Prediction of state of health of lithium-ion batteries based on the AED-CEEMD-Transformer network

doi:10.19799/j.cnki.2095-4239.2023.0440

Abstract

Abstract:

The accurate prediction and assessment of the state of health (SOH) of lithium-ion batteries are extremely important for the safe and stable operation of the battery equipment. Quickly and accurately predicting the SOH can enhance the safety of battery devices and reduce the failure risk. This study proposes an algorithm for estimating the health status of lithium-ion batteries based on the transformer network structure to address the challenge of accurately predicting their SOH. This algorithm utilizes the battery capacity as the SOH indicator, incorporating the average Euclidean distance (AED) and complementary ensemble empirical mode decomposition (CEEMD) methods. First, the AED is used to assess the similarity between the initial cycle capacities of the batteries in the battery database and the battery to be predicted. The batteries in the battery database with similar capacity degradation trends are selected as the training set for improving the model's training speed. The CEEMD method is then employed to decompose the battery capacity curve into the capacity regeneration and degradation trend parts. The degradation models for the lithium-ion batteries are separately established using the transformer network for each component. As a result, the predictions for the SOH of lithium-ion batteries are obtained. This study validates the accuracy of the proposed battery prediction algorithm using two lithium-ion battery datasets from Stanford University and the University of Maryland. These datasets comprise batteries tested under different charge-discharge strategies and testing environments. The root mean square error of the proposed model can be controlled within 4%, demonstrating its high accuracy level. The superiority of the proposed estimation method is validated by comparing it with the commonly used lithium-ion battery health estimation algorithms based on the long short-term memory, recurrent neural network, and gated recurrent unit.

Key words: lithium-ion battery, transformer network, state of health, average euclidean distance, complementary ensemble empirical mode decomposition

CLC Number:

TM 911

Rui CHEN, Kai DING, Lianxing ZU, Qingsong XU, Zongbiao WANG, Dasi LUO, Jingjiang SU, Sheng HU, Jilong MAO. Prediction of state of health of lithium-ion batteries based on the AED-CEEMD-Transformer network[J]. Energy Storage Science and Technology, 2023, 12(10): 3242-3253.

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电池	误差	前30%训练集				前50%训练集				前70%训练集
电池	误差	OURS	LSTM	RNN	GRU	OURS	LSTM	RNN	GRU	OURS	LSTM	RNN	GRU
A0	RMSE	0.04599	0.04981	0.05130	0.05337	0.04753	0.05160	0.05322	0.05334	0.04857	0.05027	0.05059	0.05109
A0	MAPE	0.03332	0.03533	0.03715	0.03857	0.03457	0.03798	0.03993	0.04000	0.03842	0.04049	0.04076	0.04106
A1	RMSE	0.05217	0.05672	0.06219	0.06051	0.05467	0.05906	0.06120	0.06363	0.04996	0.05406	0.05551	0.05541
A1	MAPE	0.03859	0.04137	0.04570	0.04429	0.04155	0.04562	0.04742	0.04928	0.04073	0.04444	0.04561	0.04542
B0	RMSE	0.04896	0.05504	0.05836	0.05666	0.04810	0.05150	0.05475	0.05305	0.04934	0.05091	0.05258	0.05206
B0	MAPE	0.03738	0.04130	0.04390	0.04240	0.03744	0.04056	0.04292	0.04162	0.04006	0.04188	0.04319	0.04277
B1	RMSE	0.04890	0.05444	0.05632	0.05516	0.05159	0.05661	0.05782	0.05914	0.04536	0.04943	0.05149	0.05171
B1	MAPE	0.03676	0.03983	0.04254	0.04176	0.04010	0.04385	0.04506	0.04612	0.03828	0.04139	0.04273	0.04293
C1	RMSE	0.03968	0.04459	0.04657	0.04710	0.04254	0.04748	0.04901	0.04779	0.04229	0.04742	0.04841	0.04808
C1	MAPE	0.02872	0.03214	0.03331	0.03356	0.03111	0.03482	0.03605	0.03495	0.03308	0.03749	0.03813	0.03783
C2	RMSE	0.04011	0.04501	0.04707	0.04869	0.04270	0.04785	0.05026	0.04898	0.04509	0.04823	0.04889	0.04996
C2	MAPE	0.02793	0.03223	0.03306	0.03414	0.03043	0.03385	0.03680	0.03554	0.03572	0.03804	0.03870	0.03963

电池	误差	前30%训练集				前50%训练集				前70%训练集
电池	误差	OURS	LSTM	RNN	GRU	OURS	LSTM	RNN	GRU	OURS	LSTM	RNN	GRU
CS1	RMSE	0.03637	0.03937	0.03988	0.03986	0.03687	0.03802	0.03882	0.03882	0.03280	0.03324	0.03313	0.03335
CS1	MAPE	0.08005	0.08188	0.08320	0.08314	0.08857	0.09069	0.09296	0.09296	0.09761	0.09977	0.09950	0.10023
CS2	RMSE	0.01584	0.01811	0.01829	0.01863	0.01451	0.01635	0.01653	0.01657	0.01060	0.01127	0.01154	0.01175
CS2	MAPE	0.01573	0.01740	0.01763	0.01789	0.01495	0.01672	0.01691	0.01695	0.01184	0.01260	0.01288	0.01307
CS3	RMSE	0.02656	0.02884	0.02905	0.02925	0.02614	0.02770	0.02819	0.02824	0.02146	0.02175	0.02189	0.02190
CS3	MAPE	0.03453	0.03639	0.03655	0.03673	0.03829	0.03861	0.03915	0.03923	0.03468	0.03540	0.03558	0.03562
CS4	RMSE	0.03259	0.03470	0.03536	0.03528	0.03030	0.03184	0.03234	0.03231	0.02546	0.02592	0.02613	0.02598
CS4	MAPE	0.05495	0.05670	0.05776	0.05766	0.05607	0.05884	0.05978	0.05966	0.05538	0.05671	0.05760	0.05709
CS5	RMSE	0.02647	0.02843	0.02885	0.02835	0.02637	0.02775	0.02794	0.02787	0.02377	0.02405	0.02422	0.02423
CS5	MAPE	0.03604	0.03743	0.03779	0.03736	0.04016	0.04059	0.04079	0.04066	0.04107	0.04120	0.04143	0.04144
CS 6	RMSE	0.02543	0.02787	0.02837	0.02833	0.02609	0.02752	0.02768	0.02772	0.02339	0.02353	0.02369	0.02366
CS 6	MAPE	0.03324	0.03491	0.03530	0.03525	0.03779	0.03802	0.03815	0.03819	0.03744	0.03762	0.03784	0.03777

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