Energy Efficient Speed Planning of Electric Vehicles for Vehicle-Following Scenarios Using Data-driven Koopman Model

In the context of urban road following scenarios, ensuring the precise speed prediction and control of electric vehicle while rigorously maintaining the safe driving conditions has persistently presented a formidable challenge. To address such issues, a novel data-driven approach for the energy efficient speed planning of electric vehicles in vehicle-following scenarios is proposed. Incorporating Koopman operator theory, the developed control-oriented vehicle dynamics prediction model leverages real-world vehicle-following data in various vehicle-following scenarios. Subsequently, a data-driven Koopman model predictive control (MPC) method is developed based on the established prediction model, which focuses on reducing the energy consumption through speed planning while ensuring safe following distances. The comparative simulation results ultimately demonstrate the effectiveness of the proposed method under various driving scenarios.