Modeling Aging Propagation State of Health in Battery Systems

Date: 
Wednesday, April 1, 2015 - 3:00pm - 4:00pm

Modeling Aging Propagation State of Health in Battery Systems

 

Giorgio Rizzoni
The Ohio State University

Batteries are inherently subject to aging. Aging is the reduction in performance, availability, reliability, and life span of a system or component. The generation of long–term predictions describing the evolution of the aging in time for the purpose of predicting the Remaining Useful Life (RUL) of a system may be understood as Prognosis.

The field of battery prognosis has seen significant progress in recent years thanks to model–based and data–driven algorithms that can model the aging process and estimate the RUL of battery cells. However, in advanced battery systems cells are interconnected into modules and packs, and aging propagates.

Aging propagation from one cell to the others results in a reduced system life. Propagation of aging has a profound effect on the accuracy of battery systems state of health (SOH) assessment and prognosis. This talk describes a systematic methodology for modeling the propagation of aging in advanced battery systems. The modeling approach is able to predict battery pack aging, thermal, and electrical dynamics under actual PHEV operation, and includes consideration of random variability of the cells, electrical topology and thermal management.

Giorgio Rizzoni is a professor at The Ohio State University in the Departments of Mechanical and Aerospace Engineering, and Electrical and computer Engineering, and is the director of the Center for Automotive Research. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Society of Automotive Engineers (SAE). His research activities are related to advanced propulsion systems for ground vehicles, energy efficiency, alternative fuels, the interaction between vehicles and the electric power grid, vehicle safety and intelligence, and policy and economic analysis of alternative fuels and vehicle fuel economy.

Nord 356
Case Western Reserve University
Cleveland, Ohio 44106