Vehicle manufacturers are beginning to express great interest in quantum computing since it shares synergies with the paradigm shifting taking place across the automotive industry with the proliferation of part-electric or fully electric vehicles (in addition to the demand for self-driving autonomous vehicles). The use of quantum computing is particularly pertinent regarding improvements in battery chemistry with this placing upper limits on electric vehicle driving range, mass, and ultimately deeper market penetration across all mobility sectors overall.

With the adoption of quantum computing, vehicle and battery manufacturers can study variations in battery chemistry and structure at faster than real-time whilst being exponentially faster at producing more precise results compared with classical computing. This demonstrates a marked difference in battery technology development that is currently limited to battery modelling techniques that are incredibly slow and require significant numbers of trial-and-error experiments. Furthermore, battery energy flow optimisations will become more complex in the future due to the plethora of information available and the suite of battery materials used. Classical computing cannot provide detailed optimisations in the short timeframes required; hence the reason for leveraging the power of quantum computing.

How can Quantum Computing Help?

Since quantum computing is based on quantum physics rather than algorithms and mathematics, it can process instructions and produce data significantly quicker than traditional computing. Furthermore, quantum computing, rather than using ones and zeros as with traditional computing, uses quantum bits, also known as ‘qubits,’ which are orders of magnitude quicker at processing commands. This allows numerous commands to be processed in parallel, again reducing time taken to complete a set of instructions compared with classical computing.

Quantum computers are naturally more suited for modelling molecular behaviour of batteries, since both systems are fundamentally governed by quantum mechanics and transport properties. Therefore, since quantum computers can model chemistry more efficiently and faster than classical computers, they naturally find favour for in-depth investigations into battery chemistry. Additionally, quantum computing offer advantages in areas such as 3D simulation modelling that has historically been extremely slow; particularly with regards to resolving complex Computational Fluid Dynamics (CFD) or Large Eddy Simulation (LES) problems.

Innovations in Quantum Computing for EV Battery Design and Development

In early 2022, Hyundai partnered with quantum computing start-up IonQ to investigate how quantum computers may be used to construct improved batteries for electric vehicles. The goal of this was to develop the largest battery-chemistry model ever run on a quantum computer. This system will be used to study and simulate the structure of lithium compounds, including lithium oxide in lithium-air batteries. Presently, there is great interest in lithium-air batteries as they have a specific energy density close to that of gasoline (40.1MJ/kg vs. ~43MJ/kg). Hyundai and IonQ have therefore used quantum computing to develop new variational quantum eigensolver algorithms that are optimised for the study of lithium chemistry. This partnership is central to Hyundai’s target of selling 560,000 electric vehicles annually by 2025 whilst introducing up to 12 new fully electric models.

In late 2021, Toyota, Japan, in collaboration with QunaSys, Japan, announced that it will use quantum computing to help build the next generation of electric vehicle batteries. Simulations will be used to assess the characteristics and suitability of various materials to provide more energy dense, robust, and safer batteries. The Density Functional Theory (DFT), which represents a material’s electric structure, will be used as the basis for this study, with quantum computing providing the computational power to implement this investigation. The quantitative algorithms used can precisely compute the behaviour of batteries, allowing for a realistic simulation of the dynamic behaviour of diverse functional materials at faster than real-time.

In 2018, Volkswagen entered partnership with Google to investigate how quantum computing could shape battery chemistry to achieve high energy density with improved safety and robustness. This research was established at VW’s CODE Lab in San Francisco, USA, and investigated how newly developed algorithms could be used to replicate the structure of electrified vehicle batteries. Early successes included precisely simulating molecules such as lithium-hydrogen and carbon chains. According to VW, their quantum computing solution will be able to simulate full battery packs, producing a battery design which will be a configurable chemical blueprint ready for production.

PsiQuantum partnered with Mercedes-Benz in 2022 to create a quantum algorithm that simulates the effects of fluoroethylene carbonate, a popular electrolyte addition that boosts battery performance. Their most current research focused on how fault-tolerant quantum computing could be used to optimise battery design. It was only through use of quantum computing that new optimal battery configurations related to battery chemistry were discovered. Use of quantum computing contributed to reduced resource overheads and a more manageable application compared to classical computing.

IBM has partnered with several automakers including Mercedes-Benz to help build the next generation of batteries for electric vehicles using quantum computing. It has partnered with Daimler to use the power of quantum computing to develop solid-state batteries for EVs. The company has also partnered with Mitsubishi Chemical to develop Lithium air batteries using quantum computing.

The Future of Quantum Computing for EV Battery Design and Development

Beyond the chemical analysis of battery chemistry for more efficient and energy dense battery materials, quantum computing can be utilised for investigations into fuel cell technologies and material durability. The latter is important for membranes within fuel cells and battery anode materials such as silicon, that shows great promise but is ultimately hindered by its poor durability during charge/discharge cycles.

Furthermore, quantum computing can be used for optimisation studies regarding battery charge/discharge cycles and solid-state batteries; the latter seen as the next major breakthrough required for widespread implementation of electrified mobility.

In summary, quantum computing is a key ingredient in unlocking the potential of batteries for use in the mobility sector. However, unless battery energy density, durability, and safety can be improved, batteries will be limited to only the light to medium duty vehicle sectors and micro-mobility. Furthermore, if continuing along the complete electrification pathway (without considering other carbon neural or zero carbon energy generation technologies), an ideal use of quantum computing would be to deploy it to enhance the solid-state battery market, since these types of batteries have the potential to help phase-out liquid electrolyte batteries in the near to midterm future.