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| Multi-objective energy management strategy of fuel cell vehicle based on nonlinear programming and XGBoost |
| Institute of Automotive Engineering Technology, Hefei University of Technology, Hefei, Anhui 230000, China |
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Abstract To solve the problems of real-time and accuracy of fuel cell vehicle power distribution in current research,the off-line nonlinear programming combining online XGBoost algorithm was used to predict the fuel cell power in fuel cell vehicles. The power system model of fuel cell hybrid vehicles was constructed, and the typically mixed driving conditions of vehicles were obtained through cluster analysis. The optimal distribution ratio of fuel cells and lithium batteries under the working condition was calculated off-line by nonlinear programming algorithm. Taking the nonlinear programming calculation results as training data, the XGBoost algorithm was used to conduct the model training verification. The comparative calculation results show that through the proposed algorithm, the multi-objective consideration of the dynamic performance optimization of fuel cell hybrid system in the current off-line calculation is strengthened, and the accuracy of the online machine learning training data is improved. The proposed XGBoost algorithm can effectively expedite the calculation speed and avoid the over-fitting of the data to realize the accurate estimation of the power of fuel cell hybrid vehicle.
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Received: 17 December 2021
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2023, Vol. 44 
