A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty

Chun Wang, Chaocheng Fang, Aihua Tang, Bo Huang and Zhigang Zhang
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Chun Wang: School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
Chaocheng Fang: School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
Aihua Tang: School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China
Bo Huang: School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
Zhigang Zhang: School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China

Energies, 2022, vol. 15, issue 12, 1-16

Abstract: An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s.

Keywords: ultracapacitor; state-of-charge (SOC); variable temperature model; neural network; adaptive extended Kalman filter (AEKF) (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: 2022
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