Diagnosis of a battery energy storage system based on principal component analysis

This paper proposes the use of principal component analysis (PCA) for the state of health (SOH) diagnosis of a battery energy storage system (BESS) that is operating in a renewable energy laboratory located in Choco, Colombia. The presented methodology allows the detection of false alarms during the operation of the BESS. The principal component analysis model is applied to a parameter set associated to the capacity, internal resistance and open circuit voltage of a battery energy storage system. The parameters are identified from experimental data collected daily. The PCA model retains the first 5 components that collect 80.25% of the total variability. During the test under real operation contidions, PCA diagnosed a degradation of state of health fastest than the comercial battery controller. A change in the charging modes lead to a battery recovery that was also monitored by the proposed algortihm, and control actions are proposed that lead the BESS to work in normal conditions. (C) 2019 Elsevier Ltd. All rights reserved.