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

Shifei Yuan, Hongjie Wu, Xuerui Ma and Chengliang Yin
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Shifei Yuan: National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai 200240, China
Hongjie Wu: National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai 200240, China
Xuerui Ma: National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai 200240, China
Chengliang Yin: National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2015, vol. 8, issue 8, 1-23

Abstract: 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.

Keywords: model stability analysis; parametric sensitivity analysis; measurement uncertainty; parameters variation; influencing factors weight (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: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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