PHEV Hybrid Vehicle System Efficiency and Battery Aging Optimization Using A-ECMS Based Algorithms

Minimizing lithium ion battery aging and maximizing overall system efficiency are key engineering design objectives for Plug-in Electric Hybrid Vehicles (PHEVs). To quantitatively optimize the aging and system efficiency, an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) based algorithm is implemented within vehicle simulation code. Battery charge and discharge cycling is modeled using equivalent circuit modeling techniques where circuit parameters are updated based on estimated aging effects. These aging effects are predicted through a so-called Single Particle Model (SPM) wherein particle interactions are neglected, and Solid Electrolyte Interface (SEI) layer aging is predicted for graphite anode. The aging model in this study is calibrated against available battery aging data for similar batteries. Steady state capacity fade map under given environmental conditions and various battery states of charge and current levels are predicted. A battery capacity fade map is generated, and then used in the A-ECMS optimization function to adjust aggressiveness of the PHEV power split decisions. The results of a single objective (pure efficiency based), and a multi-objective (battery aging and efficiency are weighted to form an objective function) hybrid control optimization are compared. It shows the effectiveness of the proposed optimization process for trading off fuel efficiency and battery life. Since ECMS strategies can operate in real-time and are on-board implementable, the methodology discussed can potentially be implemented on-board a real controller to improve battery life.