A Markov Model Accounting for Charge Recovery in Energy Harvesting Devices

Abstract

Energy harvesting is an important feature that can be implemented in mobile devices to provide them with extended autonomy, yet it poses several challenges in terms of optimal battery usage. In this paper, we highlight that some non-ideal effects, especially the so-called "charge recovery," can have a dramatic impact on the operation policy of autonomous devices. To do so, we construct a Markov model, where we introduce a bidimensional battery value, including the apparent energy level, which is what available at the electrodes to power the device, and the actual energy level stored in the battery. When power is not drained by the device, these levels tend to equalize due to charge recovery, but, in the case of intense battery usage, they can be significantly different. We show that this non-ideality leads to considerably different estimates of undesirable events such as battery outages, and may cause a general underutilization of the devices if not properly accounted for.

A Game Theoretic Approach for Cost-Effective Management of Energy Harvesting Smart Grids

“…Moreover, a more realstic battery implementation could provide a benefit in terms of accuracy. For instance, one can add the battery parametrization, such as state of health, energy depreciation, and state of charge [21]. The simulator code can be found at [22].…”

Green Network Approach for Integrated Wireless Network Used in Rural India

“…Some of the work is done on very complex models and must be frequently updated depending upon the varying conditions. A non ideal battery is discussed on the parameters like State of Charge (SOC) [8], battery degradation [9] and energy leakage [10].…”

Towards self-control of service rate for battery management in energy harvesting devices

“…We consider a model for an energy harvesting device (wireless transmitter, sensor node), which transmits data packets and is powered by a battery, recharged by a harvesting mechanism. In this work, we use the model proposed and discussed in detail in [13]. In this chapter, we will briefly describe the model parameters, rules and restrictions.…”

“…This means that our energy queuing model for the battery involves events where the battery is discharged even when data is not sent (leakage), or it gets discharged more than it should (deep discharge). This leads to an apparent energy level which is lower than the actual one [13], but on the other hand a charge recovery effect may be present, leading the apparent energy level to raise towards the actual one when the battery is not used.…”

“…• battery leakage seen as a decrease of the energy level, due to chemical degradation of the active materials [13] • imperfect state of charge knowledge expressed as unavailability of the precise level of energy [11] • charge recovery modeled as an increase of battery level, when no energy is diminished by both leakage or the discharge event due to the data transmission. This effect appears because of non-homogeneous distribution of the electroactive species within a battery, which get equalized because of the chemical diffusion phenomena [4] • deep discharge characterized by decreasing of the apparent energy level, due to the unbalanced electroactive material distribution [13], which are happening only within service (or data transmission). • charging/discharging imperfections, meaning that only a limited portion of the available energy can be stored in the battery during charging [8] We considered the operation policies, which are based on the battery models with imperfections.…”