人工智能在电动汽车上应用的准备状态:基于J-TRA方法的小型全球回顾与分析

A. H. Pandyaswargo, M. Maghfiroh, H. Onoda
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引用次数: 0

摘要

交通运输部门是全球温室气体(GHG)排放的重要贡献者。据估计,到2050年,用可持续和可再生能源驱动的电动汽车(ev)取代化石燃料汽车将有助于减少约21%的排放。为了提高电动汽车的运行效率,各种人工智能(AI)技术已经被应用。例如充电系统优化、自动驾驶汽车技术、交通控制技术等。为了了解这些技术应用的当前准备状态,我们构建了一个小型的全球人工智能在电动汽车中的应用数据库。已经确定了23个原型项目的地点。项目按AI类型、开发人员类型、操作规模和准备状态进行分类。采用日本技术准备评估(J-TRA)方法分析了战备状态。有七个分析参数:1)市场,2)技术发展,3)系统集成,4)可持续性验证,5)安全性,6)商业化和7)成本和风险。结果表明,尽管该技术具有广阔的市场前景、技术发展的稳步进展以及可验证的环境效益,但在该技术达到商业化、成本和风险应对机制的更高准备水平之前,还需要做更多的工作来确保安全性并与现有系统集成。
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Readiness Status of Artificial Intelligence Applications on Electric Vehicles: A mini global review and analysis using the J-TRA method
The transportation sector is a significant contributor to global greenhouse gas (GHG) emissions. It is estimated that replacing fossil fuel-based vehicles with electric vehicles (EVs) powered by sustainable and renewable energy could contribute to approximately 21% of emission avoidance by 2050. To improve the efficiency of EV operation, various artificial intelligence (AI) technologies have been applied. Examples include charging system optimization, self-driving car technology, and traffic control technology. To understand the current readiness status of those technologies applications, a small database of AI use in EVs that is in practice globally is constructed. There are 23 locations of prototype projects identified. The projects are categorized by the AI type, developer type, size of operation, and readiness status. Readiness status is analysed with the Japan Technology Readiness Assessment (J-TRA) methodology. There are seven analysed parameters: 1) Market, 2) Technology development, 3) System Integration, 4) Sustainability Verification, 5) Safety, 6) Commercialization and 7) Cost and Risk. The results show that while there is a promising market, steady progress in technological development, and verified environmental benefits, more work is needed to ensure safety and integration with the current systems before the technology can reach higher readiness levels of commercialization, cost, and risk-coping mechanisms.
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