A novel state of charge estimation method for lithium-ion batteries based on bias compensation. (1st July 2021)

• Record Type: Journal Article
• Authors: Ouyang, Tiancheng; Xu, Peihang; Chen, Jingxian; Su, Zixiang; Huang, Guicong; Chen, Nan
• Is Part Of: Energy. Volume 226(2021)
• Publication Date: 2021-07-01
• Subjects: State-of-charge -- H-infinity filter -- Bias compensation -- Joint estimation -- Lithium-ion battery; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120348
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate and efficient state-of-charge estimation for lithium-ion batteries are extremely crucial for electrical vehicles. A lot of researches make great progress on joint estimation algorithms. However, the combination of different algorithms brings too many design parameters, which reduces the accuracy of estimation and computational efficiency. In this paper, an adaptive H-infinity filter with bias compensation is proposed. Static condition and dynamic condition are set to verify the proposed algorithm in the experiments. In the dynamic condition, the proposed algorithm is verified and compared with the other three joint estimation algorithms at temperatures of 40 °C, 25 °C, 10 °C and 0 °C. Experiments show that the proposed algorithm achieves the highest estimation accuracy and calculation efficiency under two operating conditions and four temperatures, and the average time consumption of the proposed algorithm is reduced by 0.9%, 2.25% and 34.14%, respectively, compared with the other combinations of different algorithms. Graphical abstract: Image 1 Highlights: Bias compensation is introduced to improve the accuracy of parameters identification. An adaptive H-infinity filter is proposed for state of charge estimation. Influence of temperature on characteristics of battery and estimation is analyzed. Estimated effect of different algorithms at four temperature conditions is compared. The computational efficiency of different algorithms is compared and analyzed.