Optimal energy management with balanced fuel economy and battery life for large hybrid electric mining truck

Abstract

With the addition of an energy storage system (ESS) and advanced controls, a hybrid electric propulsion system can considerably improve the fuel economy over a pure mechanical powertrain. However, the high cost and relatively short operating life of the battery ESS constitute a significant portion of the total operation cost (TOC) of an electrified vehicle, particularly for heavy-duty vehicles with a larger ESS. In this work, a new method for generating the optimal energy management strategy (EMS), considering the TOC of a hybrid electric mining truck (HEMT), is introduced. The cost associated with battery performance degradation and operation life-shortening under different battery use patterns is added to form the globally optimal, TOC-based EMS. The optimal EMS under different vehicle operation profiles are identified using dynamic programming (DP) to serve as benchmarks. An intelligent optimal ESS energy management method for achieving the minimum TOC during real-time, open-pit HEMT operations is introduced by combining an artificial neural network (ANN) model and a fuzzy-logic controller (FLC). The new, real-time intelligent optimal EMS led to twenty-one percent TOC reduction of the HEMT over the traditional, pure fuel economy-oriented optimal EMS, and formed the foundation of TOC-based, optimal EMS development for hybrid electric vehicles (HEVs).