کنترل سلسله مراتبی و سازگار با فازی برای مدیریت انرژی هوشمند در خودروهای الکتریکی هیبریدی *

This work is concerned with the minimization of total energy consumption (summation of electric battery and fuel) of hybrid hydraulic-electric vehicles through an energy management combined approach incorporating elements of fuzzy logic, neural network and rule-based algorithms. In this paper, the global vehicle effciency is calculated by considering electrical motor, battery, engine, hydraulic pump, hydraulic motor and the transmission. An adaptive fuzzy neural algorithm is embedded in the vehicle with a fuzzy mode-switching control strategy along with proposed fuzzy tuning controllers to achieve real time control. In addition, a new formula is developed to update the proposed fuzzy controller. An intelligent hierarchical hybrid controller strategy is employed with several advantages: (i) proposed strategy does not depend on the a priori knowledge of the driving event, which makes it suitable to be implemented online; (ii) it can be easily implemented in real time based on fuzzy rule-based strategy containing five operation modes; (iii) rate of charge of the battery is limited to minimize aging effects; (iv) engine is operated near its optimal range. The effectiveness of the overall proposed architecture is demonstrated under various conditions in MATLAB/Truckmaker simulations which show increased efficiency over Pontryagin’s minimum principle. Offline and online control performance of the proposed approach are tested.