Analysis of stationary fuel cell dynamic ramping capabilities and ultra capacitor energy storage using high resolution demand data. To enhance the learning capability and decrease the effect on the initial values of Q-table, GA can also be utilized to optimize the initial values of Q-Learning based fuzzy energy management. The environment adaptation capability of fuzzy EMS is then improved needless of driving pattern recognition. By processing the accumulated experience, the Q-Learning controller progressively learns an appropriate fuzzy EMS output tuning policy that associates suitable actions to the different driving patterns. Different from the driving pattern recognition based method, Q-Learning controller observes the driving states, takes actions, and obtains the effects of these actions. Here, an adaptive fuzzy energy management control strategy (EMS) based on Q-Learning algorithm is presented for the real-time power split between the fuel cell and supercapacitor in the hybrid electric vehicle (HEV) in order to adapt the dynamic driving pattern and decrease the fuel consumption. GA can also be efficient to optimize the new emerging intelligent algorithm. With the development of intelligent algorithms, the learning-based algorithm has been considered as viable solutions to various optimization and control problems.
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