Using digital technologies to optimise electric vehicle batteries

Our Automotive Sector Lead, Tim Powell, explores how AI, large-scale modelling, and quantum computing are accelerating innovation—from smarter designs and streamlined production to sustainable second-life applications.

With mandates requiring all new cars and vans in the UK, EU and beyond to be zero-emission by 2035, the demand for battery Electric Vehicles (EVs) is surging. However, meeting this demand comes with major challenges, not just in design, development, and production, but also in consumer adoption. Concerns like range anxiety and charging times must be addressed to keep the UK competitive in electric vehicle and battery manufacturing.

While battery range has tripled in the past five years, development and testing remain bottlenecks in this fast-moving market. Digital technologies, such as large-scale modelling, AI, and quantum computing, offer powerful solutions to accelerate innovation, providing critical insights at every stage of the EV battery lifecycle.

Design: The cornerstone of battery innovation

By using large scale, complex coupled physics-based modelling and simulation, we can simulate years of battery use in mere days, providing insights into performance and degradation over time. By identifying limitations early, designers can create more resilient and robust batteries that are inherently safe. Large-scale modelling and simulation, combined with advanced data analytics and AI, enable the creation of surrogate models that accelerate access to behavioural insights. These models simplify analysis, reducing complexity and minimising the computational resources required for in-silico design testing.

Beyond optimising existing designs, we can also explore the fundamental chemistries within a battery. We can leverage digital technologies for material discovery by employing large-scale computational chemistry techniques alongside advanced deep learning models. These approaches enable us to map the physical-chemical properties to their structural-composition-performance relationships, helping to identify the most promising candidates efficiently. Implementation of an advanced computing and AI enhanced workflow helps to reduce the requirement for physical testing, leading to faster innovation.

Production: Scaling up for demand

Effective supply chain and logistics management is crucial to ensuring that the right materials are available to meet production demands efficiently. Computing can optimise logistics routing, ensuring materials arrive precisely when and where they are needed. Meanwhile, AI driven uncertainty quantifications can model how global events could affect supply chain.

With the rapid growth of demand, comes the building of gigafactories to produce the quantity of parts required. We need to ensure that these factories are being built in an efficient and optimal way. Unlike traditional automotive manufacturing, the EV battery industry lacks decades of experience in optimising factory layouts. We need to evaluate how we can learn from historic factory layouts and what the requirements are for current battery manufacturing to achieve scalability and efficiency.

A key innovation in production is the “battery passport,” a digital system that tracks a battery’s lifecycle from creation to disposal. A physical battery’s life starts at the production phase, and there are considerations as to whether data should be included about the production of the materials that make up a battery pack. By securely storing data on material sourcing, manufacturing processes, and performance metrics, this tool ensures transparency and traceability. Such data is invaluable for optimising production and ensuring compliance with environmental regulations.

In use: Enhancing performance on the road

Once deployed, EV batteries must deliver reliability, efficiency, and user-friendly experiences. Advanced Battery Management Systems (BMS) are central to this phase. Equipped with integrated sensors and enriched by historical data, next-generation BMS’s can leverage machine learning to adapt to real-world conditions. For example, a battery in a car driven in Iceland will face vastly different challenges than the same model battery and car operating in southern Spain. Smarter BMSs can optimise performance based on these conditions, ensuring longevity and efficiency.

Where, when, and duration for charging are important questions for the widespread adoption of EVs. Through data analytics, modelling, simulation, and AI we can tackle the EV infrastructure from in terms of both vehicle routing and charging location. By using travel patterns, current EV driver behaviours, market conditions, and proximity to other services, AI can be used to show locations where investments should be made to support the growing EV charging market. Through these techniques, we can design a dynamic charging infrastructure with optimal charging stations and schedules based on real-time traffic and energy demand, utilising data analytics to ensure that charging is convenient and efficient.

Second life: Promoting sustainability

The end of an EV battery’s life in a vehicle does not mean the end of its usefulness or the value of its materials. However, this is not a challenge that can be postponed for another decade—it must be addressed now through proper planning and innovation. Second-life applications, recycling, and the promotion of a circular economy are essential to ensuring sustainability.

On average, EV batteries become unsuitable for use in electric vehicles after approximately eight years or 100,000 miles. However, they still retain around 75% of their original capacity, making them valuable for less demanding applications. In some cases, EV batteries may not be viable for second-life use and must be assessed for recycling to recover and repurpose their raw materials.

Battery passports play a pivotal role in this phase. By storing detailed performance and usage data, these systems enable precise assessments of a battery’s suitability for second-life applications. On a larger scale, aggregating data from multiple passports can inform industry-wide strategies for recycling and reuse, maximising resource efficiency and minimising waste.

Looking ahead

The journey toward a zero-emission future hinges on overcoming the challenges of EV battery development. Digital technologies are integral in this endeavour, offering tools to innovate faster, optimise production, and enhance sustainability. By embracing these technologies, the industry can deliver longer-lasting, more efficient batteries while mitigating environmental impacts.

The examples outlined here are just the beginning. They demonstrate the potential of digital technologies to revolutionise every stage of the battery lifecycle. As the industry invests in these tools, it can unlock new discoveries, accelerate progress, and drive a sustainable future for EVs.