Christian Charles O'Gorman

Artificial neural network simulation of battery performance

Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery, such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, we have begun development of non-phenomenological models for battery systems based on artificial neural networks. The paper describes initial feasibility studies as well as current models and makes comparisons between predicted and actual performance.

Reliability of rechargeable batteries in a photovoltaic power supply system

We investigated the reliability of a rechargeable battery acting as the energy storage component in a photovoltaic power supply system. A model system was constructed for this that includes the solar resource, the photovoltaic power supply system, the rechargeable battery and a load. The solar resource and the system load are modelled as stochastic processes. The photovoltaic system and the rechargeable battery are modelled deterministically, and an artificial neural network is incorporated into the model of the rechargeable battery to simulate damage that occurs during deep discharge cycles. The equations governing system behaviour are solved simultaneously in the Monte Carlo framework, and a first passage problem is solved to assess system reliability.