Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions

Shengyu Tao, Ruifei Ma, Zixi Zhao, Guangyuan Ma, Lin Su, Heng Chang, Yuou Chen, Haizhou Liu, Zheng Liang, Tingwei Cao, Haocheng Ji, Zhiyuan Han, Minyan Lu, Huixiong Yang, Zongguo Wen, Jianhua Yao, Rong Yu, Guodan Wei, Yang Li, Xuan Zhang (), Tingyang Xu () and Guangmin Zhou ()
Additional contact information
Shengyu Tao: Tsinghua University
Ruifei Ma: Tsinghua University
Zixi Zhao: Tsinghua University
Guangyuan Ma: Tsinghua University
Lin Su: Tsinghua University
Heng Chang: Tsinghua University
Yuou Chen: Tsinghua University
Haizhou Liu: Tsinghua University
Zheng Liang: Tsinghua University
Tingwei Cao: Tsinghua University
Haocheng Ji: Tsinghua University
Zhiyuan Han: Tsinghua University
Minyan Lu: Tsinghua University
Huixiong Yang: Ltd.
Zongguo Wen: Tsinghua University
Jianhua Yao: Tencent
Rong Yu: Hupan Lab
Guodan Wei: Tsinghua University
Yang Li: Tsinghua University
Xuan Zhang: Tsinghua University
Tingyang Xu: Tencent
Guangmin Zhou: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: 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 unseen SOC. We predict that assuming uniform deployment of the proposed technique, this would save 4.9 billion USD in electricity costs and 35.8 billion kg CO2 emissions by mitigating data curation costs for a 2030 worldwide battery retirement scenario. This paper highlights exploiting limited data for exploring extended data space using generative methods, given data can be time-consuming, expensive, and polluting to retrieve for many estimation and predictive tasks.

Date: 2024
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DOI: 10.1038/s41467-024-54454-0

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