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A new life prediction model for Li-ion batteries in plug-in vehicles applications

Research Scholar

Fabio Todeschini, Center for Automotive Research (Italy)
Simona Onori, Co-Researcher
Giorgio Rizzoni, Faculty Mentor


Fabio Todeschini received his bachelor's and master's degrees in automation engineering from Politecnico di Milano in 2008 and 2011, respectively. After he obtained his degrees, he researched hybrid bicycles at E-shock SRL in Milan, Italy. Since June, he has been a visiting scholar at The Ohio State University Center for Automotive Research under the supervision of Professor Giorgio Rizzoni and Dr. Simona Onori. His research interests are in the area of battery modeling and identification for prognosis and life estimation. In October, he began a Ph.D. program at Politecnico di Milano.

What is the issue or problem addressed in your research?

The increased sensibility on environmental problems and the growing price of oil have steered the research during the past few years towards the development of new means of transportation systems. In particular, powertrain hybridization as well as electrical energy management are being considered as the short-medium term solution to the petroleum displacement and greenhouse gas emission reduction problem for the automotive sector. In this scenario, the battery is considered as a key component for fuel saving measures.

Among different battery chemistries, Li-ion based batteries are regarded as the most suitable technology for hybrid and electric vehicles applications as they offer greater power and energy density compared to the other available battery chemistries. Although Li-ion batteries technology seems to be most suitable to better exploit all the advantages of the powertrain hybridization, there are many aspects of this technology that need to be improved before assisting to a significant PHEV market penetration. The key issues are represented by: current battery price, battery longevity, safety, reliability and lifetime prediction.

What methodology did you use in your research?

Experimental research has shown that over the battery life cycle, battery aging manifests both through capacity loss and resistance growth. In this study focus is given to characterize the capacity loss behavior of the battery through a new modeling approach, called “damage accumulation model”.

Driven by experimental data and more physical system-based argument, a in depth analysis has been conducted to find out the most critical conditions from an aging standpoint. Furthermore, an analytical model that relates the aging factors (i.e., battery current severity or C-rate, battery temperature, operating state-of-charge) to the capacity fade has been developed.

The proposed model: 1) allows to understand which operating conditions (in term of battery usage) is preferable to avoid to prevent fast battery aging and 2) allows to forecast the battery remaining useful life.

What are the purpose/rationale and implications of your research?

The aim of this research is to study and understand the battery aging phenomenon in order to design algorithms to be used on-board of the vehicle to predict battery end-of-life.