The Electric Vehicle (EV) is not a new concept, but in recent years, attention has increased dramatically. Increased market share in the automobile industry results from EV analytics and battery technology improvements. However, hardware is not the only factor in this growth. In order to provide a complete transportation system, the modern mechatronic vehicle combines electrical storage and propulsion systems with electronic sensors, controls, and actuators. These components are tightly connected with software, secure data transfer, and data analysis. The general rise of EVs has benefited from advancements in all these fields, but data analytics is the unifying factor that connects them all.

This article will analyse how these powerful solutions might promote growth and competitive advantage, focusing on data analytics considerations in the EV space.

One could argue that the evolution of the modern electric car is the narrative of how data analysis influenced and guided the development of electrical propulsion. Unbeknownst to many, electric car technology is considerably older. The first electric automobiles were created in the 1830s, but when the storage battery was created in 1865 and improved upon in 1881, the technology was commercially practical. The early innovators were already making observations, taking notes, and devising advancements based on the information they were gathering and exchanging.

For instance, all-size electrically powered railroad locomotives were used in tunnels and mines when the poisonous fumes from combustion rendered combustion the only safe choice. Additionally, it was employed in places where energy could be produced cheaply and distributed effectively using overhead lines, including in street trolleys, urban electric buses, and interurban passenger applications. These were highly designed systems, and significant data analysis was used to optimise every aspect of their design, construction, operation, and control most effectively and economically. 

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Millions of forklifts and self-propelled pallet jacks with multiple electric subsystems or, in many cases, entirely electric components were used by industry later in the century. These lessons provide a plethora of insightful information on applying electric propulsion and control technology in challenging real-world industrial settings. This information influenced the creation of smaller, more potent, and more effective motors and battery system advancements that boosted their capacity for storage and their robustness and dependability.

All of these developments consequently accelerated the development of electric vehicles.

All of these different applications shared two characteristics:

* An electric motor drives a wheel or gear to perform work, and

* The systematic collection and analysis of data to promote value creation, cost reduction, and continual improvement.

EVs must carry their electricity aboard and convey passengers safely. Thus, EV development benefited as EV battery technology advanced in other uses. Additionally, as computers' size and weight shrank and their power and capabilities increased, they also developed practical ways to collect and use the EV's crucial data in real-time, enhancing performance and safety while enabling safe control. Modern mechatronic electronic vehicles are built on these six fundamental components: electrical storage, propulsion, data, sensors, computational power, and control.

The various EV technologies had developed to the point that they could be combined into a single car and mass production was financially feasible by the turn of the 20th century. The EV1 was the first mass-produced electric car provided by a significant American automaker and the first to be built specifically to be an electric vehicle. It was introduced by General Motors (GM) in the late 1990s. The EV1 had a favourable response from customers at first, but further research was needed to improve the technologies, legal environment, liability issues, and economic models for a more reliable car with fewer moving parts than internal combustion rivals. In the late 1990s, the Gen I and Gen II EV1 production lines were shut down, and vehicle leasing ended in 2003. There were only 1,117 EV1s made in total. However, the information this programme generated was precious.

Consumers should buy EVs today for the following reasons in particular:

* Governments are increasingly setting EV mandates.

* The cost of oil has skyrocketed.

* Consumers may benefit from a fully electric, connected, and user-friendly automobile with today's electronics and software.

* Most importantly, manufacturers can now use game-changing technology to make their vehicles more trustworthy, maintainable, and serviceable, which was not easily accessible to them in 1999: Big Data.

Big Data contains substantially more extensive and complicated collections of data than regular data sets, whether organised or unstructured, and frequently comes from new sources. These volumes of data are famously massive and challenging to manage, hence the emphasis on the word "substantially". They can be so big that they nearly choke on ordinary data transfer and processing software. Additionally, they frequently continue to expand.

One of the most crucial types of big data analytics is predictive analysis. The traits and actions of a machine, system or another object can be predicted in advance. If there were only reactive and historical data accessible, the industry would always be playing catch-up and would have no idea which direction to go in. The predictive analysis enables the EV and its supporting systems to examine user and passenger behaviour and enhance themselves to serve human requirements better. For detailed explanations of predictive analytics, you can refer to the data analytics course, and master them.  

The data scientist works alongside other engineers, technicians, and skilled professionals in today's modern automobile industry to bring today's EVs and their related systems to life. It is a very specialised talent path that is very lucrative and in high demand. It can also be highly gratifying to those who are insatiably interested.

Driving the expansion of the EV infrastructure requires predictive analytics. Terabytes of data can be produced in an eight-hour journey by an EV. There are solutions to unasked queries amongst such jumbled facts. Finding the patterns can help you choose the best questions to ask. People will pay for valuable answers if you ask them worthwhile questions.

For instance, linked car data has enormous potential for revenue generation. EVs need a global network of chargers to replenish their batteries while travelling. Installing those charging networks costs money, and only a certain amount of utilisation will allow the investment to be repaid. Revenue may also be earned if the charger is close to a complementary establishment like a convenience shop, restaurant, or dealership servicing facility. Competitors can set up their own charging infrastructure, which will drive away customers and increase the risk of EV damage from incompatible or subpar chargers. Or, even worse, they could sway consumers' brand loyalty to a rival brand of an electric vehicle.

Big data can keep a customer within their own network of chargers by calculating how far a battery will last before needing to be recharged, adding charger information and directions to an EV's GPS navigation system, automatically booking chargers before arrival, and monitoring charger states to avoid sending a customer to an inoperative charger and leaving them stranded there. When timing maintenance to coincide with a charging interval, big data may assess the vehicle's condition and recommend maintenance intervals and dealer locations, enabling the driver to perform two chores at once. While the car is charging, software upgrades may be downloaded and installed, providing a considerably faster wireless data connection.

If the charger is close to a related company, it can also obtain information on the purchasing patterns and preferences of the people on board, flashing advertisements and discounts to the driver and passengers. And if a motorist starts showing fatigue indications, hotel suggestions can be shown. Utilising big data and predictive analytics makes achieving all of these synergies and many more possible. 

Hope this article gives an insight on how data science and analytics can transform the EV manufacturing industry. Join the Learnbay’s data science course to update your knowledge of big data tools to stay ahead of the competition.