NDLI: Prediction of energy consumption for new electric vehicle models by machine learning

Content Provider	IET Digital Library
Author	Fukushima, Arika Yano, Toru Imahara, Shuichiro Aisu, Hideyuki Shimokawa, Yusuke Shibata, Yasuhiro
Abstract	Recommending suitable charging spots to drivers on expressways for both charging equipment and electric vehicles (EVs) is an important issue for the spread of EVs. Therefore, the authors developed a recommendation system based on the prediction of the driving ranges of multiple EVs running on expressways. Recommendations are calculated from the energy consumption predicted by data-driven models constructed by actual data on EV trips. In authors’ system, prediction models for popular EV models were constructed with high accuracy. However, the accuracy of prediction is lower for new EV models than for the popular EV models, because the number of trips of new EV models running on the expressway is limited. To solve this problem, the authors propose a new transfer learning method, a type of machine learning that constructs prediction models using other sufficient data on popular EV models. They also evaluated their proposed method using the data on actual EV trips. As a result, the rate of prediction error of authors’ proposed method was reduced by about 30% from that the conventional method. The authors’ proposed method has the potential to predict the energy consumption for new EV models with higher accuracy.
Starting Page	1174
Ending Page	1180
Page Count	7
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 9, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5169
Journal	IET Intelligent Transport Systems
Publisher Date	2018-07-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Actual EV Trips Charging Equipment Data-driven Model Driving Range Prediction Electric Vehicle Electric Vehicle Model Energy Consumption Energy Consumption Prediction EV Model Information Network Knowledge Engineering Technique Learning in AI Machine Learning Multiple EV Power Engineering Computing Prediction Error Rate Recommendation System Recommender System Transfer Learning Method Transportation
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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