TY - GEN
T1 - High Precision SoC Estimation of LiFePO4 Blade Batteries Using Improved OCV-Based PNGV Model
AU - Tao, Zhen
AU - Zhao, Zhenyu
AU - Fan, Fei
AU - Jie, Huamin
AU - Chang, Yongqi
AU - See, Kye Yak
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The state-of-charge (SoC) stands as a pivotal measure for ascertaining a battery's remaining capacity. Accurate SoC estimations can meaningfully enhance a battery's operational longevity, fortify safety standards, and enrich user experience. This paper presents an improved open circuit voltage (OCV)-based partnership for a new generation of vehicle (PNGV) model, specifically tailored for estimating the SoC of LiFePO4 blade batteries. These batteries are distinctively characterized by their advantages in safety, energy density, and thermal management. The proposed model uniquely integrates the SoC-dependent property of the battery's internal resistance, facilitating a marked improvement in estimation accuracy over existing PNGV models. Experimental results underscore the capability and effectiveness of the proposed model in estimating real-time discharging curves, achieving a remarkably low relative error rate of 0.85%.
AB - The state-of-charge (SoC) stands as a pivotal measure for ascertaining a battery's remaining capacity. Accurate SoC estimations can meaningfully enhance a battery's operational longevity, fortify safety standards, and enrich user experience. This paper presents an improved open circuit voltage (OCV)-based partnership for a new generation of vehicle (PNGV) model, specifically tailored for estimating the SoC of LiFePO4 blade batteries. These batteries are distinctively characterized by their advantages in safety, energy density, and thermal management. The proposed model uniquely integrates the SoC-dependent property of the battery's internal resistance, facilitating a marked improvement in estimation accuracy over existing PNGV models. Experimental results underscore the capability and effectiveness of the proposed model in estimating real-time discharging curves, achieving a remarkably low relative error rate of 0.85%.
KW - Blade battery
KW - open circuit voltage (OCV)
KW - partnership for a new generation of vehicle (PNGV) model
KW - state-of-charge (SoC) estimation
UR - https://www.scopus.com/pages/publications/85179520619
U2 - 10.1109/IECON51785.2023.10312229
DO - 10.1109/IECON51785.2023.10312229
M3 - 会议稿件
AN - SCOPUS:85179520619
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Y2 - 16 October 2023 through 19 October 2023
ER -