The paper considers a high efficiency energy management control strategy for a hybrid fuel cell vehicle using neural networks and Statistical Learning theory. Hybrid Electric Vehicles may potentially improve fuel economy, reduce emission gases, and achieve performance similar to conventional cars. The use of different power sources and the presence of different constraints make the power management problem highly nonlinear. Probabilistic and statistical learning methods are used to design the weights of a neural network to minimize the fuel consumption during a given path. Numerical results are obtained using the model of a real hybrid car, “Smile” developed by FAAM, using a stack of fuel cells as the primary power source in addition to ultracapacitors. The results are satisfactory in terms of fuel consuming and efficiency of ultracapacitors and batteries.
Proceedings of the 17th World Congress The International Federation of Automatic Control
Fuel Cell Electric Vehicle, Statistical Learning, Neural Network Control
Abdallah, Chaouki T.; M. Cavalletti; J. Piovesan; S. Longhi; P. Dorato; and G. Ippoliti. "Statistical Learning Applied to the Energy Management in a Fuel Cell Electric Vehicle." Proceedings of the 17th World Congress The International Federation of Automatic Control (2008): 4659-4664. https://digitalrepository.unm.edu/ece_fsp/78