<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/4E9F6C29-FAC1-4FEE-BC60-6774C0E63862" ns1:id="4E9F6C29-FAC1-4FEE-BC60-6774C0E63862"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/2DFBFFCF-E023-4FB4-BF19-677877FF389B" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F5D547D2-1F63-4F6B-82D1-E8BB492B8E3B" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F6FD3838-6132-4A65-AE25-80F657FC212E" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F5D547D2-1F63-4F6B-82D1-E8BB492B8E3B" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2022-03-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/0092A70C-4F19-4123-9027-A25117D4AF2A" ns1:rel="FUND" ns1:start="2021-07-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10005613</ns2:identifier></ns2:identifiers><ns2:title>Hubsta fleet: planning the electric journey</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>This project aims to significantly support fleet transition to electric vehicles through the creation of a new software product, Hubsta Fleet. The project will integrate an existing charge point back-office network with a route optimisation algorithm to provide fleet operators with a web-based tool to help plan and optimise journeys as well as gaining valuable data on CO2 and cost savings to support the increased uptake of EVs within a fleet situation.

Through a collaboration with Teesside University, the project will also look to provide enhanced features such as a recalculation of route algorithms to take into account any constraints of charging (availability, time spent at a charge point).</ns2:abstractText></ns2:project>