Featured image

Founded in 2014, enerGQmobility develops and markets low cost self-learning energy management systems to the full range of organizations from households to multinationals in all sectors of the market. Our aim is to contribute to “stop the global warming” within 5 years by licensing the technology to partners. It uses the low cost and energy saving technology to assess the performance and provide improvements in the areas of aviation, maritime, rail, and road transport.

The self-learning energy management systems of enerGQmobility make use of AI-techniques and visualize excess energy so that it can be reduced easily.

What is the goal of the project?

The goal of enerGQmobility is to achieve fuel savings in the transport industry by influencing human behaviour. We can provide any organization within the transport industry with insights that provide management, maintenance, the driver, steersman or pilot in the efficiency of the transport movement the knowledge to make well informed decisions resulting in fuel-efficiency.

What is the result of the project?

Optimizing performance and reduce fuel consumption for all transportation modes using machine learning. EnerGQmobility offers trucking companies with a data-driven approach to performance management and cargo loads to accomplish fuel savings.

Who initiated the project and which organizations are involved?

EnerGQmobility BV is a joint venture of Energy Developments Holding BV, Pond by TheRockGroup BV, and some other strategic shareholders for the purpose of offering solutions for fuel savings in the logistics and mobility industry. The goal of this new venture is to be able to combine the technical knowledge of enhanced machine learning and gamification principles of Energy Developments Holding daughter enerGQ, and the business development skills and direct access to the transport industry of TheRockGroup.

What is the next step?

Our aim is to develop an independent software solution that enables an airline, ship, train or vehicle crew to operate more fuel efficient. Key for this solution is using the impact of the human factor on decisions which can be made during the trip and which complex of trip parameters and external factors influence fuel consumption. Offering to any crew information that provides a clear indication of the actual performance, compared with similar previous trips under similar circumstances. Such insight can be provided based on the application of algorithms on anonymous historical data, to be provided by the operator.

What can other cities learn from your project?

Optimizing performance, and reducing fuel consumption of vehicles is a solution to overconsumption of natural resources worldwide. The technology being developed and applied has the potential to be used globally.