Researchers from the University of California, Berkeley, including Yuneil Yeo, Jaewoong Lee, Scott Moura, and Maria Laura Delle Monache, have developed a new model to improve the energy efficiency of autonomous electric vehicles (A-EVs). Their work, published in the journal IEEE Transactions on Intelligent Transportation Systems, focuses on enhancing the driving dynamics of A-EVs to optimize battery energy use.
The team proposed a nonlinear microscopic dynamical model that builds upon the existing Optimal Velocity Model (OVM). Their innovation lies in incorporating a control term based on battery dynamics, which enables thermally optimal and energy-efficient driving. This approach considers the battery’s energy efficiency in the car-following dynamics, a critical aspect for improving the overall energy performance of A-EVs.
The researchers rigorously proved that their proposed model achieves lower energy consumption compared to the traditional Optimal Velocity Follow-the-Leader (OVFL) model. They validated these analytical results through numerical simulations, demonstrating the model’s effectiveness in real-world scenarios. Additionally, they investigated the stability properties of the proposed model to ensure its reliability and robustness.
For the energy sector, this research offers practical applications in enhancing the efficiency of electric vehicle fleets. By optimizing the driving dynamics of A-EVs, energy providers and vehicle manufacturers can reduce energy consumption, extend battery life, and lower operational costs. This model could be particularly useful in urban environments where traffic conditions are complex and energy efficiency is paramount.
The study highlights the importance of integrating battery dynamics into the control strategies of autonomous vehicles. As the energy industry continues to evolve, such advancements in vehicle technology will play a crucial role in achieving sustainable and efficient transportation systems.
This article is based on research available at arXiv.