A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management

Abstract

Hybrid electric vehicle (HEV) improvements in fuel economy and emissions strongly depend on the energy management strategy. The control of an HEV with minimum fuel consumption and emissions is a global problem and the control action taken at each time instant affects the following. Thus, dynamic programming (DP) is a well-suited technique to find the optimal solution to the control problem. Unfortunately, this approach to solving the optimal control problem requires a priori knowledge of the driving conditions (necessary to implement the DP backward algorithm) and is therefore not suitable for HEV real-time control. It is shown that it is possible to obtain the global optimal control policy using the instantaneous minimization of a “well-defined” cost function dependent only on the system variables at the current time. The definition of such a cost function requires an equivalence factor for comparing the electrical energy with the fuel energy. This approach is known in literature as equivalent consumption minimization strategy (ECMS). The optimal value of the equivalence factor can be found through a systematic optimization only if the driving cycle is known. In this paper a new control strategy called adaptive ECMS (A-ECMS) is presented. This real-time energy management for HEV is obtained adding to the ECMS framework an on-the-fly algorithm for the estimation of the equivalence factor according to the driving conditions. The main idea is to periodically refresh the control parameter according to the current road load, so that the battery state of charge is maintained within the boundaries and the fuel consumption is minimized. The results obtained with A-ECMS show that the fuel economy that can be achieved is only slightly suboptimal and the operations are charge-sustaining.