Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration

Energies

Shifei Yuan²  Hongjie Wu¹  Xuerui Ma²  Chengliang Yin² 

[1] National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai 200240, China;

关键词: model stability analysis;    parametric sensitivity analysis;    measurement uncertainty;    parameters variation;    influencing factors weight;   

DOI  :  10.3390/en8087729

来源: mdpi

【 摘 要 】

Accurate estimation of model parameters and state of charge (SoC) is crucial for the lithium-ion battery management system (BMS). In this paper, the stability of the model parameters and SoC estimation under measurement uncertainty is evaluated by three different factors: (i) sampling periods of 1/0.5/0.1 s; (ii) current sensor precisions of ±5/±50/±500 mA; and (iii) voltage sensor precisions of ±1/±2.5/±5 mV. Firstly, the numerical model stability analysis and parametric sensitivity analysis for battery model parameters are conducted under sampling frequency of 1–50 Hz. The perturbation analysis is theoretically performed of current/voltage measurement uncertainty on model parameter variation. Secondly, the impact of three different factors on the model parameters and SoC estimation was evaluated with the federal urban driving sequence (FUDS) profile. The bias correction recursive least square (CRLS) and adaptive extended Kalman filter (AEKF) algorithm were adopted to estimate the model parameters and SoC jointly. Finally, the simulation results were compared and some insightful findings were concluded. For the given battery model and parameter estimation algorithm, the sampling period, and current/voltage sampling accuracy presented a non-negligible effect on the estimation results of model parameters. This research revealed the influence of the measurement uncertainty on the model parameter estimation, which will provide the guidelines to select a reasonable sampling period and the current/voltage sensor sampling precisions in engineering applications.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.