Accurate estimation of the State-of-Charge (SoC) is a key function of battery management system (BMS). Accurate SoC has a direct impact on safety, efficiency and longevity of battery. The traditional methods like Amphere-hour method and kalman filter methods constrained by nonlinear dynamics. With the advent of quantum computing, several methods have emerged to solve some of these issues. The current paper provides the hybrid quantum-classical framework of the State-of-Charge estimation with Variational Quantum Circuits. The model is a hybrid of classical preprocessing and quantum feature encoding. Classical algorithms amplify the circuit to project real battery data to SoC. The approach is evaluated with CALCE lithium-ion battery data. The outcomes demonstrate proper estimation with potential improvements in the model interpretability. The model is implemented on IBM quantum simulators and real quantum hardware. This article is a spectacular advancement in the implementation of quantum computing in battery diagnostics.
Accurate estimation of the State-of-Charge (SoC) is a key function of battery management system (BMS). Accurate SoC has a direct impact on safety, efficiency and longevity of battery. The traditional methods like Amphere-hour method and kalman filter methods constrained by nonlinear dynamics. With the advent of quantum computing, several methods have emerged to solve some of these issues. The current paper provides the hybrid quantum-classical framework of the State-of-Charge estimation with Variational Quantum Circuits. The model is a hybrid of classical preprocessing and quantum feature encoding. Classical algorithms amplify the circuit to project real battery data to SoC. The approach is evaluated with CALCE lithium-ion battery data. The outcomes demonstrate proper estimation with potential improvements in the model interpretability. The model is implemented on IBM quantum simulators and real quantum hardware. This article is a spectacular advancement in the implementation of quantum computing in battery diagnostics.
Sodium-ion batteries (SIBs) are becoming progressively more prevalent as a feasible substitute for lithium-ion batteries (LIBs) due to the rising need for affordable and sustainable energy storage on a worldwide scale. Because sodium is abundant and relatively inexpensive, SIBs seem appealing. However, the intricacy of their electrochemical properties, which includes ion transport, intercalation kinetics, and thermal characteristics poses a hindrance to their broad adoption. Current available classical algorithms struggle with scalability, nonlinearity, and computational intensity of electrical vehicle (EV) battery models. This paper proposes a quantum-assisted state estimation framework tailored to enhance predictive accuracy and computational robustness of battery state estimation using a hybrid quantum-classical model. Our approach proposes utilizing simulated data derived from a physics-based pseudo-twodimensional (P2D) electrochemical model to train and validate a variational quantum circuit (VQC) designed for state of charge (SOC) and voltage prediction. Although no quantum hardware execution is performed in this study, rather this work introduces a framework and simulation-ready methodology for future deployment. The results show theoretical viability and potential for superior (hybrid) quantum performance compared to classical-only models, laying the groundwork for quantumenhanced battery management systems.
Lithium-ion batteries (LIBs) are critical in modern energy storage systems, powering everything from portable electronics to electric vehicles. However, optimizing their performance and longevity remains a significant challenge. Quantum simulation has emerged as a promising tool to model the complex electrochemical processes within LIBs, offering insights into charging mechanisms and degradation pathways th'at classical methods struggle to capture. This paper presents a systematic literature review of quantum simulation techniques applied to LIBs, focusing on charging and degradation modeling. We analyze the current state-of-the-art, identify key techniques, and discuss the potential of quantum computing to revolutionize battery research. Our findings highlight the advantages of quantum simulations in capturing quantum mechanical and quantum chemistry effects which are critical for accurate battery modeling. Trends in the literature suggest a move toward algorithm optimization, integration with classical methods, and the development of quantum-inspired techniques.
Most of the traditional approaches for state of Charge (SoC) estimation are marred with model uncertainty, sensor noise, and nonlinearities. This chapter introduces a novel quantum computing approach to SoC estimation with the Qiskit framework to map classical battery parameters onto quantum states. A parameterized Ry gate is utilized to map classical values like voltage, nominal capacity, load resistance, and time onto the rotation angles that describe the charge state of a battery. The quantum states so obtained are mapped to the Q-sphere and state vector representations, which provide some indication of the probabilistic nature of quantum estimation.The proposal provides the basis under which hybrid quantum-classical battery management systems could have performed better than traditional models under certain constraint. The chapter makes a reference to battery electrochemistry and quantum information science in casting a futuristic vision towards the application of quantum computation to smart energy networks.
Accurately predicting the lifetime of lithium-ion batteries during early charge–discharge cycles remains a significant challenge due to the nonlinear and weakly expressed degradation dynamics in the initial stages of operation. Classical machine learning (ML) models—although effective in pattern recognition—often face limitations in modeling complex correlations within small, high-dimensional datasets. To address these challenges, this study proposes a Hybrid Quantum–Classical Machine Learning (HQML) framework that integrates a Variational Quantum Circuit (VQC) as a quantum feature encoder with a Gradient Boosting Regressor (GBR) as the classical learner. The proposed approach is implemented using the Qiskit Aer simulator on the MIT Battery Degradation Dataset (124 cells, 42 engineered features). By encoding multi-source degradation descriptors (voltage, capacity, temperature, internal resistance) into Hilbert space via amplitude and angle encoding, the HQML model captures intricate nonlinear feature interactions that are inaccessible to conventional kernels. Experimental results demonstrate that the hybrid model achieves an RMSE of 93 cycles and an R² of 0.94, outperforming the best classical baseline (SVM + Wrapper selection, RMSE = 115, R² = 0.90). Furthermore, quantum observables analysis reveals interpretable correlations between entanglement strengths and physical degradation indicators. These results highlight the potential of quantum machine learning as a powerful paradigm for high-fidelity battery prognostics in the early-life regime.
Energy storage devices such as lithium-ion batteries and supercapacitors are essential for portable electronics, electric vehicles, and renewable energy systems, yet their long-term reliability is limited by capacity degradation. Accurate prediction of state-of-health (SOH), state-of-charge (SOC), and capacity retention are therefore critical for extending device lifetimes and improving safety. Classical machine learning (ML) methods including LSTMs, CNNs, and ensemble approaches have achieved success in forecasting degradation trends, but they face limitations in scalability, data requirements, and capturing complex electrochemical behaviors. Quantum computing, and in particular quantum machine learning (QML), offers new opportunities by exploiting superposition and entanglement to process information more efficiently and compactly. This review surveys recent advances in applying QML to energy storage, with a focus on CQ approaches (classical data–quantum processing), distinguishing Hybrid-CQ architectures. Comparative analyses highlight trade-offs: CQ and Hybrid-CQ models achieve higher accuracy and parameter efficiency but are constrained by noise, limited qubits, and slower runtimes on real devices. Looking forward, integrating error-mitigation strategies, benchmarking on actual quantum hardware, and embedding physics-informed modeling are critical to closing the gap between theoretical promise and practical deployment. Beyond prediction tasks, quantum approaches hold potential for dynamic charging optimization, materials discovery, and quantum battery concepts. Collectively, these developments underscore how QML can complement and eventually surpass classical ML, paving the way toward more accurate, sustainable, and efficient energy storage systems.
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.