Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network
Abstract
:1. Introduction
2. Sample Entropy Extraction for Braking Signals
2.1. Brake Signal Processing
2.2. Sample Entropy Extraction
- (1)
- Construct a sequence of vectors of dimension m, . where, .
- (2)
- Take the absolute value of the maximum difference between the corresponding elements of and as the distance between the two vectors.
- (3)
- Define as the number of () for which the distance between and is less than or equal to r. When , it can be expressed as:
- (4)
- Define as:
- (5)
- Increase the dimension to m + 1 and count the number of and () whose distances are less than or equal to r. Denote the definition of as: Define as the number of with distances less than or equal to r from ( in m + 1 dimensions
- (6)
- Define as:
3. SSA-PNN Brake Intention Recognition Algorithm
3.1. PNN Brake Intention Recognition Model
3.2. PNN Smoothing Factor Optimization
3.3. SSA-PNN Braking Intent Recognition Process
4. Experimental Analysis
4.1. Data Acquisition
4.2. Validation Analysis
5. Conclusions
- (1)
- Through the analysis of the experimental results, the effectiveness of using the sample entropy of the braking signal as the recognition feature is verified. Compared with the time-domain features such as pedal position, speed, and speed change rate, the sample entropy can identify the braking intention more effectively.
- (2)
- Compared with traditional machine learning algorithms, the SSA-PNN braking intention recognition algorithm proposed in this paper has higher braking intention recognition accuracy and can effectively achieve the accurate determination of braking intention.
- (3)
- In this paper, the SSA-PNN recognition model featuring sample entropy is constructed through the study of brake intention feature parameters and the recognition model, which improves the accuracy of brake intention recognition. However, there are still some shortcomings in this paper. The method only focuses on the accuracy of recognition and ignores the consideration of real time. In addition, further research is needed on how to extract braking signal features more accurately and quickly to improve the accuracy of braking intentions.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Feature | Identification Accuracy | ||
---|---|---|---|
Train (%) | Test (%) | Kappa for Testing | |
Brake pedal position | 90.83 | 90.00 | 0.8387 |
Brake pedal position variation rate | 87.75 | 86.67 | 0.7996 |
Brake pedal speed variation rate | 81.67 | 83.33 | 0.7463 |
Sample Entropy | 94.16 | 93.33 | 0.8982 |
Algorithm | Parametric | |||
---|---|---|---|---|
PNN | RBF neuron expansion coefficient: | 0.01 | ||
Random forest | Minimum number of leaves | 50 | Number of decision trees: | 50 |
SSA | Number of sparrow groups | 20 | Iterations | 50 |
GA | Hereditary algebras: | 50 | Population size: | 5 |
SVM | RBF parameters | 0.01 | Penalty factor | 10 |
BP | Training error: 1 × 10−6 Learning rate: 0.01 Iterations: 13 |
Brake Intention Recognition Algorithm | Identification Accuracy | ||
---|---|---|---|
Train (%) | Test (%) | Kappa for Testing | |
Random forest | 79.04 | 77.84 | 0.6725 |
BP | 84.71 | 84.44 | 0.7666 |
SVM | 86.67 | 88.89 | 0.8352 |
PNN | 92.38 | 93.33 | 0.9011 |
GA-PNN | 96.19 | 95.56 | 0.9334 |
SSA-PNN | 97.14 | 97.78 | 0.9667 |
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Wen, J.; Zhang, H.; Li, Z.; Fang, X. Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network. World Electr. Veh. J. 2023, 14, 264. https://doi.org/10.3390/wevj14090264
Wen J, Zhang H, Li Z, Fang X. Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network. World Electric Vehicle Journal. 2023; 14(9):264. https://doi.org/10.3390/wevj14090264
Chicago/Turabian StyleWen, Jianping, Haodong Zhang, Zhensheng Li, and Xiurong Fang. 2023. "Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network" World Electric Vehicle Journal 14, no. 9: 264. https://doi.org/10.3390/wevj14090264