Energy management optimization of fuel cell hybrid electric vehicles based on hybrid deep neural networks
In order to improve the fuel economy of fuel cell hybrid electric vehicles during short range driving,a vehicle speed prediction model structure VBS-net based on hybrid deep neural network was constructed.This structure not only further improves the convolutional network based on the VGG-Net structure,but also introduces a bidirectional long short-term memory neural network to effectively learn the spatiotemporal dependencies of the entire vehicle speed prediction sequence.Simultaneously considering the influence of prediction time domain and input sequence length on the prediction accuracy of short-range vehicle speed problems,Bayesian optimization hyperparameters are used to further improve the prediction accuracy of VBS-Net.To address the online optimization and computational efficiency issues of energy management strategies,a multi-objective optimization based on model predictive control(MPC)energy management strategy was designed.This strategy can achieve a balance and optimization of hydrogen consumption,lithium battery state of charge(SOC)maintenance,and fuel cell utilization efficiency.Finally,under actual vehicle conditions,the proposed strategy was compared with rule-based strategies,resulting in fuel economy improvements of 7.25%,9.94%and 19.23%,and better SOC maintenance characteristics.
deep learningbayesian optimizationenergy management strategyspeed prediction
万方数据
维普
