Dynamic processes are often influenced by external conditions. We expand the neural network approximation capability to behavior modeling within an original hierarchical master-slave relation. Unlike the control theory paradigm, neural weights will replace "state variables" that may be impossible to measure. An application aiming at predicting the end of discharge for rechargeable batteries is fully described. This new battery management tool leads to accurate predictions (mean error is about 3 %) and its implementation into a portable equipment demonstrates that neural networks could be useful even for small size products. The system is further improved by on-line adaptation to actual conditions and individual behavior. This improvement reduces the error prediction to a low 1.5 %.

This article presents an original method to accurately predict the end of discharge of rechargeable batteries inserted in portable electronic equipments. The proposed method is based on two neural networks organized in a masterslave relation. A prediction accuracy of 3% (18 minutes) is reached. A further improvement of the system is introduced by adapting on-line another neural network to the actual battery currently in use. This adaptive method reduces the average error to 10 minutes. Results are promising and implementation, carried out in a portable multimeter prototype, only requires a small amount of the computing power already available inside most portable equipments.