Co-estimation of state of charge and capacity of lithium-ion battery based on GWO optimized LSTM and LSSVM

doi:10.3969/j.issn.1674-8484.2022.03.019

Abstract

Abstract:

A joint estimation model of the state of charge and capacity based on long short term memory (LSTM) and least squares support vector machine (LSSVM) was proposed to improve the accuracy of the state of charge (SOC) estimation of lithium-ion batteries after aging by analyzing the charging and discharging characteristics of lithium-ion batteries. According to the charging and discharging characteristics of the lithium-ion batteries, the charging time of the segment voltage was proposed as a new health factor. The capacity estimation module of the lithium-ion battery was established based on LSSVM, and the capacity estimation result was recorded by a memory gate. A joint estimation model of SOC and capacity of lithium-ion battery was established based on the grey wolf optimizer optimized long short term memory (GWO-LSTM) framework. The results show that the capacity estimation accuracy of the proposed method is improved by more than 43% comparing with the back-propagation neural network optimized by the particle swarm algorithm (BPNN-PSO) and the traditional LSTM model, and the SOC estimation shows better robustness.

Key words: lithium-ion batteries, state of charge (SOC), capacity estimation, long short memory (LSTM), grey wolf optimizer(GWO), least square support vector machine (LSSVM)

CLC Number:

TM911.3

WANG Qiao, WEI Meng, YE Min, LIAN Gaoqi, MA Yuchuan. Co-estimation of state of charge and capacity of lithium-ion battery based on GWO optimized LSTM and LSSVM[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 571-579.

Figures/Tables 8

References 22

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测试样本	Spearman相关性系数
测试样本	3.6~3.7 V	3.7~3.8 V	3.8~3.9 V	3.9~4.0 V	完整充电时间
CX2-1	0.699 3	0.662 1	0.912 6	0.963 5	0.957 6
CX2-2	0.700 2	0.651 2	0.927 1	0.978 5	0.972 5
CX2-3	0.733 2	0.654 3	0.931 1	0.981 4	0.977 5

测试样本	Spearman相关性系数
测试样本	3.6~3.7 V	3.7~3.8 V	3.8~3.9 V	3.9~4.0 V	完整充电时间
CX2-1	0.699 3	0.662 1	0.912 6	0.963 5	0.957 6
CX2-2	0.700 2	0.651 2	0.927 1	0.978 5	0.972 5
CX2-3	0.733 2	0.654 3	0.931 1	0.981 4	0.977 5