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摘要: State-of-health (SOH) estimation is necessary for lithium ion batteries due to ineluctable battery ageing. Existing SOH estimation methods mainly focus on voltage characteristics without considering temperature variation in the process of health degradation. In this article, we propose a novel SOH estimation method based on battery surface temperature. The differential temperature curves during constant charging are analyzed and found to be strongly related to SOH. Part of the differential temperature curves in a voltage range is adopted to establish a relationship with SOH using support vector regression. The influence of battery discrepancy, voltage range, and sampling step are systematically discussed and the best combination of voltage range and sampling step is determined using leave-one-out validation. The proposed method is then validated and compared with an incremental capacity analysis (ICA)-based SOH estimation method using the Oxford and NASA datasets, which were collected from different cells under different conditions, respectively. The results show that the proposed method is capable of estimating SOH with the root-mean-square error less than 3.62% and 2.49%, respectively. In addition, the proposed method can improve the overall SOH estimation accuracy and robustness by combining with the ICA-based method with little computational burden. |
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| 图1 (a) Test schedule for the Oxford dataset. (b) Capacity evolution of eight cells in the Oxford dataset. (c) Capacity evolution of three cells in the NASA dataset. | 图2 Combined SOH estimation results for (a) cell 1 and (b) NASA-2. | 引文信息: Tian J , Xiong R , Shen W . State of health estimation based on differential temperature for lithium ion batteries[J]. IEEE Transactions on Power Electronics, 2020, PP(99):1-1. (下载链接) | 其他相关论文: 1. Rui, Xiong, et al. Towards a smarter battery management system: A critical review on battery state of health monitoring methods[J]. Journal of Power Sources, 2018.(下载链接)
2. Tian J, Xiong R, Shen W. A review on state of health estimation for lithium ion batteries in photovoltaic systems[J]. eTransportation, 2019, 2: 100028.(下载链接)
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