Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

Munsi, Md. Shahin; Chaoui, Hicham

doi:10.1109/access.2024.3371483

Cited by 4 publications

(1 citation statement)

References 128 publications

(165 reference statements)

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“…• Energy Management System (EMS): The EMS plays a crucial role in controlling and optimizing the power flow between the internal combustion engine and the electric motor. It determines when to use electric power, when to rely on the internal combustion engine, and when to switch between different modes to maximize efficiency and performance [20]. PHEV configurations [11].…”

mentioning

confidence: 99%

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Recalde,

Cajo,

Velasquez

et al. 2024

Energies

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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 monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

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mentioning

confidence: 99%

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Recalde,

Cajo,

Velasquez

et al. 2024

Energies

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Optimum Speed Control of Permanent Magnet Synchronous Motor using Artificial Neural Network-Based Field-Oriented Controller

Ishwarya,

Srimathi,

Nithishkumar

et al. 2024

2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT)

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Sustainable Mobility Transition: A SWOT-AHP Analysis of the Case Study of Italy

Ben Ali,

Rauch

2024

Sustainability

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The significance of sustainable mobility transition projects extends beyond borders. Transportation, particularly passenger vehicles, is a crucial sector in achieving sustainability. Therefore, prioritizing sustainable green mobility has led to the inclusion of alternative solutions, with a focus on accelerating the shift towards electric vehicle (EV) technologies and implementing a ban on the sale of new petrol and diesel cars in all European countries by 2035. Italy has been making progress in this area as the country seeks to address environmental concerns, reduce emissions, and promote sustainable transportation. However, compared to other European countries in 2024, Italy still has a long way to go to achieve a sustainable market share. In this regard, this article aims to address several questions related to the promotion and scaling up of the electric mobility transition project in Italy, taking into account the current situation. Specifically, it seeks to identify internal and external factors associated with this technology ecosystem, along with their relative importance. To conduct this study, a strengths, weaknesses, opportunities, and threats (SWOT) analysis was conducted to identify the factors, which was followed by the analytical hierarchy process (AHP) methodology to determine their priority and importance. A total of 8 internal factors and 14 external factors were analyzed, and their overall priority was determined. This study reveals that it is crucial to capitalize on the opportunities and strengths related to technology ecosystems while effectively mitigating the threats and technological limitations in order to scale up technology adoption. In particular, strengths S1 and S3 were given the highest overall priority scores, suggesting that they are the most important factors to leverage for the successful adoption of the technology. These prioritized factors and subfactors are crucial for expediting the transition process and can influence consumers’ decisions. However, without a substantial increase in consumer understanding and knowledge of these technologies, public education campaigns will be necessary. The significance of this study is paramount, and its results can contribute to the continuous enhancement in the formulation of practical plans and regulations to promote sustainable transportation, taking into account the identified factors.

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Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

Cited by 4 publications

References 128 publications

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Optimum Speed Control of Permanent Magnet Synchronous Motor using Artificial Neural Network-Based Field-Oriented Controller

Sustainable Mobility Transition: A SWOT-AHP Analysis of the Case Study of Italy

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