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| Ageing Performance Modeling of Li-Ion Batteries Based on Variational Deep Embedding-Wasserstein GAN with Gradient Penalty |
| Li Yi, Zhang Jinlong, Qi Hanhong, Wei Yanjun, Zhang Di |
| School of Electrical Engineering Yanshan University Qinhuangdao 066004 China |
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Abstract Lithium-ion batteryaging tests are primarily used to study battery aging performance. However, aging experiments for a large number of batteries take time and effort. Therefore, combined with Wasserstein GAN and gradient penalty (WGANGP), the Variational deep embedding (VaDE) model is used to form a VaDE-WGANGP architecture. Then, a method of battery aging performance modelling and data generation is designed. This paper aims to generate simulated data onexternal characteristics (such as battery voltage and charge/discharge current during working). The VaDE-WGANGP generation model is effective during different battery SOH intervals, which indicates that simulated battery data can be obtained across the whole battery life span (SOH∈[100%, 80%]) and coversbattery aging performance. Furthermore, battery external characteristics differ for different battery cells even under the same SOH state. Accordingly, the diversities in battery external characteristics among different battery cells under the same SOH states are considered.
An open-source battery test data set, in cludingdata from Toyota Research Institute (TRI) in partnership with MIT and Stanford and working data on LFP/graphite cells, is used. Firstly, the three external characteristics (voltage, current, and discharge capacity during the discharge process) are selected as the input of the model. The latent space by the VaDE encoder can be mapped, and its distribution with specific rules can be obtained through optimization. Then, this distribution space is sampled, and the latent variables obtained by sampling are used as input into the VaDE decoder for data generation. SOH estimation tests show that the VaDE-WGANGP has good generation performance for battery external characteristics, and the primary battery working performance is simulated during the aging process. Besides, effective and extended battery data resources can be provided by this measure for data-driven algorithms, especially in case the primitive data amount is insufficient.
The following conclusions can be drawn. (1) Regarding battery data clustering and battery extrinsic characteristics simulation generation, the integrated model of VaDE-WGANGP has better performance than VAE and VaDE. The statistical characteristics of battery extrinsic characteristics in different SOH intervals are accurate. (2) VaDE-WGANGP can generate high-quality simulation data of battery external characteristics. The SOH estimation results prove that the data generated by VaDE-WGANGP is of high quality. This paper provides a novel idea for analyzing the external characteristics of batteries during aging. Given the shortage of original data in data-driven technology, this scheme can also provide an effective method for data extension and is helpful for battery screening.
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Received: 16 April 2023
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2024, Vol. 39 
