Abstract
This paper presents a Valve Regulated Lead Acid (VRLA) battery state of health (SOH) estimation model. The model is based on a region of the battery discharge voltage response known as the coup de fouet. Two implementation approaches are considered. The first approach utilises soft computing techniques in the form of an Adaptive Neuro-Fuzzy Inference System known as ANFIS [1], [2]. The second approach utilises the least squares estimator (LSE) hard computing technique. The comparison of approaches has highlighted the potential of soft computing for providing accurate results. It, however, also raises the need for caution in employing these techniques, as the additional accuracy obtained comes at the expense of increased complexity which may not be justified in practice.
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