Kalman filter optimize Transformer method for state of health prediction on lithium-ion battery

doi:10.19799/j.cnki.2095-4239.2024.0145

Abstract

Abstract:

Reviewing methods for predicting the lithium-ion battery state of health(SOH), we propose a battery SOH prediction method, which uses Transformer network based on Kalman Filter. Firstly, the battery data is preprocessed by adding Gaussian noise and auto-encoder reconstrution to remove the original noise from the data and strengthen the data features. Secondly, the battery data is extracted using the proposed (Kalman Filter-Transformer, KF-Transformer) model to extract the features of battery SOH changes, so that the Transformer network can better capture the nonlinear changes of battery SOH. Finally, the mapping from the features to the prediction of battery SOH is accomplished through the linear layer to obtain the prediction results of lithium-ion battery SOH. In this paper, three datasets (NASA, CALCE CS2 and CALCE CX2) are used for training and testing, and the two datasets use different temperatures and different batteries. The R² value of the predicted result in this paper is 0.987 and the mean absolute percentage error (MAPE) value is 1.8%, which is better than Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU). These results demonstrate the accuracy and stability of the proposed method for SOH prediction.

Key words: lithium-ion battery, battery state of health, auto-encoder, transformer network, Kalman Filter

CLC Number:

TM 911

Yufeng HUANG, Huanchao LIANG, Lei XU. Kalman filter optimize Transformer method for state of health prediction on lithium-ion battery[J]. Energy Storage Science and Technology, 2024, 13(8): 2791-2802.

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参数	NASA	CALCE CS2	CALCE CX2
电池容量	2000 mAh	1100 mAh	1350 mAh
电芯材质	LiNiMnCo/石墨	LiCoO₂	LiCoO₂
标称电压	3.7 V	3.7 V	3.7 V
测试温度	24 ℃	1 ℃	0.5 ℃
充/放电截止电压	4.2V/2.5 V	4.2V/2.7 V	4.2V/2.7 V
充/放电速率	2 A(1C)	1.1 A(1C)	0.675 A(0.5C)
电池编号	B0005, B0006, B0007, B0018	CS2-35, CS2-36, CS2-37, CS2-38	CX2-34, CX2-36, CX2-37, CX2-38

参数名称	参数量
epoch	5000
学习率	0.0005
注意力数量	16
隐藏层维度	16
隐藏层数量	8
注意力窗口大小	64

Index	RNN	MLP	LSTM	GRU	Ours
Index	RNN	MLP	LSTM	GRU	Before-Transformer	In-Transformer	After-Transformer
R₂	0.8214	0.8047	0.8785	0.8592	0.9627	0.9789	0.9612
RMSE	0.0911	0.0959	0.0966	0.0988	0.0497	0.0345	0.0448
MAPE	0.0540	0.0564	0.0568	0.0575	0.0385	0.0268	0.0341

Index	RNN	MLP	LSTM	GRU	Ours
Index	RNN	MLP	LSTM	GRU	Before-Transformer	In-Transformer	After-Transformer
R²	0.8063	0.8384	0.8551	0.8496	0.9765	0.9874	0.9542
RMSE	0.0992	0.1115	0.0684	0.0632	0.0305	0.0252	0.0531
MAPE	0.1211	0.1554	0.0913	0.0841	0.0237	0.0189	0.0415

Index	RNN	MLP	LSTM	GRU	Ours
Index	RNN	MLP	LSTM	GRU	Before-Transformer	In-Transformer	After-Transformer
R²	0.8973	0.9200	0.9150	0.9031	0.9674	0.9803	0.9587
RMSE	0.0354	0.0339	0.0349	0.0356	0.0268	0.0171	0.0281
MAPE	0.0336	0.0325	0.0334	0.0841	0.0215	0.0137	0.0269