Description
Quantum Computing (QC) has the potential to revolutionize battery research by not only offering
computational advantages over classical computers but aspiring at a potential paradigm shift in
computational approaches. Potential research applications of high impact are in material
discovery, electrolyte design, reaction kinetics, molecular dynamics, and optimization of charging
algorithms.
This work aims at providing an overview of these applications but focuses on optimisation of
charging algorithms. Optimization of charging algorithms for batteries is an essential aspect of
battery management and energy storage system design. The goal is to maximize the efficiency,
performance, and lifespan of batteries while ensuring safe charging operations. Long term
scientific progress may arise by simulations of the complex physical and chemical processes,
including ion diffusion, electrochemical reactions, and thermal management but also in other
domains such as rapid testing of multiple charging algorithms, safety considerations and grid
integration. The goal of this research is to introduce an application for QC for adaptive charging
where the quantum algorithm could adapt a prohibitively large number of charging parameters in
real-time based on the battery’s current state. For example, if the battery is showing signs of
heating up or increased internal resistance, the algorithm could automatically reduce the charging
rate and voltage to prevent overheating and extend the battery's life. Conversely, when the battery
is in optimal conditions, it could allow faster charging to meet user demands. The preliminary
results point at Quantum Variational Algorithms in specific the Approximate Optimization
Algorithm (QAOA) for approximating the solutions to combinatorial optimization problems.
Quantum annealers, basic Grover's algorithm, and Quantum Adiabatic optimization are also
presented in relation to the problem of adaptive charging.
This work also stresses that QC is still in its early stages, and practical applications are limited by
the current state of quantum hardware, which may not yet offer a significant advantage over
classical computers for many optimization problems. However, as quantum technology continues
to advance, it is expected that quantum optimization algorithms will find broader application in
energy research. In order to illustrate the complexity of such developments, this work also
presents the overview of a state-of-the-art atomic system.
