Adaptive Energy Management of Hybrid and Plug-in Hybrid Electric Vehicles

This report summarizes the studies done during the fall 2007 semester. It includes the literature review, and research done on the subject of the Ph.D. dissertation. Since the oil crises of the 1970's, fuel economy has been one of the dominant issues in automotive industry. Therefore, a lot of research work has been focused on finding more efficient methods for transportation. One approach is provided by the concept of electric vehicles (EV) as compared with the internal combustion engines (ICE), electric machines are much more efficient. Moreover, electric machines are emissions free which means that they are environmentally friendly. Against all benefits, the problem with EVs is in the energy storage systems. In electrical systems, the energy is mostly stored in batteries, where drive range is an issue. They are required to recharge often and the charging times are long. To solve this energy storage problem, hybrid electric vehicles (HEV) are proposed as a practical solution. HEVs are the vehicles which are using both ICE and electric machines as energy transformation medium and again a battery to store the extra energy from the regenerative breaking or ICE. Hence, they enjoy the benefits of both electric and conventional vehicles. In general, hybrid systems can be commanded by splitting the required power between the electric machine and ICE to meet the specific needs like fuel consumption, efficiency, performance, and emissions. This power splitting scenario, which is the key point of hybridization, is in fact the control strategy or energy management of the hybrid automobile. Performance of the system, therefore, depends on the control strategy which needs to be robust (independent from uncertainties and always be stable) and reliable. Moreover, in order to improve the system, the control strategy should be adaptive to track the demand changes from the driver or drive cycle for optimization purposes. To compensate all this needs an adaptive artificial intelligent (neural networks and fuzzy logics) supervisory control system is proposed in this Ph.D. research for energy management of hybrid and plug-in hybrid electric vehicles.