A New HEV Power Distribution Algorithm Using Nonlinear Programming

Abstract

An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. The optimal equivalent factor is determined by driving conditions. Previous studies have used an adaptive ECMS (A-ECMS) to determine the appropriate equivalent factor according to changing driving conditions. An A-ECMS adjusts the equivalent factor by controlling the battery’s state of charge (SOC) to follow a reference SOC trajectory. It is therefore critical to identify a reference SOC trajectory that reflects real-world driving conditions. These conditions, which are composed of the HEV’s nonlinear dynamics and complex constraints, can be formulated into a nonlinear optimal control problem (NOCP). Here, we propose applying nonlinear programming (NLP) to an A-ECMS. The NLP-based ECMS algorithm can be divided into two parts: the use of an NLP to solve an NOCP to obtain the reference SOC trajectory and the application of an NLP solution (the result of the first part) to an A-ECMS. Simulation results demonstrate that the proposed NLP-based ECMS closely resembles a global optimal solution for dynamic programming in a relatively brief calculation time.

An Economic Velocity Planning Strategy Based on Driving Style and Improved Dynamic Programming for a Hybrid Electric Truck

“…In order to save energy and reduce emissions, the selection of effective algorithms for controlling hybrid electric vehicles has become a research focus [3,4]. Common EMSs include rule-based (RB) energy management strategy [5][6][7], equivalent fuel consumption minimization strategy (ECMS) [8][9][10], Pontryagin's minimum principle (PMP) [11][12][13], model predictive control strategy (MPC) [14][15][16], etc. However, these strategies are mostly applied under fixed driving conditions, limiting the optimization of vehicle performance.…”