Reinforcement Learning-Based E-Scooter Energy Minimization Using Optimized Speed-Route Selection

Abstract

In the evolving urban transportation, the emergence of Micro-Mobility (MM), symbolized by Electric Scooters (ESs), has become a pivotal response to private automobiles’ environmental and logistical challenges. However, the limited battery capacity of ESs presents a challenge in realizing their full potential. This paper addresses the problem of optimizing energy consumption in ESs by jointly considering path and speed selection all while considering user dissatisfaction levels. Our approach considers two types of ESs, one with regenerative braking (i.e., able to recharge the battery from kinetic energy of movement) and the other without regenerative braking. In order to build a realistic environment, we considered dynamic factors such as time-varying road congestion, road conditions, and ambient temperature. We considered a comprehensive energy consumption model for the ES that includes parameters such as rolling resistance, air friction, road gradient, auxiliary power and ambient temperature influence. Moreover, we introduced a user dissatisfaction model that accounts for traffic conditions, congestion, and ambient temperature to enhance the user experience. The optimization problem was then formulated and solved with Deep Reinforcement Learning (DRL-DQN) approach considering the time-varying environment, road-specific parameters (i.e., road angle, road shading, road speed limit, and road condition), and user dissatisfaction levels. The DRL approach was designed to make timely and context-aware decisions the minimize the energy consumption of the ES. Rigorous validation and comprehensive testing demonstrate the effectiveness of our approach. We evaluated the proposed solution’s performance against alternative methodologies used by fleet operators in different tests, including energy consumption, average user dissatisfaction, and average trip duration. The results showed that the proposed approach saved nearly 53-67% of energy for regenerative braking cases and 25-55% for non-regenerative braking cases when compared with other approaches and offers high adaptability to the environment and less complexity when compared with the exhaustive solution.

Keywords

• Electric scooters
• reinforcement learning
• energy minimization
• user dissatisfaction
• route and speed selection