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Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles

Author

Listed:
  • Jaewook Lee

    (School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 412-791, Korea)

  • Woosuk Sung

    (Research and Development Division, Hyundai Motor Company, Hwaseong 445-706, Korea)

  • Joo-Ho Choi

    (School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 412-791, Korea)

Abstract

This paper presents an efficient method for estimating capacity-fade uncertainty in lithium-ion batteries (LIBs) in order to integrate them into the battery-management system (BMS) of electric vehicles, which requires simple and inexpensive computation for successful application. The study uses the pseudo-two-dimensional (P2D) electrochemical model, which simulates the battery state by solving a system of coupled nonlinear partial differential equations (PDEs). The model parameters that are responsible for electrode degradation are identified and estimated, based on battery data obtained from the charge cycles. The Bayesian approach, with parameters estimated by probability distributions, is employed to account for uncertainties arising in the model and battery data. The Markov Chain Monte Carlo (MCMC) technique is used to draw samples from the distributions. The complex computations that solve a PDE system for each sample are avoided by employing a polynomial-based metamodel. As a result, the computational cost is reduced from 5.5 h to a few seconds, enabling the integration of the method into the vehicle BMS. Using this approach, the conservative bound of capacity fade can be determined for the vehicle in service, which represents the safety margin reflecting the uncertainty.

Suggested Citation

  • Jaewook Lee & Woosuk Sung & Joo-Ho Choi, 2015. "Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles," Energies, MDPI, vol. 8(6), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:5538-5554:d:50871
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    References listed on IDEAS

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    1. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    2. Hu, Chao & Jain, Gaurav & Tamirisa, Prabhakar & Gorka, Tom, 2014. "Method for estimating capacity and predicting remaining useful life of lithium-ion battery," Applied Energy, Elsevier, vol. 126(C), pages 182-189.
    3. Yinjiao Xing & Eden W. M. Ma & Kwok L. Tsui & Michael Pecht, 2011. "Battery Management Systems in Electric and Hybrid Vehicles," Energies, MDPI, vol. 4(11), pages 1-18, October.
    4. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
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    Cited by:

    1. Yunfeng Jiang & Xin Zhao & Amir Valibeygi & Raymond A. De Callafon, 2016. "Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery," Energies, MDPI, vol. 9(8), pages 1-17, July.
    2. Gandoman, Foad H. & Ahmadi, Abdollah & Bossche, Peter Van den & Van Mierlo, Joeri & Omar, Noshin & Nezhad, Ali Esmaeel & Mavalizadeh, Hani & Mayet, Clément, 2019. "Status and future perspectives of reliability assessment for electric vehicles," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 1-16.
    3. Paul Stewart & Chris Bingham, 2016. "Electrical Power and Energy Systems for Transportation Applications," Energies, MDPI, vol. 9(7), pages 1-3, July.
    4. Boe-Shong Hong & Mei-Hung Wu, 2015. "Online Energy Management of City Cars with Multi-Objective Linear Parameter-Varying L 2 -Gain Control," Energies, MDPI, vol. 8(9), pages 1-25, September.

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