The roll out of electric vehicles (EVs) to the mass market has been a perennial challenge we have looked to overcome to meet our net zero emissions target by 2050. With most globalised economies committed to phasing out fossil fuel-powered vehicles by 2030 and continued innovation in battery engineering, the opportunity to drive the EV market and build on sustainability commitments is now clearer than ever.
And yet, patterns of market growth continue to be inconsistent. The European Union (EU) has seen disproportionate levels of EV growth compared to the likes of China and the USA, while the rollout of charging points in leading markets such as the UK continues to face criticism over the risk of creating charging blackspots in rural and less affluent areas. Without the right data to drive the global EV rollout, the market boom risks becoming inequitable, inaccessible and unsustainable.
Securing a sustainable recovery requires us to adopt meaningful, data-driven models that are responsive, agile, and capable of delivering on shifting consumer priorities. Historical data once served us well, enabling us to train models to make predictions regarding future outcomes and scenarios across a range of different industries. Shifting trends in consumer patterns of consumption and usage that have emerged out of the pandemic, however, prove that this approach is inadequate for the scale of the challenge we face. We cannot continue to rely on pre-pandemic data models in a post-pandemic world.
Getting smart with data
Predictive analytics using near real-time data can help to drive sustained growth of EV charge point infrastructure. Its main functional application is the identification of patterns found in large data sets to identify future risks and opportunities. One of its key assets is that it can help develop data systems that are able to embed intelligence through cost-efficient sensors and ubiquitous communications that can be shared openly and at pace. Historical data models are outdated insofar as they rely on monolithic systems and products that are unable to adapt to current circumstances. Predictive analytics and adaptive intelligence, however, can be integrated by developers, public sector bodies and service providers at all stages of the planning cycle to ensure that there is adequate provision for the rollout of EV charge points in towns and cities.
If we are serious about meeting our shared sustainability goals, we must move fast to adopt smart data models and implementation.
Integrating charge point data early in planning cycles for EV infrastructure can enable local authorities to predict end-user demand both in the planning and operational phases of development. Real-time data modelling enabled by predictive analytics also means better addressing the environmental challenges posed by urban planning by integrating smarter, more flexible planning cycles that account for evolving trends that become clearer with systems innovation. If we are serious about meeting our shared sustainability goals, we must move fast to adopt smart data models and implementation.
Unique systems software modelled around the technology can also provide Distributed Network Operators (DNOs) visibility over patterns of consumption and capacity across their entire network. Predictive analytics can therefore serve a dual function of enabling the mass scale-up of EV infrastructure without serious complications arising from distributed energy consumption. It is a must to ensure that EVs, PHEVs (Plug-In hybrid electric vehicles) and MHEVs (Mild hybrid electric vehicles) can safely enter the mass market. With the right innovations in data application, we can deliver a revolution in EV infrastructure development.
To meet the needs of consumers the focus must be on product innovation and the creation of the conditions for sustainable, long-term growth. This means transitioning away from outdated data models and recognising the need for greater innovation and provision in meeting end-user demand. Already, we have seen marked changes in intercity travel and a rise in private transport usage spurred on by the pandemic, but even these trends are subject to change in a world characterised by the levels of volatility we have seen since early 2020. Only predictive analytics and adaptive intelligence can secure the levels of flexibility the industry needs to meet real-time changes head on while tackling the longstanding problem of EV accessibility.
Traditional usage information from Big Data has historically relied on points of interest such as restaurants and shopping centres for developers to predict longer charging sessions. Changing trends in patterns of intercity travel, however, have strengthened the need case for data innovation to support the sustainable growth of commercial charge point infrastructure. We need predictive analytics and adaptive intelligence to accommodate fluctuating patterns of usage, especially as domestic holidays become increasingly popular.
Only by using real-time, robust data can we hope to tip the balance towards rapid mass adoption and affordability of electric vehicles.
Rolling out EV charge points en masse is the only way industry can mitigate the risks of creating unequal product demand and inaccessibility for less economically mobile and more rural communities. Measures to cut vehicle costs for consumers through interest-free loans and tariff cost cuts are surely to be welcomed, however they risk being ineffectual if industry is not using targeted data to get charge points built quickly and productively. Only by using real-time, robust data can we hope to tip the balance towards rapid mass adoption and affordability.
Driving the Bounce Back
It is important to recognise that just as EVs alone will not be the sole driver in our mission to reach net zero, so too predictive analytics and adaptive intelligence are not the panacea to addressing evolving trends in end-user demand. All data systems will inevitably be tested by new behavioural variables in patterns of usage and consumption that will require further innovations in modelling. Wider industry trends are also likely to have an impact. Innovations in hydrogen technology look set to further disrupt the rollout of EV technology, and there will likely be greater market disruption as unequal patterns of market growth continue.
These combined factors only reinforce the importance of adopting the right data-driven approach to planning and operations now if we are serious about meeting the critical challenge the industry faces. Adopting an intelligent and adaptive approach to predictive analytics systems can help empower developers and local authorities to aid the rollout of EVs to the mass market and prove we are serious about delivering a net zero that works for all.