UCLA

The existing prediction models used in Battery Management Systems (BMS) for lead-acid batteries have difficulty in applying to lead-acid batteries with changed structure or materials. Four different lifetime prediction models of lead-acid batteries to be mainly used as energy storage for PV systems, EV, and hybrid EV are examined. Equivalent full cycles to failure, “Rain flow” cycles were counting, the Schiffer weighted Ah-throughput model and recurrent Neural Network-based Model are discussed for the accessibility and availability of the lead-acid batteries with the supercapacitor anode (hybrid batteries). By examining the mechanism, “Rain flow” cycles counting, the Schiffer weighted Ah-throughput model and recurrent Neural Network-based Model were three models could be used in hybrid batteries. By comparing the accuracy of these models in photovoltaic systems, the Schiffer weighted Ah-throughput model and recurrent Neural Network-based Model were selected to be the promising solutions. A modification of the Schiffer weighted Ah-throughput model is discussed based on the chemical mechanism. All possible effects are taking into account in the modified model based on the detailed analysis. Further estimations can be simply conducted through the factory data sheet of hybrid batteries.