Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization

Energies

Jie Zhou¹  Bizhong Xia¹  Yadi Yang¹  Rui Huang¹  Guanghao Chen¹  Wei Wang²  Huawen Wang²  Yongzhi Lai²  Mingwang Wang² 

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

关键词: lithium-ion batteries;    state of charge;    state of health;    degree of polarization;    online parameter identification;    estimation in the full life cycle;   

DOI  :  10.3390/en12152939

来源: DOAJ

【 摘 要 】

The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

【 授权许可】

Unknown