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摘要: State of charge estimation of the battery is one of the core functions in the battery management system. Accurate and reliable state of charge estimation under wide temperature range is critical for the application of all-climate electric vehicles. The main work of this paper is as follows: (1) To achieve accurate closed-loop state estimation, a temperature dependent battery model is proposed; (2) The common model-based state of charge estimation methods using filters like Kalman filter assume that the state perturbations and measurement noise are white and Gaussian noises, which is not realistic in practical application. To solve this problem, set membership method which holds that the noises are unknown but bounded is used for state of charge estimation. Based on the established temperature dependent battery model and the set membership method, battery parameter and state of charge co-estimation algorithm is proposed for all-climate battery state estimation; (3) The proposed method is fully verified at -10 degrees C-40 degrees C and the comparison between the proposed method and extended Kalman filter is conducted to illustrate its superiorities. Furthermore, the validity and real time performance of the co-estimation method are verified in a hardware-in-loop test bench. Results show that the proposed co-estimation method has excellent robustness and the state of charge estimation error is bounded to 5% under wide temperature range. (C) 2019 Elsevier Ltd. All rights reserved.
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| 图1 The improvement of SOC online estimation for all-climate batteries based on the common method.
| 图2 Implementation flowchart of set membership theory based parameter and SOC co-estimation.
| 引文信息: Xiong R , Li L , Yu Q , et al. A Set Membership Theory based Parameter and State of Charge Co-Estimation Method for All-climate Batteries[J]. Journal of Cleaner Production, 2019, 249:119380.(下载链接) | 其他相关论文: 1. Sun F , Xiong R , He H . Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions[J]. Journal of Power Sources, 2014, 259(aug.1):166-176.(下载链接)
2. Yu Q , Xiong R , Lin C , et al. Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters[J]. IEEE Transactions on Vehicular Technology, 2017, 66(10):8693-8701. (下载链接)
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