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摘要： The state of charge (SOC) estimation is extremely important for the wide commercialization and safe operation of electric vehicle (EV), especially under cold conditions, which is also a critical technology for battery system in EVs used in the 2022 Beijing winter Olympics. Three efforts have been made in this paper: (1) A general joint estimation framework with dual estimators is set up. Based on this frame, a joint algorithm using the recursive least square (RLS) and the adaptive H infinity filter (AHIF) is realized. (2) Four filter-based algorithms have been systematically compared and analyzed at the wide temperature range. The results show that RLS-AHIF algorithm has better performance for SOC estimation even at low temperatures, such as -10 degrees C, and the SOC error is within 3.5%. (3) A hardware-in-loop validation platform including the battery management system (BMS) and battery test instruments has been built to verify the proposed method. The results from the platform show that the maximum error of SOC is less than 2% at 0 degrees C and 25 degrees C. Consequently, the proposed algorithm can achieve the application over a wide temperature range in an actual BMS. |
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图1 The procedure of RLS-AHIF frame. | 
图2 Battery experimental setup and procedure: (a) test bench; (b) test procedure. | 引文信息： Ju W , Rui X , Linlin L , et al. A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach[J]. Applied Energy, 2018, 229:648-659.(下载链接) | 其他相关论文： 1. Xiong R , Gong X , Mi C C , et al. A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter[J]. Journal of Power Sources, 2013, 243(6):805-816.（下载链接）
2.Xiong R, Yu Q, Lin C. A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter[J]. Applied energy, 2017, 207: 346-353.(下载链接)
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