MORP: Data-Driven Multi-Objective Route Planning and Optimization for Electric Vehicles

The Wireless Power Transfer (WPT) system that enables in-motion charging (or wireless charging) for Electric Vehicles (EVs) has been introduced to resolve battery-related issues (such as long charging time, high cost, and short driving range) and increase the wide-acceptance of EVs. In this paper, we study the WPT system with the objectives of minimizing energy consumption, travel time, charging monetary cost on the way, and range anxiety for online EVs. Specifically, we propose the Multi-Objective Route Planner system (MORP) to guide EVs for the multi-objective routing. MORP incorporates two components: traffic state prediction and optimal route determination. For the traffic state prediction, we conducted analysis on a traffic dataset and observed spatial-temporal features of traffic patterns. Accordingly, we introduce the horizontal space-time Autoregressive Integrated Moving Average (ARIMA) model to predict vehicle counts (i.e., traffic volume) for locations with available historical traffic data. And, we use the spatial-temporal ordinary kriging method to predict vehicle counts for locations without historical traffic data. Based on vehicle counts, we use the non-parametric kernel regression method to predict velocity of road sections, which is used to predict travel time and then, energy consumption of a route of an EV with the help of the proposed energy consumption model. We also estimate charging monetary cost and EV related range anxiety based on unit energy cost, predicted travel time and energy consumption, and current onboard energy. We design four different cost functions (travel time, energy consumption, charging monetary cost, and range anxiety) of routing and formulate a multi-objective routing optimization problem. We use the predicted parameters as inputs of the optimization problem and find the optimal route using the adaptive epsilon constraint method. We evaluate our proposed MORP system in four different aspects (including traffic prediction, velocity prediction, energy consumption prediction, and EV routing). From the experimental studies, we find the effectiveness of the proposed MORP system in different aspects of the online EV routing system.