Online SOC estimation based on modified covariance extended Kalman filter for lithium batteries of electric vehicles

To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.

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.

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.

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.

Experimental Investigation on Cooling/Heating Characteristics of Ultra-Thin Micro Heat Pipe for Electric Vehicle Battery Thermal Management

Due to the heat pipes’ transient conduction,phase change and fluid dynamics during cooling/heating with high frequency charging/discharging of batteries,it is crucial to investigate in depth the experimental dynamic thermal characteristics in such complex heat transfer processes for more accurate thermal analysis and design of a BTMS. In this paper,the use of ultra?thin micro heat pipe(UMHP) for thermal management of a lithium?ion battery pack in EVs is explored by experiments to reveal the cooling/heating characteristics of the UMHP pack. The cooling performance is evaluated under di erent constant discharging and transient heat inputs conditions. And the heating e ciency is assessed under several sub?zero temperatures through heating films with/without UMHPs. Results show that the pro?posed UMHP BTMS with forced convection can keep the maximum temperature of the pack below 40 °C under 1 ~ 3 C discharging,and e ectively reduced the instant temperature increases and minimize the temperature fluctuation of the pack during transient federal urban driving schedule(FUDS) road conditions. Experimental data also indicate that heating films stuck on the fins of UMHPs brought about adequate high heating e ciency comparing with that stuck on the surface of cells under the same heating power,but has more convenient maintenance and less cost for the BTMS. The experimental dynamic temperature characteristics of UMHP which is found to be a high?e cient and low?energy consumption cooling/heating method for BTMSs,can be performed to guide thermal analysis and optimiza?tion of heat pipe BTMSs.

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.

Recursive calibration for a lithium iron phosphate battery for electric vehicles using extended Kalman filtering

In this paper,an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical open circuit voltage versus state of charge(SOC) map is applied to characterize the voltage behavior of a lithium iron phosphate battery for electric vehicles(EVs).As a result,the overpotentials of the battery can be depicted using a second-order circuit network and the model parameterization can be realized under any battery loading profile,without a special characterization experiment.In order to ensure good robustness,extended Kalman filtering is adopted to recursively implement the calibration process.The linearization involved in the calibration algorithm is realized through recurrent derivatives in a recursive form.Validation results show that the recursively calibrated battery model can accurately delineate the battery voltage behavior under two different transient power operating conditions.A comparison with a first-order model indicates that the recursively calibrated second-order model has a comparable accuracy in a major part of the battery SOC range and a better performance when the SOC is relatively low.

Improved Safety for Automotive Lithium Batteries:An Innovative Approach to include an Emergency Cooling Element

This paper describes a concept for an independent and redundant safety concept for Lithium batteries in Electric and Hybrid Electric Vehicles. This concept includes an emergency cooling system based on pressurized carbon dioxide (CO2). Since carbon dioxide (CO2) is a possible medium of future mobile air conditioning (MAC) systems, the MAC system can be utilized for the one-time emergency cooling described in this paper. In the first part of the paper, some major safety aspects of automotive Li batteries are highlighted. In the second section, the paper describes a technical approach, how these batteries can be made safer. Pressurized CO2, which is a promising candidate for cooling liquids used in future mobile air conditioning (MAC) systems, is used to effectively cool down an overheating or up-heating battery in a critical state. The safety system thereby is not based on an electrical effect, but on a direct and fast-reacting thermal conduction, avoiding a thermal runaway of individual cells. The application of the proposed system is to act preventively just before the thermal runaway gets uncontrollable. In this case, the limited amount of CO2, which is available in the MAC system, fulfils the emergency cooling requirements. The combination of standard car components for the concept leads to an only moderate increase of the total weight and the additional system costs. Therefore, the described system might be of interest for car, battery and air conditioning system producers. This paper explains that the synergetic combination of CO2-based MAC systems and Li-based batteries is an innovative approach to improve environmental compatibility in future vehicles. The concept is proven experimentally on a lab scale with battery cells and battery packs consisting of four serially connected cells, respectively.

A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries

State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC estimation.In this article,we compare five state-of-the-art FOMs in terms of SOC estimation.To this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM parameters.Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy.The model R(RQ)W shows superior identification accuracy than the other four FOMs.Secondly,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current drifts.The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs.Although more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory accuracy.Validation results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other models.Moreover,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

Fractional order battery modelling methodologies for electric vehicle applications:Recent advances and perspectives

Accurate modelling of lithium ion batteries is crucial for battery management in electric vehicles.Recent studies have revealed the fractional order nature of lithium ion batteries,leading to fractional order modelling techniques.In this paper,a comprehensive review of the fractional order battery models and their applications in battery management of electric vehicles is provided from the perspectives of frequency and time domains.In the frequency domain,the fractional order models to fit electrochemical impedance spectroscopy data are investigated,followed by their applications in health diagnosis,battery heating and charging strategies.In the time domain,the fractional order models adopted for voltage simulation are discussed,followed by their applications in battery state estimation and fault diagnosis.Finally,from the perspectives of time domain and frequency domain applications,critical challenges and research trends for future work in terms of fractional order modelling are highlighted to advance the development of next-generation battery management.

新能源汽车储能系统快速充电策略研究综述

电动汽车在近十年得到大力发展与推广,但续航里程一直限制着电动汽车的进一步发展。车载储能系统快速充电技术能够有效缓解现阶段电动汽车用户续航里程焦虑,同时提高车辆安全性。电动汽车快速充电技术包括基于经验的充电策略和基于优化的充电策略。本文首先总结了各种传统充电方法的优点和缺点,其次根据应用场景优化目标的不同,归纳了不同优化目标的快充策略的应用,此外总结了电池内部的电极材料、电解质和电极/电解质界面(EEI)对快充性能的影响,并归纳了不同材料的改良措施,并对该领域未来发展方向进行了展望。

基于SCSSA-CNN-BiLSTM的行驶工况下锂电池寿命预测

随着锂离子电池广泛应用,电池寿命预测的重要性日益突显。锂离子电池剩余寿命(RUL)的准确预测是其健康管理的关键组成部分。基于此提出了一种名为SCSSA-CNN-BiLSTM的算法,旨在实现应用于整车的锂离子电池RUL预测。采用卷积神经网络(CNN)和双向长短时记忆神经网络(BiLSTM),并结合了正余弦和柯西变异的麻雀优化算法(sine-cosine and Cauchy mutation sparrow search algorithm, SCSSA),形成了一种新型的混合神经网络,以提高锂离子电池RUL预测的准确性和稳定性。CNN用于电池健康状态(SOH)深度特征的全面提取,而BiLSTM以双向方式研究这些深度特征,并通过密集层生成锂离子电池的RUL预测。为验证所提出方法的有效性,首先使用NASA的电池数据,将多个常用模型与所提出的混合神经网络模型进行比较。研究结果显示,混合模型的决定系数(R2)提高了4%~23%,RUL绝对误差降至1,这表明模型具备更高的预测准确性。随后,在整车层面进行了CLTC动态工况下的循环试验,并对寿命衰减数据进行了预测。最终的结果显示,SCSSA-CNN-BiLSTM模型对应的均方根误差(RMSE)、R2分别为1.64、0.98 Ah,展现出了良好的预测和泛化性能。

面向实车应用的磷酸铁锂电池容量辨识及特异性优化方法研究

锂离子电池作为电动汽车的重要部件之一,其健康程度直接影响车辆的续航能力、安全性能以及整体运行效率。其中,容量作为描述电池健康状态的重要指标,实车条件下的准确估计是一个难题。为此,本文提出一种结合安时积分与等效电路模型,将容量作为待辨识参数之一,通过粒子群优化算法以实现容量辨识的方法。在此基础上,聚焦于磷酸铁锂电池的电压特殊性,提出了一种面向慢充工况的特异性优化方法,以解决容量辨识过程中模型端电压拟合较差的问题,主要通过截取充电末期电压片段与使用双维度目标函数两种方法实现。本文基于两款搭载磷酸铁锂电池的电动汽车车型进行了精度验证。鉴于实车数据缺乏容量标签,本文首先基于静置充电片段计算容量作为标签值。由于标签数量不足,又采用小里程下的标称容量作为标签,通过这两种方法进行精度验证。结果显示,两款车型的平均绝对百分比误差分别为2.33%和3.38%,表明该方法具有较高的精度与适用性,为实车容量估计提供了一种思路与方法。

水轮型翅片组耦合均温板的多目标及拓扑优化

为解决传统通道型液冷板功耗高、散热效率低等问题,提出了用于电动车的一种异形化的水轮形扰流翅片组耦合均温板电池热管理系统。水轮组的组合方式、布置位置和数量等因素确定基础模型后,使用多目标优化方法,分析了翅片长度、翅片倾角、中心距离出口边距离和两出口间距离。为了进一步提升液冷板均温性,在液冷板外侧添加均温板,以温度为目标函数对其进行拓扑优化。结果表明:与初始模型相比平均温度和标准差分别降低了1.83℃和0.45,综合评价指标—热性能因子(TPF)提升77.8%。翅片长度、倾角、出口位置及其附件扰流对液冷板各项性能有着显著影响。在流体通道所占体积分数为0.8时,液冷板的平均温度进一步下降2.6℃,温度标准差下降0.206,TPF相较添加前继续提升18.9%。

锂离子电池汽车全尺寸火灾试验研究进展综述

分析近年来国内外发生的锂离子电池电动汽车火灾事故,对电动汽车的火灾风险进行简要概括。重点针对美国、加拿大、奥地利、瑞典、中国等国家在锂离子电池电动汽车全尺寸火灾试验及火灾防控方面的科研成果进行综述。根据国内外研究成果,对不同电池容量的锂离子电池电动汽车的热释放速率进行汇总,重点总结燃烧总热释放量、热释放速率峰值和释放有毒氟化氢气体与电池容量的关系。旨在为我国锂离子电池电动汽车火灾防控和火灾风险评估技术研究提供必要的数据参考。

基于数据驱动的锂离子电池健康状态估计研究进展综述

锂离子电池(LIBs)在电气化交通、电化学储能和移动电子产品等领域广泛使用,精准评估其健康状态(SOH)是确保安全可靠应用的基础。数据驱动法是当前评估SOH的主流方法,该方法无需考虑电池内部复杂的物理化学反应,依赖于直接的数据分析,且具有较高的精度。本文从锂离子电池SOH影响因素入手分析了基于数据驱动的电池SOH估计方法的研究现状,着重比较了机器学习、滤波器和时间序列等方法实施SOH估计的原理、优缺点。最后,针对电动汽车实际应用场景,对SOH估计方法的未来发展趋势进行了展望。

GB/T 31467—2023《电动汽车用锂离子动力电池包和系统电性能试验方法》标准解读与分析

近年来,我国新能源汽车和动力蓄电池产业快速发展,相关标准的部分内容已不能适应产业发展需要,面向当前我国新能源汽车和动力蓄电池的使用场景需求,结合我国动力蓄电池电性能测试经验,充分参考相关国际标准,GB/T 31467—2023经修订后发布,为电动汽车用锂离子动力电池包和系统的电性能测试提供了方法。本文详细对比了GB/T 31467—2023与GB/T 31467.1—2015、GB/T 31467.2—2015以及ISO 12405-4:2018的测试项目与技术内容,并对通用测试条件、通用测试、基本性能测试等部分的修订内容进行了解释说明,可为相关动力电池产品研发和验证提供参考。标准在修订过程中,一方面考虑国内技术水平及整车对动力电池包和系统电性能的需求,增加了外观、极性、能量密度、充电性能以及工况放电的测试方法;另一方面,结合整车实际运行过程中的充放电电流以及运行环境,对所有测试项目的充放电电流进行修改,并对无负载容量损失、能量效率、高低温启动功率等测试项目的测试温度进行协调统一,有利于电动汽车用锂离子动力电池包和系统的研发和测试。

锂离子电池及其电解液发展状况及市场预测

锂离子电池由于工作电压高、循环寿命长、安全性能好、成本低、环境友好等特点,近年来在移动电话、笔记本电脑、电动汽车等领域得到了巨大发展。对锂离子电池工作原理进行了简介,对锂离子电池及其电解液的发展历程及过去5年的发展规模、产量、市场销售等进行了综述,对未来3年锂离子电池的发展趋势进行了预测,为锂离子电池和电动汽车等行业的进一步发展及其他新能源二次电池的研发和生产提供参考和借鉴。

基于Comsol的锂电池模组热失控仿真研究

随着越来越多纯电动车安全事故的发生,锂电池的热安全性引起越来越多的关注。论文基于Comsol软件,对锂电池包建立集总模型,用外部试验测得的参数模拟内部电芯的电化学特性,研究电池包内部某一电芯热失控状态下,电池包其他电芯热失控状态以及整个电池包热失控蔓延的过程。电池SOC即荷电状态,用来反映电池的剩余容量,研究发现SOC越大,热失控蔓延的现象越严重。集总模型建模仿真结果显示SOC越小,电压下降的趋势也越明显。

纯电动汽车低压电源管理优化策略研究

随着汽车技术的不断发展,汽车的电气设备种类越来越多,电器消耗的电能占整车能量比重不断上升,对汽车低压电源管理提出更高要求。为满足用户日益增长的汽车电子设备用电需求,达到减少整车能量消耗、提高电池充电效率的目的,在对纯电动汽车负载进行分类的基础上,利用遗传算法对用于电量安全分级的低压电池荷电状态(state of charge,SOC)进行优化,并提出一种基于SOC的4级恒流低压锂电池充电管理策略;利用AVL-Cruise和MATLAB-Simulink软件联合仿真搭建车辆模型,采用不同工况进行仿真验证和对比分析。结果表明,低压锂电池电源管理策略能够满足纯电动汽车的电量安全性要求,在一定程度上提高了整车经济性;优化后的锂电池充电效率有一定的提高,充电时间也有所减少。