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Go to Editorial ManagerThe hybrid electric vehicle (HEV) is considered an effective technique to reduce fuel consumption and exhaust emissions. The effectiveness of the HEVs in reducing fuel consumption and exhaust emissions is required an accurate division of the total power demand between energy sources. This aim is reached by an accurate design of energy management strategy (EMS) in the HEVs. Dynamic programming is an effective strategy to found the optimal solution for energy management. This technique requires the driving cycle to be known previously, wherefore it's not suitable to implement in real-time. The Equivalent Consumption Minimization Strategy (ECMS) is an effective technique that can be implemented in real-time. This strategy is used to estimate and adapt the equivalent factor (EF) in real-time, which is used to convert the electric energy from the battery to equivalent fuel cost. The value of the (EF) varies with the driving cycle, therefore, the (EF) is suitable for a certain driving cycle and may lead to weak performance to another. This work proposed a technique based on the battery state of charge feedback called adaptive prediction (AP) to estimate and adapt the equivalent factor in real-time. The best-obtained results are ranged between (11.1 to 32.889) % for several different driving cycles.
This paper is concerned with the design of a new controller for active suspension system. The model is considered as a quarter-car. The presented controller depends on the fuzzy technique and NARMA-L2 linearization algorithm. The compensation system that added by the fuzzy rules improves the performance of the controller, while the neural network produces the required control signal. The new controller can achieve an improvement of the ride comfort with a reasonable value of power consumption. The mathematical analysis of the mechanical power used by the model is focused on the average and the RMS of the power supplied to the system, regardless of the frequency content of the vibration signal. The simulation results which are verified by a practical examples of road profiles, demonstrate the efficacy of the proposed controller.