Model prediction for ranking lead-acid batteries according to expected lifetime in renewable energy systems and autonomous power-supply systems

Predicting the lifetime of lead-acid batteries in applications with irregular operating conditions such as partial state-of-charge cycling, varying depth-of-discharge and different times between full charging is known as a difficult task. Experimental investigations in the laboratory are difficult because each application has its own specific operation profile.

Therefore, an experimental investigation is necessary for each application and, moreover, for each operation strategy. This paper presents a lifetime model that allows comparison of the impact of different operating conditions, different system sizing and different battery technologies on battery lifetime.

It is a tool for system designers and system operators to select appropriate batteries, to do a proper system design (sizing of the battery, power generators and loads), and to implement an optimized operation strategy (end-of-charge voltage, frequency of full charging, gassing periods, maximum depth-of-discharge).

The model is a weighted Ah throughput approach based on the assumption that operating conditions are typically more severe than those used in standard tests of cycling and float lifetime. The wear depends on the depth-of-discharge, the current rate, the existing acid stratification, and the time since the last full charging.

The actual Ah throughput is continuously multiplied by a weight factor that represents the actual operating conditions. Even though the modelling approach is mainly heuristic, all of the effects that are taken into account are based on a detailed analysis and understanding of ageing processes in lead-acid batteries.

The ' normal ' user can adapt the model to different battery types simply from the data sheet information on cycle lifetime and float lifetime. (C) 2006 Elsevier B.V. All rights reserved.