Electric vehicles with four individually controlled drivetrains are over-actuated systems and therefore the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energyefficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and therefore extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an electric vehicle demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies.

Citation: Kamjoo, Azadeh, Maheri, Alireza, Dizqah, Arash and Putrus, Ghanim (2016) Multiobjective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. International Journal of Electrical Power & Energy Systems, Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Citation: Dizqah, Arash, Maheri, Alireza and Busawon, Krishna (2014) Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) An Accurate Method for the PV Model Identification Based on a GeneticAlgorithm and the Interior-Point Method AbstractDue to the PV module simulation requirements as well as recent applications of model-based controllers, the accurate photovoltaic (PV) model identification method is becoming essential to reduce the PV power losses effectively. The classical PV model identification methods use the manufacturers provided maximum power point (MPP) at the standard test condition (STC). However, the nominal operating cell temperature (NOCT) is the more practical condition and it is shown that the extracted model is not well suited to it. The proposed method in this paper estimates an accurate equivalent electrical circuit for the PV modules using both the STC and NOCT information provided by the manufacturers. A multi-objective global optimization problem is formulated using only the main equation of the PV module at these two conditions that restrains the errors due to employing the experimental temperature coefficients. A novel combination of a genetic algorithm (GA) and the interior-point method (IPM) allows the proposed method to be fast and accurate regardless the PV technology. It is shown that the overall error, which is defined by the sum of the MPP errors of both the STC and the NOCT conditions, is improved by a factor between 5.1% to 31% depending on the PV technology.

No abstract

Article (Accepted Version) http://sro.sussex.ac.uk Kanarachos, Stratis, Dizqah, Arash Moradinegade, Chrysakis, Georgios and Fitzpatrick, Michael E (2018) Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based fruit fly optimisation. Applied Soft Computing, 62. pp. 463-477. ISSN 1568 4946 -Original citation: Kanarachos, S; Moradinegade Dizqah, A; Chrysakis, G. and Fitzpatrick, M.E. (2017) Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation Applied Soft Computing (in press). Abstract:In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithmbased on a recent study on how well a fruit fly's tiny brain finds foodwas developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity.

No abstract

It is well-known that, due to bimodal operation as well as existent discontinuous differential states of batteries, standalone microgrids belong to the class of hybrid dynamical systems of non-Filippov type. In this work, however, standalone microgrids are presented as complementarity systems (CSs) of the Filippov type which is then used to develop a multivariable nonlinear model predictive control (NMPC)-based load tracking strategy as well as Modelica models for long-term simulation purposes. The developed load tracker strategy is a multi-source maximum power point tracker (MPPT) that also regulate the DC bus voltage at its nominal value with the maximum of ±2.0% error despite substantial demand and supply variations.

In order to design model-based controllers applicable to hybrid renewable energy systems (HRES), it is essential to model the HRES mathematically. In this study, a standalone HRES, consisting of a photovoltaic (PV) array, a lead-acid battery bank, a pitch-controlled wind turbine, and a three-phase permanent magnet synchronous generator (PMSG), supplies a variable DC load demand through two boost-and buck-type DC-DC converters. It is shown that the mathematical model of the HRES can be represented by a system of nonlinear hybrid differential algebraic equations (hybrid DAEs). The developed model in this paper employs the Modelica language that allows object-oriented and acausal modelling of the multimode systems. The OpenModelica environment is utilised to compile the model and simulate the system. It is shown that the simulation provides a sufficiently accurate prediction of all the differential and algebraic states including mode transitions. The results of the simulation show a good match with the information available in the components datasheet.