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

Abstract

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.

Authors:
 [1];  [1];  [2];  [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Utah State University
  3. Ford Motor Company
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E)
OSTI Identifier:
1378881
Report Number(s):
NREL/CP-5400-68030
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 American Control Conference (ACC), 24-26 May 2017, Seattle, Washington
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; life prediction; lithium-ion batteries; prognostic life model; state of health estimate; active life balancing strategy; battery chemistry; capacity fade; resistance growth

Citation Formats

Shi, Ying, Smith, Kandler A, Zane, Regan, and Anderson, Dyche. Life Prediction of Large Lithium-Ion Battery Packs with Active and Passive Balancing. United States: N. p., 2017. Web. doi:10.23919/ACC.2017.7963682.
Shi, Ying, Smith, Kandler A, Zane, Regan, & Anderson, Dyche. Life Prediction of Large Lithium-Ion Battery Packs with Active and Passive Balancing. United States. doi:10.23919/ACC.2017.7963682.
Shi, Ying, Smith, Kandler A, Zane, Regan, and Anderson, Dyche. Mon . "Life Prediction of Large Lithium-Ion Battery Packs with Active and Passive Balancing". United States. doi:10.23919/ACC.2017.7963682.
@article{osti_1378881,
title = {Life Prediction of Large Lithium-Ion Battery Packs with Active and Passive Balancing},
author = {Shi, Ying and Smith, Kandler A and Zane, Regan and Anderson, Dyche},
abstractNote = {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.},
doi = {10.23919/ACC.2017.7963682},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jul 03 00:00:00 EDT 2017},
month = {Mon Jul 03 00:00:00 EDT 2017}
}

Conference:
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