Towards Electric Vehicle Battery Health Prediction

Dr. Mohammad Rezvani WorkHorse Group Inc.
When Oct 30, 2015
from 03:30 PM to 04:30 PM
Where Duthie Center for Engineering, Room 117
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The seminar is free and open to the public. A reception and social time begins at 3:00 p.m., Seminar at 3:30 p.m.

Abstract: As hybrid and electric vehicle technology advance further, car manufacturers have employed lithium ion batteries, as the electrical energy storage device of choice, in existing and envisioned vehicles. However, since the lithium ion battery is the most expensive part of the electric vehicle, the battery health should be monitored over time to ensure the batteries’ reliability and capability of delivering power and energy when required. The battery health information is obtained through two key factors: State of Charge (SoC) and State of Health (SoH).

To extract the information from the battery and predict the SoC and SoH, data mining techniques are used. The data is collected using inbuilt sensors in battery of the electric vehicles. Using feature extraction techniques, the useful features are extracted, and an Artificial Neural Network (ANN) predictive model is built which accurately predicts the SoC and SoH, relieving the information regarding the battery health in various charged and discharged conditions. This presentation will cover data management best practices such as feature extraction and data-driven classification techniques to predict the remaining useful life of batteries.

Bio: Dr. Mohammad Rezvani is currently a battery system engineer in WorkHorse Group Inc. WorkHorse manufactures electric drive systems for medium-duty, class 3-6 commercial truck platforms. Dr. Rezvani current research includes electric vehicle-terrain interactions, battery life cycle management, mobility estimation, battery modeling, vehicle health monitoring, and fault diagnosis systems. He received his B.Eng. in Mechanical Engineering from Iran University of Science and Technology and his M.Eng. in Maintenance Engineering from Luleå University of Technology, Sweden. He obtained his PhD in Mechanical Engineering from University of Cincinnati with a specialization in Prognostics and Health Management for Lithium-ion batteries and Electric Vehicles.