Predictive analytics key to driving, and meeting, EV demand

Making the transition from old data models to map consumer behaviour is key to driving the uptake and subsequent rollout of EV infrastructure, writes Geoff McGrath

Despite a considerable downturn in the global automotive industry, the electric vehicle (EV) sector has, like most low carbon industries, continued to grow.  A 43% rise in global sales and renewed environmental policy commitments from governments around the world all point towards the sector making a tangible difference in the endeavour to ‘Bounce Back Better’ from the socio-economic damage caused by the pandemic.

Still, patterns of growth have been inconsistent. Europe has seen disproportionate growth compared to China and the US, and the rollout of charging points in leading markets such as the UK has faced heavy criticism owing to the risk of creating ‘charging blackspots’ in rural and less affluent areas.

Delivering for consumers

Committing to the shared ambition to ‘Bounce Back Better’ and deliver equal growth means recognising where greater innovation and greater precision are needed in meeting end-user demand. Outdated data models risk failing to account for changes in mobility that will emerge as industry recovers from the pandemic. To remain abreast of these changes, a flexible approach is needed towards analysing consumer behaviour and to prepare for how these changes will affect urban transport planning cycles.

Outdated data models risk failing to account for changes in mobility that will emerge as industry recovers from the pandemic

Usage information provided by Big Data, for example, typically relies upon points of interest such as restaurants and shopping centres, where developers can predict longer charging sessions for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). It does not, however, account for changes in long-distance travel.

Data collected over the last year and predictive analytics has already indicated that long-distance journeys by car did increase and are expected to continue to rise in line with expected growth in the popularity of domestic holidays. If we limit planning for the mass roll-out of charging points by only accounting for location data, we risk stifling growth for the industry just as it is about to enter the mass market and may miss the opportunity to meet consumer demand as their pattern of behaviour evolves. Simply put, we need to focus on resilience, not optimisation.

Building back greener

Transitioning away from outdated data models will also enable the public and private sectors to share data and modelling scenarios in real-time. Integrating charging point data early in planning cycles for essential infrastructure can enable local authorities to predict end-user demand both in the planning and operational phases of development. Real-time data modelling also means better addressing the environmental challenges posed by urban planning by integrating smarter, more flexible planning cycles that account for evolving trends made clearer through the adoption of predictive analytics technology. Delivering on our sustainable planning priorities means we must transition to more diverse data sets and think seriously about their potential to overcome challenges posed by the transition to net zero.

Real-time data modelling also means better addressing the environmental challenges posed by urban planning by integrating smarter, more flexible planning cycles that account for evolving trends made clearer through the adoption of predictive analytics technology

The new normal

Just as agile and responsive data can only enhance the potential to address evolving trends in end-user demand, so too must we understand that EVs will not be the panacea in our mission to reach net zero. Innovations in hydrogen technology look set to further disrupt the rollout of EV technology, and patterns of unequal growth are likely to remain if costs stay high. Using data flexibly and smartly to map out consumer behaviour, however, will bring us closer to adapting to market changes and prove we are serious about delivering on our sustainable development goals as we begin to define the ‘new normal’.


The opinions expressed here are those of the author and do not necessarily reflect the positions of Automotive World Ltd.

Geoff McGrath is an entrepreneur, strategist, innovator and technologist. For over eight years, Geoff was the former chief innovation officer for McLaren Applied, where he took insights from the world of Formula 1 to drive change in the wider transport sector and beyond, Geoff is now the Managing Director of data science business, CKDelta. To find out more, please visit: https://www.ckdelta.ie/

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