Statistical learning controller for the energy management in a fuel cell electric vehicle

This paper considers a high efficiency energy management control strategy for a hybrid fuel cell vehicle. The proposed switching architecture consists of a bank of neural network based controllers designed using statistical learning theory. 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 and the switching strategy is used to implement different controllers designed on the considered operative conditions. The proposed controller increases the efficiency of the whole system and reduces 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 and a lithium battery pack. The results are compared with those of a single neural network based controller and the performance is shown to be satisfactory in terms of fuel consumption and the efficiency of the whole system.