Predictive Energy Management of Hybrid Electric Vehicles via Multi-Layer Control

Abstract

This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/off, engine clutch state, and power-split decisions subject to constraints on the battery state of charge (SOC) and charge sustenance. Then, we split it into two control layers, including a supervisory control in a higher layer and a local power-split control in a lower layer. In the supervisory layer, a gear and powertrain mode manager (PM) is designed, and optimal gear, engine on/off and clutch states are obtained by using a combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP). Moreover, a real-time iteration Secant method is proposed to calculate optimal battery costate such that the constraint on charge sustenance is satisfied. In the local controller layer, a linear quadratic tracking method (LQT) is used to optimally split power between the engine and the electric machine and keep battery SOC within its bounds.