Adaptive charging strategy for lithium-ion battery based on differential voltage platform

doi:10.19799/j.cnki.2095-4239.2023.0382

Abstract

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

CLC Number:

TM 912

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.

Figures/Tables 15

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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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