The Effectiveness of Merge the (A-ECMS) with Heuristics Rule-Based Control Strategy for Energy Management in a Parallel HEVs


The 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.