An application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles. (29th June 2021)

• Record Type: Journal Article
• Authors: Zhang, Shuzhi; Peng, Nian; Zhang, Xiongwen
• Is Part Of: International journal of energy research. Volume 45:Number 13(2021)
• Page Start: 18554
• Page End: 18576
• Publication Date: 2021-06-29
• Subjects: maximum available energy -- state‐of‐charge -- state‐of‐energy -- state‐of‐health -- state‐of‐power; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.6964
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Considering prediction accuracy and adaptability to unpredictable operating conditions simultaneously, this paper presents an application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles. Under static and dynamic operating conditions, three commonly used online model parameters identification algorithms, including extended Kalman filter (EKF), particle swarm optimization, and recursive least square, are compared first, whose comparison results show that EKF's comprehensive performance is optimal. Taking identified open‐circuit voltage as observation information, two first‐order EKFs are established to online estimate state‐of‐charge (SOC) and state‐of‐energy (SOE). To maintain high accuracy and reliability under unpredicted operating conditions, fixed accumulation charge and fixed accumulation energy are innovatively seen as triggers, successfully realizing periodically capacity (state‐of‐health) and maximum available energy prediction with estimated SOC and SOE. Finally, with identified model parameters and estimated battery states, peak discharge/charge power can be further calculated in real time. Notably, parameters tuning for multistate estimation is also discussed in this work. Furthermore, the feasibility and prediction accuracy of the proposed multistate estimation framework is verified with sophisticated driving simulation under different temperatures. The validation results indicate that the presented framework can provide precise and reliable multistate estimation with relatively low computation cost. Highlights: An application‐oriented multistate estimation framework is proposed Different online model parameters identification methods are compared Parameters tuning for multistate estimation is discussed Fixed accumulation charge is innovatively taken as capacity updating trigger The feasibility of the proposed framework is verified under various temperatures