Qatar Environment and Energy Research Institute (Qeeri), part of Hamad Bin Khalifa University (HBKU), and Mowasalat (Karwa), Qatar’s public transport company, have launched the Electric Bus lithium-ion battery cell testing project to improve the operating system of electric buses.
The project supports the research industry in exploring and experimenting with actual batteries that are being used in Mowasalat operations.
Mowasalat began electrifying its public transit routes earlier this year and is set to operate a fleet of eco-friendly e-buses embedded in Qatar’s public transit system during the FIFA World Cup 2022.
According to the statement, “The joint project will span from battery testing to various technical and operational aspects of the e-bus system. The first step is to develop an understanding and analysis of challenges described in similar international case studies in the field of bus electrification and techniques for problem-solving and decision-making.”
Ahmed Al Obaidly COO – Karwa
“Mowasalat (Karwa) has invested heavily in fulfilling a commitment to providing modern, eco-friendly transport solutions using the latest technological innovation. We are delighted to now have the expertise of a national research entity like Qeeri to conduct such an in-depth scientific study. The results of our efforts will have a significant role in Qatar’s sustainable development legacy.”
Dr. Marc Vermeersch, Qeeri executive director, said that, “Sustainable, green mobility, with its shift to eco-friendly transportation systems, has an important role in Qatar’s long-term sustainability strategy. By supporting the efforts of such an important stakeholder, Qeeri aligns our research and innovation with the country’s target of having fully eco-friendly public transportation by 2030.”
Commenting on the project, Dr. Veronica Bermudez, senior research director at Qeeri’s Energy Centre, stated that, “We aim to address the fundamental challenges in grid integration of EV charging infrastructure in Qatar by exploiting the synergy between innovative autonomous control schemes, real-time grid modeling, deep learning-based methods for optimal charging or discharging scenarios.”
