How AI is unlocking battery technology that will power the future of electric vehicles

A seismic shift is predicted for the automotive industry that could see worldwide sales of electric vehicles surpassing 30 million by 2030. Safety, energy density and charging capability of batteries will need to improve dramatically though. Does artificial intelligence hold the key?

The stakes for the global battery market are incredibly high. Some predictions estimate that it will be worth over £250bn per year from 2025, with the possible creation of four million jobs in the EU alone. And batteries – already essential for most consumer goods – will be even more vital for widespread adoption of electric vehicles.

Currently, no automotive or battery manufacturer can claim to offer an EV battery that charges as quickly as it takes to fill the tank of a traditional fossil-fuel-based vehicle, nor can it offer the same range. The Volkswagen e-Up offers 99 miles at full charge, the Tesla Model S 100D 335 miles. However, none can be fully charged in a matter of minutes. Today, a Tesla supercharging station will take 75 minutes to reach full charge, whereas SP Group, the largest EV network in Singapore, takes only half an hour.

The potential for lithium-ion batteries to solve some of these issues is enormous. However, there are a number of challenges that prevent a rapid charge, from the need for higher energy density to pre-eminent rate performance and improved safety requirements. Overcoming issues in battery chemistry is a slow research process, largely based on an iterative process of design, experimentation and systematic trial and error. Indeed, many new advances fail before they make it to market.

In R&D facilities, cyclers gather data from battery cells every second, including performance parameters such as cell temperature, real-time resistance, operating voltage window, charge and discharge current, and swelling levels. Collecting this information simultaneously from thousands of batteries means that terabytes of data are accumulated in every experiment. As a result, the number of combinations for these materials is endless, and the number of experiments needed to test each combination equally so. Analysis using traditional statistical or manual methods is extremely challenging.

A holistic approach to employing data science in battery development could hold the key to solving such complex models. Artificial intelligence – or machine learning – can assess information and construct a mathematical model at a far quicker pace than the human brain. Systems can automatically learn and improve from experience, without being explicitly programmed.

AI’s current and potential impact across multiple industries is staggering. In manufacturing, some of the world’s largest companies are already using it with impressive results. Royal Dutch Shell’s Smart Manufacturing System uses AI to predict demand for oil, measure shortages of supply and analyse the correct mix/blends for an exact refining process. BASF and SAP claim to have automated 94 per cent of payment processing with AI.


The Ring coordinator stated that Ring had “learned to avoid” these terms as they “might confuse residents into thinking this program requires a Ring device or other system to participate or that it provides any sort of direct access to user devices and information”. A Ring representative told Gizmodo that: “Ring requests to look at press releases and any messaging prior to distribution to ensure our company and our products and services are accurately represented.”

Motherboard previously reported on the concerning terms of Ring’s contracts with US police departments, revealing, for instance, that Florida’s Lakeland Police Department is required to “encourage adoption” of Ring products as part of its partnership with the company. This includes regularly positing positive content about how Ring has been used to apprehend suspects. The agreement also requires police to “keep the terms of this program confidential”.

According to Gizmodo, Ring has a sought a close relationship with police departments, including by seeking to access to real-time 911 caller data from police forces in order to personalise crime news for the Neighbors app. Ring confirmed to Gizmodo that it has access to data such as GPS coordinates, incident time, and incident description from some of its law enforcement partners.

Motherboard also reported this week that local police departments in at least three cities have requested that Ring shares the personal data – names, addresses, and email addresses – of all customers who purchase a subsidised Ring device. A Ring representative has disputed this report, stating that Ring customers submit their own information with the knowledge that it will be used to verify their suitability for the subsidy program.

Advocacy groups such as the Electronic Frontier Foundation and Fight for the Future have accused Ring of using these law enforcement partnerships to build surveillance networks which “undermine our democratic processes and basic civil liberties”.

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