NSGA-II genetic algorithm-based optimization of the lithium battery equalization index

doi:10.19799/j.cnki.2095-4239.2023.0088

Abstract

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

CLC Number:

TM 912

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.

Figures/Tables 10

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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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