Kaveh Sarrafan - Academia.edu (original) (raw)

Papers by Kaveh Sarrafan

Physica C: Superconductivity, 2013

Abstract Recently, producing power with smaller amount of losses become as a goal in our daily li... more Abstract Recently, producing power with smaller amount of losses become as a goal in our daily life. Today, large amount of energy waste in power networks all around the world. The main reason is “resistive electric equipments” of power networks. Since early 1980s, simultaneous with the development of high temperature superconductive (HTS) technology, superconductors gently attracted the mankind attentions. Using superconductive equipments instead of conventional resistive ones are result in salient electric loss reduction in power systems. Especially to reduce losses in power networks superconductive industrial rotating machines can potentially perform a significant role. In early recent century, first generation of HTS rotating machines was born. But unfortunately they have long way to penetrate the commercial markets yet. In HTS rotating machines the conventional copper made windings are replaced with the HTS superconductors. In this paper an industrial HTS synchronous motor with YBCO coated conductor field windings was designed. As a new approach, model was equipped with a compound rotor that includes both magnetic and non-magnetic materials. So, large amount of heavy iron made part was replaced by light non-magnetic material such as G-10 fiberglass. Furthermore, in this structure iron loss in rotor could be reduced to its lowest value. Also less weight and more air gap energy density were the additional advantages. Regarding zero electric loss production in field windings and less iron loss in rotor construction, this model potentially is more effective than the other iron made HTS motors.

In this paper, the steady-state temperature of a sample 500 KW two rotor one stator Non-slotted a... more In this paper, the steady-state temperature of a sample 500 KW two rotor one stator Non-slotted axial flux permanent magnet motor is calculated using the finite element simulator software package. Due to the high temperature in various parts of the machine, especially at stator winding, a cooling system is designed for the motor and the temperature is recalculated. The results show that the temperature obtained for the parts is within the permissible range.

Higher penalties may apply, and higher damages may be awarded, for offences and infringements inv... more Higher penalties may apply, and higher damages may be awarded, for offences and infringements involving the conversion of material into digital or electronic form. Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong. represent the views of the University of Wollongong.

IEEE Transactions on Intelligent Vehicles, 2020

Range anxiety has remained a critical technological bottleneck in electric vehicles (EVs). The is... more Range anxiety has remained a critical technological bottleneck in electric vehicles (EVs). The issue lies in the fact that the estimation of the EV's batteries state of charge (SoC) is still inaccurate and unreliable due to the complex and nonlinear characteristics of the batteries. To tackle the problem, this paper presents a novel real-time mixed SoC estimation algorithm for the EV's lithium-ion batteries for implementation in an advanced driver assistance system (ADAS). The mixed estimation algorithm combines: i) an improved Coulomb counting method (CCM) that takes into account the battery state-of-health, operating temperature, and aging effect, ii) a model-based method (MBM) that represents a real-time recursive structure of the battery, and iii) a bottom-up based method (BUBM) that takes into account a variety of the environmental conditions, traffic conditions, auxiliary loads, and driver's behavior. To validate the proposed algorithm, several laboratory tests under real-time driving cycles have been conducted on a Manganese-oxide Li-ion cell of a 2012 Nissan Leaf battery cell. Furthermore, the effectiveness of the model has been demonstrated by driving the Nissan Leaf along a selected route in Australia. The results demonstrate a great accuracy for the SoC estimation compared to previous models.

IEEE Transactions on Vehicular Technology, 2018

Most of the commercially available range indicator systems for electric vehicles (EVs) do not pro... more Most of the commercially available range indicator systems for electric vehicles (EVs) do not provide a sufficiently accurate range to destination, as the environmental factors and driver's behavior are generally not taken into account. In this paper, a real-time range indicator system is developed and implemented using online environmental data from various internet resources to estimate accurately the real-time state of charge and the remaining range for the EV while it is on the road. The estimation considers 1) the dynamic wind speed and wind direction with respect to vehicle position, 2) the probability of rain and ambient temperature, 3) the dynamic effective rolling resistance and terrain adhesion coefficient (based on the condition of the road surface), 4) the time-domain efficiency analysis of the propulsion system, 5) the online traffic conditions and auxiliary loads, and 6) the braking force distribution used in commercially available EVs. The real-time range indicator system is validated using measured data from a 2012 Nissan Leaf driven along a selected route in Australia with the maximum error of 8% for the entire route and less than 1% error at the destination.

2016 IEEE International Conference on Power System Technology (POWERCON), 2016

In this paper, a mathematical model for calculating regenerative braking (RB) energy considering ... more In this paper, a mathematical model for calculating regenerative braking (RB) energy considering the time-varying efficiency of a common electric vehicle (EV) is presented, taking into account environmental conditions. The contribution factor (CF) for estimating the impact of the RB system on the total consumed energy is calculated. To validate the calculated CF, a Blade Electron EV (Converted Hyundai Gets) was driven along a selected route in Australia and the measured CF is then compared with that of the calculated one. The results clearly demonstrate the accuracy of the mathematical model. The effect of the change in the elevation and speed limit profiles along the route on the total consumed energy using the RB system is also discussed. Finally, the estimated state of charge (SoC) of the battery is then compared with the measured SoC along the selected route.

IET Electrical Systems in Transportation, 2017

2016 IEEE Industry Applications Society Annual Meeting, 2016

This paper proposes a state-of-the-art algorithm for a real time charging recommendation for an e... more This paper proposes a state-of-the-art algorithm for a real time charging recommendation for an electric vehicle (EV) driver based on an accurate real-time range indicator system to avoid range anxiety. The charging recommendation algorithm alerts the driver when charging is deemed required for the selected route. This algorithm determines the nearest charging location obtained using GPS based on an accurate estimation of SoC at the destination and when charging determines the optimum charging time required by the battery to have sufficient energy to reach the destination. The graphical user interface (GUI) of the real-time range indicator system is also used to show the driver an accurate estimation of the remaining range to destination and the current state of charge (SoC). The results from simulations of a range of routes validate the proposed algorithm.

A precise estimation of the lithium-ion battery’s inner state, such as the state of health (SoH) ... more A precise estimation of the lithium-ion battery’s inner state, such as the state of health (SoH) and the state of charge (SoC) of the battery, is crucial for a reliable and effective performance of a battery management system in an electric vehicle. In this paper, an improved real-time model-based battery parameters estimation method using the recursive least-square algorithm with forgetting factor (RLS-FF) is proposed. Compared to the traditional methods, the proposed model yields the capability to accurately estimate the battery SoC and SoH by including the real-time variation of open circuit voltage and internal resistance of a battery, respectively. Moreover, a forgetting factor is used to capture the online parameter variations by reducing the impact of the older data to keep the model simple and suitable for EV applications. To verify the validity of the proposed model, an experimental test is carried out on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell.

2018 IEEE Industry Applications Society Annual Meeting (IAS)

The state-of-charge (SoC) in electric vehicles (EVs) is a key piece of information, which plays a... more The state-of-charge (SoC) in electric vehicles (EVs) is a key piece of information, which plays a crucial role in reducing drivers' range anxiety (fear of being stranded due to insufficient EV battery power) and also in amping up EVs uptake into the global transport market. This paper proposes a new real-time mixed SoC estimation algorithm for lithium-ion batteries used in EVs. The mixed algorithm is a combination of an improved Coulomb Counting (CC), a Model-Based (MB), and a Bottom-up (BU) methods in order to improve the accuracy of the SoC estimation. To extract the battery parameters, experimental tests have been conducted on a 2012 Nissan Leaf battery cell (i.e. 31.1 Ah Manganese-oxide Li-ion cell). The effectiveness of the proposed algorithm is then validated using the measured data from an actual driving cycle test on the battery cell. The results demonstrate a great accuracy with a maximum error of 0.15% for the SoC estimation in comparison with conventional models.

2017 IEEE Transportation Electrification Conference (ITEC-India)

Battery modeling plays a crucial role in improving the performance of battery powered systems esp... more Battery modeling plays a crucial role in improving the performance of battery powered systems especially in electric vehicle (EV) applications. To date, many state-of-the-art battery models have been proposed by researchers to improve the performance of electric vehicles. In this paper, an electric circuit based approach for electric vehicle battery model capable of capturing dynamic capacity rate effects for runtime prediction, state of charge tracking and I-V performance is proposed. To compare the results, two well-known electrical circuit based battery models are accurately modeled in MATLAB Simulink and the accuracy and the simplicity of each model are then compared with the proposed model in this paper with the emphasis on rate capacity effects for state of charge tracking and runtime prediction. To extract the battery parameters and to verify the results of each battery model, experimental tests have also been conducted on four Li-ion LGHG2 3 Ah battery cells connected in series.

IEEE Transactions on Industry Applications

A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reduc... more A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reducing range-anxiety and improving the performance of the electric vehicle (EV) battery management system. An accurate estimation of the SoC, however, has remained elusive due to the complex and nonlinear behavior of the battery. In this article, a new mixed estimation model (MEM) for the battery parameters and the SoC estimation is proposed, where the route is specified before the travel. The new MEM uses a combination of a battery power-based method (BPBM), a combined model, and a partial adaptive forgetting factor recursive least-square (PAFF-RLS) SoC calibration algorithm to make use of the best characteristics of each model to determine a better and more accurate SoC estimation. The partial adaptive forgetting factors solves the issue of the different rate changes in the battery parameters and reduces the complexity of the algorithm compared to the fully adaptive recursive models. The BPBM allows various traveling factors to be included in the model, such as the environmental conditions, the effect of auxiliary loads, and the traffic congestion. To verify the validity of the PAFF-RLS algorithm, two laboratory tests using real-time driving cycles have been conducted on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell. The effectiveness of the MEM model has been demonstrated by driving the Nissan Leaf along two selected routes in Australia. The results demonstrate the great accuracy of the proposed method for the SoC estimation, when compared with those from the previous models.