State-of-Health Prediction of Lithium-Ion Batteries Based on Diffusion Model with Transfer Learning

Chenqiang Luo (), Zhendong Zhang (), Shunliang Zhu and Yongying Li
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Chenqiang Luo: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Zhendong Zhang: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Shunliang Zhu: Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co., Ltd., Shanghai 201805, China
Yongying Li: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Energies, 2023, vol. 16, issue 9, 1-14

Abstract: An accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) is crucial to their safe and reliable. Although recently the data-driven methods have drawn great attention, owe to its efficient deep learning, it is worthwhile to continue devoting many efforts to prediction performance. In practice, fast charging mode has been widely applied in battery replenishing, which poses challenges for SOH prediction due to the diversity of charging conditions and electrochemical properties of LIBs; although, the process is stable and detectable. Furthermore, most previous data-driven prediction methods based discriminative model cannot describe the whole picture of the problem though sample data, affecting robustness of model in real-life applications. In this study, it is presented a SOH prediction model based on diffusion model, as an efficient new family of deep generative model, with time series information tackled through Bi-LSTM and the features derived from the voltage profiles in multi-stage charging process, which can identify distribution characteristics of training data accurately. The model is further refined by means of transfer learning, by adding a featured transformation from the base model for SOH prediction of different type LIBs. Two different types of LIBs datasets are used to evaluate the proposed model and the verified results revealed its better performance than those of other methods, reducing efforts required to collect data cycles of new battery types with the generality and robustness.

Keywords: lithium-ion battery; diffusion model; transfer learning; SOH prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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