Open Access

ARTICLE

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

Qiang Zhang¹, Xianguang Zha¹, Jun Wu¹, Liang Zhang¹, Wei Dai², Gang Ren³, Shiqian Li³, Ning Ji^3,*, Xiangjun Zhu³, Fengwei Tian³

1 State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210024, China
2 State Grid Jiangsu Electric Power Company Research Institute, Nanjing, 211103, China
3 Technican Training Center of State Grid Jiangsu Electric Power Co., Ltd., Suzhou, 215004, China

* Corresponding Author: Ning Ji. Email:

Structural Durability & Health Monitoring 2022, 16(1), 37-51. https://doi.org/10.32604/sdhm.2022.018422

Received 11 August 2021; Accepted 10 December 2021; Issue published 11 February 2022

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.

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Keywords

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