基于差分电压平台的锂电池自适应充电策略

doi:10.19799/j.cnki.2095-4239.2023.0382

摘要/Abstract

摘要：

为应对锂离子电池在充电过程中由于其复杂电化学特性所引发的多因素不平衡问题，本文在综合考量充电时间、充电效率和电池健康状态(state of health，SOH)3个因素的基础上，提出一种基于差分电压平台(differential voltage platform，DVP)的自适应多阶恒流(DVP-based multistage constant current，DMCC)充电策略。首先，建立电-热-老化耦合模型以模拟充电过程中电池参数特性的变化。其次，为实现充电过程中的动态优化和自适应分阶，将充电电压差分处理并以DVP作为恒流切换条件，利用改进的灰狼算法(grey wolf optimizer，GWO)优化各阶段充电电流。然后，基于优化结果，采用帕累托最优前沿(Pareto optimal frontier)分析比较不同权重值组合对于充电优化的影响。最后，在MATLAB/Simulink平台搭建锂离子电池充电仿真系统，与传统恒流恒压(constant current-constant voltage，CC-CV)策略和基于截止电压的多阶恒流(voltage-based multistage constant current，VMCC)策略进行对比试验，仿真结果表明，本文所提充电控制策略可有效降低充电引起的电池容量衰减，缩短电池充电时间。

关键词: 锂离子电池, 充电策略, 多目标优化, 多阶恒流

Abstract:

Considering the three factors of charging time, charging efficiency, and battery state of health, this study proposes an adaptive multistage constant current charging strategy (i.e., differential voltage platform (DVP)-based multistage strategy) based on the DVP to cope with the multifactor imbalance caused by the complex electrochemical characteristics of lithium-ion batteries during charging. First, an electrical-thermal-aging coupling model is established to simulate the change in the battery parameter characteristics during charging. Next, the charging voltage is processed differentially to realize dynamic optimization and adaptive grading in the charging process. Accordingly, the DVP is used as the constant current switching condition, whereas the improved grey wolf optimizer algorithm is employed to optimize the charging current at each stage. Based on the optimization results, the Pareto optimal frontier analysis is used to compare the effects of different weight value combinations on charging optimization. Finally, the lithium-ion battery charging simulation system is built on the MATLAB/Simulink platform, and the traditional constant current-constant voltage and voltage-based multistage constant current strategies based on the cut-off voltage are compared and tested. The simulation results show that the proposed charging control strategy effectively reduces the battery capacity attenuation caused by charging and shortens the battery charging time.

Key words: lithium-ion battery, charging strategy, multi-objective optimization, multistage constant current

中图分类号:

TM 912

段双明, 董鹏来. 基于差分电压平台的锂电池自适应充电策略[J]. 储能科学与技术, 2023, 12(10): 3170-3180.

Shuangming DUAN, Penglai DONG. Adaptive charging strategy for lithium-ion battery based on differential voltage platform[J]. Energy Storage Science and Technology, 2023, 12(10): 3170-3180.

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参数	值	单位
I	[0, 4]	A
U_k	[2.5, 4.2]	V
SOC	[10, 90]	%
t	[0, 36000]	s
T	[10, 45]	℃
η	[90, 100]	%
SOH	[0, 100]	%

策略	权重因子
策略	α	β	γ
策略1	0.92	0.05	0.03
策略2	0.88	0.04	0.08
策略3	0.8	0.14	0.06
策略4	0.6	0.2	0.2

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