Advances in Lithium-ion Battery System Equalization Strategy Research
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摘要: 相比电池单体，成组后电池系统的容量、寿命和安全性均会大幅度下降，其原因在于内部参数和外部环境导致的不一致性问题。因此，需要均衡管理系统保障电池的一致性，其中均衡策略是关键之一。从均衡动机、均衡目标、均衡算法和均衡策略评价四方面，对国内外电池均衡策略研究进展进行了综述。首先深入分析电池组一致性影响因素，确定均衡动机。其次，从均衡目标出发，总结了电池组、电路和多目标融合三方面研究进展。再次，根据不同的算法分类详细阐述均衡算法。之后，对均衡策略的评价进行总结并提出一种新的评价方法。最后，系统梳理了目前亟待解决的均衡技术关键问题，对未来均衡策略的研究进行了展望。
相比电池单体，成组后电池系统的容量、寿命和安全性均会大幅度下降，其原因在于内部参数和外部环境导致的不一致性问题。因此，需要均衡管理系统保障电池的一致性，其中均衡策略是关键之一。从均衡动机、均衡目标、均衡算法和均衡策略评价四方面，对国内外电池均衡策略研究进展进行了综述。首先深入分析电池组一致性影响因素，确定均衡动机。其次，从均衡目标出发，总结了电池组、电路和多目标融合三方面研究进展。再次，根据不同的算法分类详细阐述均衡算法。之后，对均衡策略的评价进行总结并提出一种新的评价方法。最后，系统梳理了目前亟待解决的均衡技术关键问题，对未来均衡策略的研究进行了展望。
Abstract: Compared to battery cells, the capacity, life and safety of the battery system will be significantly reduced after forming a group, which is due to the inconsistency problem caused by internal parameters and external environment. Therefore, an equalization management system is needed to guarantee the consistency of the battery, of which the equalization strategy is one of the keys. The research progress of battery equalization strategy at home and abroad is reviewed from four aspects: equalization motivations, equalization objectives, equalization algorithms and equalization strategies evaluation. Firstly, the battery pack consistency influencing factors are analyzed in depth to determine the equalization motivations. Secondly, from the equalization objectives, three research advances in battery pack, circuit and multi-objective fusion are summarized. Again, the equalization algorithms are elaborated according to different algorithm classifications. After that, the evaluation of the equalization strategies are summarized and a new evaluation method is proposed. Finally, the key problems of the current equalization technology that need to be solved are systematically sorted out, and the next research on equalization strategies are prospected. -
表 1 均衡目标的原理、优点和缺点
均衡目标 原理 优点 缺点 基于电压一致 
1.简单，测量方便
2.数据来源可靠
3.充放电边界清晰
4.扩展性强1.易受电池内部参数影响
2.温度和老化对其有影响
3.LFP电池电压有平台
4.有些均衡电路构型复杂，成本高
5.低容量利用率基于SOC一致 
OCV-SOC估计
1.占用计算资源少
2. 准确反应电池内部状态
3.更加稳定
安时法SOC估计
1.需要精确初始SOC
卡尔曼滤波SOC估计
1.计算量大
整体
1.电池组容量增大
2.鲁棒性强OCV-SOC
1.一致性影响曲线形状
2.滞后特性
3.静置的单体
4.老化影响
5.LFP电池电压平台
安时法SOC估计
1.累积误差大
卡尔曼滤波SOC估计
1.高和低SOC区间误差大
整体
1.计算量大且复杂
2.老化影响
3.主动均衡电路构型复杂基于容量一致 
1.约束条件宽泛
2.电池组容量最大化利用
3.延长电池组使用寿命，减缓老化1.需要稳定、精确的计算资源
2.主动均衡的均衡电流需要额外计算均衡时间最小化 减少均衡过程的持续时间 1.时间易于测量
2.对被动均衡而言，缩短均衡时间意味着有更高的效率1.受多个因素影响
2.对主动均衡而言，缩短均衡时间意味着有更高的能耗均衡能耗最小化 降低元件传输过程能量损耗 1.提高能量利用率 1.有些均衡能耗数据很难获得
2.跟电池组容量大小有关多目标融合 多个均衡目标同时控制 1.弥补单个均衡目标的缺点
2.改善均衡速度和均衡效果1.计算量大且复杂
2. 电路更加复杂、成本高
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表 2 均衡算法的原理和优缺点
均衡算法 原理 优点 缺点 经典控制算法 经典代数值 使用经典算术运算的结果，例如极值、均值等作为均衡标准 1.计算灵活 1.均衡时间受影响 2.适用性强 2.均衡效率受影响 PID 比例系数、积分系数和微分系数 1.结构简单 1.需要选取合适的参数 2.稳定可靠 2.突变之后无法快速稳定 3.调节方便 现代控制算法 OC 在给定条件下寻找受控系统的控制规律，根据目标函数寻找指定的性能指标的最优解 1.适用于复杂系统 1.近似求解 2.指定性能最优解 2.复杂系统建模困难 MPC 根据测量信息在每个采样时间在线解决有限时域中的开环优化问题 1.控制多参数 1.运行条件要已知 2.简单高效 2.有时建模复杂 3.可拓展性强 智能控制算法 AC 一种用来寻找优化路径的概率型算法 1.应用广泛 1.经验选择参数 2.正反馈逼近最优解 GA 属于进化算法的一种, 它通过模仿自然界的选择与遗传的机理来寻找最优解 1.复杂优化问题求解 1.收敛速度慢 2.高度可拓展性 2.局部搜索能力差 3.鲁棒性强 3.控制变量多 PSO 基于迭代的优化算法 1.简单、调整的参数少 1.局部寻优能力较差 FLC 一种使用隶属度代替布尔真值的逻辑 1.鲁棒性强 1.规则定制依赖经验 2.实时性好 2.灵活性差 3.容错性好 3.可移植性差 混合控制算法 分布式控制 基于多智能体共识算法的均衡系统多目标分布式控制 1.扩展性和灵活性好 1.稳定性有待验证 2.易于实现和调整 2.算法结构复杂 3.不需要系统模型 3.集成困难
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表 3 定性和定量结合的均衡策略评价方法
评价方法 评价指标 说明 评价等级(单位) 定性评价 均衡路径 能量转移路径的简洁性评价 复杂、适中、简单 复杂性 均衡算法复杂性评价 复杂、适中、简单 实用性 均衡算法实际应用性评价 好、中、差 拓展性 均衡算法的内存占用评价 大、中、小 均衡能耗 均衡能耗大小评价 大、中、小 定量评价 均衡电流 均衡电流大小评价 单位：A 均衡时间 均衡持续时间大小评价 单位：s 成本 EMS硬件成本大小评价 单位：$ 温度 EMS整体温度大小评价 单位：℃ 电池组容量利用率 电池组容量与最小电池单体容量比值评价 单位：%
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