Lithium-ion battery model based on sliding window and long short term memory neural network

doi:10.19799/j.cnki.2095-4239.2021.0373

Abstract

Abstract:

This study proposes a lithium-ion battery model based on the sliding window and long short-term memory (LSTM) neural network to improve the model accuracy under complex working conditions. First, a lithium-ion battery model based on the LSTM neural network is established. Next, the basic structure of the neural network is determined. The time series feature extraction, feature fusion, and regression prediction are realized by combining the LSTM, vector splicer, and full connection layers. A sliding window input vector processing method is then proposed. The sliding window is advanced one time point at a time, and the data volume is limited by restricting the maximum number of letter elements within the time window. A computational margin is reserved for the parallel computation of the multiple LSTM layers and the deep hidden layers of the splicing and fully connected layers. Subsequently, the optimal selection of the depth of the recurrent network layer in the model is achieved. A training method using offline data set pre-training and online data parameter modification is proposed to solve the generalization problem under various complex working conditions. The model learns the common parts of the battery through the repetitive training of a large number of offline data sets. The network parameters are adjusted and used in the prediction by using a part of the online data. Finally, the datasets of the constant current/constant voltage, random current pulse, high-power pulse, and other working condition test profiles are applied for validation. The results show that the proposed modeling method can accurately predict the battery's output voltage and state of charge.

Key words: lithium-ion battery, battery model, neural network, long short term memory, feature extraction, sliding window

CLC Number:

TM 912

Shaofeng ZHANG, Qingyong ZHANG, Yesen YANG, Yixin SU, Binyu XIONG. Lithium-ion battery model based on sliding window and long short term memory neural network[J]. Energy Storage Science and Technology, 2022, 11(1): 228-239.

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预测目标	层定义	神经元数	激活函数
SOC	隐层1	2L_tw	tanh
	隐层2	L_tw	tanh
	输出层	L_tw	linear
电压	隐层	2L_tw	tanh
电压	输出层	L_tw	linear

编号	工况	来源
1	电池1恒流恒压充放电	实验平台
2	电池2恒流恒压充放电	实验平台
3	低温随机电流脉冲	NASA RW10
4	高温随机电流脉冲	NASA RW23
5	平抑波动的随机脉冲	Simulink模型
6	大倍率脉冲放电	Simulink模型
7	温度多变环境	Simulink模型

性能	数值
性能	镍钴锰酸锂 (HLY18650-2500)	钴酸锂 (ICR18650/15P)
额定容量/(mA·h)	2500	1500
最小容量/(mA·h)	2500	1500
工作电压/V	2.75～4.20	2.50～4.20
内阻/Ω	≤60	≤18
放电温度/℃	-20～60	-20～60

训练方法	Step1		Step2		Step3		Step4		Step5
训练方法	SOC	电压	SOC	电压	SOC	电压	SOC	电压	SOC	电压
离线	32.1×10^-4	14×10^-4	25.1×10^-4	14.2×10^-4	31.4×10^-4	14.4×10^-4	28.6×10^-4	14.5×10^-4	30×10^-4	14.7×10^-4
在线	8.95×10^-4	22×10^-4	9.61×10^-4	22.1×10^-4	8.54×10^-4	22.1×10^-4	9.22×10^-4	22.2×10^-4	9.31×10^-4	22.5×10^-4
离线+在线	1.71×10^-4	12.3×10^-4	1.32×10^-4	12.3×10^-4	1.31×10^-4	12.4×10^-4	1.50×10^-4	12.4×10^-4	1.21×10^-4	12.7×10^-4

模型	平抑波动		大倍率放电		温度多变
模型	SOC	电压	SOC	电压	SOC	电压
MLP1	4.9×10^-5	1.4×10^-5	1.37×10^-4	1.16×10^-3	2.01×10^-3	1.53×10^-4
MLP2	2.11×10^-4	1.85×10^-4	2.94×10^-3	1.48×10^-3	8.4×10^-3	3.62×10^-4
提出的模型	9.52×10^-4	8.57×10^-5	6.66×10^-3	1.97×10^-2	1.30×10^-2	1.81×10^-3