On the Effects of Battery Imperfections in an Energy Harvesting Device

TL;DR

This paper investigates how battery imperfections like storage inefficiencies and uncertain charge levels impact the throughput optimization in energy harvesting devices, revealing that ignoring these effects leads to sub-optimal performance.

Contribution

It models real battery effects in energy harvesting devices using a Markov Decision Process, highlighting the importance of considering these imperfections for optimal operation.

Findings

Real battery effects significantly reduce achievable throughput.

Policies ignoring battery imperfections are highly sub-optimal.

Considering battery inefficiencies improves energy management strategies.

Abstract

Energy Harvesting allows the devices in a Wireless Sensor Network to recharge their batteries through environmental energy sources. While in the literature the main focus is on devices with ideal batteries, in reality several inefficiencies have to be considered to correctly design the operating regimes of an Energy Harvesting Device (EHD). In this work we describe how the throughput optimization problem changes under \emph{real battery} constraints in an EHD. In particular, we consider imperfect knowledge of the state of charge of the battery and storage inefficiencies, \emph{i.e.}, part of the harvested energy is wasted in the battery recharging process. We formulate the problem as a Markov Decision Process, basing our model on some realistic observations about transmission, consumption and harvesting power. We find the performance upper bound with a real battery and numerically discuss the novelty introduced by the real battery effects. We show that using the \emph{old} policies obtained without considering the real battery effects is strongly sub-optimal and may even result in zero throughput.