Elaboration of a training system and algorithm to improve batteries performance

Abstract

In the process of battery operation, an important factor is the identity of the individual elements characteristics, on which the lifetime of the entire battery depends. In charge-discharge cycles, an element with worse characteristics leads to a deviation of the charge parameters of the entire battery, as a rule it does not receive a full charge. Most well known battery training systems control a limited list of parameters, such as charge current, initial and final voltage, and electrolyte temperature. Automatic control of the density of the electrolyte and the moment of gas evolution is quite difficult to perform; they are performed manually by the operator or are excluded for non-served batteries.