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.

/pdf/artificial-neural-network-simulation-of-battery-performance-3uxrdsjbhg.pdf

Artificial neural network simulation of battery performance

Many dynamical systems tested in the field and the laboratory display significant nonlinear behavior. Accurate characterization of such systems requires modeling in a nonlinear framework. One construct forming a basis for nonlinear modeling is that of the artificial neural network (ANN). However, when system behavior is complex, the amount of data required to perform training can become unreasonable. The authors reduce the complexity of information present in system response measurements using decomposition via canonical variate analysis. They describe a method for decomposing system responses, then modeling the components with ANNs. A numerical example is presented, along with conclusions and recommendations.

/pdf/dynamical-system-modeling-via-signal-reduction-and-neural-12azm8km8c.pdf

Dynamical system modeling via signal reduction and neural network simulation

Complex Structure Simulation with Neural Networks

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Complex Structure Simulation with Neural Networks