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

Energies

Bizhong Xia¹  Ruifeng Zhang¹  Rui Huang¹  Zizhou Lao¹  Weiwei Zheng²  Wei Wang²  Huawen Wang²  Yongzhi Lai²  Mingwang Wang² 

[1] Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;Sunwoda Electronic Co. Ltd., Shenzhen 518108, China;

关键词: battery management system;    state of charge estimation;    multiple forgetting factor;    recursive least square;    online parameter identification;   

DOI  :  10.3390/en11113180

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown