Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold regions.Alternating current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating advantages.This paper introduces the recent advances in AC heating from the perspective of practical EV applications.First,the performance degradation of EVs in low-temperature environments is introduced briefly.The concept of AC heating and its research methods are provided.Then,the effects of various AC heating methods on battery heating performance are reviewed.Based on existing studies,the main factors that affect AC heating performance are analyzed.Moreover,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and compared.The evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted out.Finally,the perspectives and challenges of AC heating are discussed.This paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

With the dramatic increase in electric vehicles(EVs)globally,the demand for lithium-ion batteries has grown dramatically,resulting in many batteries being retired in the future.Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery aging level on service and provide regroup strategy of the retied batteries in secondary use.There are still limitations on the current rapid battery capacity estimation methods,such as direct current internal resistance(DCIR)and electrochemical impedance spectroscopy(EIS),in terms of efficiency and robustness.To address the challenges,this paper proposes an improved version of DCIR,named pulse impedance technique(PIT),for rapid battery capacity estimation with more robustness.First,PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency,in which the coherence analysis is used to guide the selection of a reliable frequency band.The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method,which obtains more information on the battery capacity evaluation.Second,various statistical variables are used to extract aging features,and Pearson correlation analysis is applied to determine the highly correlated features.Then a linear regression model is developed to map the relationship between extracted features and battery capacity.To validate the performance of the proposed method,the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series.The results reveal that the proposed PIT can provide comparative indicators to EIS,which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

Online model identification of lithium-ion battery for electric vehicles

In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.

SOC distribution-based modeling for lithium-ion battery for electric vehicles using numerical optimization

In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.

Fault detection of new and aged lithium-ion battery cells in electric vehicles

In this paper,a novel model-based fault detection in the battery management system of an electric vehicle is proposed.Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults,considering the impact of battery aging.Battery aging primarily affects capacity and resistance,becoming more pronounced in the later stages of a battery lifespan.By incorporating aging effects into our fault diagnosis scheme,our proposed approach prevents false or missed alarms for the aged battery cells.The aging effect of battery,capacity fading and resistance growth,are considered unknown parameters.An adaptive observer is employed to design a fault detector,considering unknown parameters in the battery model.The adaptive observers are designed for two different scenarios:In the first scenario,it is presumed that aging effects remain constant over time due to their slow rate of change.Then,it is assumed that aging effects are time-varying.Therefore,the fault detection scheme can detect faults of new battery cells as well as aged cells.Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack,to demonstrate the performance of the proposed approach in more real-world scenarios.The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.

Identification of dynamic model parameters for lithium-ion batteries used in hybrid electric vehicles

An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.

Fuzzy Model for Estimation of the State-of-Charge of Lithium-Ion Batteries for Electric Vehicles

A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.

A model-based driving cycle test procedure of electric vehicle batteries

The battery test methods are the key issues to investigate the energy-storage characteristics and dynamic characteristics of electric vehicle(EV) batteries.In this paper,the research advances of existing battery test methods as well as driving cycles are reviewed.An electric vehicle model that consists of EV dynamics model,battery model and electric motor model is built.The dynamic characteristics of the battery in frequency domain are analyzed.Based on the EV model and the frequency domain characteristics of the battery,a driving cycle test procedure of EV battery is proposed.The battery test procedure is able to reflect the real-world characteristics of EV batteries,and can be used as a universal EV battery test method.

A review of lithium-ion battery safety concerns:The issues,strategies,and testing standards

Efficient and reliable energy storage systems are crucial for our modern society.Lithium-ion batteries(LIBs)with excellent performance are widely used in portable electronics and electric vehicles(EVs),but frequent fires and explosions limit their further and more widespread applications.This review summarizes aspects of LIB safety and discusses the related issues,strategies,and testing standards.Specifically,it begins with a brief introduction to LIB working principles and cell structures,and then provides an overview of the notorious thermal runaway,with an emphasis on the effects of mechanical,electrical,and thermal abuse.The following sections examine strategies for improving cell safety,including approaches through cell chemistry,cooling,and balancing,afterwards describing current safety standards and corresponding tests.The review concludes with insights into potential future developments and the prospects for safer LIBs.

Modeling and Experimental Verification of Electric Vehicles Off-Grid Photovoltaic Powered Charging Station

With the increasing development of EVs, the energy demand from theconventional utility grid increases in proportion. On the other hand, photovoltaic(PV) energy sources can overcome several problems when charging EVs from theutility grid especially in remote areas. This paper presents an effective photovoltaic stand-alone charging station for EV applications. The proposed charging station incorporates PV array, a lithium-ion battery representing the EV battery, and alead-acid battery representing the energy storage system (ESS). A bidirectionalDC-DC converter is employed for charging/discharging the ESS and a unidirectional DC-DC converter is utilized for charging the EV battery. The proposed controllers achieve maximum power extraction from the PV and regulate the DC-linkvoltage. It also controls the voltage and current levels of both the ESS and the EVduring the charging/discharging process. The study has been applied to two caseswith different power levels. Analysis, simulation, and implementation of the proposed system are presented. A 120 W laboratory prototype is carried out to verifythe system performance, experimentally. Design guides for higher power levelsare proposed to help in choosing the proper parameters of the converters. Boththe simulation and experimental results are matched and verify the highperformance of the proposed system.

Analysis of Hybrid Rechargeable Energy Storage Systems in Series Plug-In Hybrid Electric Vehicles Based on Simulations

In this paper, an extended analysis of the performance of different hybrid Rechargeable Energy Storage Systems (RESS) for use in Plug-in Hybrid Electric Vehicle (PHEV) with a series drivetrain topology is analyzed, based on simulations with three different driving cycles. The investigated hybrid energy storage topologies are an energy optimized lithium-ion battery (HE) in combination with an Electrical Double-Layer Capacitor (EDLC) system, in combination with a power optimized lithium-ion battery (HP) system or in combination with a Lithium-ion Capacitor (LiCap) system, that act as a Peak Power System. From the simulation results it was observed that hybridization of the HE lithium-ion based energy storage system resulted from the three topologies in an increased overall energy efficiency of the RESS, in an extended all electric range of the PHEV and in a reduced average current through the HE battery. The lowest consumption during the three driving cycles was obtained for the HE-LiCap topology, where fuel savings of respectively 6.0%, 10.3% and 6.8% compared with the battery stand-alone system were achieved. The largest extension of the range was achieved for the HE-HP configuration (17% based on FTP-75 driving cycle). HP batteries however have a large internal resistance in comparison to EDLC and LiCap systems, which resulted in a reduced overall energy efficiency of the hybrid RESS. Additionally, it was observed that the HP and LiCap systems both offer significant benefits for the integration of a peak power system in the drivetrain of a Plug-in Hybrid Electric Vehicle due to their low volume and weight in comparison to that of the EDLC system.

Experimental study on heat generation and dissipation performance of PEV Lithium-ion battery

Based on the lithium-ion battery pure electric vehicle （PEV） application, two capacity types of batteries are applied in thermal characteristic experiments. With the experimental comparison method, battery thermal characteristics and heat generation mechanism are studied. Experiments of batteries in cases of different dimensions, batteries with different air cooling velocity and two capacity types of batteries in free convection environment are put forward. Battery heat generation performance, heat dissipation performance and comparison of different capacity types＇ batteries are researched and summarized. Conclusions of battery heat generation and dissipation in PEV applications, important battery thermal management factors and suggestions are put forward.

Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications

For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identification method for lithium-ion batteries in electric vehicles.In this article,we propose an equivalent circuit model structure.Based on the model structure we derive the recursive mathematical description.The recursive extended least square algorithm is introduced to estimate the model parameters online.The accuracy and robustness are validated through experiments and simulations.Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%.In addition,it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.

Preheating method of lithium-ion batteries in an electric vehicle

To improve the low-temperature charge-discharge performance of lithium-ion battery,low-temperature experiments of the charge-discharge characteristics of 35 Ah high-power lithium-ion batteries have been conducted,and the wide-line metal film method for heating batteries is presented.At-40℃,heating and charge-discharge experiments have been performed on the battery pack.The results indicate the charge-discharge performance is substantially worse in cold climates,and can be significantly improved by heating the battery pack with a wide-line metal film.Pulse charge-discharge experiments show that at-40℃ambient temperature,the heated battery pack can charge or discharge at high current and offer almost80%power.

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

Lithium-ion(Li-ion)cells degrade after repeated cycling and the cell capacity fades while its resistance increases.Degra-dation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used,the working conditions and the battery temperature.At present,charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health(SOH)via methods such as incremental capacity(IC)analysis.In this paper,a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method.The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries.This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling,analysis and diagnosis.

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

Research on EV Battery Swapping System and Standards Requirements

Combined with the perspective of State Grid Corporation of China （SGCC）, the paper researches the EV battery swapping mode and its advantages, analyses standards requirements of battery swapping system and proposes the corresponding standards system

Modeling and optimization of an enhanced battery thermal management system in electric vehicles

This paper models and optimizes an air-based battery thermal management system(BTMS)in a battery module with 36 battery lithium-ion cells.A design of experiments is performed to study the effects of three key parameters(i.e.,mass flow rate of cooling air,heat flux from the battery cell to the cooling air,and passage spacing size)on the battery thermal performance.Three metrics are used to evaluate the BTMS thermal performance,including(i)the maximum temperature in the battery module,(ii)the temperature uniformity in the battery module,and(iii)the pressure drop.It is found that(i)increasing the total mass flow rate may result in a more non-unifbi*m distribution of the passage mass flow rate among passages,and(ii)a large passage spacing size may worsen the temperature uniformity on the battery walls.Optimization is also performed to optimize the passage spacing size.Results show that the maximum temperature difference of the cooling air in passages is reduced from 23.9 K to 2.1 K by 91.2%,and the maximum temperature difference among the battery cells is reduced from 25.7 K to 6.4 K by 75.1%.

Comparison of the topologies for a hybrid energy-storage system of electric vehicles via a novel optimization method

The combination of batteries and ultracapacitors has become an effective solution to satisfy the requirements of high power density and high energy density for the energy-storage system of electric vehicles.Three aspects of such combination efforts were considered for evaluating the four types of hybrid energy-storage system(HESS)topologies.First,a novel optimization framework was proposed and implemented to optimize the voltage level of a battery pack and an ultracapacitor pack for the four types of HESS topologies.During the optimization process,the dynamic programming(DP)algorithm was iteratively applied to determine the optimal control actions.The simulation results with DP were used to evaluate the energy efficiency of different HESS topologies at different voltage levels.Second,the optimized voltage level of the battery and ultracapacitor in each topology indicates that a higher voltage level usually results in a better system performance.The simulation results also illustrate that the optimized rated voltage level of the battery pack is approximately 499.5 V,while for the ultracapacitor pack,the optimized voltage level is at its maximum allowed value.Note that the constraint of the battery voltage is initialized at200–600 V.Third,the control rules for different HESS topologies were obtained through the systematic analysis of the simulation results.In addition,advantages and disadvantages of the four topologies were summarized through evaluation of the efficiency and operating currents of the batteries and the ultracapacitor.

Thermal Stability of Supercapacitor for Hybrid Energy Storage System in Lightweight Electric Vehicles:Simulation and Experiments

Recent research findings indicate that the non-monotonic consumption of energy from lithium-ion(Li-ion)batteries results in a higher heat generation in electrical energy storage systems.During peak demands,a higher heat generation due to high discharging current increases the temperature from 80℃ to 120℃,thereby resulting in thermal runaway.To address peak demands,an additional electrical energy storage component,namely supercapacitor(SC),is being investigated by various research groups.This paper provides insights into the capability of SCs in lightweight electric vehicles(EVs)to address peak demands using the worldwide harmonized light-duty driving test cycle(WLTC)driving profile in MATLAB/Simulink at different ambient temperatures.Simulation results indicate that temperature imposes a more prominent effect on Li-ion batteries compared with SCs under peak demand conditions.The effect of the discharging rate limit on the Li-ion battery current is studied.The result shows that SCs can accommodate the peak demands for a low discharging current limit on the battery,thereby reducing heat generation.Electrochemical impedance spectroscopy and cyclic voltammetry are performed on SCs to analyze their thermal performance at different temperatures ranging from 0℃ to 75℃ under different bias values of-0.6,0,0.6,and 1 V,respectively.The results indicate a higher specific capacitance of the SC at an optimum operation temperature of 25℃ for the studied bias.This study shows that the hybrid combination of the Li-ion battery and SC for a lightweight EV can address peak demands by reducing thermal stress on the Li-ion battery and increasing the driving range.