Optimal Situation-Based Power Management and Application to State Predictive Models for Multi-Source Electric Vehicles

Abstract

This paper presents an optimized situation-based power management strategy (SB-PMS) for multi-source electric vehicles. The presented SB-PMS is based on defining optimal control strategies offline for different vehicles states. In such control schemes, multiple characteristic variables, i.e. vehicle speed and power demand, are implemented to define vehicle states. The selection and discretization of characteristic variables have a significant impact on the solution optimality of SB-PMSs. Based on a comprehensive analysis of literature, the variables vehicle speed, power demand, speed dynamics, and on-board state-of-charge have been selected to investigate their impact solution optimality of SB-PMS. These variables are depicted as axes in a multi-dimensional space (grid-space), whereinto vehicle states can be mapped. Multiple structures of grid-space are generated based on different constellations of axes and different discretization levels for each axis. Comparative evaluation of grid-space structures is based on two aspects: first, the optimality of situated solutions related to vehicle states; second, the ability to develop predictive models using the statistics of state transitions in grid-space. Results analysis, compared to rule-based (RB) and dynamic programming (DP), reveals the significant impact of certain grid-space structures to achieve near optimal results in both energy saving and prediction accuracy at different trip conditions.