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

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: 



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