A parametric battery state of health estimation method for electric vehicle applications

被引：3
|
作者：
Sarikurt, Turev [1 ]
Ceylan, Murat [1 ]
Balikci, Abdulkadir [1 ]
机构：
[1] Gebze Tech Inst, Fac Engn, Dept Elect Engn, Kocaeli, Turkey
来源：
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES | 2017年 / 25卷 / 04期
关键词：
Lithium-ion batteries; battery management systems; battery state of health; electric vehicles; LITHIUM-ION BATTERIES; LEAD-ACID-BATTERIES; OF-HEALTH; IMPEDANCE MEASUREMENTS; PROGNOSTICS; MANAGEMENT; SUPPORT; CHARGE; MODEL;
D O I：
10.3906/elk-1602-118
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Lithium-ion batteries are commonly preferred in electric vehicle applications. The relative capacity and state of health of a battery decrease with age. Therefore, accurate estimation of these parameters is essential. In this study a parametrical approach for estimation of battery state of health is proposed. A hybrid battery model that has a maximum error less than 3% is used. The relative capacity of the battery is estimated by using performance decrement with age. The method is validated by two different set of experiments. The first set is conducted with batteries that were aged by a controlled process and the second set is conducted with randomly aged batteries. The proposed method works successfully in both conditions with maximum error less than 5%.
引用
收藏
页码：2860 / 2870
页数：11
相关论文
共 50 条
  • [41] The Estimation of State of Charge for Power Battery Packs used in Hybrid Electric Vehicle
    Xie, Shanshan
    Xiong, Rui
    Zhang, Yongzhi
    He, Hongwen
    8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016), 2017, 105 : 2678 - 2683
  • [42] Estimation of state-of-charge and power capability of battery used in electric vehicle
    Dong, Xiong-He
    Qi, Guo-Guang
    Feng, Xi-Kang
    Chen, Ai-Song
    Dianyuan Jishu/Chinese Journal of Power Sources, 2002, 26 (03):
  • [43] State estimation algorithms for electric vehicle 48 V battery management system
    Chaitanya K.V.V.K.
    Bayya M.
    Materials Today: Proceedings, 2023, 72 : 199 - 203
  • [44] AN ADAPTIVE ALGORITHM OF NiMH BATTERY STATE OF CHARGE ESTIMATION FOR HYBRID ELECTRIC VEHICLE
    Qiang, Jiaxi
    Ao, Guoqiang
    He, Jianhui
    Chen, Ziqiang
    Yang, Lin
    2008 IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1-5, 2008, : 2049 - 2054
  • [45] State of charge estimation for electric vehicle battery based on amended Ah metrology
    Zhao, Qiang
    Shao, Cheng-Jun
    Han, Ying-Hua
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMMUNICATION AND ELECTRONIC INFORMATION ENGINEERING (CEIE 2016), 2016, 116 : 475 - 484
  • [46] VLSI design and FPGA implementation of state-of-charge and state-of-health estimation for electric vehicle battery management systems
    Kim, Minjoon
    So, Jaehyuk
    JOURNAL OF ENERGY STORAGE, 2023, 73
  • [47] Online Estimation of State of Health During Discharging of Vehicle Power Battery
    Liu F.
    Liu Y.-P.
    Li J.-D.
    Bu F.-T.
    Dongbei Daxue Xuebao/Journal of Northeastern University, 2022, 43 (11): : 1544 - 1551
  • [48] Electric Vehicle Battery State of Charge Estimation and Simulated Research Based on Improved Ampere-Hour Method
    Zhang, Xin
    Zeng, Qiu-Yong
    Li, Zhen
    Liu, Hua-Dong
    Yang, Jia-Zhi
    Sun, Yang
    Fan, Xing-Ming
    INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND MECHANICAL AUTOMATION (ICEEMA 2015), 2015, : 27 - 32
  • [49] Supercapacitors State-of-Health diagnosis for electric vehicle applications
    El Mejdoubi A.
    Chaoui H.
    Gualous H.
    Oukaour A.
    Slamani Y.
    Sabor J.
    World Electric Vehicle Journal, 2016, 8 (02): : 379 - 387
  • [50] Machine Learning-based Electric Vehicle Battery State of Charge Prediction and Driving Range Estimation for Rural Applications
    Eissa, Magdy Abdullah
    Chen, Pingen
    IFAC PAPERSONLINE, 2023, 56 (03): : 355 - 360
12345