Predicting SoH in Lithium-ion Batteries using a Variational Quantum Neural Network

Accurate prediction of the State of Health (SoH) in Lithium-ion (Li-ion) batteries is critical for increasing Electric Vehicles (EV) performance and lifespan. However, capturing complex patterns remains challenging as traditional Machine Learning (ML) models struggle with non-linear and high-dimensional data. We propose a Variational Quantum Neural Network (VQNN) model that uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to predict SoH. QML applications in EVs are scarce, and VQNN addresses this gap by applying quantum computing in battery SoH prediction. Our VQNN model achieves a Root Mean Squared Error (RMSE) of 0.0346 on the NASA B0005 and B0006 battery datasets, outperforming the LSTM model by 0.0017. However, the VQNN requires a longer training time of 8021.81 s compared to the Neural Network (NN) at 4.45 s and the Long Short Term Memory (LSTM) at 9.46 s. We demonstrate that Quantum Machine Learning (QML) can be used to capture complex degradation patterns, paving the way for quantum-driven Battery Management Systems (BMS) in connected EVs. Our findings suggest that despite the computational overheads, advancements in quantum hardware could enable real-time SoH prediction, enhancing EV reliability in the transport ecosystem.