TY - GEN
T1 - Online state of charge EKF estimation for LiFePO4 battery management systems
AU - Zhu, Zheng
AU - Sun, Jinwei
AU - Liu, Dan
PY - 2012
Y1 - 2012
N2 - State of charge (SOC) is the most important status parameter of energy storage system, which is able to predict the available mileage of electric vehicle. In fact, the accuracy of SOC estimation plays a vital role in the usability and security of the battery. In this paper, we designed an battery characteristic experimental system in which LiFePO4 battery was tested under different conditions. Based on quantities of LiFePO4 battery experiments, an improved second-order battery model was proposed in this paper. To fully consider the practical demands, parameters were acquired by the HPPC composite pulse condition under different factors, such as temperature, charging and discharging rates and SOC. At last, the SOC of the battery was estimated through a state equation established by the Extended Kalman Filter (EKF). Experiments proved that the maximum error of SOC estimation is less than 4.2%. Compared with the original Ah method, the improved method has a better ability to reflect the dynamic performance of batteries suitably, and a better dynamic adaptability. What's more, the SOC estimation algorithm is realized by DSP 5509A in an Battary Manage System (BMS).
AB - State of charge (SOC) is the most important status parameter of energy storage system, which is able to predict the available mileage of electric vehicle. In fact, the accuracy of SOC estimation plays a vital role in the usability and security of the battery. In this paper, we designed an battery characteristic experimental system in which LiFePO4 battery was tested under different conditions. Based on quantities of LiFePO4 battery experiments, an improved second-order battery model was proposed in this paper. To fully consider the practical demands, parameters were acquired by the HPPC composite pulse condition under different factors, such as temperature, charging and discharging rates and SOC. At last, the SOC of the battery was estimated through a state equation established by the Extended Kalman Filter (EKF). Experiments proved that the maximum error of SOC estimation is less than 4.2%. Compared with the original Ah method, the improved method has a better ability to reflect the dynamic performance of batteries suitably, and a better dynamic adaptability. What's more, the SOC estimation algorithm is realized by DSP 5509A in an Battary Manage System (BMS).
KW - BMS
KW - EKF
KW - SOC
UR - https://www.scopus.com/pages/publications/84875645387
U2 - 10.1109/ISPACS.2012.6473562
DO - 10.1109/ISPACS.2012.6473562
M3 - 会议稿件
AN - SCOPUS:84875645387
SN - 9781467350815
T3 - ISPACS 2012 - IEEE International Symposium on Intelligent Signal Processing and Communications Systems
SP - 609
EP - 614
BT - ISPACS 2012 - IEEE International Symposium on Intelligent Signal Processing and Communications Systems
T2 - 20th IEEE International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2012
Y2 - 4 November 2012 through 7 November 2012
ER -