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Neural networking is a new approach to modeling batteries for electric vehicle applications. This modeling technique is much less complex than a first principles model but can consider more parameters than classic empirical modeling. Test data indicates that individual cell size, geometry, and operating conditions affect battery performance (energy density, power density and life). Given sufficient experimental data, system parameters, and operating conditions, a neural network model could be used to interpolate and perhaps even extrapolate battery performance under wide variety of operating conditions. As a result, the method could be a valuable design tool for electric vehicle battery design and application. This paper describes the on going modeling method at Texas A&M University and presents preliminary results of a tubular lead acid battery model.

Electric power generation and conditioning have experienced revolutionary development over the past two decades. Furthermore, new materials such as high energy magnets and high temperature superconductors are either available or on the horizon. Our work is based on the promise that new technologies are an important driver of new power system concepts and architectures. This observation is born out by the historical evolution of power systems both in terrestrial and aerospace applications. This paper will introduce new approaches to designing space power systems by using several new technologies.