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

VHDL-AMS modeling of an automotive vibration isolation seating system

This paper presents VHDL-AMS model of an automotive vibration isolation seating system with an active electromechanical actuator. Five control algorithms for the actuator are implemented and their efficiencies are investigated by subjecting the system to a number of stimuli, such as a single jolt or noisy harmonic excitations. Simulations were carried out using the SystemVision simulator and results are shown to compare the relative performance merits of the control methods

SystemC-A modeling of an automotive seating vibration isolation system

A modeling methodology for mixed physical domains system in a new modelling Language is presented. The system is automotive seating vibration isolation system with electronic control. It is described and simulated in SystemCA, an extended version of SystemC which provides analogue, mixed-signal and mixed-domain modeling capabilities. Results show that SystemC-A provides efficient means to model and investigate performance of complex mixed-domain systems for automotive applications

Comparisons of energy sources for autonomous in-car wireless tags for asset tracking and parking applications

AbstractThis study compares the energy available on the car dashboard for powering in-car wireless tags for asset tracking and parking applications. Three energy sources available on the dashboard of a vehicle were investigated, i.e. vibration energy, thermal energy and light energy. The area available for the energy harvester is the same as a credit card (85×54mm2). Simulations were carried out to estimate the potential electrical power that can be generated from the three energy sources. It was found that a vibration harvester can generate tens of μW under all weather conditions. The other two types of energy harvesters can generate tens of mW on a sunny day. However, the output power of a thermogenerator drops to 0 while the power density of a solar cell drops by up to 40% on an overcast or rainy day

Integrated approach to energy harvester mixed technology modelling and performance optimisation

An energy harvester is a system consisting of several components from different physical domains including mechanical, magnetic and electrical as well as the external circuits which regulate and store the generated energy. To design highly efficient energy harvesters, we believe that the various components of the energy harvesters need to be modelled together and in systematic manner using one simulation platform. We propose an accurate HDL model for the energy harvester and demonstrate its accuracy by validating it experimentally and comparing it with recently reported models. It is crucial to consider the various parts of the energy harvester in the context of a complete system, or else the gain at one part may come at the price of efficiency loss else where, rending the energy harvester much less efficient than before. The close mechanical-electrical interaction that takes place in energy harvesters, often lead to significant performance loss when the various parts of the energy harvesters are combined. Therefore, to address the performance loss, we propose an integrated approach to the energy harvester modelling and performance optimisation and demonstrate the effectiveness of employing such an approach by showing that it is possible to improve the performance of vibration-based energy harvester, in terms of the effective energy stored in the super-capacitor, by 33% through optimising the micro-generator mechanical parameters and the voltage booster circuit components

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 booster’s 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 mechanicalelectrical 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%

Fast design space exploration of vibration-based energy harvesting wireless sensors

An energy-harvester-powered wireless sensor node is a complicated system with many design parameters. To investigate the various trade-offs among these parameters, it is desirable to explore the multi-dimensional design space quickly. However, due to the large number of parameters and costly simulation CPU times, it is often difficult or even impossible to explore the design space via simulation. This paper presents a response surface model (RSM) based technique for fast design space exploration of a complete wireless sensor node powered by a tunable energy harvester. As a proof of concept, a software toolkit has been developed which implements the proposed design flow and incorporates either real data or parametrized models of the vibration source, the energy harvester, tuning controller and wireless sensor node. Several test scenarios are considered, which illustrate how the proposed approach permits the designer to adjust a wide range of system parameters and evaluate the effect almost instantly but still with high accuracy. In the developed toolkit, the estimated CPU time of one RSM estimation is 25s and the average RSM estimation error is less than 16.5

Integrated approach to energy harvester mixed technology modelling and performance optimisation

An energy harvester is a system consisting of several components from different physical domains including mechanical, magnetic and electrical as well as the external circuits which regulate and store the generated energy. To design highly efficient energy harvesters, we believe that the various components of the energy harvesters need to be modelled together and in systematic manner using one simulation platform. We propose an accurate HDL model for the energy harvester and demonstrate its accuracy by validating it experimentally and comparing it with recently reported models. It is crucial to consider the various parts of the energy harvester in the context of a complete system, or else the gain at one part may come at the price of efficiency loss else where, rending the energy harvester much less efficient than before. The close mechanical-electrical interaction that takes place in energy harvesters, often lead to significant performance loss when the various parts of the energy harvesters are combined. Therefore, to address the performance loss, we propose an integrated approach to the energy harvester modelling and performance optimisation and demonstrate the effectiveness of employing such an approach by showing that it is possible to improve the performance of vibration-based energy harvester, in terms of the effective energy stored in the super-capacitor, by 33% through optimising the micro-generator mechanical parameters and the voltage booster circuit components