ORCID Profile
0000-0001-8137-9507
Current Organisation
Queensland University of Technology
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Electrical and Electronic Engineering | Power and Energy Systems Engineering (excl. Renewable Power) | Renewable Power and Energy Systems Engineering (excl. Solar Cells) | Applied Economics not elsewhere classified | Automotive Engineering | Industrial Electronics | Social Theory | Road And Rail Transportation | Software Engineering | Decision Making | Transport Engineering | Applied Economics | Electrical Engineering | Automotive Engineering | Marketing Management (incl. Strategy and Customer Relations) |
Energy Storage, Distribution and Supply not elsewhere classified | Information Services not elsewhere classified | Renewable energy | Electricity transmission | Road safety | Other road transport | Energy Storage (excl. Hydrogen) | Solar-Photovoltaic Energy | Energy Conservation and Efficiency in Transport | Energy distribution not elsewhere classified | Behaviour and Health | Expanding Knowledge in the Agricultural and Veterinary Sciences | Transport not elsewhere classified
Publisher: Institution of Engineering and Technology
Date: 2014
DOI: 10.1049/CP.2014.0303
Publisher: Institution of Engineering and Technology (IET)
Date: 18-06-2019
Publisher: IEEE
Date: 2002
Publisher: MDPI AG
Date: 06-02-2023
DOI: 10.3390/EN16041628
Abstract: This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine the state of charge of lithium-ion batteries, we assessed their accuracy and drawbacks. Five models were selected from various published state-of-charge estimation models, based on cell types with GRU or LSTM, and optimisers such as stochastic gradient descent, Adam, Nadam, AdaMax, and Robust Adam, with extensions via momentum calculus or an attention layer. Each method was examined by applying training techniques such as a learning rate scheduler or rollback recovery to speed up the fitting, highlighting the implementation specifics. All this was carried out using the TensorFlow framework, and the implementation was performed as closely to the published sources as possible on openly available battery data. The results highlighted an average percentage accuracy of 96.56% for the correct SoC estimation and several drawbacks of the overall implementation, and we propose potential solutions for further improvement. Every implemented model had a similar drawback, which was the poor capturing of the middle area of charge, applying a higher weight to the voltage than the current. The combination of these techniques into a single custom model could result in a better-suited model, further improving the accuracy.
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 06-2006
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 05-2016
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 12-2016
Publisher: Institution of Engineering and Technology (IET)
Date: 30-04-2019
Publisher: IEEE
Date: 06-2015
Publisher: Institution of Engineering and Technology
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2003
Publisher: IEEE
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 06-2015
Publisher: Institution of Engineering and Technology
Date: 2016
DOI: 10.1049/CP.2016.0239
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/63.737594
Publisher: Institution of Engineering and Technology
Date: 2021
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 09-2014
Publisher: Institution of Engineering and Technology (IET)
Date: 30-04-2019
Publisher: Institution of Engineering and Technology (IET)
Date: 16-04-2019
Publisher: IEEE
Date: 09-2016
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 09-2014
Publisher: MDPI AG
Date: 24-04-2018
DOI: 10.3390/EN11041022
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2003
Publisher: IEEE
Date: 09-2017
Publisher: SAE International
Date: 07-09-2005
DOI: 10.4271/2005-01-3479
Publisher: SAE International
Date: 23-06-2003
DOI: 10.4271/2003-01-2300
Publisher: Elsevier BV
Date: 2018
Publisher: IEEE
Date: 05-12-2021
Publisher: Journal of Modern Power Systems and Clean Energy
Date: 2020
Publisher: IEEE
Date: 05-12-2022
Publisher: Elsevier BV
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2015
Publisher: Institution of Engineering and Technology (IET)
Date: 26-04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2004
Publisher: IEEE
Date: 06-2015
Publisher: MDPI AG
Date: 03-02-2023
DOI: 10.3390/EN16031517
Abstract: In recent times, wireless power transfer systems have been identified as a reliable option to supply power to medical implants. Up to now, Wireless Power Transfer Systems (WPTS) have only been used to charge batteries of low-power medical implants. However, for medical implants requiring a relatively higher power, such as a ventricular assist device, which is an implanted blood pump in the patient’s abdominal cavity, an external power supply has been used. When WPTS is used for medical implants, it increases the number of required power converter stages and hardware complexity along with the volume, which tends to reduce the overall efficiency. In addition, the existence of uncertainties in WPTS-based medical implants, such as load and mutual inductance variations, can lead to system instability or poor performance. The focus of this paper is to design a WPTS to supply power to the pump motor directly through its inverter based on the requirements of the motor drive system (MDS) without resorting to an additional DC-to-DC converter stage. To this end, the constraints that the drive system imposes upon WPTS have been identified. In addition, to make a reliable closed-loop operation, a µ-synthesis robust controller is designed to make sure the system maintains its stability and performance with respect to the system’s existing uncertainties. A number of experimental results are provided to verify the effectiveness of the adopted WPTS design approach and the corresponding closed-loop controller for WPTS. Furthermore, the experimental findings for the maximum efficiency tracking (MET) approach (to minimize WPTS coil losses) and constant DC link voltage control approach are shown and compared. According to experimental results and system efficiency analysis, the former appears to perform better. The system dynamic performance analysis, on the other hand, demonstrates the latter’s advantage.
Publisher: Institution of Engineering and Technology
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 07-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Informa UK Limited
Date: 20-09-2020
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 09-10-2022
Publisher: Journal of Modern Power Systems and Clean Energy
Date: 2022
Publisher: IEEE
Date: 2004
Publisher: Journal of Modern Power Systems and Clean Energy
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Institution of Engineering and Technology (IET)
Date: 09-05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Institution of Engineering and Technology
Date: 2014
DOI: 10.1049/CP.2014.0470
Publisher: Elsevier BV
Date: 11-2019
Publisher: IEEE
Date: 11-2019
Publisher: IEEE
Date: 12-2016
Publisher: Institution of Engineering and Technology
Date: 2016
DOI: 10.1049/CP.2016.0188
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: MDPI AG
Date: 25-08-2023
DOI: 10.3390/S23177409
Abstract: IEEE 802.11ah, or Wi-Fi HaLow, is a long-range Internet of Things (IoT) communication technology with promising performance claims. Being IP-based makes it an attractive prospect when interfacing with existing IP networks. Through real-world performance experiments, this study evaluates the network performance of Wi-Fi HaLow in terms of throughput, latency, and reliability against IEEE 802.11n (Wi-Fi n) and a competing IoT technology LoRa. These experiments are enabled through three proposed network evaluation architectures that facilitate remote control of the devices in a secure manner. The performance of Wi-Fi HaLow is then assessed against the network requirements of various smart grid applications. Wi-Fi HaLow offers promising performance when compared to rival technology LoRa. This study is the first to evaluate Wi-Fi HaLow in an authentic experimental way, providing performance data and insights that are not possible through simulation and modelling alone. This work provides the basis for further evaluation and implementation of this emerging technology.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 06-2015
Publisher: Inderscience Publishers
Date: 2003
Publisher: IEEE
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 08-2019
Start Date: 2003
End Date: 2003
Funder: Australian Research Council
View Funded ActivityStart Date: 2014
End Date: 2017
Funder: Australian Research Council
View Funded ActivityStart Date: 2008
End Date: 2008
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2014
End Date: 06-2017
Amount: $169,256.00
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2004
End Date: 12-2003
Amount: $10,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2022
End Date: 08-2026
Amount: $4,282,859.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2008
End Date: 06-2009
Amount: $600,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 09-2015
End Date: 12-2018
Amount: $525,000.00
Funder: Australian Research Council
View Funded Activity