Life Prediction of Large Lithium-Ion Battery Packs with Active and Passive Balancing

Ying Shi, Kandler Smith, Regan Zane, Dyche Anderson

Research output: Contribution to conferencePaperpeer-review

14 Scopus Citations


Lithium-ion battery packs take a major part of large-scale stationary energy storage systems. One challenge in reducing battery pack cost is to reduce pack size without compromising pack service performance and lifespan. Prognostic life model can be a powerful tool to handle the state of health (SOH) estimate and enable active life balancing strategy to reduce cell imbalance and extend pack life. This work proposed a life model using both empirical and physical-based approaches. The life model described the compounding effect of different degradations on the entire cell with an empirical model. Then its lower-level submodels considered the complex physical links between testing statistics (state of charge level, C-rate level, duty cycles, etc.) and the degradation reaction rates with respect to specific aging mechanisms. The hybrid approach made the life model generic, robust and stable regardless of battery chemistry and application usage. The model was validated with a custom pack with both passive and active balancing systems implemented, which created four different aging paths in the pack. The life model successfully captured the aging trajectories of all four paths. The life model prediction errors on capacity fade and resistance growth were within ±3% and ±5% of the experiment measurements.

Original languageAmerican English
Number of pages6
StatePublished - 29 Jun 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: 24 May 201726 May 2017


Conference2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2017 American Automatic Control Council (AACC).

NREL Publication Number

  • NREL/CP-5400-68030


  • active life balancing strategy
  • battery chemistry
  • capacity fade
  • life prediction
  • lithium-ion batteries
  • prognostic life model
  • resistance growth
  • state of health estimate


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