Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management

被引：3

作者：

Cavus, Muhammed ^{[1
]}

Dissanayake, Dilum ^{[2
]}

Bell, Margaret ^{[3
]}

机构：

[1] Northumbria Univ, Dept Math Phys & Elect Engn, Newcastle Upon Tyne NE1 8SA, England

[2] Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham B15 2TT, England

[3] Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, England

来源：

ENERGIES | 2025年 / 18卷 / 05期

关键词：

artificial intelligence; battery energy management; electric vehicle; internet of things; predictive maintenance; reinforcement learning; state of charge; state of health; system control; sustainable transportation; ENERGY MANAGEMENT; CHARGE ESTIMATION; HEALTH ESTIMATION; STATE; ALGORITHM;

D O I：

10.3390/en18051041

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and safety. AI-driven techniques-such as machine learning (ML), neural networks (NNs), and reinforcement learning (RL)-enhance battery state of health (SOH) and state of charge (SOC) predictions, as well as temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered control frameworks optimise energy distribution, regenerative braking, and power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve energy efficiency and extend battery life, even in dynamic environments. This review also examines the integration of the Internet of Things (IoT) and big data analytics in EV systems, enabling predictive maintenance and fleet-level optimisation. By analysing these advancements, this paper highlights AI's pivotal role in shaping next-generation, energy-efficient EVs.

引用

页数：41

共 123 条

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