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Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Bizhong Xia, Rui Huang, Zizhou Lao, Ruifeng Zhang, Yongzhi Lai, Weiwei Zheng, Huawen Wang, Wei Wang and Mingwang Wang
Additional contact information
Bizhong Xia: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Rui Huang: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Zizhou Lao: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Ruifeng Zhang: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Yongzhi Lai: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Weiwei Zheng: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Huawen Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Wei Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Mingwang Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China

Energies, 2018, vol. 11, issue 11, 1-19

Abstract: The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

Keywords: battery management system; state of charge estimation; multiple forgetting factor; recursive least square; online parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (3)

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