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.

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.

Estimation Method of State-of-Charge For Lithium-ion Battery Used in Hybrid Electric Vehicles Based on Variable Structure Extended Kalman Filter

Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.

State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

Model based SOC estimation for high-power Li-ion battery packs used on FCHVs

A model based method which recruited the extended Kalman filter （EKF） to estimate the full state of charge （SOC） of Li-ion battery was proposed. The underlying dynamic behavior of the cell pack was described based on an equivalent circuit comprising of two capacitors and three resistors. Measurements in two tests were applied to compare the SOC estimated by model based EKF estimation with the SOC calculated by coulomb counting. Results have shown that the proposed method is able to perform a good estimation of the SOC of battery packs. Moreover, a corresponding battery management systems （BMS） including software and hardware based on this method was designed.

电动汽车动力电池关键状态参数估计及管理

通过分析电动汽车电池管理系统相关技术,提出了二阶等效电路模型并考虑了均衡电路的影响,建立了基于扩展卡尔曼滤波器(Extended Kalman Filter, EKF)的荷电状态估计算法规则,并将实验数据与真实数据进行了对比,验证了该模型SOC算法满足设计要求,同时对该设计现存的不足提出改进建议,并对今后的发展进行了预期。

风光互补式电动汽车充电站储能锂电池的实时状态估计

为了解决风光互补式电动汽车充电站储能系统中,传统静态锂电池模型不能实时更新参数导致相应的开路电压和荷电状态(state of charge, SOC)估计误差大等问题,提出一种基于动态一阶RC等效电路模型的锂电池自适应实时状态估计方法。首先,采用滑模控制方法追踪锂电池的实时输出电压,基于动态一阶RC等效电路模型,考虑锂电池内部参数欧姆内阻、极化内阻、极化电容和开路电压的动态变化情况,修正锂电池的端电压状态估计方程;然后,通过李雅普诺夫函数和稳定性判据推导出状态估计方程参数与实时电压追踪误差、工作电流之间的关系,得出锂电池内部参数的实时更新方法;进一步,通过实验确定开路电压与锂电池SOC之间的函数关系;在此基础上,实现锂电池状态的自适应实时估计。仿真结果表明:在风光互补式电动汽车充电站储能系统的连续变化负载工况下,所提自适应实时状态估计方法可以使锂电池估计状态快速收敛至模型参考值,避免了开路电压估计值波动问题;以安时积分和卡尔曼滤波方法修正的SOC为参考,自适应实时估计SOC的最大误差为0.72%,均方根误差和平均绝对误差分别为0.002 3和0.001 9;与开路电压-内阻模型估计SOC进行比较,自适应实时估计SOC的精度提高了一个数量级。

电池健康状态对电动汽车充电负荷计算的影响

充电负荷是计算研究电动汽车(electric vehicle,EV)对电网冲击和充电设施规划的基础。为此提出一种考虑电池健康状态(state of health,SOH)的充电需求计算方法。首先,利用EV出行链中与充电负荷相关的特征量的概率分布,实现单个EV用户出行行为的完整模拟。接着,基于电池健康状态修正EV实际容量和充电特征,并提出由用户里程焦虑系数修正用户下次出行后所允许的最小剩余SOC(state of charge,SOC)值,改进充电负荷计算模型。最后,基于美国家庭出行调查(national household travel survey,NHTS)数据的仿真表明,SOH影响EV用户出行的多个特征,EV规模越大,充电负荷计算越应考虑SOH。

改进非线性系统滤波算法AGV动力电池荷电状态估算

智能工厂中运行的纯电动自动导引车(AGV),对锂离子动力电池的荷电状态(SOC)进行实时估算是一项重要的任务.其目的在于,对AGV任务分配、路径规划以及充电管理等工作提供有效的参考依据.通过建立锂离子动力电池的等效电路模型以及运用次优多重渐消因子扩展卡尔曼滤波(SMFFEKF)算法,对电池SOC进行估算.首先,利用特性实验数据和经典最小二乘算法对锂离子动力电池进行建模和参数辨识;然后,利用SMFFEKF算法对SOC进行实时估算,最后,介绍了锂离子动力电池及AGV的实际应用场景.通过计算机软件仿真和实验室环境下测试设备获取的电池数据结果的对比,表明该算法具有很好的实时性和预测精度,能够满足实际应用的需求.

电动汽车用Ni MH电池建模及基于状态空间的SOC预测方法

电动汽车的电池管理系统需要精确、可靠的电池荷电状态 (SOC)预测器。针对氢镍 (NiMH)电池 ,提出了一种集总参数等效电路模型 ,介绍了通过实验获得电池参数的方法。该模型考虑了温度对电池参数的影响 ,能反映电池充放电的动态过程。文中用状态空间描述了R C电池模型 ,在此基础上建立了一种新的基于状态空间的SOC递推算法 ,并对电池的充放电过程进行了仿真计算。分析表明 。

电动汽车动力电池模型及SOC预测方法

简要介绍了铅酸蓄电池的电化学过程,提出了一种考虑温度影响的动态R-Q电池模型,并在此基础上建立了一种新的基于状态空间的SOC递推算法。对电池的两种放电过程仿真计算表明,该算法精确可靠,计算速度快,易于实现,适合电动汽车上使用。基于状态空间的SOC预测方法通过电池稳态开路电压来计算SOC,在电池充放电循环过程中没有误差累积,同样可用于其它类型的动力电池。

基于神经网络的磷酸铁锂电池SOC预测研究

利用神经网络进行了电动汽车用的磷酸铁锂(LiFePO4)电池荷电状态(state of charge,SOC)预测研究。在分析磷酸铁锂电池充放电机理的基础上,采用levenberg-marquardt(LM)算法建立了磷酸铁锂电池的BP(back propagation)神经网络模型,并进行了电池SOC值的预测。结果表明,基于神经网络的电池SOC预测方法具有较高的精度,可用来预测磷酸铁锂电池的SOC值。

锂电池动态系统Thevenin模型研究

建立高精度的电池模型对于电动汽车动力锂电池的应用研究有重大意义。锂电池在使用过程中,其系统参数会跟随外界环境及荷电状态变化而改变,选用固定参数的电池模型会导致模型精度差。提出一种动态系统Thevenin模型。结合影响锂电池特性的荷电状态和环境温度因素,将经典Thevenin模型中的欧姆内阻、极化内阻、极化电容等固定参数,在动态系统Thevenin模型中描述为随荷电状态与温度动态变化的变量。最后选取单体锂电池为实验对象,采用HPPC实验辨识模型参数,对经典模型与动态系统模型分别进行仿真分析,结果表明,动态系统Thevenin模型能更准确描述锂电池性能。

电动汽车动力电池特性仿真系统

为了更好地模拟动力电池的动态特性,开发相应的控制策略,建立了适用于电动汽车的动力电池仿真平台,并且分别建立了动力电池剩余电量模型、最大充放电功率模型和电池热模型.基于电动汽车的不同工况进行了动力电池系统的仿真.结果表明,所建立的动力蓄电池模型能够很好地模拟电动汽车运行过程中蓄电池组的动态行为,为动力蓄电池管理系统软、硬件的开发和电动汽车整车控制策略的开发提供了良好的平台.

基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法

该文针对传统扩展卡尔曼滤波(EKF)算法对电池数学模型精确的高度依赖与动态电池模型难以精确获得之间的矛盾问题,提出一种完全数据驱动的基于改进EKF算法的动力电池全生命周期荷电状态(SOC)估计方法。该方法为数据驱动的SOC估计方法和基于模型的SOC估计方法的良好结合,其优点在于:一方面抑制数据驱动方法存在累积误差的问题,并保留其良好的动态特性;另一方面改善基于模型的算法过度依赖电池模型的缺点,并保留其很好的鲁棒特性。该方法的创新之处在于将等效电路中难以获知的一部分视为以电池电流为输入,以内部电压为输出,以电池内部阻抗为时变参数的黑箱系统,并加以动态在线辨识,获得实时的动力电池真实状态,从而保证电池模型的准确性和动态性,真正实现动力电池全生命周期的SOC估算。仿真结果表明,该方法具有较好的鲁棒性和实用性。

基于双CAN总线的电动汽车电池管理系统

根据所选用的锂离子动力电池组单体只数多、位置分布比较分散的特点,设计了基于双CAN总线的分布式电池管理系统(BMS),系统由若干采样模块和一个主控模块组成。介绍了电池信息采集和双CAN通信模块的硬件设计、电池荷电状态(SOC)的估算策略以及电池组安全管理策略。

混合动力客车锂离子动力电池管理系统

根据混合动力客车锂离子动力电池组单体只数多、分布比较分散的特点,设计了基于双 CAN 总线的分布式电池管理系统(BMS)。该系统由若干采样模块和一个主控模块组成,与动力电池之间的连线数量少,可扩展性强,而且采用复杂可编程逻辑器件(CPLD)技术实现了串联电池组单体电压的采样方法,实现了温度的低成本采样方法,建立了基于"预测-修正"方法的动力电池荷电状态(SOC)的估算方法,可以实时地修正 SOC估计的误差和可靠地实现对动力电池运行时状态参数的监测,提高电池 SOC 的估算精度。

锂离子电池SOC与模型参数联合估算研究

电池荷电状态(SOC)的估算精度是影响新能源汽车性能的重要因素之一。针对电池参数动态变化影响SOC估算精度的问题,在确定二阶RC等效电路模型的基础上,采用渐衰记忆的递推最小二乘算法和扩展卡尔曼滤波算法对模型参数与SOC在线联合估算。经过实验与仿真验证,在模拟城市道路工况的放电条件下,与安时法和卡尔曼法相比,联合估算方法得到的SOC估算值与真实值的误差缩小到1.29%。该方法能够适应电池特性的动态变化,保证较高的SOC估算精度。

基于改进卡尔曼滤波的电池SOC估算

以研究电动汽车动力电池管理系统为背景,以电池荷电状态估算为关键技术,介绍了荷电状态与其主要影响因素的非线性动态关系,建立了二阶RC等效电池模型.在此基础上,考虑了温度对电池内阻的影响,采用卡尔曼滤波算法、改进的安时计量法和开路电压法,结合基于温度的电池模型参数在线辨识,对电池荷电状态进行估算,通过MATLAB仿真,并与基于经验公式的卡尔曼滤波算法进行了对比,平均误差为2.46%,提高了估算精度,验证了算法的可行性和可靠性.

基于滑模观测器的锂离子动力电池荷电状态估计

基于混合动力脉冲功率特性试验数据辨识得到了锂离子动力电池的等效电路模型参数,提出了基于滑模观测器算法的动力电池荷电状态估计方法。设计了滑模观测器,最后进行了算法验证。结果表明,对FUDS工况,应用滑模观测器算法进行荷电状态估计的最大误差为1.17%,方差为0.000 04,比传统算法获得了更好的估计精度和鲁棒性。