Intelligent Incipient Fault Detection System for Electric Vehicle Battery: Fault Isolation Schemes and Prototype Development

Lithium-ion (Li-ion) batteries are the primary power source for electric vehicles (EVs) due to their high energy and power density, and long life-cycle. The recent variants of the high-end plug-in EVs, with Li-ion battery pack, offer a range of approximately 300 miles on a single full charge close to their gasoline counterparts. Further, to bridge the gap between the fueling time of the gas-powered vehicles and the charging time of EVs, high power chargers have also been introduced, reducing the charging time to less than 30 minutes. The Li-ion battery packs operate at maximum limits to deliver the required power to achieve these optimal performances. The extreme operating conditions and abusive operations may lead to internal and external faults, such as short circuits, cell internal temperature rise, lithium plating and loss of lithium, and mechanical failure due to vibration. These internal faults have a cumulative effect on the battery’s health, aggravating the vulnerability to thermal runaway. Although various external safety technologies are employed in the battery monitoring system and battery management system (BMS) to protect the battery from external fault conditions, it is still challenging to detect the internal faults from the available measurements (e.g., voltage, current, and surface temperature). The state-of-the-art internal fault detection approaches use Li-ion battery models with constant parameters to develop model-based fault detection algorithms, which may lead to inaccurate results since these parameters change with health degradation. In a companion research project funded by Tran-SET in Cycle-4, the research team proposed developing a real-time learning-based fault detection scheme. However, the scheme proposed in the TranSET Cycle-4 project requires significant improvements from the perspective of hardware implementation. Therefore, the development of an intelligent incipient fault detection system, which addresses the challenges of the computational complexity of real-time machine learning using neural networks for the embedded-hardware implementation, is critical to exploit the advantages of the real-time learning schemes in the field of Li-ion battery management for EVs. The proposed research project’s overall objective is to develop, implement, and validate an intelligent fault detection scheme capable of detecting a Li-ion battery’s internal faults in its incipient stage. This involves significant intellectual challenges related to root-cause analysis for determining the interrelation between internal parameters and type of fault and develop a computationally efficient neural network algorithm for hardware implementation. The team will address these challenges by (1) developing failure mode analysis schemes to identify the root-causes, (2) developing computationally efficient fault detection algorithms using real-time machine learning, (3) developing Field Programmable Gate Array (FPGA)-based hardware architecture to implement fault detection scheme, and (4) validating the prototype experimentally. The success of the project will lead to a significant improvement in the safety of EVs. The research aligns with the vision of TranSET of overcoming transportation challenges in Region-6 by using innovative approaches. The proposed research is highly relevant to multiple disciplines, such as control systems, machine learning, and EVs. This project’s success will provide necessary validation results and a prototype of the fault detection system for the safe operation of the Li-ion battery fostering its adoption by stakeholders. The project will also educate students and working professionals on this innovative multidisciplinary research and technology development. The team will also integrate the research results into mechatronics courses and reach out to secondary school students hosting lab visits and workshops to motivate them to seek STEM as a career.

Language

  • English

Project

  • Status: Active
  • Funding: $130000
  • Contract Numbers:

    69A3551747106

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Transportation Consortium of South-Central States (Tran-SET)

    Louisiana State University
    Baton Rouge, LA  United States  70803
  • Project Managers:

    Mousa, Momen

  • Performing Organizations:

    Oklahoma State University, Stillwater

    School of Civil & Environmental Engineering
    207 Engineering South
    Stillwater, OK  United States  74078
  • Principal Investigators:

    Sahoo, Avimanyu

  • Start Date: 20210801
  • Expected Completion Date: 20230201
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01833048
  • Record Type: Research project
  • Source Agency: Transportation Consortium of South-Central States (Tran-SET)
  • Contract Numbers: 69A3551747106
  • Files: UTC, RIP
  • Created Date: Jan 19 2022 2:24PM