Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles

Jiapeng Yan, Huifang Kong () and Zhihong Man
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Jiapeng Yan: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Huifang Kong: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Zhihong Man: School of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Energies, 2022, vol. 15, issue 24, 1-17

Abstract: In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP). Recurrent neural networks (RNNs) are advantageous in many folds for solving NOPs, yet existing RNNs’ convergence usually requires convexity with calculation of second-order partial derivatives. In this paper, a recurrent neural network-based NOP solver (RNN-NOPS) is developed. It is seen that the RNN-NOPS is designed to drive all state variables to asymptotically converge to the feasible region, with loose requirement on the NOP’s first-order partial derivative. In addition, the RNN-NOPS’s equilibria are proved to meet Karush–Kuhn–Tucker (KKT) conditions, and the RNN-NOPS behaves with a strong robustness against the violation of the constraints. The comparative studies are conducted to show RNN-NOPS’s advantages for solving the EHB force allocation problem, it is reported that the overall regenerative energy of RNN-NOPS is 15.39% more than that of the method for comparison under SC03 cycle.

Keywords: recurrent neural network (RNN); nonlinear optimization problems (NOP); electric vehicle (EV); electro-hydraulic braking (EHB); asymptotical convergence (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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