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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.

Due to ever-tightening CO2 regulations on passenger vehicles, it is necessary to find novel methods to improve powertrain system efficiency. These increases in efficiency should generally be cost effective so that the customer perceives that they add value. One approach for improving system efficiency has been the use of thermal energy management. For example, changing the flow of, or reusing “waste” heat from the powertrain to improve efficiency. Due to the interactions involved with thermal management, a system level approach is useful for exploring, selecting, and developing alternative solutions. It provides a structured approach to augment the right kind of synergies between subsystems and mitigate unintended consequences. However, one challenge with using these approaches early in a program is having appropriate metrics for assessing key aspects of the system behaviors.