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illustrates the integration of Machine Learning in hybrid electric vehicles to harness energy from the environment. Sensors gather real-time environmental data such as solar radiation and wind speed. These data are analyzed by ML models to identify energy harvesting opportunities and optimal capture methods. The Energy Management System (EMS) adjusts energy distribution based on these predictions, extending vehicle range and reducing fuel consumption.
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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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Citations
... Since programmable electric vehicles can use a range extender to complete charging, unlike traditional plug-in hybrid electric vehicle models, it is possible to develop an efficient charging management control strategy to better achieve energy savings and emission reduction for programmable electric vehicles. 14 The focus of this paper is on a mass-produced range extender model developed by a company, as illustrated in Figure 1. The system structure mainly comprises three modules: the power battery system, power drive system and range extender system. ...
... As this misalignment accumulates, the error increases, potentially resulting in a suboptimal control sequence. To minimize this error, for state quantities that do not align with the grid points, the J DP values for the two adjacent grid points can be calculated using equation (14). ...
The main challenge facing current energy management strategies for extended-range electric vehicles is effectively balancing power demand and energy utilization to enhance fuel economy under complex and variable driving conditions. Therefore, to optimize the distribution between the two energy sources of extended-range electric vehicles and improve their fuel economy, this paper proposes an energy management strategy incorporating speed prediction. Firstly, the long short-term memory neural network speed prediction scheme is investigated, and its effectiveness under different cyclic conditions is verified. Secondly, the four hyperparameters of the long short-term memory neural network structure were optimized using the sparrow algorithm (SA) to further enhance the prediction accuracy of the long short-term memory speed prediction algorithm. After optimization, the mean square deviation and mean absolute error are reduced by 46.46% and 54.46%, respectively, compared with the pre-optimization period. Finally, an energy management strategy based on speed prediction was designed using the sparrow algorithm-long short-term memory model. The results show that the speed prediction-based energy management strategy reduces fuel consumption by 6.05% and 3.50% under the New European Driving Cycle and World Light Vehicle Test Cycle conditions, respectively, compared to the rule-based hybrid control strategy.
... One of the primary areas where AI has demonstrated its potential is in energy management systems (EMS) for electric drivetrains. The review "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles" extensively explores advancements in machine learning (ML) techniques and optimization strategies in energy management systems (EMS) for plug-in hybrid electric vehicles (PHEVs), emphasizing the integration of various ML methodologies to enhance decision-making and optimize energy distribution between the internal combustion engine and electric motor for improved efficiency, while discussing optimization algorithms like genetic algorithms and particle swarm optimization that aim to minimize energy consumption and maximize performance, highlighting realworld implications for emissions reduction and sustainability, future trends including deep learning, and challenges such as data availability and computational complexity, ultimately demonstrating the significant opportunities for innovation and efficiency improvements that ML integration offers in EMS for PHEVs [5]. By analyzing driving patterns and environmental conditions, AI-driven EMS can make realtime decisions that improve energy efficiency and extend battery life, ultimately enhancing the overall performance of electric vehicles. ...
... 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.
