Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network
The accurate identification of a driver’s braking intention is crucial to the formulation of regenerative braking control strategies for electric vehicles. In this paper, a braking intention recognition model based on the sample entropy of the braking signal and a probabilistic neural network (PNN)...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/9/264 |
| _version_ | 1797576330964893696 |
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| author | Jianping Wen Haodong Zhang Zhensheng Li Xiurong Fang |
| author_facet | Jianping Wen Haodong Zhang Zhensheng Li Xiurong Fang |
| author_sort | Jianping Wen |
| collection | DOAJ |
| description | The accurate identification of a driver’s braking intention is crucial to the formulation of regenerative braking control strategies for electric vehicles. In this paper, a braking intention recognition model based on the sample entropy of the braking signal and a probabilistic neural network (PNN) is proposed to achieve the accurate recognition of different braking intentions. Firstly, the brake pedal travel signal is decomposed to extract the effective components via variational modal decomposition (VMD); then, the features of the decomposed signal are extracted using sample entropy to obtain the multidimensional feature vector of the braking signal; finally, the sparrow search algorithm (SSA) and probabilistic neural network are combined to optimize the smoothing factor with the sparrow search algorithm and the cross-entropy loss function as the fitness function to establish a braking intention recognition model. The experimental validation results show that combining the sample entropy features of the braking signal with the probabilistic neural network can effectively identify the braking intention, and the SSA-PNN algorithm has higher recognition accuracy compared with the traditional machine learning algorithm. |
| first_indexed | 2024-03-10T21:50:44Z |
| format | Article |
| id | doaj.art-95f73dbce639445ea2c50d9abe8b8f74 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T21:50:44Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-95f73dbce639445ea2c50d9abe8b8f742023-11-19T13:27:31ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-09-0114926410.3390/wevj14090264Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural NetworkJianping Wen0Haodong Zhang1Zhensheng Li2Xiurong Fang3College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaThe accurate identification of a driver’s braking intention is crucial to the formulation of regenerative braking control strategies for electric vehicles. In this paper, a braking intention recognition model based on the sample entropy of the braking signal and a probabilistic neural network (PNN) is proposed to achieve the accurate recognition of different braking intentions. Firstly, the brake pedal travel signal is decomposed to extract the effective components via variational modal decomposition (VMD); then, the features of the decomposed signal are extracted using sample entropy to obtain the multidimensional feature vector of the braking signal; finally, the sparrow search algorithm (SSA) and probabilistic neural network are combined to optimize the smoothing factor with the sparrow search algorithm and the cross-entropy loss function as the fitness function to establish a braking intention recognition model. The experimental validation results show that combining the sample entropy features of the braking signal with the probabilistic neural network can effectively identify the braking intention, and the SSA-PNN algorithm has higher recognition accuracy compared with the traditional machine learning algorithm.https://www.mdpi.com/2032-6653/14/9/264electric vehiclebraking intentionvariational modal decompositionsample entropyneural networks |
| spellingShingle | Jianping Wen Haodong Zhang Zhensheng Li Xiurong Fang Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network World Electric Vehicle Journal electric vehicle braking intention variational modal decomposition sample entropy neural networks |
| title | Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network |
| title_full | Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network |
| title_fullStr | Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network |
| title_full_unstemmed | Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network |
| title_short | Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network |
| title_sort | research on electric vehicle braking intention recognition based on sample entropy and probabilistic neural network |
| topic | electric vehicle braking intention variational modal decomposition sample entropy neural networks |
| url | https://www.mdpi.com/2032-6653/14/9/264 |
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