@article{1476197,
      recid = {1476197},
      author = {Huang, Qi, and Wang, Shunli, and Chen, Zonghai, and Xiong,  Ran, and Fernandez, Carlos, and Stroe, Daniel-I.,},
      title = {Long-term health state estimation of energy storage  lithium-ion battery packs /},
      pages = {1 online resource (xi, 92 pages) :},
      abstract = {This book investigates in detail long-term health state  estimation technology of energy storage systems, assessing  its potential use to replace common filtering methods that  constructs by equivalent circuit model with a data-driven  method combined with electrochemical modeling, which can  reflect the battery internal characteristics, the battery  degradation modes, and the battery pack health state.  Studies on long-term health state estimation have attracted  engineers and scientists from various disciplines, such as  electrical engineering, materials, automation, energy, and  chemical engineering. Pursuing a holistic approach, the  book establishes a fundamental framework for this topic,  while emphasizing the importance of extraction for health  indicators and the significant influence of electrochemical  modeling and data-driven issues in the design and  optimization of health state estimation in energy storage  systems. The book is intended for undergraduate and  graduate students who are interested in new energy  measurement and control technology, researchers  investigating energy storage systems, and structure/circuit  design engineers working on energy storage cell and pack.},
      url = {http://library.usi.edu/record/1476197},
      doi = {https://doi.org/10.1007/978-981-99-5344-8},
}