Review on implementation method analysis and performance comparison of lithium battery state of charge estimation

doi:10.19799/j.cnki.2095-4239.2022.0078

Abstract

Abstract:

The lithium battery state of charge (SOC) estimation technology is the core technology to ensure the reasonable application of electric energy storage and electric vehicles, as well as the control, operation, monitoring, and maintenance of lithium battery systems. It demonstrates the problems of nonlinearity, time variability, complexity, and uncertainty of influencing factors in the practical application of lithium batteries, thereby resulting in the difficulty, low accuracy, and insufficient adaptability of the state of charge estimation. As a result, numerous lithium battery state of charge estimation algorithms and improvement strategies have emerged. At the same time, some researchers have analyzed and compared the implementation methods, advantages, and disadvantages of various estimation methods, and improvement strategies, but the relevant review lacks a systematic summary and insufficient discussion on the technical characteristics and applicability of estimation methods. To begin, this paper examines the influencing factors and test standards of lithium battery state of charge estimation. Next, the traditional methods based on experimental calculation, filtering algorithms based on battery model, data-driven machine learning technology, and digital-analog hybrid estimation methods are compared and analyzed, as well as technical characteristics, implementation process, applicable conditions, problems, pain points, and application advantages. The research focus and application status of the existing state of charge estimation technology for lithium batteries are systematically and comprehensively discussed. Finally, future research directions for lithium battery state of charge estimation algorithms are proposed.

Key words: SOC estimation of lithium battery, experimental calculation method, filter estimation algorithm, machine learning technology, data-model hybrid drive

CLC Number:

TM 92

Chong LI, Chenhui WANG, Gao WANG, Zonghu LU, Chengzhi MA. Review on implementation method analysis and performance comparison of lithium battery state of charge estimation[J]. Energy Storage Science and Technology, 2022, 11(10): 3328-3344.

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常见因素	影响原因
温度	温度越高，电解液的活性越强，进而锂离子扩散能力增强，受到的阻力减弱，内部化学反应更加激烈，故锂电池的容量随之增大
充放电倍率	充放电流越大，锂电池内部的化学反应越剧烈，故在充电电压相同的前提下，电流越大，锂电池的容量越大
电池自放电率	电池自放电率显示在非工作状态保存电量的能力，该值越大，锂电池的真实容量越大
充放电效率	大电流放电会使得电池内部的锂离子不能完全化学反应，导致电池容量减小

标准编号	标准名称	SOC估计技术要求
GB/T 36558—2018^[18]	电力系统电化学储能系统通用技术条件	能量计算误差不应大于3%，计算更新周期不应大于3 s
GB/T 38661—2020^[19]	电动汽车用电池管理系统技术条件	对于不可外接充电的混合动力电动汽车，锂电池动力电池管理系统SOC估算的累积误差应不大于15%
GB/T 34131—2017^[20]	电化学储能电站用锂离子电池管理系统技术规范	电能量计算误差应不大于3%

混合形式	技术特点	技术优势
安时积分+ELM^[73]	安时积分法估计电池SOC， ELM修正预测误差	ELM算法预测估计误差弥补安时积分法误差较大的问题
EKF+ Elman^[77]	EKF估计电池SOC，Elman神经网络实时修正电池极化电压	使用Elman神经网络模型提高了锂电池等效模型精度，提升EKF的SOC估计精度
EKF+ BP^[78]	EKF估计电池SOC，BP神经网络计算EKF误差并进行修正	BP神经网络具有非线性建模能力，能够建立温度变量与SOC值差值，形成误差修正
UKF+ BP^[23]	UKF估计电池SOC，BP神经网络估计初始电池SOC	初始SOC更为准确，加快UKF的收敛速度
EKF+ ANN^[75]	EKF估计神经网络权值及阈值等参数，利用神经网络预测电池SOC	通过EKF获取更为准确的神经网络权值及阈值，提升神经网络SOC预测准确性
EKF+ LSTM^[76]	UKF以及LSTM共同估计SOC，利用LSTM估计结果校正UKF结果	结合UKF以及LSTM对SOC的估计效果，在多种情况下具有良好SOC估计效果
EKF+ SVM^[74]	EKF估计电池SOC，SVM模型根据电池状态补偿电池参数误差	使用SVM保证电池等效模型参数更为准确
EKF+ LSSVM^[79]	EKF估计电池SOC，LSSVM估计电池SOH，建立SOC与SOH联合估计模型	LSSVM适合短周期SOH估计，从电池健康状态的角度修正电池SOC估计结果

方法类型	估计方法	方法精度	方法复杂度	方法数据量	方法计算量	实时检测性
基于实验测试计算的估计方法	开路电压法^[22]	**	*	*	*	*
	放电法^[21]	**	***	*	*	*
	安时积分法^[4]	***	***	*	**	***
	电导法^[23]	***	**	**	**	*
	交流阻抗法^[24]	****	****	***	**	*
基于模型驱动的估计方法	卡尔曼及其改进滤波^[35-36]	***	***	***	***	****
	粒子滤波^[50-52]	****	***	***	***	****
	H无穷滤波^[53]	***	****	***	***	****
	基于递推最小二乘滤波^[54]	***	***	***	***	****
基于数据驱动的估计方法	神经网络类^[61,62]	****	****	*****	****	****
	支持向量类^[67-69]	***	***	****	***	****
	高斯过程回归^[70]	***	**	****	***	****
基于数模驱动的估计方法	卡尔曼+ 神经网络^[73]	*****	*****	*****	*****	****
基于数模驱动的估计方法	卡尔曼+ 支持向量机^[74]	*****	****	****	****	****

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