Digital twin technology in electric and self-navigating vehicles: Readiness, convergence, and future directions

IF 7.6 Q1 ENERGY & FUELS Energy Conversion and Management-X Pub Date : 2025-03-07 DOI:10.1016/j.ecmx.2025.100949
Uma Ravi Sankar Yalavarthy , N Bharath Kumar , Attuluri R Vijay Babu , Rajanand Patnaik Narasipuram , Sanjeevikumar Padmanaban
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Abstract

Digital Twin (DT) technology, which creates digital replicas of physical systems, significantly enhances the lifecycle of complex items, systems, and processes. It is especially important in the automotive industry for improving the design, construction, and operation of Electric Vehicles (EVs). Digital Twins make EVs safer, more comfortable, and more enjoyable to drive, thereby enhancing user experience. As mobility systems evolve to become more intelligent and eco-friendlier, electric and self-navigating vehicles are increasingly replacing internal combustion engine vehicles by leveraging technologies such as IoT, Big Data, AI, ML, and 5G. Significant contribution of transportation to global CO2 emissions underscores the need for sustainable practices. Smart EVs, capable of significantly reducing emissions, require innovative architectures like DTs for optimal performance. The advancement of data analytics and IoT has accelerated the adoption of DTs to increase the efficiency of system design, construction, and operation. EV batteries, being the most expensive components, necessitate thorough analysis for State of Charge (SoC) and State of Health (SoH). This review examines the application of DT technology in Intelligent Transportation Systems (ITS), addressing challenges with particular attention on issues regarding monitoring, tracking, battery and charge administration, communication, assurance, and safety. It also explores current trends in EV energy storage technologies and the crucial role of Digital Twins in optimizing battery systems. This technology enables comprehensive digital lifecycle analysis, enhancing battery management efficiency through optimal models for SoC and SoH assessments. Additionally, this review provides insights into various models, future challenges, and discusses DTs for EV battery systems, highlighting case studies, characteristics, and technological opportunities.
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电动汽车和自动导航汽车中的数字孪生技术:准备、融合和未来方向
数字孪生(DT)技术可以创建物理系统的数字副本,大大提高了复杂项目、系统和流程的生命周期。在汽车工业中,改进电动汽车的设计、制造和运行尤为重要。Digital Twins让电动汽车更安全、更舒适、驾驶更愉快,从而提升用户体验。随着移动系统向更智能、更环保的方向发展,电动汽车和自动导航汽车正越来越多地利用物联网、大数据、人工智能、机器学习和5G等技术取代内燃机汽车。交通运输对全球二氧化碳排放的巨大贡献凸显了可持续实践的必要性。能够显著减少排放的智能电动汽车需要像DTs这样的创新架构来实现最佳性能。数据分析和物联网的发展加速了DTs的采用,以提高系统设计、构建和运营的效率。电动汽车电池是最昂贵的部件,需要对其充电状态(SoC)和健康状态(SoH)进行彻底的分析。这篇综述探讨了DT技术在智能交通系统(ITS)中的应用,解决了在监控、跟踪、电池和充电管理、通信、保障和安全等问题上特别关注的挑战。它还探讨了电动汽车储能技术的当前趋势以及数字双胞胎在优化电池系统中的关键作用。该技术可实现全面的数字生命周期分析,通过优化SoC和SoH评估模型提高电池管理效率。此外,本综述还提供了对各种模型、未来挑战的见解,并讨论了电动汽车电池系统的dt,重点介绍了案例研究、特点和技术机遇。
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来源期刊
CiteScore
8.80
自引率
3.20%
发文量
180
审稿时长
58 days
期刊介绍: Energy Conversion and Management: X is the open access extension of the reputable journal Energy Conversion and Management, serving as a platform for interdisciplinary research on a wide array of critical energy subjects. The journal is dedicated to publishing original contributions and in-depth technical review articles that present groundbreaking research on topics spanning energy generation, utilization, conversion, storage, transmission, conservation, management, and sustainability. The scope of Energy Conversion and Management: X encompasses various forms of energy, including mechanical, thermal, nuclear, chemical, electromagnetic, magnetic, and electric energy. It addresses all known energy resources, highlighting both conventional sources like fossil fuels and nuclear power, as well as renewable resources such as solar, biomass, hydro, wind, geothermal, and ocean energy.
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