News and Insights

Part 2: The EV Ecosystem and the Thirst for Data

The Importance of Data to Fleet Electrification

This is one in a series of blog posts exploring the role of data in fleet electrification.

Data is valuable

Electric vehicle data analytics is in high demand. And that is not surprising. A key feature of the drive to electrify vehicles is the manoeuvring by key players in the EV ecosystem to generate, control and utilise data. This can take several forms across the many and differently motivated players:

  • Policyholders require usage and geospatial data to design policy directives & incentives that drive net zero adoption and charging infrastructure provision, as well as directing connected vehicle development.
  • Corporates must demonstrate their net zero compliance and the ability to report on fleet transition is an integral part of statutory reporting and schemes such as CDP, used to prove the ESG credentials of major players.
  • Vehicle OEMs use vehicle data from thousands of embedded sensors to make informed decisions regarding EV production, predictive maintenance scheduling and locking in customer value through user satisfaction and loyalty schemes.
  • Telematics Providers use tools that evaluate the real-world driving data collected by telematics devices fitted in your fleet vehicles and compare this against large datasets of real-world EV performance. This allows for an analysis of which EV model may be the best replacement for a current vehicle; how different driving styles affect achievable range; and how transitioning to EV can reduce a fleet’s carbon footprint.
  • Charge Point Operators operate back-office systems that connect charge points to energy management systems to charge intelligently, monitoring, managing and restricting the use of charging devices remotely to optimise energy consumption, i.e. “smart charging”
  • Routing Technology Providers use geospatial AI (which includes geographic information systems) to informs map-based analyses – essential in both B2B and B2C operations.
  • Finance Providers (including leasing companies) use proprietary data models to make recommendations on vehicle replacement and increasingly partner with charge point OEMs and operators to extend their offer to EV + charging infrastructure.
  • Fleet Management Companies similarly use cloud-based software incorporating numerous data points based to gain real-time visibility, historic reporting and predictive analytics to help decision-making around driver satisfaction and performance, safety and fuel consumption.
  • Warehouse & Yard Management uses software to manage data for automated asset tracking and vehicle movements.

EV Fleet Specialists such as FPS aggregate the above in producing an end-to-end solution that is coherent across the organisation and across all stakeholders, bridging the skills gap left by single focus providers or a partnered offering based on vehicle + infrastructure. Collaboration throughout the supply chain network is critical to a company’s success, but individual systems must also be able to speak together and together produce a coherent view of operations. This is achieved by unifying applications using Big Data. 

Questions fleets ask themselves, and getting to answers

The first key transition question is whether electrification is feasible and achievable for your fleet? The key considerations here are scale, geography, duty cycles, payload and energy capacity on site(s). Furthermore, can you charge your vehicles at a time and in a way that works for your operational needs? This depends on other site demands on energy and what implications this has for charging patterns (likely using smart charging) or upgrade needs. Site generation may also play into this equation.

Many players within the EV ecosystem can adeptly help to answer the first question. Simply modelling historic data to match operational patterns with vehicle range is a simple data modelling exercise. But that is not enough.

You have now selected vehicles that are physically capable of transporting goods to customers. Hopefully you have modelled vehicle capability and battery pack with charger attributes in a way that does not consume more capital outlay than is necessary as you test your transition model. What next? How do you ensure that those vehicles are charged and ready to go so that your customers’ expectations are fully met? So that your vehicles are not left stranded in the depot due to capacity unavailability or reigning back to avoid capacity breaches. So that your operation is not dependent on expensive public chargers that may not be available when or where your driver needs them and, even if they are, involves unwanted downtime.

Achieving an efficient ICE-to-EV transition is no simple task

Successful fleet electrification is not achieved by loading the depot with as many high-powered chargers as are affordable in an attempt to ‘future-proof’. A more calculated approach is required for successful transition if operational efficacy and capital efficiency is to be assured. How is this achieved so as to avoid costly mistakes or vehicle/ charger decisions that do not speak adequately to operational needs?

A fundamental and necessary mindset shift involves the realisation that EV transition is not achieved simply with vehicle and charger procurement and implementation. ICE-world was simple, EV-world is not. But it does offer up significant benefits if transition is well controlled and executed.(1) Aside from vehicle and charger choices that match operational need and minimise cost, there are multi-layered infrastructure and operations elements that need to be brought together.

Digital twins help right size solutions

This is where a further level of modelling complexity is required. Using digital twins to model big data sets allows real-time data insights to make sense of the sea of data needed to operate an EV fleet effectively and optimally with maximum uptime:

  • when to charge
  • on which charger
  • at what power
  • to fulfil which orders
  • in which vehicle
  • on which route
  • with what topology (affecting battery performance)
  • and in what weather conditions (affecting battery performance)

to maximise vehicle utilisation at minimum cost, with sufficient system intelligence to account – in real-time – for unexpected interruptions to schedules. Furthermore, past and current data can be used to “train” the model to predict need in accordance with forecast growth & activity patterns.

Analytical sophistication using digital twins is now sufficiently refined that it should provide confidence to fleet operators to move from “Just-in-case” management to “right-sized” operations. Indeed, fleets that have engaged in pilots with a single location or portion of fleet are already benefitting from the data around some of the key questions that needed answering. This precisely because they have witnessed how their operations have been efficiently and effectively electrified because of the work they have done in optimising fleet operations in pilot studies – chosen as the ‘low hanging fruit’ that can deliver quick wins and huge learning.

Fleet electrification is much more than the limited data set that underlies TCO. The wider challenge – and opportunity – cuts across energy, chargers, vehicles, site energy requirements, site energy generation assets, task schedules, order and stock management, through to performance, cost and emissions reporting. Moreover, it cuts across different time horizons: the transition plan phase; the pilot phase; the roll-out phase; right the way through to transitioned steady state/ replacement cycle. The ideal world solution is real-time data insights providing the means to iterate future planning based on fleet performance and forecast activity to refine the speed and nature of EV adoption. Digital twins help to achieve this.

In the complex EV world of stakeholder, systems and operational interdependencies, an integrated enterprise management software solution is essential to making sense of the sea of data needed to operate an EV fleet effectively and optimally with maximum uptime, thereby helping organisations to realise operational, management and infrastructure efficiencies.

Put more simply, an integrated software solution can analyse data to direct optimised operations, provide a clear view of real-life performance and better inform the EV transition roadmap – allowing companies have a clear and galvanised view of the present and future of transition with confidence and at scale to realise financial and emissions benefits faster.


(1) Switching large commercial fleets from internal-combustion engines (ICEs) to electric vehicles (EVs) could cut the total cost of ownership by between 15 and 25 per cent, according to an estimate by McKinsey and Company. FPS performance data bears out this estimate, and potentially translates into significant financial savings for fleets.