Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions
Tsinghua–Berkeley Shenzhen Institute · Tsinghua University · +4 more institutions
Abstract
Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement conditions. Here, we show that the generative learning-assisted SOH estimation is promising in alleviating data scarcity and heterogeneity challenges, validated through a pulse injection dataset of 2700 retired lithium-ion battery samples, covering 3 cathode material types, 3 physical formats, 4 capacity designs, and 4 historical usages with 10 SOC levels. Using generated data, a regressor realizes accurate SOH estimations, with mean absolute percentage errors below 6% under…
Citation impact
- FWCI
- 20.83
- Percentile
- 100%
- References
- 72
Authors
22- STShengyu TaoCorresponding
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
- RMRuifei Ma
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
- ZZZixi Zhao
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
- GMGengyu Ma
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
- LSLin Su
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
Topics & keywords
- Computer science
- Reuse
- Estimation
- Battery (electricity)
- Scarcity
- Generative model
- Electricity
- Machine learning