@article{oai:ipsj.ixsq.nii.ac.jp:02005279,
 author = {何,新 and 劉,屹 and 田中,悠斗 and 中島,誠敬 and 唐戸,涼太 and 藤田,歩夢 and 富井,尚志 and Xin He and Yi Liu and Yuto Tanaka and Masataka Nakajima and Ryouta Karato and Ayumu Fujita and Takashi Tomii},
 issue = {4},
 journal = {情報処理学会論文誌データベース（TOD）},
 month = {Oct},
 note = {電気自動車（EV）の長距離走行において，走行前に正確なエネルギー消費量を予測し可視化することは，適切な充電計画やルート選択を行ううえで重要である．特に冬季には低気温により，エアコンの電力需要が増加するとともに，転がり抵抗も増加する．そのため，従来の速度と勾配のみを考慮した手法では不十分であった．それに対して本研究では，気温を説明変数としたエアコンの消費電力量予測モデルの改善を行った．さらに，気温が転がり抵抗に及ぼす影響もモデル化し，予測精度を向上させた．次に，冬季の長距離運転計画を支援するため，このモデルを用いてエネルギー消費量可視化システムを構築した．提案システムでは，地点ごとのエネルギー消費量およびSOC（State Of Charge）の推移を地図上に可視化する．加えて，異なるエアコン設定温度や複数ルート選択時における目的地到達の可能性，必要な充電回数，推奨される充電地点を直観的に提示する．これにより，冬季におけるEVユーザが，より現実的かつ有用な長距離走行計画を立案できることを示した．, Electric vehicles (EVs) require precise predictions and visualizations of energy consumption prior to long-distance trips to ensure effective charging planning and optimal route selection. In winter conditions, low temperatures significantly increase both the power demand of air conditioning systems and the vehicle's rolling resistance. As a result, conventional prediction models that only consider driving speed and road gradient are insufficient. To address this, we developed an enhanced energy consumption prediction model that incorporates ambient temperature as an explanatory variable for accurately estimating the energy use of air conditioning systems. Additionally, we modeled the temperature-dependent changes in rolling resistance to further improve prediction accuracy. Based on this improved model, we constructed an energy consumption visualization system designed to support long-distance EV travel planning specifically in winter conditions. The proposed system visualizes energy consumption and state-of-charge (SOC) trajectories along selected routes. It also intuitively presents the likelihood of reaching the destination, required number of charging stops, and recommended charging locations, considering different air conditioning temperature settings and multiple routing options. Our findings demonstrate that this system enables EV users to plan more practical and reliable long-distance journeys during winter.},
 pages = {64--78},
 title = {EVの冬季長距離運転支援のための事前エネルギー消費量可視化システム},
 volume = {18},
 year = {2025}
}