Leran Wang

A comparison of power output from linear and nonlinear kinetic energy harvesters using real vibration data

The design of vibration energy harvesters (VEHs) is highly dependent upon the characteristics of the environmental vibrations present in the intended application. VEHs can be linear resonant systems tuned to particular frequencies or non-linear systems with either bi-stable operation or a Duffing-type response. This paper provides detailed vibration data from a range of applications, which has been made freely available for download through the Energy Harvesting Network’s online data repository. In particular, this research shows that simulation is essential in designing and selecting the most suitable vibration energy harvester for particular applications. This is illustrated through C-based simulations of different types of VEHs, using real vibration data from a diesel ferry engine, a combined heat and power pump, a petrol car engine and a helicopter. The analysis shows that a bistable energy harvester only has a higher output power than a linear or Duffing-type nonlinear energy harvester with the same Q-factor when it is subjected to white noise vibration. The analysis also indicates that piezoelectric transduction mechanisms are more suitable for bistable energy harvesters than electromagnetic transduction. Furthermore, the linear energy harvester has a higher output power compared to the Duffing-type nonlinear energy harvester with the same Q factor in most cases. The Duffing-type nonlinear energy harvester can generate more power than the linear energy harvester only when it is excited at vibrations with multiple peaks and the frequencies of these peaks are within its bandwidth. Through these new observations, this paper illustrates the importance of simulation in the design of energy harvesting systems, with particular emphasis on the need to incorporate real vibration data.

An integrated approach to energy harvester modeling and performance optimization

This paper proposes an integrated approach to energy harvester (EH) modeling and performance optimization where the complete mixed physical-domain EH (micro generator, voltage booster, storage element and load) can be modeled and optimized. We show that electrical equivalent models of the micro generator are inadequate for accurate prediction of the voltage boosters performance. Through the use of hardware description language (HDL) we demonstrate that modeling the micro generator with analytical equations in the mechanical and magnetic domains provide an accurate model which has been validated in practice. Another key feature of the integrated approach is that it facilitates the incorporation of performance enhanced optimization, which as will be demonstrated is necessary due to the mechanical-electrical interactions of an EH. A case study of a state-of-the-art vibration-based electromagnetic EH has been presented. We show that performance optimization can increase the energy harvesting rate by about 40%.

An efficient and accurate MEMS accelerometer model with sense finger dynamics for applications in mixed-technology control loops

This contribution presents a novel MEMS accelerometer model implemented in VHDL-AMS. The model includes sense finger dynamics, which allow accurate performance prediction of a MEMS accelerometer in a mixed-technology control loop. A distributed mechanical sensing element model is developed and the effect of the sense finger dynamics is analyzed. The sense finger dynamics might cause a failure of the Sigma-Delta control loop which is captured by the proposed model but cannot be correctly modeled using the conventional approach.

An automated design flow for vibration-based energy harvester systems

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%.

Response-surface-based design space exploration and optimisation of wireless sensor nodes with tunable energy harvesters

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 systems 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 MATLABs 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.

An Explicit Linearized State-Space Technique for Accelerated Simulation of Electromagnetic Vibration Energy Harvesters

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 systems 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 harvesters 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.

Accelerated simulation of tunable vibration energy harvesting systems using a linearised state-space technique

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.

VHDL-AMS based genetic optimisation of fuzzy logic controllers

Purpose – This paper presents a VHDL-AMS based genetic optimisation methodology for fuzzy logic controllers (FLCs) used in complex automotive systems and modelled in mixed physical domains. A case study applying this novel method to an active suspension system has been investigated to obtain a new type of fuzzy logic membership function with irregular shapes optimised for best performance. Design/methodology/approach – The geometrical shapes of the fuzzy logic membership functions are irregular and optimised using a genetic algorithm (GA). In this optimisation technique, VHDL-AMS is used not only for the modelling and simulation of the FLC and its underlying active suspension system but also for the implementation of a parallel GA directly in the system testbench. Findings – Simulation results show that the proposed FLC has superior performance in all test cases to that of existing FLCs that use regular-shape, triangular or trapezoidal membership functions. Research limitations – The test of the FLC has only been done in the simulation stage, no physical prototype has been made. Originality/value – This paper proposes a novel way of improving the FLC’s performance and a new application area for VHDL-AMS.

VHDL-AMS based genetic optimization of a fuzzy logic controller for automotive active suspension systems

This paper presents a new type of fuzzy logic controller (FLC) membership functions for automotive active suspension systems. The shapes of the membership functions are irregular and optimized using a genetic algorithm (GA). In this optimization technique, VHDL-AMS is used not only for the modeling and simulation of the fuzzy logic controller and its underlying active suspension system but also for the implementation of a parallel GA. Simulation results show that the proposed FLC has superior performance to that of existing FLCs that use triangular or trapezoidal membership functions.

Energy Efficient Sensor Nodes Powered by Kinetic Energy Harvesters – Design for Optimum Performance

In an energy harvester powered wireless sensor node system, as the energy harvester is the only energy source, it is crucial to configure the microcontroller and the sensor node so that the harvested energy is used efficiently. This paper outlines modelling, performance optimisation and design exploration of the complete, complex system which includes the analogue mechanical model of a tunable kinetic microgenerator, its magnetic coupling with the electrical blocks, electrical power storage and processing parts, the digital control of the microgenerator tuning system, as well as the power consumption models of sensor node. Therefore not only the energy harvester design parameters but also the sensor node operation parameters can be optimised in order to achieve the best system performance. The power consumption models of the microcontroller and the sensor node are built based on their operation scenarios so that the parameters of the digital algorithms can be optimised to achieve the best energy efficiency. In the proposed approach, two Hardware Description Languages, VHDL-AMS and SystemC-A is used to model the systems analogue components as well as the digital control algorithms which are implemented in the microcontroller and the sensor node. Simulation and performance optimisation results are verified experimentally. In the development of the fast design exploration tool based on the response surface technique, the response surface model (RSM) is constructed by carrying out a series of simulations. The RSM is then optimised using MATLABs optimisation toolbox and the optimisation results are presented.