Immediate remaining capacity estimation of heterogeneous second-life lithium-ion batteries via deep generative transfer learning

This work proposes a novel deep generative transfer learning algorithm to estimate the relative remaining capacity of second-life batteries using minimal field data, enabling safe and sustainable reuse under data scarce and heterogeneous conditions.

• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2023B1515120099
• TS
  Tsinghua Shenzhen International Graduate School

  Awards: SZPR2023007, JC2021006