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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.

Lithium-ion batteries (LIBs) have been widely used as the energy storage system in plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) due to their high power and energy density and long cycle life compared to other chemistries. However, LIBs are sensitive to operating conditions, including temperature, current demand and surface pressure of the cell. One very well understood phenomenon of lithium-ion battery is the reduction in charge capacity over time due to cycling and storage commonly known as capacity fade. Considering the need for predicting the behavior of an aged cell and the need for estimating battery useful life for warranty purpose, it is crucial to predict the capacity fade with reasonable accuracy. To accommodate this need, a novel cell level empirical aging model is built based on storage tests and cycle tests. The storage test captures the calendar aging of the lithium-ion cell while the cycle test estimates the cycle aging of the cell.