Design for Failure Prevention of Lithium-Ion Battery

Prognostics of Li-Ion Battery

Prognostics and health management (PHM) utilizes sensory signals acquired from an engineered system to quantify the system’s health condition and predict its remaining useful life, which provides an advance warning of potential failures and a window of opportunity for implementing measures to avert these failures. To improve the reliability and safety of lithium-ion (Li-ion) battery, it is essential to develop effective PHM techniques that can be used to detect battery anomalies in advance and preventcatastrophic failures from occurring. Boeing’s recent trouble with using Li-ion battery technology in the 787 Dreamliner planes exemplifies the cascading impact of battery failures when there is little to no way to detect abnormal degradation before a major incident occurs.
My research in this area has led to the development of a multiscale filtering method to estimate the state of health (SOH) of Li-ion battery. This method greatly simplifies the complex process of inferring battery capacity from voltage, current and temperature measurements, while improving prediction accuracy and efficiency. Research has also been conducted to investigate how machine learning techniques (e.g., k-nearest neighbor regression, sparse Bayesian learning and deep belief network) can be used to analyze in real-time large amounts of battery measurement data for health monitoring and life prediction.


  1. Bai G., Wang P., and Hu C., “A Self-Cognizant Dynamic System Approach for Prognostics and Health Management,” Accepted, Journal of Power Sources, 2014.
  2. Bai G., Wang, P., Hu C., and Pecht M., “A Generic Model-Free Approach for Lithium-ion Battery Health Management,” Applied Energy, v135, p247–260, 2014. [ DOI ]
  3. Fathi R., Burns J.C., Stevens D.A., Ye H., Hu C., Jain G., Scott E., Schmidt C., and Dahn J.R., “Ultra High-Precision Studies of Degradation Mechanisms in Aged LiCoO2/Graphite Li-Ion Cells,” Journal of The Electrochemical Society, v161, n10, A1572–A1579, 2014.  [ DOI ]
  4. Hu C., Jain G., Zhang P., Schmidt C., Gomadam P., and Gorka T., “Data-Driven Approach Based on Particle Swarm Optimization and K-Nearest Neighbor Regression for Estimating Capacity of Lithium-Ion Battery,” Applied Energy, v129, p49–55, 2014. [ DOI ]
  5. Hu C., Jain G., Tamirisa P., and Gorka T., “Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-Ion Battery,” Applied Energy, v126, p182–189, 2014. [ DOI ]
  6. Hu C., Youn B.D., and Chung J., “A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation,” Applied Energy, v92, p694–704, 2012. [ DOI ]
  7. Hu C., Youn B.D., and Chung J., “Online Estimation of Lithium-Ion Battery State-of-Charge and Capacity with a Multiscale Filtering Technique,” Annual Conference of the Prognostics and Health Management (PHM) Society 2011, Sep 25-29 2011, Montreal, Canada. (Invited Talk at BMS Workshop of Annual PHM Conference 2011)
  8. Hu C., Youn B.D., Chung J., and Ortanez R., “A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation,” 15th International Meeting on Lithium Batteries (IMLB), June 27-July 2 2010, Montreal, Quebec, Canada.