State-of-charge estimation based on model-adaptive Kalman filters. (August 2021)

• Record Type: Journal Article
• Authors: Locorotondo, Edoardo; Lutzemberger, Giovanni; Pugi, Luca
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 7(2021)
• Page Start: 1272
• Page End: 1286
• Publication Date: 2021-08
• Subjects: Lithium-ion batteries -- SOC evaluation -- Kalman filter -- hysteresis model -- adaptive model -- online parameter identification -- electric vehicle; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651820965406
• Languages: English
• ISSNs: 0959-6518

Abstract

This article presents a set of algorithms for the estimation of state of charge, specifically deployed for lithium-ion batteries. These algorithms are based on appropriate battery models. These models can be developed having different levels of accuracy, also including the possibility to correctly represent the hysteresis voltage behaviour of the selected lithium cells. In addition, different identification methods of the battery model parameters may also be considered, considering tabulated parameters, calibrated in previous tests, or online parametrization tools. State of charge is then evaluated using non-linear Kalman filter techniques. Effectiveness of identification methods, also with the performance offered by Kalman filter itself, has been accurately evaluated through experimental tests. To verify the robustness of the proposed algorithms, some disturbances were introduced and evaluation was also conducted at different state of charge initial conditions and sampling times.