In an energy harvester powered wireless sensor node, the energy harvester is often the only energy source, therefore it is crucial to configure the microcontroller and the sensor node so that the harvested energy is used efficiently. This paper presents a response surface model (RSM) based design space exploration and optimisation of a complete wireless sensor node system. In our work the power consumption models of the microcontroller and the sensor node are defined based on their digital operations so that the parameters of the digital algorithms can be optimised to achieve the best energy efficiency. In the proposed technique, SystemC-A is used to model the system's analogue components as well as the digital control algorithms implemented in the microcontroller and the sensor node. A series of simulations are carried out and a response surface model is constructed from the simulation results. The RSM is then optimised using MATLAB's optimisation toolbox and the results show that the optimised system configuration can double the total number of wireless transmissions with fixed amount of harvested energy. The great improvement in the system performance validates the efficiency of our technique.

Vibration energy harvesting systems pose significant modeling and design challenges due to their mixed-technology nature, extremely low levels of available energy and disparate time scales between different parts of a complete harvester. An energy harvester is a complex system of tightly coupled components modeled in the mechanical, magnetic, as well as electrical analog and digital domains. Currently available design tools are inadequate for simulating such systems due to prohibitive CPU times. This paper proposes a new technique to accelerate simulations of complete vibration energy harvesters by approximately two orders of magnitude. The proposed technique is to linearize the state equations of the system's analog components to obtain a fast estimate of the maximum step-size to guarantee the numerical stability of explicit integration based on the Adams-Bashforth formula. We show that the energy harvester's analog electronics can be efficiently and reliably simulated in this way with CPU times two orders of magnitude lower than those obtained from two state-of-the-art tools, VHDL-AMS and SystemC-A. As a case study, a practical, complex microgenerator with magnetic tuning and two types of power-processing circuits have been simulated using the proposed technique and verified experimentally.

Using wireless communication and energy harvesting in automobiles might have significant advantages considering dependability (no wires and contacts) and weight (no cable tree). In this paper, we give a brief overview of the related technologies, surrounding conditions, and methods for design and optimization. As examples, we focus on methods for harvesting kinetic energy and wireless transmission in a tire pressure metering system (TPMS).

This paper proposes a linearised state-space technique to accelerate the simulation of tunable vibration energy harvesting systems by at least two orders of magnitude. The paper provides evidence that currently available simulation tools are inadequate for simulating complete energy harvesting systems where prohibitive CPU times are encountered due to disparate time scales. In the proposed technique, the model of a complete mixed-technology energy harvesting system is divided into component blocks whose mechanical and analogue electrical parts are modelled by local state equations and terminal variables while the digital electrical part is modelled as a digital process. Unlike existing simulation tools that use Newton-Raphson method, the proposed technique uses explicit integration such as Adams-Bashforth method to solve the state equations of the complete energy harvester model in short simulation time. Experimental measurements of a practical tunable energy harvester have been carried out to validate the proposed technique.

This paper proposes, for the first time, an automated energy harvester design flow which is based on a single HDL software platform that can be used to model, simulate, configure and optimise energy harvester systems. A demonstrator prototype incorporating an electromagnetic mechanical-vibration-based micro-generator and a limited number of library models has been developed and a design case study has been carried out. Experimental measurements have validated the simulation results which show that the outcome from the design flow can improve the energy harvesting efficiency by 75%.

This paper presents an integrated approach to energy harvester modelling and performance optimisation where the complete mixed physical-domain energy harvester system (micro generator, voltage booster, storage element and load) can be modelled and optimised in a systematic manner using one simulation platform. We developed an accurate HDL model for the energy harvester and demonstrated its accuracy by validating it experimentally and comparing it with recently reported models. To address the performance loss due to the close mechanical-electrical interaction that takes place in energy harvesters, we proposed a holistic methodology to the energy harvester optimisation based on the HDL model. The effectiveness of employing such an approach has been demonstrated by showing that it is possible to improve vibration-based energy harvester efficiency (energy delivered to load/harvested energy) by 30% through optimising the micro-generator size and the voltage booster circuit components.