Solar Battery » Optimising smart EV fleet charging: the EFLES project is another milestone in Moixa’s success story

Optimising smart EV fleet charging: the EFLES project is another milestone in Moixa’s success story

By Mara Jun 24, 2021

Moixa is delighted to be part of the EFLES project and leverage its GridShare platform to manage EV charging and enable grid participation in flexibility services.

Always at the forefront of technological innovation, a year ago Moixa started working on EFLES – or EV Fleet-Centred Local Energy Systems – an exciting innovation project aimed at optimising smart EV fleet charging. The project is funded through the UK Government’s Industrial Strategy Challenge Fund for research and innovation; and it will run until 2022.

The aim of the EFLES project is to develop an innovative smart charging system which maximises the use of existing electrical infrastructure while incorporating vehicle telemetry and machine learning to reduce operational costs and unlock additional revenue streams via grid support activity.
To capitalise on shared expertise and successfully deliver this forward-thinking project, Moixa joined forces with other thought-leading partners, such as UK Power Network Services (UKPNS), UPS and Cross River Partnership (CRP).

EFLES has made great use of Moixa’s GridShare Software and has shown how artificial intelligence (AI) can break down the barriers to electrification for global fleet operators, by maximising the cost and carbon savings from EVs. In fact, the Camden Depot’s smart charging system is being integrated with our GridShare platform, in order to monitor and forecast energy demand as well as:

  • Optimise EV charger management within the existing grid connection limit
  • Enable participation in grid flexibility services

The EFLES project will also run a series of simulations investigating benefits of more dynamic time of use tariffs and expansion to a fully electrified fleet as well as the inclusion of an onsite Energy Storage System (ESS), roof mounted solar PV array and a private wire connection to a publicly available charging hub.

Currently, UK Power Network Services is developing a business model that will include flexibility services options, while Cross River Partnership is working on the exploitation plan and commercial strategy. Moixa has developed the local charging control system and completed lab and offsite testing. Testing at the Camden depot will begin next month, July 2021.
UPS is using the installation at Camden to test the system’s feasibility for other centres.

  • The project will also serve as a template for other fleet operators interested in:
  • Optimising energy usage and creating new revenue streams
  • Accelerating the electrification of their fleets
  • Encouraging EV take-up by improving access to rapid chargers
  • Supporting a more sustainable and resilient energy network
  • Reducing transport emissions and improving local air quality

Aiming at supporting those responsible for the evolution of our energy systems, Moixa is thrilled to be able to contribute to this pioneering project, a milestone in making the transition to net zero emissions as intelligent, fast, and efficient as possible.

Moixa joined forces with other thought-leading partners to share know-how and boost innovation:

UPS: The international logistics company lends the project its expertise in fleet management and demonstrates the project’s feasibility for its broader operation.

UK Power Network Services: EFLES lead partner is working with Moixa to integrate GridShare into the existing infrastructure and develop a business model for UPS. They previously worked with UPS to implement their current smart charging system.

Cross River Partnership: The London-based public-private partnership will develop the exploitation plan for the project and examine how it can deliver environmental and economic benefits within London and beyond.

If you are interested in learning more about Moixa’s GridShare Software and collaborating with us, please contact our business services team: