A novel battery identification method based on pattern recognition

Development of an intelligent battery diagnostic system is a necessity for future transportation industry. These technologies will have the potential to create profound impact in other industries such as portable electronics. The present article reports on a pattern recognition method that is primarily engineered to detect the chemistry, number of cells, and state of charge in an unknown package of batteries. The proposed technique has the potential to be used for condition monitoring in a known set of batteries thereby, creating a health monitoring apparatus that can be an integral part of a battery management system in an Electric or Hybrid Electric vehicle using any of the prominent lead acid, lithium-ion, and Nicle Metal Hydride batteries. The proposed method is based on distinct signatures that one can identify in a relatively straightforward equivalent circuit of a battery. These signatures are extracted using time domain diagnostics and are used in combination with nonlinear mappings such as exponential regression and artificial neural networks for pattern recognition purposes.