This work aims to address the issue of inaccuracy in Battery Electric Vehicles (BEVs) range prediction and the resulting range anxiety among BEV drivers. Vehicle range being a vector quantity, depends upon multiple static and dynamic factors. If calculated inappropriately, it negatively impacts subsequent driving decisions leading to poor vehicle performance. The work proposes a real-time driver and vehicle specific Range Polygon (RP) which is representing the maximum distance in the map that can be covered by the BEV in different directions along a specific route, starting from the source. The RP being dynamic, is a function of terrain, traffic, battery state of charge (SoC), driving habits, and charging stations. The methodology involves first predicting future driving behavior and subsequently integrating it with other factors to calculate energy consumption along each potential route. The RP range was validated against commonly used scaler matrices for range estimation and was found to be more appropriate to predict directional EV range. Field trials suggested 92-95% accuracy of predicted range against actual vehicle movement. With changes in the driving pattern, RP could learn and readjust itself on a real time basis to bridge the error gap. The authors suggest considering dynamic vehicle weight model into RP to improve the range prediction accuracy under different laden conditions. In cases where the RP is to be drawn for a larger area, opportunity cost for accuracy increases against available computation resources, but it can be optimized by focusing on driver’s main area of driving and also the product category. The RP is a novel approach for BEV’s range anxiety related issues and can be further utilised for turn-by-turn navigation, driving assistance, return trip planning, re-routing etc. Overall, it is an immensely useful tool for BEV drivers, fleet owners, and trip planners which optimally balances static and dynamic range prediction parameters and transforms range anxiety to Range tranquility on a real time basis.
Mr. MANISH KONDHARE, Deputy General Manager- AI ML, TATA MOTORS LIMITED