**EV Assist -- Feasibility Study of Vehicle-User-Network Optimisation**

The aim of the project is to assess the technical feasibility and commercial potential of "EV Assist", a personalised travel assistant that will use artificial intelligence and machine learning to increase use of electric vehicles and infrastructure.

Every car that is replaced with an electric vehicle contributes a CO2 saving of around 133.1g/km. Lower running costs, less maintenance, a lower carbon footprint and cleaner air all add up to significant social, economic, and environmental benefits. But the EV industry is struggling to achieve the necessary adoption to realise these benefits.

'Range anxiety' is at the route of the EV paradox. A perceived lack of access to charge points among would-be EV users, discourages them from switching from traditional combustion engine vehicles, with their established fuel supply chain. This could be overcome with additional charge-point infrastructure but currently operators have no means of capturing granular demand, which is key to calculating return on investment.

The UK Government is currently picking up the bill, offering tax incentives, congestion and clean air zone concessions to drive EV adoption. This is costly and unsustainable.

EV ASSIST will be designed to enable real-time information exchanges between electric vehicle owners and charge point infrastructure operators regarding near-term charging requirements and the availability of charging infrastructure at any given time.

The successful realisation of EV ASSIST can improve the overall customer experience of travelling by EV, directly addressing range anxiety, and helping operators extract maximum value from their charging assets by optimising utilisation per customer. In combination, these EV ASSIST enabled outcomes can make a significant contribution to supporting the UK's electric transition over the next decade.

A 6-month research programme has been put together by partners to investigate vehicle-user-network optimisation through desktop research, EV community engagement, and Proof-of-Concept test exercises asking:

* How can information be most efficiently captured and shared by EV ASSIST?
* How to incentivise end-users to share their EV travel requirements with operators?
* How can operators most effectively exploit EV ASSIST generated demand forecasts?
* How to scale and commercially exploit EV ASSIST to best effect?
* What is the precise impact EV ASSIST can have on the UK government's "Road to Zero" strategy?

The project is led by Birmingham-based travel demand and route optimisation platform 'You. Smart. Thing.' (YST) and supported by Transport for West Midlands ("TfWM"), owner and operator of charge point assets across the region.