Intelligent Energy Management Strategy Based on Artificial Neural Fuzzy for Hybrid Vehicle

This paper proposes an intelligent energy management strategy for a hybrid hydraulic-electric vehicle in order to minimize its total energy consumption. It proposes first to model the vehicle total energy consumption and investigates the minimization of an expended energy function, formulated as the sum of electrical energy provided by the on-board batteries and consumed fuel. More precisely, it is proposed in this paper an intelligent controller that shows its capabilities of increasing the overall vehicle energy efficiency and, therefore, minimizing total energy consumption. The proposed strategy consists of an advanced supervisory controller at the highest level (third), which corresponds to a fuzzy system deciding the most appropriate operating mode of the system. In the second level, an intelligent optimal control strategy is developed based on neuro-fuzzy logic. Then, in the first level, there are local fuzzy controllers to regulate vehicle subsystems set points to reach the best operational performance. The advantage of the proposed strategy could be summarized as follows: first, it can be implemented online and second, reduces total energy consumption compared with several traditional methods. The proposed strategy validations are performed using a mix of automotive TruckMaker and MATLAB/Simulink developed software on several (standard or not) driving cycles.