A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives

World Electric Vehicle Journal, Journal Year: 2024, Volume and Issue: 15(8), P. 364 - 364

Published: Aug. 13, 2024

This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using preferred reporting items reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in intelligence, including machine learning, deep genetic algorithms, have been analyzed their impact on improving vehicle performance, efficiency, range. study highlights significant optimization, route planning, demand forecasting, real-time adaptation to driving conditions through advanced control algorithms. Additionally, this explores intelligence’s role diagnosing faults, predictive maintenance propulsion batteries, personalized experiences based driver preferences environmental factors. Furthermore, addressing security cybersecurity threats vehicles’ is discussed. The findings underscore potential foster innovation efficiency sustainable mobility, emphasizing need further research overcome challenges optimize practical applications.

Language: Английский

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Multi-objective optimization of PI controller for BLDC motor speed control and energy saving in Electric Vehicles: A constrained swarm-based approach

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 402 - 417

Published: June 20, 2024

Language: Английский

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Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Sensors, Journal Year: 2024, Volume and Issue: 24(7), P. 2205 - 2205

Published: March 29, 2024

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes uncertainty channel state information, which makes it difficult to guarantee Quality Service (QoS) in process computation offloading and resource allocation a Server (VECS). A multi-user optimization model algorithm based on Deep Deterministic Policy Gradient (DDPG) are proposed address this problem. Firstly, problem is modeled as Mixed Integer Nonlinear Programming (MINLP) according objective minimizing total system delay. Then, response large space coexistence discrete continuous variables action space, reinforcement learning DDPG proposed. Finally, method used solve compared with other three benchmark schemes. Compared baseline algorithms, scheme can effectively select task mode reasonably allocate VECS computing resources, ensure QoS execution, have certain stability scalability. Simulation results show that completion time be reduced by 24–29% existing state-of-the-art techniques.

Language: Английский

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Proposal automatic water purging system for machinery in high humidity environments controlled by an ECU

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109775 - 109775

Published: Oct. 16, 2024

Language: Английский

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Hydrogen storage capability optimization based on multi-objective function for decision of hydrogen production and utilization

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 95, P. 1095 - 1110

Published: Nov. 26, 2024

Language: Английский

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