@Article{sdhm.2022.018422,
AUTHOR = {Qiang Zhang, Xianguang Zha, Jun Wu, Liang Zhang, Wei Dai, Gang Ren, Shiqian Li, Ning Ji, Xiangjun Zhu, Fengwei Tian},
TITLE = {PSO-LSSVM-based Online SOC Estimation for Simulation Substation Battery},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {16},
YEAR = {2022},
NUMBER = {1},
PAGES = {37--51},
URL = {http://www.techscience.com/sdhm/v16n1/46791},
ISSN = {1930-2991},
ABSTRACT = {As the emergency power supply for a simulation substation, lead-acid batteries have a work pattern featuring non-continuous operation, which leads to capacity regeneration. However, the accurate estimation of battery state of charge (SOC), a measurement of the amount of energy available in a battery, remains a hard nut to crack because of the non-stationarity and randomness of battery capacity change. This paper has proposed a comprehensive method for lead-acid battery SOC estimation, which may aid in maintaining a reasonable charging schedule in a simulation substation and improving battery’s durability. Based on the battery work pattern, an improved Ampere-hour method is used to calculate the SOC during constant current and constant voltage (CC/CV) charging and discharging. In addition, the combined Particle Swarm Optimization (PSO) and Least Squares Support Vector Machine (LSSVM) model is used to estimate the SOC during non-CC discharging. Experimental results show that this method is workable in online SOC estimation of working batteries in a simulation substaion, with the maximum relative error standing at only 2.1% during the non-training period, indicating a high precision and wide applicability.},
DOI = {10.32604/sdhm.2022.018422}
}