State of charge estimation method based on linearization of voltage hysteresis curve

被引:3
|
作者
Lu, Chusheng [1 ]
Hu, Jian [1 ]
Zhai, Yuanyi [1 ]
Hu, Haibin [1 ]
Zheng, Hangyu [1 ]
机构
[1] Midea Corp Res Ctr, Foshan, Peoples R China
关键词
Voltage hysteresis curve; Lithium-ion battery equivalent model; Open circuit voltage; Neural network; NEURAL-NETWORK; BATTERY MANAGEMENT; OF-CHARGE; PACKS; SOC;
D O I
10.1016/j.est.2023.108481
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The lithium-ion battery equalization method with voltage equalization is relatively mature and convenient to implement. However, this equalization method may cause excessive energy consumption. The equalization method with state of charge (SOC) equalization can reduce this kind of energy consumption. To use the equalization method with SOC, we should estimate the SOC first. So this paper proposes a SOC estimation method based on linearization of voltage hysteresis curve (EMBL). This paper linearizes the voltage hysteresis curve of lithium-ion battery and proposes a novel equivalent model of lithium-ion battery based on linear neural network (LEM). The performance superiority of the LEM is verified through the intermittent charging and dis-charging experiments. Then the SOC estimation method based on linearization of voltage hysteresis curve is constructed. The equalization experiments with voltage equalization and SOC equalization are carried out respectively. And the results prove that the method with SOC equalization can significantly reduce energy consumption compared to the method with voltage equalization, and the EMBL based SOC equalization can slightly reduce energy consumption compared to the extended Kalman filter (EKF) algorithm based SOC equalization.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Open-circuit voltage-based state of charge estimation of lithium-ion power battery by combining controlled auto-regressive and moving average modeling with feedforward-feedback compensation method
    Dang, Xuanju
    Yan, Li
    Jiang, Hui
    Wu, Xiru
    Sun, Hanxu
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2017, 90 : 27 - 36
  • [22] A Method for State of Charge and State of Health Estimation of LithiumBatteries Based on an Adaptive Weighting Unscented Kalman Filter
    Fang, Fengyuan
    Ma, Caiqing
    Ji, Yan
    ENERGIES, 2024, 17 (09)
  • [23] A novel parameter adaptive method for state of charge estimation of aged lithium batteries
    Kong, Depeng
    Wang, Shuhui
    Ping, Ping
    JOURNAL OF ENERGY STORAGE, 2021, 44
  • [24] A Comprehensive Analysis of the Open-Circuit Voltage (OCV) Method for the Estimation of State of Charge
    P. R. Dhabe
    S. R. Paraskar
    S. S. Jadhao
    Transactions of the Indian National Academy of Engineering, 2025, 10 (1) : 235 - 242
  • [25] Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique
    Xu, Zhicheng
    Wang, Jun
    Fan, Qi
    Lund, Peter D.
    Hong, Jie
    JOURNAL OF ENERGY STORAGE, 2020, 32 (32)
  • [26] Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage
    Gismero, Alejandro
    Schaltz, Erik
    Stroe, Daniel-Ioan
    ENERGIES, 2020, 13 (07)
  • [27] State of charge estimation of lithium-ion battery under time-varying noise based on Variational Bayesian Estimation Methods
    Yun, Zhonghua
    Qin, Wenhu
    Shi, Weipeng
    JOURNAL OF ENERGY STORAGE, 2022, 52
  • [28] IoT and artificial intelligence enabled state of charge estimation for battery management system in hybrid electric vehicles
    Kiran, Siripuri
    Polala, Niranjan
    Phridviraj, M. S. B.
    Venkatramulu, S.
    Srinivas, Chintakindi
    Rao, V. Chandra Shekhar
    INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS, 2022, 29 (05) : 463 - 479
  • [29] An execution time optimized state of charge estimation method for lithium-ion battery
    Celikten, Baran
    Eren, Ozan
    Karatas, Yusuf Selim
    JOURNAL OF ENERGY STORAGE, 2022, 51
  • [30] Battery State of Charge Estimation Based on a Pure Hardware Implementable Method
    Tang, Xiaopeng
    Gao, Furong
    Yao, Ke
    Wang, Yujie
    JOINT INTERNATIONAL CONFERENCE ON ENERGY, ECOLOGY AND ENVIRONMENT ICEEE 2018 AND ELECTRIC AND INTELLIGENT VEHICLES ICEIV 2018, 2018,