Health-Conscious vehicle battery state estimation based on deep transfer learning

Shuangqi Li, Hongwen He, Pengfei Zhao, Shuang Cheng

Research output: Contribution to journalArticlepeer-review

21 Citations (SciVal)
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Establishing an accurate mathematical model is fundamental to managing, monitoring, and protecting the battery pack in electric vehicles (EVs). The application of the deep learning algorithm-based state estimation method can significantly improve the accuracy and stability of the battery model but is hindered by the great demand for training data. This paper addresses the challenge of health-conscious battery modeling by utilizing multi-source data based on a novel deep transfer learning method. Firstly, a cloud-based battery management framework is designed, which is able to collect and process battery operation data from various EVs and provide a foundation for deploying the transfer learning method. Battery healthy state information in the collected dataset is labeled by a generic perception model, which can be commonly used to quantify the aging state of different battery packs and facilitate the knowledge transfer process. Additionally, a deep transfer learning method is developed to boost the training process of the battery model, where the operation data from different types of EVs can be used for establishing state estimators. The method is verified by the battery operation data collected from two types of electric buses. With the developed healthy state perception model and transfer learning method, battery model error can be limited to 2.43% and 1.27% in the whole life cycle.
Original languageEnglish
Article number119120
Number of pages10
JournalApplied Energy
Early online date18 Apr 2022
Publication statusPublished - 15 Jun 2022


  • Battery energy storage
  • Battery management system
  • Battery state estimation
  • Deep transfer learning
  • Electric vehicles
  • Transportation electrification

ASJC Scopus subject areas

  • Building and Construction
  • Mechanical Engineering
  • Energy(all)
  • Management, Monitoring, Policy and Law


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