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Peer-reviewedCC BY 4.0Batteries

Real Electric Vehicle Lithium-Ion Battery Capacity Estimation Method Based on the Fusion Model of Discrete Arrhenius and Double Extended Kalman Filter

  • Dongdong Qiao1
  • Zhicheng Zhu2
  • Xiaowei Shen1
  • Zhiwei Zhao3
  • Xiangjun Li4
  • Xin Lai1
4 affiliations
  1. 1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  2. 2School of Automotive Engineering, Jiangxi Polytechnic University, Jiujiang 332007, China
  3. 3The China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
  4. 4The China Electric Power Research Institute, Beijing 100192, China
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Abstract

Accurate capacity estimation of lithium-ion batteries (LIBs) in real-world electric vehicles (EVs) remains a critical challenge due to complex degradation mechanisms, sparse cloud-platform data, and the limitations of existing single-model approaches. To address these issues, this paper proposes a novel fusion estimation method that integrates a discretized Arrhenius empirical lifetime model with a first-order equivalent circuit model (ECM) identified via particle swarm optimization (PSO) and employs a dual extended Kalman filter (DEKF) to synergistically update model parameters and fuse capacity estimates. Then, the proposed method is validated using real-world charging data from 36 EVs across three vehicle brands. Experimental results demonstrate that the fusion model achieves capacity estimation errors within 2.974% maximum and delivers state-of-health (SOH) estimation with R2 values of 0.966, 0.983, and 0.981 for the three vehicle types, respectively. The method effectively overcomes data sparsity and noise issues inherent in cloud-platform data, and provides accurate, adaptive, and robust capacity tracking under diverse real-world operating conditions. This work offers a practical and scalable solution for cloud-based battery health management in next-generation EV applications.

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Cite

BibTeXRIS
BibTeX
@article{qiao2026real,
  title = {{Real Electric Vehicle Lithium-Ion Battery Capacity Estimation Method Based on the Fusion Model of Discrete Arrhenius and Double Extended Kalman Filter}},
  author = {Dongdong Qiao and Zhicheng Zhu and Xiaowei Shen and Zhiwei Zhao and Xiangjun Li and Xin Lai},
  journal = {Batteries},
  year = {2026},
  month = nov,
  doi = {10.3390/batteries12110455},
  url = {https://www.mdpi.com/2313-0105/12/11/455},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Real Electric Vehicle Lithium-Ion Battery Capacity Estimation Method Based on the Fusion Model of Discrete Arrhenius and Double Extended Kalman Filter
AU  - Dongdong Qiao
AU  - Zhicheng Zhu
AU  - Xiaowei Shen
AU  - Zhiwei Zhao
AU  - Xiangjun Li
AU  - Xin Lai
PY  - 2026
DA  - 2026/11/02
JO  - Batteries
DO  - 10.3390/batteries12110455
UR  - https://www.mdpi.com/2313-0105/12/11/455
AB  - Accurate capacity estimation of lithium-ion batteries (LIBs) in real-world electric vehicles (EVs) remains a critical challenge due to complex degradation mechanisms, sparse cloud-platform data, and the limitations of existing single-model approaches. To address these issues, this paper proposes a novel fusion estimation method that integrates a discretized Arrhenius empirical lifetime model with a first-order equivalent circuit model (ECM) identified via particle swarm optimization (PSO) and employs a dual extended Kalman filter (DEKF) to synergistically update model parameters and fuse capacity estimates. Then, the proposed method is validated using real-world charging data from 36 EVs across three vehicle brands. Experimental results demonstrate that the fusion model achieves capacity estimation errors within 2.974% maximum and delivers state-of-health (SOH) estimation with R2 values of 0.966, 0.983, and 0.981 for the three vehicle types, respectively. The method effectively overcomes data sparsity and noise issues inherent in cloud-platform data, and provides accurate, adaptive, and robust capacity tracking under diverse real-world operating conditions. This work offers a practical and scalable solution for cloud-based battery health management in next-generation EV applications.
SN  - 2313-0105
ER  -