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The vehicle speed, acceleration along with wind speed, road elevation, temperature, battery\u2019s SOC, and auxiliary loads are used as input to a multi-channel Convolutional Neural Network (CNN) to predict the energy consumption. The prediction is further fine-tuned using a Bagged Decision Tree (BDT). Unlike other existing techniques, the proposed system can be easily generalized for other vehicles as it is independent of internal vehicle parameters. Comparison with other benchmark techniques shows that the proposed system performs better and has a least mean absolute percentage error of 1.57%.<\/jats:p>","DOI":"10.1007\/s40747-022-00727-4","type":"journal-article","created":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T06:02:41Z","timestamp":1650261761000},"page":"4727-4751","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A system for electric vehicle\u2019s energy-aware routing in a transportation network through real-time prediction of energy consumption"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4134-4849","authenticated-orcid":false,"given":"Shatrughan","family":"Modi","sequence":"first","affiliation":[]},{"given":"Jhilik","family":"Bhattacharya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,18]]},"reference":[{"issue":"January","key":"727_CR1","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.apenergy.2018.03.105","volume":"220","author":"H Zhang","year":"2018","unstructured":"Zhang H, Song X, Xia T, Yuan M, Fan Z, Shibasaki R, Liang Y (2018) Battery electric vehicles in Japan: human mobile behavior based adoption potential analysis and policy target response. 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