Event prediction algorithm using neural networks for the power management system of electric vehicles

Abstract

The power management system for electronic vehicles selectively activates Electronic Control Units (ECUs) in the electronic control system according to time-series vehicle data and predefined operation states. However, at an operation state transition, the energy overheads used for the selective ECU activation could be higher than the energy saved by deactivating ECUs. To prevent these energy-inefficient state transitions, we apply two main ideas to our proposed algorithm: (A) unacceptable state transitions and (B) adaptive training speed. For the unacceptable transitions, our energy model evaluates the breakeven time where energy saving equals to energy overheads. Based on the breakeven time, our algorithm classifies training dataset as unacceptable and acceptable event sets. Especially when the algorithm trains neural networks for the two event sets, the adaptive training speed expedites its training speed based on a history of training errors. Consequently, without violating in-vehicle time constraints, the algorithm could provide real-time predictions and save energy overheads by avoiding unacceptable transitions. In the simulation results on real driving datasets, our algorithm improves the energy dissipation of the electronic control system by 5% to 7%.