Optimal Transmission Policies for Energy Harvesting Devices With Limited State-of-Charge Knowledge

Abstract

Wireless sensors can be integrated with energy harvesting (EH) devices to enable long-term, autonomous operation, necessitating efficient energy management. Existing research assumes knowledge of the state-of-charge (SOC) of the rechargeable battery; however, accurate SOC estimation in real-world devices is typically costly or impractical. This paper investigates the impact of imperfect SOC knowledge and the design of policies to cope with such uncertainty. The optimization complexity is reduced by decoupling the different time scales of the system: first, the short-term average performance is optimized with respect to fast-varying exogenous state variables, under an average energy consumption constraint, but neglecting battery dynamics; then, the policy dictating the average energy consumption as a function of state variables evolving over longer time scales is optimized, based on the detailed battery dynamics. A local search algorithm is presented to determine a locally optimal policy. The performance degradation compared to the scenario with perfect SOC knowledge is shown to decrease with increasing storage capacity and decreasing uncertainty in the EH source, and is within 5% for most cases of practical interest. Moreover, near-optimal performance is achieved by only a loose SOC knowledge, which distinguishes between high/low SOC levels. Finally, the impact of time correlation in the EH source is investigated. EH state knowledge is shown to be more critical than SOC knowledge, hence precise knowledge of the former can obviate the need for accurate information about the latter.