Data-Driven Health Management of Electrical Vehicle Battery Systems

Details

• Creators:
  Wang, Pingfeng ; Krishnan, Krishna ; Twomey, Janet ; Bai, Guangxing
• Corporate Creators:
  Wichita State University
• Corporate Contributors:
  Midwest Transportation Center
• Subject/TRT Terms:
  • Electric vehicle charging
  • Electric vehicles
  • Energy storage systems
  • Hybrid vehicles
  • Intelligent transportation systems
  • Intelligent vehicles
  • Lithium batteries
  • dynamic systems
  • battery health management
  • safety
  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Midwest Transportation Center
• Abstract:
  The objectives of this research were to conduct theoretical and experimental investigations to develop a new battery health management paradigm based on a novel, self-cognizant dynamic system (SCDS) approach to predict and prevent failures of safety-critical battery systems (e.g., lithium plating and thermal runaway) for electric vehicles (EVs) and hybrid electric vehicles (HEVs) and develop an onboard diagnostics tool and alarm system for early awareness of these potential impending failures. This research developed a technique that can adaptively recognize the dynamic characteristics of an operating battery system over time without relying on expensive, time-consuming battery tests for the prediction and prevention of safety-critical battery system failures. Battery failure prognostics employing the proposed SCDS-based health management paradigm can not only account for normal battery capacity fading over time but also identify abnormal safety-critical failures that usually happen in a relatively shorter time period.
• Format:
  PDF
• Funding:
  DTRT13-G-UTC37
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/36551/dot_36551_DS1.pdf
• File Type:
  [PDF - 855.23 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:b2fa125ed5998332cc7c012db76e39fc0b8cf0dd810df684b2189264405135ae