Electric Vehicles

Electrification of the truck fleet has the potential to reduce the “harder-to-abate” emissions of logistics significantly, but is generally considered to be very challenging. In this study, we focus on the energy-efficient routing of a mixed fleet of conventional and electric heavy-duty trucks with pickup and delivery under energy consumption uncertainty. We propose an energy consumption model that accounts for realistic driving dynamics, road conditions, weight, and distances. Integrating this model into the routing problem, we address energy consumption uncertainty using second-order...

Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning. Through seamless integration of these disciplines, the efficacy of mathematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlighting the transformative impact of integrating physics and machine learning to address those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and...

The famous Cannonball Run, a cross-country driving challenge from New York City to Los Angeles, highlights the unique challenges of long-distance electric vehicle (EV) route planning. The time record for an internal combustion vehicle is 25 h and 39 min. Comparing this to the EV record of 42 h and 17 min achieved with Tesla Model S, which elucidates the complexities inherent to optimal EV route planning. To bridge this divide, our study introduces a system designed for real-time vehicle-to-cloud (V2C) interaction aimed at enhancing online long-distance EV route planning. Our approach...

Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and heterogeneous traffic flow. To address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our...