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Battery Management System Development for Electric Vehicles and Fast Charging Infrastructure Improvement
The proposed project will deliver three modules:
- a new machine learning and equivalent circuit hybrid model for battery fast charging;
- state-of-charge (SOC) estimation, state-of-health (SOH) estimation, and a balancing algorithm for fast charging;
- an onboard BMS controller developed on an FPGA.
Our research will answer the following questions:
- How can machine learning and empirical battery models be integrated to improve SOC/SOH estimation?
- How does an onboard BMS for fast charging prolong the travel range of EVs and the battery's lifespan?
Principal Investigator:
Yu Yang, Ph.D.
PI Contact Information:
California State University, Long Beach
Dates:
March 2023
Impacts/Benefits of Implementation:
The proposed research has broad societal impacts. It aligns with SB1 objectives in a multifaceted manner. First, EVs with a more efficient BMS can improve their cruise range and thus reduce energy consumption and traffic congestion. Second, the resulting BMS can be applied in the solar-power and battery-assisted charging stations to make more reliable infrastructure towards the age of zero-emission transportation. Third, the developed system and software will be used for workforce training in electrical engineering and chemical engineering courses.
Project Number:
2325
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Contact Us
SJSU Research Foundation 210 N. 4th Street, 4th Floor, San Jose, CA 95112 Phone: 408-924-7560 Email: mineta-institute@sjsu.edu
