IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v182y2016icp558-568.html
   My bibliography  Save this item

State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Runzhe Shan & Yaxuan Wang & Shilong Guo & Yue Cui & Lei Zhao & Junfu Li & Zhenbo Wang, 2025. "From Empirical Measurements to AI Fusion—A Holistic Review of SOH Estimation Techniques for Lithium-Ion Batteries in Electric and Hybrid Vehicles," Energies, MDPI, vol. 18(13), pages 1-42, July.
  2. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
  3. Haochen Qin & Xuexin Fan & Yaxiang Fan & Ruitian Wang & Qianyi Shang & Dong Zhang, 2023. "A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(14), pages 1-23, July.
  4. Lyu, Zhiqiang & Wang, Geng & Gao, Renjing, 2022. "Synchronous state of health estimation and remaining useful lifetime prediction of Li-Ion battery through optimized relevance vector machine framework," Energy, Elsevier, vol. 251(C).
  5. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
  6. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
  7. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
  8. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Data cleaning and restoring method for vehicle battery big data platform," Applied Energy, Elsevier, vol. 320(C).
  9. Shao-Xun Liu & Ya-Fu Zhou & Yan-Liang Liu & Jing Lian & Li-Jian Huang, 2021. "A Method for Battery Health Estimation Based on Charging Time Segment," Energies, MDPI, vol. 14(9), pages 1-15, May.
  10. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
  11. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Health-Conscious vehicle battery state estimation based on deep transfer learning," Applied Energy, Elsevier, vol. 316(C).
  12. Li, Yang & Gao, Guoqiang & Chen, Kui & He, Shuhang & Liu, Kai & Xin, Dongli & Luo, Yang & Long, Zhou & Wu, Guangning, 2025. "State-of-health prediction of lithium-ion batteries using feature fusion and a hybrid neural network model," Energy, Elsevier, vol. 319(C).
  13. Li, Junfu & Wang, Lixin & Lyu, Chao & Wang, Dafang & Pecht, Michael, 2019. "Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries," Applied Energy, Elsevier, vol. 256(C).
  14. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  15. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
  16. Li, Yang & Wang, Shunli & Chen, Lei & Qi, Chuangshi & Fernandez, Carlos, 2023. "Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 282(C).
  17. Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
  18. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
  19. Lehtola, Timo, 2025. "Vehicle-to-grid applications and battery cycle aging: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
  20. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
  21. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
  22. Rahbari, Omid & Omar, Noshin & Firouz, Yousef & Rosen, Marc A. & Goutam, Shovon & Van Den Bossche, Peter & Van Mierlo, Joeri, 2018. "A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm," Energy, Elsevier, vol. 155(C), pages 1047-1058.
  23. Xiaohui Xu & Ke Deng & Jibin Yang & Pengyi Deng & Xiaohua Wu & Linsui Cheng & Haolan Zhou, 2025. "Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators," Sustainability, MDPI, vol. 17(3), pages 1-24, January.
  24. Shuo Sun & Junzhong Sun & Zongliang Wang & Zhiyong Zhou & Wei Cai, 2022. "Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism," Energies, MDPI, vol. 15(12), pages 1-17, June.
  25. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Lei, Zhenzhen & Zhang, Yuanjian, 2023. "State of health estimation for lithium-ion batteries based on hybrid attention and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
  26. Wu, Muyao & Zhong, Yiming & Wu, Ji & Wang, Yuqing & Wang, Li, 2023. "State of health estimation of the lithium-ion power battery based on the principal component analysis-particle swarm optimization-back propagation neural network," Energy, Elsevier, vol. 283(C).
  27. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
  28. Wu, Muyao & Wang, Li & Wu, Ji, 2023. "State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction," Energy, Elsevier, vol. 282(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.