TY  - EJOU
AU  - Zhang, Qiang 
AU  - Zha, Xianguang 
AU  - Wu, Jun 
AU  - Zhang, Liang 
AU  - Dai, Wei 
AU  - Ren, Gang 
AU  - Li, Shiqian 
AU  - Ji, Ning 
AU  - Zhu, Xiangjun 
AU  - Tian, Fengwei 

TI  - PSO-LSSVM-based Online SOC Estimation for Simulation Substation Battery
T2  - Structural Durability \& Health Monitoring

PY  - 2022
VL  - 16
IS  - 1
SN  - 1930-2991

AB  - 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.
KW  - Simulation substation; lead-acid batteries; SOC; PSO; LSSVM

DO  - 10.32604/sdhm.2022.018422