Lookahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks

The shift towards electric heavy-duty trucks marks a significant move in sustainable freight transportation, aiming to reduce carbon emissions and decrease reliance on fossil fuels. While these trucks offer enhanced powertrain efficiency and lower operating costs, maximizing their potential requires a focus on minimizing energy consumption during operation. Efficient energy management through optimized driving strategies is essential to extend the battery life and reduce the overall environmental impact of electric trucks in long-haul logistics. This research paper introduces a predictive cruise controller that combines look-ahead information with the powertrain controller to generate an optimal speed profile for electric trucks. By optimizing the vehicle’s speed based on upcoming road conditions, the system enhances the energy efficiency of the entire powertrain, leading to more sustainable and cost-effective truck operation. The Predictive Cruise Controller (PCC) optimizes electric truck operation by focusing on three primary factors in its cost function: 1) energy consumption, 2) total trip time, and 3) vehicle acceleration. Energy utilization is a key aspect, ensuring that the truck uses power efficiently. Total trip time ensures that the truck meets delivery or schedule requirements, while vehicle acceleration is included to ensure smooth and safe operation. These are balanced with constraints such as speed limits, time restrictions, and the state of charge (SOC) of the battery, ensuring the truck operates within safe and practical boundaries. To achieve the optimal speed profile, Sequential Quadratic Programming (SQP) is used. A comparison was made with a conventional cruise controller, which simplifies the vehicle model by treating it as a point mass and ignores powertrain losses in its energy calculations. The results show that the proposed PCC offers a significant improvement in energy efficiency, making it more effective in optimizing the overall energy usage of electric trucks