A lithium battery life-prediction method based on mode decomposition and machine learning

doi:10.19799/j.cnki.2095-4239.2022.0341

Abstract

Abstract:

Lithium-ion battery's remaining useful life (RUL) is an important indicator of battery health management. Therefore, in this paper, using battery capacity as an indicator of health status, including modal decomposition and machine learning algorithms, a CEEMDAN-RF-SED-LSTM method was proposed to predict lithium battery RUL. First, adaptive white-noise full-ensemble empirical-mode decomposition (CEEMDAN) was used to decompose the battery capacity data. Then, to avoid the influence of the noise in the fluctuation component on the prediction ability of the model and not completely discard the characteristic information in the fluctuation component, this paper used the Random Forest algorithm to obtain important values for each fluctuation component, after which sexual ranking and numerical values were used as weights for each component's ability to explain the original data. Subsequently, the weight value and prediction result obtained by the neural network model constructed by different fluctuation components were weighted and reconstructed, resulting in the RUL prediction of the lithium-ion battery. Next, this research compared the prediction accuracy of the single model and the combined model, followed by the addition of the combined model prediction accuracy of RF, to improve the performance of the five neural networks further, after which the Simple Encoder-Decoder (SED) mechanism was introduced for the two networks with better performance, LSTM and GRU, to better learn the global temporal features and long-range dependencies of sequence data. We finally tested the method's performance using the NASA dataset as the research object. The experimental results showed that although the CEEMDAN-RF-SED-LSTM model performed well in battery RUL prediction, the prediction results had lower errors than the single model.

Key words: Li-ion battery, life prediction, adaptive white noise full ensemble empirical mode decomposition, random forest, neural network

CLC Number:

TM 912

Haoyi XIAO, Xiaoxia HE, Jiajia LIANG, Chunli LI. A lithium battery life-prediction method based on mode decomposition and machine learning[J]. Energy Storage Science and Technology, 2022, 11(12): 3999-4009.

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电池编号	拟合优度	IMF1	IMF2	IMF3
B0005	0.999284	0.023469	0.064610	0.109861
B0006	0.999052	0.072530	0.035768	0.124490
B0007	0.998983	0.027500	0.088808	0.076621
B0018	0.998613	0.025708	0.041343	0.174602

电池编号	神经网络模型	模型方法	运行时间/s	MAE	RMSE	MAPE	RE
B0005	LSTM	LSTM	18.77	0.058592	0.070959	0.043809	0.040650
		CEEMDAN-LSTM	70.65	0.043490	0.057525	0.026992	0.026260
		CEEMDAN-RF-LSTM	74.93	0.040182	0.053559	0.024962	0.024390
	RNN	RNN	38.07	0.280588	0.360674	0.180698	0.335772
		CEEMDAN-RNN	87.56	0.245580	0.332067	0.169977	0.390244
		CEEMDAN-RF-RNN	91.95	0.071269	0.087691	0.047033	0.195122
	GRU	GRU	17.84	0.060329	0.072215	0.044820	0.073171
		CEEMDAN-GRU	61.86	0.045586	0.060776	0.028049	0.028130
		CEEMDAN-RF-GRU	66.39	0.041444	0.055729	0.025512	0.024390
	CNN	CNN	13.09	0.068268	0.081806	0.039025	0.440650
		CEEMDAN-CNN	47.96	0.062904	0.073718	0.040348	0.304553
		CEEMDAN-RF-CNN	52.11	0.062832	0.073593	0.040437	0.280813
	MLP	MLP	13.02	0.070968	0.081720	0.047639	0.325203
		CEEMDAN-MLP	45.63	0.068850	0.080262	0.045182	0.329496
		CEEMDAN-RF-MLP	49.97	0.068734	0.080241	0.044737	0.305152

电池编号	神经网络模型	模型方法	运行时间/s	MAE	RMSE	MAPE	RE
B0006	LSTM	LSTM	20.72	0.054751	0.069080	0.037688	0.068037
		CEEMDAN-LSTM	64.83	0.044735	0.058687	0.028165	0.056075
		CEEMDAN-RF-LSTM	69.37	0.039161	0.049791	0.024893	0.054112
	RNN	RNN	38.66	0.207933	0.325623	0.177686	0.439252
		CEEMDAN-RNN	87.25	0.249714	0.345865	0.180759	0.355140
		CEEMDAN-RF-RNN	91.36	0.086660	0.110239	0.061186	0.196262
	GRU	GRU	17.84	0.064288	0.082282	0.044646	0.046729
		CEEMDAN-GRU	58.64	0.042328	0.052737	0.027216	0.065421
		CEEMDAN-RF-GRU	62.91	0.041394	0.051566	0.026716	0.065421
	CNN	CNN	13.19	0.083262	0.095125	0.054467	0.233645
		CEEMDAN-CNN	47.86	0.046205	0.062880	0.030333	0.029597
		CEEMDAN-RF-CNN	51.37	0.041894	0.054645	0.027848	0.028037
	MLP	MLP	13.06	0.085937	0.096994	0.064232	0.088037
		CEEMDAN-MLP	39.78	0.051744	0.066709	0.034459	0.084112
		CEEMDAN-RF-MLP	44.08	0.048342	0.063234	0.032276	0.037383

电池编号	模型方法	MAE	RMSE	MAPE	RE
B0005	CEEMDAN-RF-SED-LSTM	0.025569	0.031899	0.016515	0.018130
B0005	CEEMDAN-RF-SED-GRU	0.037491	0.044218	0.024056	0.089431
B0006	CEEMDAN-RF-SED-LSTM	0.032727	0.039157	0.021635	0.056075
B0006	CEEMDAN-RF-SED-GRU	0.041408	0.049697	0.028029	0.093458
B0007	CEEMDAN-RF-SED-LSTM	0.021852	0.026331	0.013319	0.059524
B0007	CEEMDAN-RF-SED-GRU	0.028275	0.036218	0.017499	0.061905
B0018	CEEMDAN-RF-SED-LSTM	0.025835	0.031901	0.016380	0.094737
B0018	CEEMDAN-RF-SED-GRU	0.030818	0.040062	0.019301	0.081053

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