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

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. Index Terms-Connected vehicles, energy management strategy (EMS), intelligent transportation systems (ITS), optimal control, plug-in hybrid electric vehicle (PHEV). I. INTRODUCTION A IR quality has become a serious concern in cities and urban areas in recent years. This has promoted new legislation, affecting the European automotive sector through Euro I-VI, which limits emissions of CO, HC, NO x , and particulate matter [1]. As Euro VI became into force, the spotlight is nowadays on CO 2 emissions. The European Commission has established a 130 g CO 2 /km target for 2015, which will be reduced to 95 g CO 2 /km in 2021 [2]. Similar policies have been imposed in other automotive markets, such as the USA, China, and Japan. This legislation has encouraged the introduction of Manuscript

Energy management system for smart grid: An overview and key issues

“…The paper reviewed the existing studies of EMS in microgrids and the existing decentralized optimization approaches. More review articles such as IoE‐based BEMS by Hannan et al, energy management in PEV by Martinez et al, energy savings by EMS by Lee and Cheng, and energy management strategies in hybrid renewable energy systems by Oltamiwa et al do not cover all the major aspects of EMS. Yousefi et al give a comprehensive analysis and comparison of different forecasting models PV, PEV, heat pump, and load demand.…”

A novel interval‐based approach for quantifying practical parameter identifiability of a lithium‐ion battery model

“…One major function of BMS is to estimate internal states such as state of charge (SoC), state of health (SoH), state of power (SoP), and so forth, using model‐based algorithms . These states are used as necessary inputs by a vehicle‐level controller to perform supervisory control of the vehicle to ensure safe and energy‐efficient driving . Therefore, accurately inferring battery states is extremely important for any electrified powertrains.…”

“…[1][2][3] These states are used as necessary inputs by a vehicle-level controller to perform supervisory control of the vehicle to ensure safe and energy-efficient driving. 4 Therefore, accurately inferring battery states is extremely important for any electrified powertrains.…”

Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability

“…Plug-in hybrid electric vehicles (PHEVs) have attracted much attention due to their combination of the advantages of pure electric vehicles and fueled vehicles [1][2][3]. The powertrain of PHEVs is usually composed of motors, engines, and other power sources, which are coordinated to form a variety of working modes through the clutch or the planetary gear.…”