Abstract
Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.
Highlights
Air quality has become a serious concern in cities and urban areas in the recent years
We provide a comprehensive survey of the state-of-the-art of optimal plug-in hybrid electric vehicles (PHEVs) energy management strategies (EMSs), including the main approaches considered in the literature to date
This review on PHEVs EMSs algorithms highlights strengths and weakness of virtually all the existing approaches in the open literature. It does not conclude with a single algorithm preferred for PHEVs energy management, but advocates mixing more than one to compensate for each own deficiencies
Summary
Air quality has become a serious concern in cities and urban areas in the recent years. The fulfilment of optimal control of PHEVs hinges on key information about drive cycle, which is necessary to schedule conveniently the battery depletion Such desirable strategy depends on the selected route, congestion level, road profile, weather condition, as well as other information available through global position system (GPS), Intelligent Transportation Systems (ITS), Geographical Information Systems (GIS), and traffic modelling [6], [7]. We review most the optimization-based PHEV EMSs to date, especially covering the most recently proposed methods, e.g., convex programming (CP), game theory (GT), and numerous metaheuristic algorithms It includes plentiful examples of their applications in simulation environment, which evidences the importance of theses novel algorithms in research trend nowadays.
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