The present paper introduced a smart prognostic system of lithium-ion batteries that combines both real-time monitoring using an IoT along with learning-based predictive modelling to improve battery health status and life expectancy. With outstanding data-driven analytics together with continuous monitoring of the important operating conditions, the proposed system has the advantage of capturing multifaceted and nonlinear degradation mechanisms, which are hard to model with traditional methods. The experimental analysis proved that the framework provides a better accuracy in predicting State of Health and Remaining Useful Life in comparison with current machine learning and deep learning-based frameworks. The findings validate the fact that the access to high-resolution and real-time battery information is critical to enhancing the reliability of prognostics, especially in situations with variable load and environmental conditions. The architecture of IMMSS allows endemic deployment to distributed battery assets through a layered system architecture with robustness and scalability in computing efficacy. Moreover, forecasting the results and combining them with the decision support system enable the establishment of the timely maintenance measures that minimize a threat of the unforeseen failures and prolong battery service. In sum, the suggested framework is an effective and implements-ready solution of the next-generation battery management systems in electric vehicle and energy storage applications. The development of edge intelligence, quantification of uncertainty, and adaptive learning methods will be considered in future work to enhance the robustness of predictions and autonomically manage the health of the battery in large-scale IoT settings.
The present paper introduced a smart prognostic system of lithium-ion batteries that combines both real-time monitoring using an IoT along with learning-based predictive modelling to improve battery health status and life expectancy. With outstanding data-driven analytics together with continuous monitoring of the important operating conditions, the proposed system has the advantage of capturing multifaceted and nonlinear degradation mechanisms, which are hard to model with traditional methods. The experimental analysis proved that the framework provides a better accuracy in predicting State of Health and Remaining Useful Life in comparison with current machine learning and deep learning-based frameworks. The findings validate the fact that the access to high-resolution and real-time battery information is critical to enhancing the reliability of prognostics, especially in situations with variable load and environmental conditions. The architecture of IMMSS allows endemic deployment to distributed battery assets through a layered system architecture with robustness and scalability in computing efficacy. Moreover, forecasting the results and combining them with the decision support system enable the establishment of the timely maintenance measures that minimize a threat of the unforeseen failures and prolong battery service. In sum, the suggested framework is an effective and implements-ready solution of the next-generation battery management systems in electric vehicle and energy storage applications. The development of edge intelligence, quantification of uncertainty, and adaptive learning methods will be considered in future work to enhance the robustness of predictions and autonomically manage the health of the battery in large-scale IoT settings.
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.
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.
Given their high power density, low discharge rate, and decreasing cost, rechargeable lithium-ion batteries (LiBs) have found a wide range of applications such as power grid-level storage systems, electric vehicles (EVs), and mobile devices. Developing a framework to accurately model the nonlinear degradation process of LiBs, which is indeed a supervised learning problem, becomes an important research topic. This paper presents a classical-quantum hybrid machine learning approach to capture the LiB degradation model that assesses the loss of battery cell life from the operating profiles. Our work is motivated by recent advances in quantum computers as well as the similarity between neural networks and quantum circuits. Similarly to adjusting weight parameters in conventional neural networks, the parameters of the quantum circuit, namely, the degree of freedom of the qubits, can be tuned to learn a nonlinear function in a supervised learning fashion. As a proof of concept paper, our obtained numerical results with the battery dataset provided by NASA demonstrate the ability of the quantum neural networks in modeling the nonlinear relationship between the degraded capacity and the operating cycles. We also discuss the potential advantage of the quantum approach compared to conventional neural networks in classical computers in dealing with massive data, especially in the context of future penetration of EVs and energy storage.
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is critical for enhancing the reliability and safety of battery-powered systems, especially in electric vehicles. While recent machine learning approaches such as Transformers and recurrent neural networks have shown promising results, their deployment in resource-constrained environments is limited due to high computational costs and large model sizes. In this paper, we propose a novel hybrid quantum-classical denoising Transformer (HQDeTransformer) architecture that integrates variational quantum circuits (VQCs) and quantum-enhanced attention mechanisms into a Transformer-based framework. The proposed HQDeTransformer employs a hybrid quantum denoising autoencoder to mitigate noise in capacity degradation sequences and utilizes hybrid quantum-classical neural networks to construct lightweight multi-head attention and feedforward blocks. Experimental evaluations on the NASA and CALCE benchmark datasets demonstrate that HQDeTransformer achieves superior performance compared to conventional machine learning models and quantum machine learning models, while reducing the number of trainable parameters. Furthermore, the use of angle-amplitude quantum embeddings and deeper quantum layers contributes to enhanced generalization and expressivity. These results highlight the potential of hybrid quantum-classical models for lightweight, accurate, and noise-resilient RUL prediction in practical battery management systems.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
Predictive maintenance (PdM) functions as a proven industrial 4.0 element which enables organizations to detect issues ahead of time while minimizing system downtimes and improving their maintenance procedures. The predictive maintenance success of Random Forest (RF), Gradient Boosting Machines (GBM) and Deep Neural Networks (DNNs) remains limited by their high computational costs and feature selection inefficiencies and difficulties in scaling for real-time industrial applications. New Quantum Machine Learning (QML) advancements manage to improve predictions through quantum superposition methods alongside kernel transformations and entanglement mechanics for better efficiency. The research introduces Q-PdM methodology that combines QSVM with VQC and Q-DNN for device failure detection in industrial systems through predictive maintenance. The research introduces Q-CBAM as a metric that enables financial feasibility evaluation of quantum-enhanced PdM models. The methodology demonstrates superior performance when implemented with AI4I 2020 Predictive Maintenance Dataset because it surpasses traditional prediction methods in all operational aspects including accuracy rates, processing speed and operational expense reduction. This research validates the Q-DNN model because it delivers 96.2% accuracy alongside 93.5% cost-benefit efficiency however its quantum models minimize computation demands and speed up inference time to suit industrial IoT real-time environments. The presented study enables a key connection between theoretical quantum machine learning research and industrial predictive maintenance practice leading to future quantum-driven monitoring systems for industries.