Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles
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 wi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/24/9486 |
| _version_ | 1827640042222256128 |
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| author | Jiapeng Yan Huifang Kong Zhihong Man |
| author_facet | Jiapeng Yan Huifang Kong Zhihong Man |
| author_sort | Jiapeng Yan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:53:25Z |
| format | Article |
| id | doaj.art-0999fc7874b649d5bc4c2f46a34a790e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:53:25Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0999fc7874b649d5bc4c2f46a34a790e2023-11-24T14:37:50ZengMDPI AGEnergies1996-10732022-12-011524948610.3390/en15249486Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric VehiclesJiapeng Yan0Huifang Kong1Zhihong Man2School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaIn 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.https://www.mdpi.com/1996-1073/15/24/9486recurrent neural network (RNN)nonlinear optimization problems (NOP)electric vehicle (EV)electro-hydraulic braking (EHB)asymptotical convergence |
| spellingShingle | Jiapeng Yan Huifang Kong Zhihong Man Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles Energies recurrent neural network (RNN) nonlinear optimization problems (NOP) electric vehicle (EV) electro-hydraulic braking (EHB) asymptotical convergence |
| title | Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles |
| title_full | Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles |
| title_fullStr | Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles |
| title_full_unstemmed | Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles |
| title_short | Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles |
| title_sort | recurrent neural network based nonlinear optimization for braking control of electric vehicles |
| topic | recurrent neural network (RNN) nonlinear optimization problems (NOP) electric vehicle (EV) electro-hydraulic braking (EHB) asymptotical convergence |
| url | https://www.mdpi.com/1996-1073/15/24/9486 |
| work_keys_str_mv | AT jiapengyan recurrentneuralnetworkbasednonlinearoptimizationforbrakingcontrolofelectricvehicles AT huifangkong recurrentneuralnetworkbasednonlinearoptimizationforbrakingcontrolofelectricvehicles AT zhihongman recurrentneuralnetworkbasednonlinearoptimizationforbrakingcontrolofelectricvehicles |