Ultracapacitor Energy Storage Systems based on Dynamic Setting and Coordinated Control for Urban Trains

Wang, Xin; Luo, Yingbing; Qin, Bin; Peng, Junming; Zhou, Yu; Sun, Zhongcan

doi:10.1016/j.ifacol.2020.12.1987

Cited by 1 publication

(1 citation statement)

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“…At present, the power source technology is still one of the significant factors restricting the rapid development of electric vehicles [3]. Meanwhile, the ultracapacitors are selected as new energy storage device and gradually applied in auxiliary power source of electric vehicles for its ultrahigh power density, long-term cycle life and excellent safety [4,5]. The State-of-Charge (SOC) indicates the current available capacitance, which is considered as a crucial state parameter of ultracapacitor.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%