Ni-MH battery modelling for ambient intelligence applications

TL;DR

This paper discusses Ni-MH battery modeling to optimize energy usage in ambient intelligence devices like sensor networks and MEMS actuators, emphasizing the slow development of battery properties compared to circuit improvements.

Contribution

It introduces a new model for Ni-MH batteries tailored for ambient intelligence applications, aiding in system-level energy optimization.

Findings

Battery properties develop slowly compared to circuit improvements

Modeling helps optimize battery usage in mobile devices

Enhanced understanding of battery behavior in ambient intelligence

Abstract

Mobile devices, like sensor networks and MEMS actuators use mobile power supplies to ensure energy for their operation. These are mostly batteries. The lifetime of the devices depends on the power consumption and on the quality and capacitance of the battery. Though the integrated circuits and their power consumption improve continually, their clock frequency also increases with the time, and the resultant power consumption seems not to vary, or slightly increase. On the other hand, the properties of batteries are developing much slower, necessitating the optimization of their usage on system level.