PREDICTION OF INDIVIDUAL CELL PERFORMANCE IN A LONG-STRING LEAD ACID PEAK-SHAVING BATTERY - APPLICATION OF ARTIFICIAL NEURAL NETWORKS/

This work represents the culmination of several years of study of an o perating large energy storage battery with the purpose of determining if computerized pattern recognition of maintenance data (and/or availa ble fabrication data) could be used for the early detection of poorly performing cells. Also investigated was the possible identification of cells with predicted high performance. Previous studies using k-neare st neighbor pattern recognition have been augmented with the investiga tion of artificial neural network analysis. Both methods have achieved practical levels of prediction, but the neural network prediction res ults are somewhat better. It was possible to select 70% of the high-pe rforming cells, without any false selections from the low-performing c ells; it was possible to identify nearly 96% of the poor-performance c ells, with none of the high-performance cells mis-selected These resul ts suggest the feasibility of the routine application of neural networ ks for performance prediction as part of a maintenance strategy for lo ng-string energy storage systems.