Open AccessPrediction of energy consumption for new electric vehicle models by machine learning
Author(s) -
Fukushima Arika,
Yano Toru,
Imahara Shuichiro,
Aisu Hideyuki,
Shimokawa Yusuke,
Shibata Yasuhiro
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5169
Subject(s) - trips architecture , electric vehicle , predictive modelling , energy consumption , computer science , energy (signal processing) , mean squared prediction error , machine learning , artificial intelligence , engineering , statistics , power (physics) , mathematics , physics , quantum mechanics , electrical engineering , parallel computing
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.