A two-level diagnosis method for energy storage battery anomalies based on battery module reconfiguration
Accurate monitoring of energy storage battery degrada-tion anomalies is the key to ensure the safe operation of battery en-ergy storage systems.The adoption of reconfigurable battery to-pology is a major trend for future battery energy storage systems,and existing data-driven battery health assessment methods pri-marily emphasize improvements at the algorithmic level,making it difficult to leverage the advantages of this framework.Aiming at this problem,this paper proposes a two-level diagnosis method for abnormal batteries;the primary diagnosis adopts a least-squares support vector machine classification model trained by full-condition full-life cycle simulation dataset to screen out the suspected abnormal battery modules;the secondary diagnosis adopts a health state estimation model based on residual connec-tion and gated cyclic unit to realize the accurate estimation of the health state of the storage batteries and to verify the results of the primary diagnosis.The experimental results show that the diagno-sis method proposed in this paper has a high accuracy rate in both diagnostic links,and realizes the accurate monitoring of energy storage battery attenuation anomalies on the basis of reconfigu-rable battery topology.
storage battery state of healthleast squares support vector machineresidual connectiongated cyclic unittwo-level anomaly diagnosis
万方数据
维普
