基于NSGA-II遗传算法的锂电池均衡指标优化

doi:10.19799/j.cnki.2095-4239.2023.0088

摘要/Abstract

摘要：

锂离子电池均衡系统主要用于解决电池组工作过程中出现的不一致现象，但现有研究在需要权衡多个均衡指标时，均衡阈值的选取缺乏理论基础。为解决该问题，本工作提出了基于非支配排序遗传算法(non-dominated sorting genetic algorithm-II，NSGA-II)对锂离子电池均衡系统的均衡指标进行优化的计算框架。首先，以均衡阈值(Δ_V )作为问题参数，兼顾均衡速度、开关次数、荷电状态(state of charge，SOC)一致性最小作为多个均衡指标建立目标函数，并给出阈值与均衡指标关系式求解的方法，建立优化锂电池均衡指标的问题模型；然后，使用NSGA-II算法对多个均衡指标进行优化，并设计相应的决策策略；最后，在新欧洲驾驶循环(new European driving cycle，NEDC)工况和高速燃油经济性测试循环(highway fuel economy test，HWFET)工况下对所提算法的有效性进行验证。结果表明，电池组一致性、均衡速度相近的情况下，NEDC工况下最佳阈值Δ_V =0.0232的开关频率是经验阈值Δ_V =0.01的42%；同样，在HWFET工况下最优阈值Δ_V =0.0156的开关频率是经验阈值Δ_V =0.01的43.6%。本工作所提方法解决了以往均衡阈值难以确定的问题，使均衡系统的设计变得科学有效。

关键词: NSGA-II遗传算法, 多目标优化, 锂电池均衡, 均衡指标, 均衡阈值

Abstract:

Lithium-ion battery equalization systems are primarily used to address inconsistencies during battery pack operation. However, existing studies lack a theoretical basis for selecting the equalization threshold when considering multiple equalization metrics. To address this problem, the paper proposes a computational framework based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the equalization metrics of the Li-ion battery equalization system. First, the equalization threshold (Δ_V ) is used as the problem parameter, and the equalization speed, the number of switching actions, and SOCconsistency are considered as multiple equalization indicators to establish the objective function. A method for determining the relationship between the threshold and equalization indicators is given to establish the problem model for optimizing Li-ion battery equalization indicators. Then, the NSGA-II algorithm is used to optimize the multiple equalization indicators and design the corresponding decision strategy. Finally, the effectiveness of the proposed algorithm is verified under New European Driving Cycle (NEDC) and Highway Fuel Economy Test (HWFET) conditions. The results show that the switching frequency of the optimal threshold Δ_V = 0.0232 is 42% of the empirical threshold Δ_V =0.01 for the NEDC condition, with similar battery pack consistency and equalization speed. Similarly, the switching frequency of the optimal threshold Δ_V =0.0156 is 43.6% of the empirical threshold Δ_V =0.01 for the HWFET condition. The proposed method in this paper addresses the challenge of determining the equalization threshold and enables a more scientific and effective design of equalization systems.

Key words: NSGA-II algorithm, multiobjective optimization, lithium battery equalizationequalization, equalization index, equalization thresholds

中图分类号:

TM 912

刘宇龄, 孟锦豪, 彭乔, 刘天琪, 王扬, 蔡永翔. 基于NSGA-II遗传算法的锂电池均衡指标优化[J]. 储能科学与技术, 2023, 12(6): 1946-1956.

Yuling LIU, Jinhao MENG, Qiao PENG, Tianqi LIU, Yang WANG, Yongxiang CAI. NSGA-II genetic algorithm-based optimization of the lithium battery equalization index[J]. Energy Storage Science and Technology, 2023, 12(6): 1946-1956.

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次序	阈值Δ_V	均衡目标			得分
次序	阈值Δ_V	N_sw/次	T_eq/s	σ_SOC	得分
1	0.0232	2777	2020	0.0182	8.8978
2	0.0230	2782	2018	0.0181	8.8967
3	0.0233	2775	2024	0.0182	8.8951
4	0.0229	2784	2017	0.0180	8.8945
5	0.0234	2772	2028	0.0182	8.8913

次序	阈值Δ_V	均衡目标			得分
次序	阈值Δ_V	N_sw/次	T_eq/s	σ_SOC	得分
1	0.0156	552	392	0.01807	9.6334
2	0.0153	554	392	0.0178	9.6235
3	0.0152	555	392	0.0177	9.6175
4	0.0151	556	392	0.0176	9.6097
5	0.0150	557	392	0.0176	9.6014

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