithium‐ion batteries using a comparative study. [ABSTRACT FROM AUTHOR]

Summary: Uncertainty prediction of lithium‐ion battery state‐of‐charge (SOC) is key for electric vehicle battery management systems. Aiming at the shortcomings of a single equivalent circuit model (ECM) and traditional SOC fusion estimation algorithms, this paper proposes a new multi‐model SOC fusion method. First, three sub‐models are established. Second, an adaptive extended Kalman filter is applied to each sub‐model in parallel to predict the battery terminal voltage and SOC simultaneously. Then, based on the ordered weighted averaging (OWA) operator theory, the real covariance matrix of the output voltage error of each model is obtained, and the weight factor of each sub‐model is calculated using this matrix. Finally, the SOC estimation of each model is weighted and synthesized to realize the SOC fusion estimation. The experimental results show that the maximum absolute error of the multi‐model SOC fusion estimation based on the OWA operator is close to the optimal value of a single model, whether it is the fusion of three ECMs or two ECMs with a degraded electrochemical model, and the multi‐model SOC fusion estimation based on OWA operator has better robustness than the single model SOC estimation. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the experimental validation of the proposed off‐board electric vehicle (EV) charging station that complies with the IEEE 519‐2014 power quality (PQ) standard. The proposed EV charging station constitutes a two‐stage power conversion, ac‐dc conversion at the front‐end (interfacing the grid and dc‐link), and dc‐dc conversion at the end‐stage (interfacing dc‐link and EV battery). The ac‐dc conversion stage at the front‐end of the proposed EV charging station with a fewer active switch count makes it unique from other available EV charging. It utilizes multi‐phase transformers to reduce the total harmonic distortion (THD) in the line current drawn, and to improve the power factor (PF). The multi‐phase transformers convert the grid power supply to a multi‐phase supply that makes the input line current stepped and reduces the THD, thus facilitating the reduction of active front‐end power conversion, filter circuitry, and PF correctors. The multi‐phase power supply is rectified to feed the dc‐link from which the EV battery is charged with a dc‐dc converter in constant current (CC) and constant voltage (CV) mode. The experimental validation was performed in a laboratory on a developed prototype of the proposed EV charger. The THD measured in the input line current was 3.78% with high PF (about unity) that meets the recommended IEEE 519‐2014 PQ standard. The efficiency of the charger recorded was 90.4%. A comparative analysis of the proposed EV charge is presented on five metrics namely: PQ, cost, power density, control complexity, and robustness, which confirms the effectiveness of the proposed EV charger. [ABSTRACT FROM AUTHOR]

Summary: The paper presents a novel methodology based on a BARON solver in general algebraic modeling system (GAMS) computational environment to solve the stochastic profit‐based unit commitment (SPBUC) problem in the deregulated energy market. The profit is considered as the difference between the total revenue collected by selling energy in the competitive energy market and the production cost of energy. The concepts of the deregulated energy market like market clearing price and market reserve price are employed for the determination of the expected revenue. Energy storage systems and electric vehicles have the benefit to act as load as well as generating sources during the uncertain and intermittent nature of renewable energy sources (RES). Thus, the RES in combination with battery energy storage systems and plug‐in hybrid electric vehicles (PHEVs) are modelled and included in the present study. The Monte Carlo Simulation technique is employed to deal with the uncertain nature of wind speed, solar irradiance and PHEVs. First, the proposed solution methodology is tested on a 10‐unit thermal system with and without valve point loading wherein it has provided an enhanced profit of 1.28% when compared with existing results reported in published literature. Secondly, the comparative results obtained by solving the SPBUC problem integrated with RES with different solvers available in GAMS computational environment demonstrate that the BARON solver outperformed the other solvers in terms of computation time and expected profit attained. [ABSTRACT FROM AUTHOR]

Summary: Calendar life accounts for most of the lifetime of the power lithium‐ion battery applied in electric vehicles, and thus should be investigated in detail. In this paper, a mechanistic calendar aging model of lithium‐ion battery is developed by adding the solid electrolyte interface (SEI) growth side reaction to the pseudo‐two‐dimensional electrochemical model. The model can accurately simulate the capacity degradation and evolution of the charging voltage profiles of a Lix(NiCoMn)1/3O2 ‐ graphite battery during high‐temperature (55°C) storage under various states of charge. The model is further validated by comparing the electrode degradations inside the batteries after calendar aging. The model predicted electrode degradations are consistent with the results identified using a dual‐tank model and post‐mortem analysis, confirming that SEI growth is the dominant degradation mechanism. Based on the validated model, internal changes in electrodes' structure induced by SEI growth during calendaring aging are discussed. The anodes of the calendar‐aged batteries suffer from severe pore clogging and film resistance increase, resulting in apparent power performance degradation of lithium‐ion battery. Finally, modeling analysis is conducted to find possible solutions to mitigate the battery calendar aging process. Rational designs of SEI by reducing the porosity and solvent diffusivity inside the SEI and the kinetic rate constant turn out to be effective in improving the calendar lifetime of lithium‐ion batteries. Novelty Statement: Calendar life accounts for most of the lifetime of the lithium‐ion battery in electric vehicles. This study establishes a mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth. The model can not only simulate the battery capacity degradation and the evolution of battery charging voltage profiles but also capture the internal degradation of the electrodes during the aging process. Modeling analysis is further conducted to find possible solutions to mitigate the calendar aging process of lithium‐ion battery. [ABSTRACT FROM AUTHOR]

Summary: Claw‐type pump is one of the most promising candidates in the circulating system of the hydrogen fuel cell because of its compact structure and high reliability. In this paper, claw‐type hydrogen circulating pump with a rated flowrate of 0.4 Nm3·min−1 was designed and fabricated. An internal flow model of the claw pump was established to investigate the thermodynamic performances with air. Then, a test rig was carried out to validate the feasibility of the numerical model with air. Finally, the numerical model was used to predict the thermodynamic performance with the fluid medium of hydrogen mixtures. Moreover, the effects of axial clearance and the content of water vapor and nitrogen were examined. The results showed that decreasing the axial clearance contributed to the increase in volumetric efficiency, hydrogen mass flow rate, power consumption, and the decrease in specific power. Meanwhile, increasing the contents of water vapor and nitrogen helped to increase the volumetric efficiency and decrease the specific power. However, it had a negative influence on increasing power consumption and decreasing hydrogen flow rate. The specific powers of all the hydrogen mixtures decreased much slower at the smaller axial clearances. [ABSTRACT FROM AUTHOR]

Summary: The inconsistency in the mass production of lithium‐ion battery (LIB) packs stem from the inconsistency in the capacity, voltage and internal resistance of single batteries that compose packs. The inconsistency issue of these battery packs can greatly reduce the output performance of a large power pack. This paper proposed the machine learning approach based on self‐organization mapping (SOM) neural networks for establishing the consistency of LIBs. This method comprehensively compares and analyzes the real‐LIB parameters (internal resistance, capacity and voltage) data obtained during charging and discharging to form the clusters of similar performing LIBs. Experimental result validated the clustering analysis and it indicates that the performance of clustered battery pack typically precedes than that of original. The capacity of clustered battery pack increased 1.9% compared with brand‐new pack. The temperature distribution of the battery pack assembled after screening is consistent. The peak temperature is 4°‐5° lower than the ordinary battery, and the temperature fluctuation is reduced by 2.6°. In addition, the application of cluster analysis is expanded and some key research directions are pointed out. [ABSTRACT FROM AUTHOR]

Summary: The State of Charge (SOC) estimation of the battery is used to display the operating conditions of the battery to make charging/discharging decisions to reduce range anxiety. For this purpose, precision in SOC estimation is always essential. The Extended Kalman Filter (EKF) algorithm is an effective method for accurate SOC estimation of nonlinear batteries. However, the performance of the filter strongly depends on process noise covariance matrix (Q) and measurement noise covariance matrix (R) values. The improper value of these matrices reduces the convergence rate and increases the estimation error. In this paper, the impact of these matrices on SOC estimation is analyzed by the trial & error method, then the Harris Hawks Optimization (HHO) algorithm based EKF is proposed. As a first step, HHO is used to obtain the optimal values for covariance matrices. In the second step, optimized matrix values are injected into the EKF, which confirms the filter accuracy in the SOC estimation. To justify the necessity of an optimized EKF, a model‐based SOC estimation algorithm is executed for a Li‐ion battery discharging scenario. The effectiveness of the HHO based EKF is compared with the traditional particle swarm optimization‐based EKF and the Adaptive Extended Kalman filter (AEKF) for SOC estimation. The simulation results show the proposed HHO based EKF algorithm gives an accurate SOC estimation of the battery even when the initial SOC is incorrect. The numerical results show that the performance indices such as maximum error, Mean Absolute Error, Mean Square Error, and Root Mean Square Error of both SOC and voltage obtained by the proposed HHO‐EKF are low compared with AEKF. The robustness of the proposed algorithm is also verified with the static load conditions and the dynamic profile of an electric vehicle. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a combined AC‐DC power flow model is purposed for the existing AC distribution system, where a certain amount of DC is superimposed from the battery swapping station on the usual AC feeder line with the help of a proposed zig‐zag transformer. In the proposed feeder system, the line‐to‐line voltages will be pure AC, hence all the three‐phase balanced load can be directly connected through lines. But, the line to ground voltages will be shifted upward, as same as the amount of DC voltages imposed by the battery swapping station which is demanded by the distribution system operator. To support the proposed methodology, power transmitted through different levels of distribution lines (33 kV, 11 kV, 400 V) is analyzed theoretically and numerically and simulated on MATLAB/Simulink Further, the proposed system is validated through small scale prototype designed and performed in the laboratory. The obtained results show the significant enhancement of the power flow capability such as up to 118.3% capacity improvement in the case of 400 V line. This purposed distribution model is a new subject that has great economic efficiency with the availability of DC sources such as battery storage plant to grid. It provides direct access to both AC and DC loads in the distribution system. [ABSTRACT FROM AUTHOR]

Summary: The performance of batteries, in their lifespan, efficiency, and safety, is affected by the uniformity of temperature within the batteries. Therefore, battery thermal management is crucial in the development in electric vehicles. In this paper, a novel battery pack with a wedge‐shaped runner is proposed. A 2‐dimensional computation fluid dynamics model was built, and the battery pack structure was optimized, including changes in the positions of the inlet/outlet, the width of the wedge‐shaped flow path, the inclination angle of the batteries, and the clearance between the batteries. Finally, the structure is studied and simulated under different ambient temperature and air velocity. The results indicated that the temperature distribution exacerbated with the increase of the inlet temperature and improved with the increase of the inlet velocity. Furthermore, the inlet temperature had little result on the temperature consistency, and the inlet velocity had a greater result on the temperature consistency. The conclusions drawn in this research are expected to play the part of good guidance for the future design of a new battery thermal management system. In our paper, a novel flow channel structure is proposed. Wedge‐shaped flow channels are used in the top and bottom parts of the battery case. In addition, that optimal structure is found by changing the position of the cooling air inlet and outlet and changing the arrangement mode of the battery pack. Finally, the structure is studied and simulated under different ambient temperatures and air velocities. [ABSTRACT FROM AUTHOR]

Summary: Considering prediction accuracy and adaptability to unpredictable operating conditions simultaneously, this paper presents an application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles. Under static and dynamic operating conditions, three commonly used online model parameters identification algorithms, including extended Kalman filter (EKF), particle swarm optimization, and recursive least square, are compared first, whose comparison results show that EKF's comprehensive performance is optimal. Taking identified open‐circuit voltage as observation information, two first‐order EKFs are established to online estimate state‐of‐charge (SOC) and state‐of‐energy (SOE). To maintain high accuracy and reliability under unpredicted operating conditions, fixed accumulation charge and fixed accumulation energy are innovatively seen as triggers, successfully realizing periodically capacity (state‐of‐health) and maximum available energy prediction with estimated SOC and SOE. Finally, with identified model parameters and estimated battery states, peak discharge/charge power can be further calculated in real time. Notably, parameters tuning for multistate estimation is also discussed in this work. Furthermore, the feasibility and prediction accuracy of the proposed multistate estimation framework is verified with sophisticated driving simulation under different temperatures. The validation results indicate that the presented framework can provide precise and reliable multistate estimation with relatively low computation cost. Highlights: An application‐oriented multistate estimation framework is proposedDifferent online model parameters identification methods are comparedParameters tuning for multistate estimation is discussedFixed accumulation charge is innovatively taken as capacity updating triggerThe feasibility of the proposed framework is verified under various temperatures [ABSTRACT FROM AUTHOR]

Summary: Balancing the lithium‐ion battery pack is essential to enhance the energy usage and life cycle of the battery. This paper analyses passive cell balancing method of Li‐ion battery for e‐mobility application based on the energy loss and cost estimation through simulation and hardware implementation. As a part of the simulation, an electrical equivalent circuit battery model is developed and model parameters are obtained using electrochemical impedance spectroscopy test. The experimented passive balancing topologies include a switched shunting resistor circuit with appropriate control logic. Final voltage‐based balancing algorithm is implemented to balance the cells at high state of charge. Experimental results are compared against the theoretical investigation. Based on the outcome of this experiment, the most significant characteristics of the passive balancing system can be developed by considering the impact on the battery performance, energy loss, and cost. [ABSTRACT FROM AUTHOR]

Summary: The parallel air‐cooled system is commonly applied in electric vehicles to cool the battery pack, in which flow pattern significantly influences the system cooling performance. In this paper, the curved divergence and convergence plenums are used to design the flow pattern in the parallel air‐cooled system for battery thermal management. The computational fluid dynamics (CFD) method is used for performance evaluation of the cooling system. The height distribution of control points is used to describe the plenum shape. During the optimization process, flow resistance network model is employed to obtain the velocity distribution of the system. An effective strategy is developed to adjust the height distribution of the control points for flow pattern design. Experiments are conducted to verify the CFD method and calculation model. Then typical cases are adopted to evaluate the proposed strategy for flow pattern design. The numerical results show that the strategy can effectively control the system flow pattern by designing the plenum shape. The cell temperature difference of the optimized system achieves an 86% reduction with the pressure drop only increased by 26%. Furthermore, compared with the optimized system with linear plenums in the reference, the optimized system with curved plenums achieves 48% reduction for the cell temperature difference without the pressure drop increased. Finally, experiments are conducted for the optimized system to verify the effectiveness of the developed strategy. The present study can provide a guide for the design of efficient parallel air‐cooled BTMS. Highlights: Shape optimization of the curved plenums for air‐cooled BTMSs is conducted.Effective strategy is developed to optimize the shape of curved plenums.ΔTmax of the optimized BTMS is reduced by 86% with ΔP only increased by 26%.Optimized BTMS with curved plenums performs better than that with linear plenums.Experiments are done to verify the calculation models and optimization strategy. [ABSTRACT FROM AUTHOR]

Summary: The batteries have been continuously for obtaining the high voltage platform and high density of energy with long lifecycle. The operating temperature of the battery cell has a significant effect on the thermal performance. This paper aims to consider the 18 650‐type lithium‐ion battery pack's thermal characteristics with the thermoelectric module using ferrofluid as a coolant. The experiment apparatus is test to determine the lithium‐ion battery pack's temperature distributions. Effects of the relevant parameters; hot and cold side flow rates (0.03‐0.05 m3/hr), supplied voltage through thermoelectric (8‐12 V), coolant types (De‐ionized water and ferrofluid), and ferrofluid concentrations (0.005%‐0.015% by volume) on the battery pack's cooling performance are considered. It is found that the thermoelectric cooling system significantly affects the battery pack cooling and gives the temperature of battery below 30°C. Higher cold and hot side flow rates can decrease average battery cell temperature by 3°C to 5°C, and the obtained uniformity temperature is below 3°C. Besides, ferrofluid concentration significantly reduces the average battery cell temperature when compared with deionized water. The proposed cooling system demonstrates the advantage of the electrical vehicle battery pack with the thermal cooling system. However, the optimized battery thermal management system still performs much better than the original one for various coolant flow rates and for the situation of heat generation rate. [ABSTRACT FROM AUTHOR]

Summary: Due to the significant influence of temperature on battery charging and discharging performance, exact evaluation of state of charge (SOC) under complex temperature environment becomes increasingly important. This paper develops an advanced framework to estimate the SOC for lithium‐ion batteries with consideration of temperature variation. First, an accurate electrical model with wide temperature compensation is established, and a series of experiments are carried out under wide range time‐varying temperature from −20°C to 60°C. Then, the genetic algorithm is leveraged to identify the temperature‐dependent model parameters. On this basis, the battery SOC is accurately estimated based on the square root cubature Kalman filter algorithm. Finally, the availability of the proposed method at different temperatures is validated through a complicated mixed working cycle test, and the experimental results manifest that the devised framework can accurately evaluate SOC under wide time‐varying temperature range with the maximum error of less than 2%. [ABSTRACT FROM AUTHOR]

Summary: The temperature of high‐power lithium‐ion batteries has a great influence on their performance, safety, and stability; thus the battery thermal management system (BTMS) is a key component of electric vehicles. In this paper, a novel liquid‐cooling BTMS with a thin plate and a slender tube is proposed for prismatic batteries, four different cooling structures of the tube are designed to achieve a high temperature uniformity, and a three‐dimensional numerical model is established. The effects of different cooling structures, battery discharge rates, coolant inlet velocities and inner diameters of the slender tube on the thermal performance are investigated. The results indicate that design S3, for which the length of the effective heat transfer tube distributed by each battery increases gradually along the flow direction of the coolant, exhibits the best thermal management performance among the four designed cooling structures, especially in terms of the temperature uniformity. When the inner diameter of the slender tube is 2 mm and the inlet velocity is not less than 0.5 m/s, the maximum temperature difference between the batteries is reduced to 4.23°C at a 2C discharge rate. This study can provide guidance for the structural design of BTMSs for prismatic batteries. [ABSTRACT FROM AUTHOR]

Summary: The rapid growth of transportation demand has been enlarged strongly which has promoted electric vehicles powered by lithium‐ion batteries. However, the inconsistencies within the battery pack will deteriorate over the lifecycle and affect the performance of electric vehicles. Therefore, various thermal management systems and equalization systems have been applied in battery management system to deal with the inconsistencies, extend battery service life, and improve safety performance. This review summarizes the origination of inconsistency within lithium‐ion batteries from production to usage process, and then introduces the classification methods and application scenarios of the balance management system in detail. Based on the circuit topology, equalization systems can be classified into passive and active topologies. Active topologies are widely researched due to the advantages of high equalization efficiency and high speed, and the state‐of‐art innovations are presented and compared from the prospective of circuit, energy flow, efficiency and system complexity. In addition, this review focuses on the mainstream equalization strategies based on the analysis of balancing variables and control algorithms in terms of efficiency, complexity and stability, especially in the areas of variables optimal selection and advanced control algorithms. It is expected that innovations such as cloud control methods and hybrid balancing systems equipped with thermal management will become the future direction of lithium‐ion equalization technologies. [ABSTRACT FROM AUTHOR]

Summary: Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency‐oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta‐model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed‐road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium‐ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV. [ABSTRACT FROM AUTHOR]

Summary: Safe and efficient operation of lithium‐ion batteries in electric vehicles (EVs) relies on the performance of the thermal management system. For optimum thermal management, precise estimation of heat generation rate is crucial. This paper illustrates the criticality of the inclusion of explicit in‐situ temperature dependence of heat generation rate as opposed to the state of the art models. We test a cylindrical LiFePO4 (LFP) cell over a broad range (typical of EV cells) of discharge rate (2.3 C‐13.6 C) and temperature (20°C‐70°C) and observe a non‐monotonic trend where heat rate decreases first up to 55°C to 60°C (depending upon the discharge rate) and then starts to increase. The trend does not fit into any existing heat generation model of Li‐ion battery, and hence we separately include it to simulate the heat transfer behaviour of a module. Simulations confirm the substantial difference of temperature from the conventional analysis where the temperature dependency is characterized inadequately. Current work demonstrates the counter‐intuitive non‐monotonic temperature dependence of heat rate, the effect of the same on peak module temperature and thus assists in improving the design methodology of the thermal management system of EV battery packs. Highlights: Dynamic correlation between temperature and heat generation is measured experimentally.Cylindrical Li‐ion cells are tested for typical electric vehicle operation ranges (2.3 C‐13.6 C and 20°C‐70°C).Counter‐intuitive decreasing‐first‐increasing‐later trend is observed for all discharge rates.Temperature dependence of in‐situ heat rate is explained and included in analysis.Major modification is observed for the peak cell temperature, a critical design parameter. [ABSTRACT FROM AUTHOR]

Summary: The performance of battery management systems relies on the core temperature estimation, which is one of the major technical bottlenecks for electric vehicles. Aiming to tackle this problem, a lumped‐parameter two‐state thermal model for lithium‐ion batteries is established in this paper. Then, this thermal model is coupled with a temperature‐dependent second‐order RC equivalent circuit model to form an electro‐thermal model for lithium‐ion batteries. Using the proposed electro‐thermal model, an adaptive estimation algorithm based on joint Kalman filtering is proposed for battery core temperature estimation, considering the heat transfer condition variations between the battery surface and ambient media. The verification results show that the proposed algorithm enhances the temperature estimation accuracy, compared to the results obtained directly from the electro‐thermal model. Besides, the verification results demonstrate the high adaptability of the proposed algorithm, that is, it is robust to variations in ambient temperature, as well as variations in thermal resistance between the battery surface and ambient media. Considering the influences of temperature and SOC on the thermal generation rate of the battery, an electro‐thermal model for lithium‐ion batteries is established. This model is composed of a lumped‐parameter two‐state thermal model and a temperature‐dependent second‐order RC equivalent circuit model. An adaptive core temperature estimation algorithm based on joint Kalman filtering is proposed, considering the heat transfer condition variations between the battery surface and the ambient media. [ABSTRACT FROM AUTHOR]

Summary: As the main form of energy storage for new energy automobile, the performance of lithium‐ion battery directly restricts the power, economy, and safety of new energy automobile. The heat‐related problem of the battery is a key factor in determining its performance, safety, longevity, and cost. In this paper, parallel liquid cooling battery thermal management system with different flow path is designed through changing the position of the coolant inlet and outlet, and the influence of flow path on heat dissipation performance of battery thermal management system is studied. The results and analysis show that when the inlet and the outlet are located in the middle of the first collecting main and the second collecting main, respectively; system can achieve best heat dissipation performance, the highest temperature decrease by 0.49°C, while the maximum temperature difference of system decreases by 0.52°C compared with typical Z‐type BTMS under the discharge rate of 1 C. Then an optimization strategy is put forward to improve cooling efficiency compared with single‐inlet and single‐outlet symmetrical liquid cooling BTMS; the highest temperature of three‐inlet and three‐outlet is 27.98°C while the maximum temperature difference of three‐inlet and three‐outlet is 2.69°C, decrease by 0.7 and 0.67°C, respectively. [ABSTRACT FROM AUTHOR]

Summary: Aiming at solving the problem of poor battery cell consistency caused by excessive decay of cell capacity or increased internal resistance during the operation of lithium‐ion battery packs for vehicles, the paper proposes an active equalization control with 12‐V power supply as an equalization energy source, which achieves efficient energy replenishment of individual cells with low power. The electrochemical‐thermal coupling model of lithium‐ion battery is built, and the order reduction of large‐scale system theory ensures that the model had higher accuracy and lower amount of calculation, which is suitable for vehicle battery management system (BMS). Then the extended Kalman filter algorithm is used to calculate the real‐time state of charge (SOC) of each cell and set as an equalization variable. The equalization simulation circuit is built with MATLAB/Simulink, the experimental platform of active equalization system for battery packs is constructed, and the battery packs are tested for equalization in static state. The simulation and experimental results show that the proposed active equalization control strategy can rapidly improve the voltage inconsistency between single cells, and the energy transfer efficiency can reach about 85% during the equalization process. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters‐based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies. [ABSTRACT FROM AUTHOR]

Summary: The spatial resolving of temperature gradient is a key but challenging issue for the loading performance, aging evaluation, and safety guarantee of large format lithium battery, while the internal temperature cannot be measured directly in field. Notable temperature difference of large format battery emerges in heavy load applications. This paper tries to solve spatial‐distributed temperature gradient by proposing a novel method. The multilayer thermal model and the real‐time method based on this model are firstly proposed to noninvasively identify the spatial temperature gradient of the battery. And the thermal conduction resistance of the battery along aging are estimated by the extended lumped‐parameter model and forgetting factor recursive least square algorithm, while the convection resistance and heat capacity can be estimated by the lumped parameter model and least square algorithm. The relation between the temperature sensor number and total thermal nodes is then derived by the experiment. Finally, the proposed method is systematically verified through the simulation and experiment. Both the spatial distribution and the highest node of the battery temperature are obtained with promising accuracy and robustness. And it can be implemented in battery management systems for various online applications such as electric vehicles and hybrid electric vehicles. A multilayer thermal model is proposed for the identification of spatial‐resolved temperature gradient in battery.The extended lumped‐parameter model is proposed to estimate the conduction resistance of battery.The relation between the temperature sensor number and total thermal nodes is derived by the experiment data.The spatial‐resolved temperature gradient and the highest temperature in large format laminated battery are real‐time identified based on the battery surface temperature measurement.The proposed method is systematically verified through simulation and experiment with promising outcomes. [ABSTRACT FROM AUTHOR]

Summary: To enhance the estimation accuracy of battery's state of charge, it is imperative to estimate the battery model parameter. To reduce the calculation efforts, the number of the battery model parameter to be estimated should be less while ensuring the state of charge estimation accuracy. Especially in engineering applications, the calculating ability is usually limited. So, it needs to choose the critical battery model parameter to be estimated. This paper's contributions are as follows: The global sensitivity analysis of the battery model parameter is achieved by the Monte Carlo simulation method. The results show that the open circuit voltage and the ohmic resistance are the high sensitivity parameters. Guided by the results of parameter sensitivity analysis, a dual extended Kalman filters method is utilized to achieve online battery model parameter estimation. The experiments prove that the state of charge estimation accuracy is improved by the online parameter estimation. Estimating high sensitivity parameters can reduce running time. And the SOC estimation accuracy can be guaranteed. [ABSTRACT FROM AUTHOR]

Summary: The state of charge and state of health estimations are two of the most crucial functions of a battery management system, which are the quantified evaluation of driving mileage and remaining useful life of electric vehicles. This paper investigates a novel data‐driven–enabled battery states estimation method by combining recurrent neural network modeling and particle‐filtering–based errors redress. First, a recurrent neural network with long‐short time memory is employed to learn the long‐term nonlinear relation between batteries states and measurable signals of lithium‐ion batteries, such as current, voltage, and temperature. Second, to denoise the estimation errors of the neural network model, particle filtering is employed to smooth the state of charge estimation results. Third, the terminal voltage difference of battery is highly related to the internal resistance of the battery, which is thus taken as a new input to track the internal resistance of the battery. The performance of the proposed method is verified by multiple comparisons with conventional techniques under randomized loading profiles and different temperatures. [ABSTRACT FROM AUTHOR]

Summary: Battery modeling plays an important role in remaining range prediction and battery management system development. An accurate and realistic battery model is essential to design an efficient electric storage system. The goal of this paper is to investigate the performance of different circuit topologies for diffusion process in the equivalent circuit models (ECMs). The theory of diffusion process approximation by using resistive‐capacitor (RC) networks is explained in frequency domain. The terminal voltage predictive capabilities of the ECMs are compared and validated with test data. The numerical simulation results show that model prediction accuracy and computation burdens increase along with the number of RC pairs. The ECM with three RC networks is the best choice in terms of the balance between accuracy and complexity for ternary lithium batteries. In addition, a novel method of combining unscented Kalman filter (UKF) algorithm with initial state of charge (SOC) acceleration convergence for SOC estimation is proposed. The results of urban dynamometer driving schedule (UDDS) show that ECM with three RC networks has the best comprehensive performance on calculation cost and SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

Summary: Power lithium‐ion batteries have been widely utilized in energy storage system and electric vehicles, because these batteries are characterized by high energy density and power density, long cycle life, and low self‐discharge rate. However, battery charging always takes a long time, and the high current rate inevitably causes great temperature rises, which is the bottleneck for practical applications. This paper presents a multiobjective charging optimization strategy for power lithium‐ion battery multistage charging. The Pareto front is obtained using multiobjective particle swarm optimization (MOPSO) method, and the optimal solution is selected using technique for order of preference by similarity to ideal solution (TOPSIS) method. This strategy aims to achieve fast charging with a relatively low temperature rise. The MOPSO algorithm searches the potential feasible solutions that satisfy two objectives, and the TOPSIS method determines the optimal solution. The one‐order resistor‐capacitor (RC) equivalent circuit model is utilized to describe the model parameter variation with different current rates and state of charges (SOCs) as well as temperature rises during charging. And battery temperature variations are estimated using thermal model. Then a PSO‐based multiobjective optimization method for power lithium‐ion battery multistage charging is proposed to balance charging speed and temperature rise, and the best charging stage currents are obtained using the TOPSIS method. Finally, the optimal results are experimentally verified with a power lithium‐ion battery, and fast charging is achieved within 1534 s with a 4.1°C temperature rise. [ABSTRACT FROM AUTHOR]

Summary: The performance of power battery is a significant factor affecting the overall quality of electric vehicles. To optimize the thermal management effect of battery pack, cold plate with wedge‐shaped microchannels was proposed in this paper. On the basis of the models of the independent cold plate and the battery‐cooling module, the effects of outlet aspect ratio, flow rate, and branching structure on the heat dissipation performance of the cold plate were studied at first. Afterwards, the effects of cooling surface, flow rate, and branching structure on the temperature distribution of the battery module were simulated. The results showed that the wedge‐shaped channels provided a good cooling efficiency and surface temperature uniformity. When the wedge‐shaped channel was used in thermal management of the battery module, the side‐cooling method reduced the temperature difference of batteries by more than 35.71% compared with front cooling under the mass flow rate of 2 × 10−5 kg/s. At a discharge rate of 3.5 C, the flow rate of 1 × 10−4 kg/s controlled the battery temperature to within 45°C, and the branching structure designed for the module successfully decreased the maximum temperature difference from 7.27°C to 4.67°C, which has been reduced by approximately 35.78%. [ABSTRACT FROM AUTHOR]

Summary: To improve the economic performance of dual‐motor battery electric vehicles, a novel driving pattern recognition–based energy management strategy (NDPREMS) is proposed in this paper. The NDPREMS firstly employs principal component analysis method to reduce the dimension of characteristic parameters of driving patterns and uses hierarchical cluster method for classifying driving patterns to construct a database of typical driving patterns, based on which a driving pattern recognizer is achieved using generalized regression neural network (GRNN) and the accuracy of this recognizer reaches 96.08%. In order to reasonably allocate the power between two motors, on the basis of rule‐based energy management strategy (REMS), a dynamic programming–based energy management strategy (DPEMS) under typical driving patterns is formulated. By doing so, the logic thresholds of REMS are optimized, and thus, the NDPREMS is achieved. Comparison simulations of control effect concerning the REMS, DPEMS, and NDPREMS are performed under typical driving patterns. Results indicate that the proposed NDPREMS exhibits greater energy conservation compared with REMS, the economic improvement under urban driving pattern is the most obvious at 11.04%, the improvement under the comprehensive test driving pattern is 5.65%, and the performance of the NDPREMS is similar to that of DPEMS. [ABSTRACT FROM AUTHOR]

Summary: Li‐ion cells are used for energy storage and conversion in electric vehicles and a variety of consumer devices such as hoverboards. Performance and safety of such devices are severely affected by overheating of Li‐ion cells in aggressive operating conditions. Multiple recent fires and accidents in hoverboards are known to have originated in the battery pack of the hoverboard. While thermal analysis and measurements have been carried out extensively on large battery packs for electric vehicles, there is relatively lesser research on smaller devices such as hoverboards, where the extremely limited thermal management design space and the critical importance of user safety result in severe thermal management challenges. This paper presents experimental measurements and numerical analysis of a novel approach for thermal management of the battery pack of a hoverboard. Measurements indicate that temperature rise in cells in the pack can be as large as 30°C at 4C discharge rate, which, although unlikely to be a standard discharge rate, may result from a malfunction or accident. A novel thermal management approach is investigated, wherein careful utilization of air flow generated by hoverboard motion is shown to result in significant temperature reduction. Measurements also indicate the key role of the metal housing around the battery pack in thermal management. Measurements are found to be in good agreement with finite element simulations, which indicate that the battery pack can be cooled as effectively in presence of a perforated metal casing as without the casing at all. Experimental data and simulation model presented here offer critical insights into the design of hoverboard thermal management and may result in safer, high performance hoverboard battery packs. Investigates temperature rise in Li‐ion battery pack of a hoverboard through experiments and simulations.Shows significant temperature rise and thermal nonuniformity in adverse operating conditions.Demonstrates a passive and effective thermal management mechanism by utilizing the flow of air relative to the moving hoverboard. [ABSTRACT FROM AUTHOR]