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A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries

Author

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  • Yuechen Liu

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
    Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA)

  • Linjing Zhang

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Jiuchun Jiang

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Shaoyuan Wei

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Sijia Liu

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Weige Zhang

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Lithium ion (Li-ion) batteries work as the basic energy storage components in modern railway systems, hence estimating and improving battery efficiency is a critical issue in optimizing the energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion batteries accurately since it varies continuously under working conditions and is unmeasurable via experiments. This paper offers a learning-based simulation method that employs experimental data to estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries, taking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge current rates are considered as the main variables that may affect the efficiencies. Over eight million empirical datasets are collected during a series of experiments performed to investigate the efficiency variation. A back propagation (BP) neural network efficiency estimation and simulation model is proposed to estimate the continuous-time energy efficiency and coulombic efficiency. The empirical data collected in the experiments are used to train the BP network model, which reveals a test error of 10 −4 . With the input of continuous SOC regions and discharge currents, continuous-time efficiency can be estimated by the trained BP network model. The estimated and simulated result is proven to be consistent with the experimental results.

Suggested Citation

  • Yuechen Liu & Linjing Zhang & Jiuchun Jiang & Shaoyuan Wei & Sijia Liu & Weige Zhang, 2017. "A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries," Energies, MDPI, vol. 10(5), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:597-:d:97138
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    References listed on IDEAS

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    Cited by:

    1. Hossam M. Hussein & Ahmed Aghmadi & Mahmoud S. Abdelrahman & S M Sajjad Hossain Rafin & Osama Mohammed, 2024. "A review of battery state of charge estimation and management systems: Models and future prospective," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.
    2. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
    3. Renxin, Xiao & Yi, Yang & Xianguang, Jia & Nan, Pan, 2023. "Collaborative estimations of state of energy and maximum available energy of lithium-ion batteries with optimized time windows considering instantaneous energy efficiencies," Energy, Elsevier, vol. 274(C).

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