PSO-LSSVM-based Online SOC Estimation for Simulation Substation Battery

摘要

As the emergency power supply for a simulation substation,lead-acid batteries have a work pattern featuring noncontinuous 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.