TY - GEN
T1 - Readiness Status of Artificial Intelligence Applications on Electric Vehicles A mini global review and analysis using the J-TRA method
AU - Pandyaswargo, Andante Hadi
AU - Maghfiroh, Meilinda Fitriani Nur
AU - Onoda, Hiroshi
N1 - Funding Information:
There are 11 locations of AI for charging system optimization projects collected in our database (Figure 2). Many of them are located in Canada and some European countries, such as France, Estonia, Spain, the Netherlands, and Italy. There is also evidence from Asian countries such as Malaysia and Taiwan. The market parameter of the project in Ottawa, Canada, has the highest readiness status. A pilot project supported by a PPP mechanism provides funding for grid efficiency improvement innovation projects [29]. Regulated private companies, such as an electricity distribution company, are already involved in the project. Therefore, the market and distribution system are already fully established. The safety parameter has the lowest TRL score, and it is the lowest score for many other AIs for charging system optimization projects. Potential hazards such as fire, leakage, explosion, and cybersecurity issues should be assessed. The system must be equipped with countermeasure equipment to reach the higher TRL in this parameter.
Publisher Copyright:
© 2022 ACM.
PY - 2022/9/21
Y1 - 2022/9/21
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - TRA
KW - electric vehicle
KW - self-driving car
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=85143256367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143256367&partnerID=8YFLogxK
U2 - 10.1145/3557738.3557848
DO - 10.1145/3557738.3557848
M3 - Conference contribution
AN - SCOPUS:85143256367
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the International Conference on Engineering and Information Technology for Sustainable Industry, ICONETSI 2022
PB - Association for Computing Machinery
T2 - 2nd International Conference on Engineering and Information Technology for Sustainable Industry, ICONETSI 2022
Y2 - 21 September 2022 through 22 September 2022
ER -
