An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation. (15th September 2020)

• Record Type: Journal Article
• Authors: Shu, Xing; Li, Guang; Shen, Jiangwei; Lei, Zhenzhen; Chen, Zheng; Liu, Yonggang
• Is Part Of: Energy. Volume 207(2020)
• Publication Date: 2020-09-15
• Subjects: Adaptive extended kalman filter -- State of charge -- State of health -- State of power -- Temperature compensation; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118262
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate estimation of inner status is vital for safe reliable operation of lithium-ion batteries. In this study, a temperature compensation-based adaptive algorithm is proposed to simultaneously estimate the multi-state of lithium-ion batteries including state of charge, state of health and state of power. In the proposed co-estimation algorithm, the state of health is identified by the open circuit voltage-based feature point method. On the basis of accurate capacity prediction, the state of charge is estimated by the adaptive extended Kalman filter with a forgetting factor considering temperature correction. The state of power is determined according to the multi constraints subject to state of charge, operating temperature and maximum current duration. The substantial experimental validations in terms of different current profiles, aging status and time-varying temperature operating conditions highlight that the proposed algorithm furnishes preferable estimation precision with certain robustness, compared with the traditional extended Kalman filter and the adaptive extended Kalman filter. Moreover, the battery pack validation is performed to further justify the feasibility of proposed algorithm when employed in a product battery management system. Highlights: An adaptive co-estimator is proposed for battery inner state estimation. The algorithm offers high accuracy at different temperature and aging status. The state of charge, state of health and state of power are cooperatively estimated. Comparisons of the proposed algorithm with traditional ones are conducted. The co-estimator is validated effective in a product battery management system.