Estimation of Residual Capacity and Deterioration of Sealed Lead-acid Batteries by Neural Networks and Its Application to Electric Bicycles

Abstract

Since measuring the electrolyte density is impossible for sealed lead-acid batteries, it is difficult to accurately estimate the residual capacity in any non-standard condition. A popular application like the electric bicycle is therefore problematic because discharge conditions are extremely variable but at the same time an accurate residual capacity estimate is desired. To solve this problem, neural networks were developed to perform this estimation using externally measurable electrical parameters. This is the first neural network implementation to perform this task. It was also found that this solution works reliably even under changing environmental conditions. Moreover, this network solution can estimate the deterioration state of the batteries in just 30s. As a result of this study, a battery checking system using two independent neural networks was developed to estimate the deterioration state and residual capacity of sealed lead-acid batteries in near real-time. This kind of system has large potential in a vast range of battery applications.