Electric Vehicles

The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or on-board sensor information is required. A comparative study is conducted between the ANN-based method and two other velocity predictors: generalized exponentially varying and Markov-chain models. The...

This paper examines modeling and control of a large population of grid-connected plug-in electric vehicles (PEVs). PEV populations can be leveraged to provide valuable grid services when managed via model-based control. However, such grid services cannot sacrifice a PEV's primary purpose - mobility. We consider a centrally located fleet of identical PEVs that are distributed to and collected from drivers. The fleet also provides regulation services to the grid, contracted a priori. We develop a partial differential equation (PDE)- based technique for aggregating large populations of PEVs....

Recent advances in the traffic monitoring systems have made traffic velocity information accessible in real time. This paper proposes a supervised predictive energy management framework aiming to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV) by incorporating dynamic traffic feedback data. Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SOC) planning level is constructed in this framework. A power balance PHEV model is developed for this upper level to rapidly generate optimal battery SOC trajectories,...

A plug-in electric vehicle (PEV) fleet utilizing vehicle-to-grid (V2G) technology, i.e., a V2G fleet, can behave as a storage system, e.g., promoting integration of distributed wind power resources. However, because the PEVs' behaviors are stochastic and a V2G fleet's population is large, three technical difficulties hinder the utilization of V2G: charging demand forecasting; ahead-of-time charge and discharge scheduling; real-time charge and discharge power dispatching. This paper utilizes a storage-like aggregate model (SLAM) of a V2G fleet that employs aggregated parameters to represent...