Electric Vehicle technology isn’t new, it was first invented in the 1830s. But with all the roadblocks getting defeated, EV is the new sunrise sector, and to boost its growth, the landscape is being impelled by data analytics. Ever since the fertile land of EVs was discovered, the inventors were constantly working based on the data they were collecting and sharing.  

Data, Data, and Data

From raw data to solutions, data analysis enables top-notch efficiency of electric-powered vehicles. EVs significantly enhance the IoT network by connecting vehicles, charging stations, smart meters, IEDs, and PMUs. They utilize sensors to collect data on user driving behaviour, battery management systems (BMS), and grid charge management. The charging and discharging patterns of EVs directly impact the functionality, security, and efficiency of the smart grid during EV Grid Integration (EVGI). Thus, effective data analytics are crucial for EVGI, green smart cities, and sustainable infrastructure. Efficient and reliable data analytics methods are particularly important for EV synchronization, charging planning, and the ability to sell surplus power back to the grid.

Collecting data generated by vehicles is essential for improving the user experience, just as apps and search engines enhance their functionality with increased user data. Automakers and electric vehicle companies are leveraging these electric insights (data) to develop and incorporate advanced technology, ensuring a smooth and futuristic driving experience as the auto industry expands. As per Counterpoint Research, in the next ten years, a minimum of 2TB to up to 11TB storage will be required for in-vehicle storage across various autonomy levels. Given the EV revolution, it is crucial for OEMs to proactively evaluate their data strategy and prioritize storage rather than considering it as an insignificant aspect.

How is data analysis contributing to the EV revolution?

Government Policies and Incentives: 

Data analysis plays a critical role in formulating government policies and subsidies for electric vehicles. It allows policymakers to analyse market trends related to EV adoption like sales figures, charging infrastructure growth, future demand, and consumer choices. Furthermore, data analysis helps assess the environmental impact (air pollution, GHG emissions, sound pollution) of EVs against their conventional counterparts. Policymakers can evaluate the economic implications of EV adoption by assessing job creation opportunities, evaluating the effects on the domestic automotive industry, and estimating the potential revenue generated from the EV industry. Such insights aid in formulating effective suitable policies, and tax benefits and even gauge the impact of their initiatives to make the necessary adjustment to optimise outcomes. 

Customer Insights:

By analysing data on consumer preferences, purchasing patterns and barriers to adoption, policymakers can better understand the factors that influence EV adoption rates. This further helps in designing policies and incentives that address specific consumer concerns, like affordability, charging infrastructure availability and range anxiety. Data analysis enables EV manufacturers to gain insights into customer behaviour from their feedback about their preferences, driving behaviour and usage patterns. This way, manufacturers can better comprehend their target audience and tailor their marketing, product development and customer support strategies accordingly.

Charging Infrastructure Planning:

Data analysis can assist in planning the deployment of charging infrastructure for EVs. By analysing data on charging patterns, renewable energy availability, population density, traffic patterns, usage, and commuting routes, stakeholders can identify high-demand areas and strategically place charging stations where they are most needed. By collecting data on charging sessions, usage patterns, and station downtime, planners can identify underutilised or congested stations, assess user satisfaction, and make informed decisions regarding infrastructure expansion, investment, maintenance, and improvements. Furthermore, it helps in understanding the charging preferences of EV owners, such as fast charging versus slow charging, and the adoption of various charging technologies – DC fast charging, and AC charging, thereby helping determine the appropriate mix of charging speeds to alleviate range anxiety and support the growth of the EV market.

Energy Grid Integration:

EVs have the potential to impact the electrical grid due to their charging requirements. Data analysis helps in optimising the management of charging infrastructure load and integrating it with the electrical grid. By analysing charging patterns, energy demand profiles, and peak usage times, planners can implement load management strategies to avoid grid strain, balance electricity supply and demand, and reduce peak-load stress. This allows for better coordination between EV charging and renewable energy generation, grid management, and demand response programs.

Range Optimization and Battery Technology: 

One of the chief concerns for EV owners is the range or driving distance the vehicle can cover on a single charge. Data analytics helps improve EV battery technology and optimize the vehicle's range. Manufacturers collect data on battery performance, usage patterns, and environmental factors to understand how batteries degrade over time and under different conditions. By analysing this data, manufacturers can develop better battery management systems, optimize charging algorithms, and enhance the overall range of EVs, addressing one of the major concerns of potential EV buyers.

Performance Optimisation and Predictive Maintenance: 

EVs generate vast amounts of data related to performance, energy consumption, and vehicle health. Manufacturers and service providers can leverage this data to monitor the performance of EVs in real-time, diagnose issues, and schedule preventive maintenance. Predictive maintenance based on data analytics further helps optimize vehicle uptime, reduce downtime, identify areas of improvement, and ensure the longevity of EV components like batteries, and motors, thereby increasing customer satisfaction.