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![Figure 1. PHEV configurations [11].](publication/381628993/figure/fig1/AS:11431281254274294@1719097097098/PHEV-configurations-11_Q320.jpg)
This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitorin...
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... surge is attributed to their ease of implementation, adaptability to diverse optimization challenges, and robust search capabilities that facilitate the attainment of effective global optima. While various metaheuristic optimization techniques exist, they can be categorized into four main groups, as illustrated in Figure 6. In order to clarify the different applications of each heuristic optimization technique in the context of EMS, the Table 7 is presented: The first group encompasses evolutionary algorithms (EAs), grounded in the principles of natural evolution. ...
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... In the case of electric and hybrid vehicles, the convenient formulation of MPC with ML and efficient optimization methods drives development as shown in [72], in this research the primary difficulty was modeling the drive system in the hybrid case. Zhang et al. [73] developed an ML methodology for modeling plug-in hybrid electric vehicles using support vector machines and random forests to validate a virtual test controller. ...
The following article presents a high-level overview of how Model Predictive Control (MPC) is leveraged in passenger vehicles and their subsystems for improved performance. This overview presents the fundamental concepts of MPC algorithms and their common variants. After building some understanding of MPC methods, the paper discusses state-of-the-art examples of how MPC methods are leveraged to perform low- to high-level tasks within a typical passenger vehicle. This review also aims to provide the reader with intuition in formulating MPC systems based on the strengths and weaknesses of the different formulations of MPC. The paper also highlights active areas of research and development.
... We have found many significant holes in the existing research landscape that must be addressed. These include developing more adaptive control techniques capable of dynamically responding to real-time changes in motor conditions to improve EV dependability and efficiency [110,111]. Furthermore, more improved sensor technologies with real-time feedback on motor parameters are required to optimize control techniques [96]. There are four categories of EVs: HEV, PHEV, BEV, and FCEV. ...
... While FOC, DTC, and MPC have demonstrated their efficacy in controlling torque, future research could concentrate on creating adaptive control strategies that dynamically adapt to changing driving conditions and motor states. By using machine learning algorithms to forecast the best course of action for control based on real-time data, these adaptive systems may be able to improve torque management's responsiveness and efficiency [110]. Research might look at combining modern sensor technologies, such as fiber optic sensors or MEMS-based devices, to offer real-time input on motor characteristics. ...
The use of electric automobiles, or EVs, is essential to environmentally conscious transportation. Battery EVs (BEVs) are predicted to become increasingly accepted for passenger vehicle transportation within the next 10 years. Although enthusiasm for EVs for environmentally friendly transportation is on the rise, there remain significant concerns and unanswered research concerns regarding the possible future of EV power transmission. Numerous motor drive control algorithms struggle to deliver efficient management when ripples in torque minimization and improved dependability control approaches in motors are taken into account. Control techniques involving direct torque control (DTC), field orientation control (FOC), sliding mode control (SMC), intelligent control (IC), and model predictive control (MPC) are implemented in electric motor drive control algorithms to successfully deal with this problem. The present study analyses only sophisticated control strategies for frequently utilized EV motors, such as the brushless direct current (BLDC) motor, and possible solutions to reduce torque fluctuations. This study additionally explores the history of EV motors, the operational method between EM and PEC, and EV motor design techniques and development. The future prospects for EV design include a vital selection of motors and control approaches for lowering torque ripple, as well as additional research possibilities to improve EV functionality.
The rapid growth of the transportation sector in the past few decades has contributed significantly to global warming issues, leading to extensive research on vehicles having nearly zero or total zero tailpipe carbon emissions. The automobiles within this classification belong to hybrid electrical vehicles (HEVs), plug‐in HEVs, battery–electric vehicles (BEVs), fuel‐cell (FC) EVs (FCEVs), and FC HEVs. FCHEVs are powered by a combination of FC systems, rechargeable batteries, ultracapacitors, and/or mechanical flywheels. FC technology appears to hold potential in terms of extended driving distances and quicker refueling times for vehicles that emit no exhaust fumes. A significant number of research studies have examined various types of energy‐storage devices as vehicle power supply, their interfacing with the drive mechanism using power converters and their energy management strategies (EMS). In this article, various EMS for FC‐based EVs are discussed. Classifications of FCEVs, BEVs, and EMSs for FCHEVs are developed by various researchers. In this review report, it is indicated that the existing EMS are capable of performing well, yet further research is required for better reliability and intelligence toward achieving greater fuel efficiency and lifetime of upcoming FCHEVs.
