State-of-health estimator based-on extension theory with a learning mechanism for lead-acid batteries

The main objective of this paper is to design and implement an improved intelligent state-of-health (SOH) estimator for estimating the useful life of lead-acid batteries. Laboratory studies were carried out to measure and record the distributed range of characteristic values in each SOH cycle for the battery subject to cycles of charging and discharging experiments. The measured coup de fouet voltage, internal resistance, and transient current are used as characteristics to develop an intelligent SOH evaluation algorithm. This method is based on the extension matter-element model that has been modified in this research by adding a learning mechanism for evaluation SOH of batteries. The proposed algorithm is relatively simple so that it can be easily implemented with a programmable system-on-chip (PSOC) microcontroller achieve rapid evaluation of battery SOH with precision by using a concise hardware circuit.