The highly nonlinear dynamics of vehicles present a major challenge for the practical implementation of optimal control and Model Predictive Control (MPC) approaches in path planning and tracking applications. Koopman operator theory offers a global linear representation of nonlinear dynamical systems, making it a promising framework for optimization-based vehicle control. This paper introduces a novel deep learning-based Koopman modeling approach that employs deep neural networks to capture the full vehicle dynamics, from pedal and steering inputs to chassis states, within a curvilinear Frenet frame. The superior accuracy of the Koopman model compared to identified linear models is shown for a double lane change maneuver. Furthermore, it is shown that an MPC controller deploying the Koopman model provides significantly improved performance while maintaining computational efficiency comparable to a linear MPC.

Plug-in electric vehicles (PEVs) have the highest promise for dramatically reducing transportation emissions. No other option has comparable emission reduction potential or as a promising pathway. Still, PEVs can offer more than green transportation. In particular, their onboard storage can further serve the society by providing an energy buffer to increase the reliability, affordability, and sustainability of electric services. These benefits are only achievable by fully exploiting the multifaceted flexibility provided by PEVs' mobility, charging adaptability, and bidirectional flow of power, as well as adopting effective decision-making and control algorithms, while minding the likely unfavorable side effects, such as shortened battery life span. This work takes a closer look at different elements of this puzzle. The main subject of this survey is behind the meter energy management with vehicle to building (V2B). We focus on different V2B application ideas and review energy management methods in smart buildings with V2B integration. Recent findings on battery capacity fade resulting from the bidirectional flow of power and extra discharging cycles with V2B are reviewed, and the methods for integrating the battery degradation in energy management formulation are discussed. Finally, the main findings of this review and research gaps are summarized and clarified.