Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

TLDR

Plug‑in hybrid electric vehicles can reduce emissions and fuel use, but most energy‑management strategies are limited by the lack of future trip information, hindering full exploitation of their potential. This study surveys the evolution of energy‑management strategies toward blended‑mode and optimal‑control approaches and examines future trends in optimization, criteria, grid integration, and fleet deployment. The analysis is conducted within a connected‑vehicle framework, emphasizing how intelligent transportation systems, traffic data, and cloud computing can improve PHEV energy management.

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.