A Hierarchical Energy Management for Hybrid Electric Tracked Vehicle Considering Velocity Planning With Pseudospectral Method

Abstract

This article proposes a hierarchical energy management strategy (EMS) for hybrid electric tracked vehicle (HETV) considering the two tracks velocity planning based on pseudospectral method (PM). Constrained by the reference path known a priori , the upper layer of the hierarchical EMS finds the optimal velocity of the two tracks, in which the two motor torques are chosen as the control variable to minimize an objective function, trading off the energy consumption, and path tracking accuracy. Based on the obtained optimal velocity profile, the lower layer distributes the power demand to the engine-generator and the battery to minimize the energy consumption. The hierarchical EMS is designed to minimize energy consumption while ensuring the premise of the vehicle path tracking performance. Both layers adopt the PM which transforms the optimal control problem (OCP) into nonlinear programming (NLP) problem, and the Sparse Nonlinear OPTimizer (SNOPT) solver is used. Simulation results show that the fuel economy of the PM outperforms that of dynamic programming (DP). Compared with DP, the hierarchical EMS can save fuel consumption by 3.92% with a significantly reduced computation burden. Finally, field experiments show that the proposed method improves fuel economy by 14.85% compared with the rule-based EMS without velocity optimal planning.