A novel entropy-based fault diagnosis and inconsistency evaluation approach for lithium-ion battery energy storage systems. (September 2021)

• Record Type: Journal Article
• Authors: Qiu, Yishu; Cao, Wenjiong; Peng, Peng; Jiang, Fangming
• Is Part Of: Journal of energy storage. Volume 41(2021)
• Publication Date: 2021-09
• Subjects: Lithium-ion battery -- Energy storage system -- Shannon entropy -- Internal short circuit -- Fault diagnosis -- Inconsistency evaluation; BESS battery energy storage system -- BMS battery management system -- BTMS battery thermal management system -- CFD computational fluid dynamics -- ECM equivalent circuit model -- EKF extended Kalman filter -- EMS energy management system -- ESC external short circuit -- ESS energy storage system -- EV electric vehicle -- HEV hybrid electric vehicle -- ICC interclass correlation coefficient -- ISC internal short circuit -- KF Kalman filter -- LIB lithium-ion battery -- LOF local outlier factor -- LSTM long short-term memory -- OCV open circuit voltage -- PHEV plug-in hybrid electric vehicles -- PF particle filter -- RC resistance capacitance -- RVM relevance vector machine -- SGCC State Grid Corporation of China -- SOC state of charge -- SOH state of health -- SVM support vector machine -- TR thermal runaway; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2021.102852
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Multi-level algorithms based on Shannon entropy are proposed. The cell-level Shannon entropy is used to detect LIB faults at early stage of ISC. The module-level and cluster-level algorithms are used for inconsistency evaluation. The investigated BESS consists of 18240 40Ah Li-ion batteries (1MW/2MWh capacity). The availability of the proposed algorithms is testified by simulated data. Abstract: Detection and diagnosis of faults at the early stage, as well as inconsistency monitoring and control are of extreme importance for operating Li-ion batteries (LIBs) safely and reliably, handling performance degradation and cell unbalancing, and avoiding accidents like thermal runaway (TR). In this work, a general procedure based on multi- level Shannon entropy algorithms is put forward to perform fault diagnosis as well as inconsistency evaluation for LIB-based energy storage systems (ESSs). More specifically, the cell-level Shannon entropy algorithm is used to detect faults by comparing Shannon entropies of different LIB cells in each module while the module-level and cluster-level Shannon entropy algorithms are used to evaluate the overall inconsistency among LIB cells in each module and in each cluster respectively. The proposed approach is then applied in a large-scale LIB-based ESS (1 MW/2 MWh). Through simulated data, the availability of the cell-level Shannon entropy algorithm to detect small changes in gradual faults is testified while the module-level and the cluster-level Shannon entropy algorithms are demonstrated to be effective for assessing inconsistences of LIBs in every module and in every cluster respectively, by comparing results of the normal case with those from two cases each with a different faulty LIB cell at the early stage of internal short circuit (ISC).