Description:FIELD OF THE INVENTION
This invention relates to Quantum Computing-Based Approach for Accurate Battery State of Health Estimation
BACKGROUND OF THE INVENTION
Accurately assessing the State of Health (SOH) of lithium-ion batteries is crucial for electric vehicles (EVs) and energy storage systems. However, existing methods, including machine learning and deep learning, face difficulties in processing large amounts of data, capturing complex battery aging patterns, and performing real-time predictions. These challenges often result in lower accuracy, high computational costs, and limited adaptability to practical applications. Despite ongoing advancements, current SOH estimation techniques struggle to provide precise, scalable, and efficient battery health monitoring solutions.
Various methods are currently used to estimate the State of Health (SOH) of lithium-ion batteries, but they often struggle with processing large datasets, capturing complex battery aging patterns, and making real-time predictions. One of the most common approaches is the Battery Management System (BMS), which monitors key parameters such as voltage, temperature, and charge cycles to assess battery health. Such firms as Tesla, LG Chem, and Panasonic combine high-end BMS technology to maximize battery performance and safety. Another common practice is machine learning-based SOH estimation, in which AI models scan battery data to forecast health and performance. Automotive manufacturers such as Nissan and Toyota, as well as firms like Bosch, apply AI-powered diagnostics to optimize battery lifespan and efficiency. Nonetheless, these methods require a lot of computational resources and data, and real-time estimation is thus difficult. Some sectors use electrochemical models like the Equivalent Circuit Model (ECM) and Electrochemical Impedance Spectroscopy (EIS), to analyse battery health based on internal chemical properties. Companies like AVL and Keysight Technologies offer such solutions, but these are computation-intensive techniques with specialized hardware. Cloud-based battery analytics is also the new buzz, utilizing real-time battery information and predictive analytics to estimate SOH. Remote monitoring and predictive maintenance features are offered by platforms like GE Digital's Asset Performance Management (APM) and ION Energy's Battery Intelligence System. Despite these advances, existing SOH estimation methods remain plagued by cost, processing speed, and scalability. Quantum computing presents a potential solution in the form of increased data processing speeds, more efficient management of intricate calculations, and improved accuracy in battery health prediction.
Estimation techniques of the Current State of Health (SOH) suffer from a number of limitations that hinder them in solving battery health monitoring problems in their entirety. Traditional machine learning algorithms struggle to duplicate the complex and nonlinear nature of battery aging. Electrochemical models require internal battery data with high resolution, which is hard to obtain under realistic usage. Numerous SOH estimation methods require considerable computational power and, therefore, are not efficient for large applications like an electric vehicle fleet or energy storage systems. Techniques such as Electrochemical Impedance Spectroscopy (EIS) require intricate calculations that raise the computational cost. Battery Management Systems (BMS) work well for individual battery packs but face difficulties in handling data from multiple batteries in large-scale networks. Cloud-based solutions provide remote monitoring but may experience delays due to data transfer limitations. Most existing methods rely on historical data, which slows down real-time decision-making and reduces predictive maintenance efficiency. AI-based models require large amounts of high-quality training data, but collecting consistent and reliable data across different battery chemistries and manufacturers remains a challenge. Additionally, some techniques need specialized hardware and frequent sensor calibration, which increases implementation costs. Due to these challenges, current SOH estimation methods struggle with accuracy, scalability, and real-time processing. Quantum computing presents a potential solution by processing large datasets more efficiently, solving complex problems faster, and improving the accuracy of battery health predictions.
The proposed quantum computing-based State of Health (SOH) estimation method offers significant improvements over traditional approaches, such as machine learning, deep learning, and electrochemical modeling. The table below highlights the key differences and advantages.
Feature Existing Methods (ML/DL/Electrochemical Models) Proposed Quantum Computing-Based Method
Computational Efficiency Requires high processing power and takes longer for large datasets Uses quantum parallelism, reducing computational load and increasing speed
Accuracy in SOH Estimation Faces challenges in identifying complex battery aging trends Quantum Machine Learning (QML) enhances accuracy by recognizing hidden degradation patterns
Real-Time Processing Slower due to sequential data processing Quantum parallel computing allows real-time SOH estimation
Optimization Efficiency Classical optimization techniques can be slow and inefficient Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing optimize predictions faster
Handling of Large Datasets Computationally demanding for high-dimensional data Quantum Principal Component Analysis (QPCA) extracts key battery health indicators efficiently
Scalability Limited by hardware and algorithm constraints Supports large-scale applications in EVs, energy storage, and smart grids
Energy Consumption High due to intensive training and computations Quantum computing provides more energy-efficient processing
Pattern Recognition Requires large datasets and extensive training Quantum Support Vector Machines (QSVMs)) improve learning fewer data points
Predictive Maintenance Limited due to slow analysis and model constraints Enables faster fault detection and proactive battery maintenance
Battery Aging Model Accuracy Struggles with nonlinear and unpredictable degradation trends Quantum Boltzmann Machines (QBM) enhances the identification of degradation patterns

SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed invention applies quantum computing to improve the accuracy, efficiency, and scalability of State of Health (SOH) estimation for lithium-ion batteries. Traditional methods often struggle with computational limitations and real-time analysis, but quantum computing provides a faster and more precise solution by using principles such as superposition, entanglement, and parallel processing. This invention integrates Quantum Machine Learning (QML), quantum optimization methods, and quantum-enhanced predictive modeling to process large-scale battery data more effectively.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed invention applies quantum computing to improve the accuracy, efficiency, and scalability of State of Health (SOH) estimation for lithium-ion batteries. Traditional methods often struggle with computational limitations and real-time analysis, but quantum computing provides a faster and more precise solution by using principles such as superposition, entanglement, and parallel processing. This invention integrates Quantum Machine Learning (QML), quantum optimization methods, and quantum-enhanced predictive modeling to process large-scale battery data more effectively.
1.Quantum Computing Methods Used
Quantum Support Vector Machines (QSVMs): QSVMs employ quantum kernels for better prediction of battery health conditions by identifying nonlinear aging patterns.
2.Quantum Algorithms for Predictive Modeling
Quantum algorithms improve predictive modeling by solving complex optimization and data processing issues. The invention utilizes
Quantum Boltzmann Machines (QBM): They improve predictive models with lesser requirements for extensive training data.
3.Quantum Optimization for SOH Estimation
Optimization is very important in improving SOH prediction. This invention employs:
Quantum Annealing: It efficiently solves battery degradation issues of high complexity in a short time, leading to more accurate predictions and improved performance.
4. Quantum-Enhanced Data Processing for Real-Time SOH Monitoring
Real-time decision-making is typically difficult due to the time-consuming data processing in conventional approaches. The proposed quantum computing technique improves this by:
Quantum Entanglement for Data Correlation: The technique enhances feature extraction by the discovery of hidden relationships in battery data, leading to more accurate predictions.
Advantages of Quantum Computing in SOH Estimation
Higher Accuracy: Quantum models better capture battery degradation behavior compared to conventional methods.
Faster Processing: Quantum computing significantly reduces computational time through parallel execution and optimized algorithms.
Scalability: The approach efficiently processes data from multiple battery systems, making it suitable for large-scale applications in electric vehicles, smart grids, and industrial energy storage.
Enhanced Real-Time Estimation: The system provides rapid and reliable SOH predictions, supporting predictive maintenance and improving battery life management.
By integrating quantum computing techniques into SOH estimation, the proposed invention addresses the limitations of existing machine learning and electrochemical models, offering a more advanced and reliable method for battery health monitoring.
NOVELTY:
The invention introduces quantum computing as a state-of-the-art approach to predicting the State of Health (SOH) of lithium-ion batteries. Unlike conventional machine learning and electrochemical approaches, the invention applies quantum superposition, entanglement, and parallel processing to improve forecasting accuracy, calculation speed, and scalability. Existing SOH estimation techniques are prone to using large datasets, high computational capacity, and complex optimization models, limiting their real-time applications. By integrating Quantum Support Vector Machines (QSVMs) to analyze battery degradation patterns faster and more accurately than traditional models. Quantum Annealing are better at forecasting by efficiently extracting optimal parameters for SOH calculation. Quantum Entanglement also achieves maximum predictability by identifying the hidden interrelationships of battery data so that fault detection can be achieved early on as well as improved management of battery life. This invention is new because it is the first application of quantum computing for SOH estimation that combines quantum machine learning, quantum optimization, and quantum-enhanced predictive modeling. Unlike traditional methods, which are marred with computation expense and sluggishness, this approach is faster, more precise, and scalable for SOH estimation. By enabling real-time battery health monitoring, predictive maintenance, and better energy management, this invention is a game-changer for electric vehicle and massive energy storage system battery health analysis.
ADVANTAGES OF THE INVENTION
1. Faster Processing: Quantum computing speeds up SOH estimation by executing multiple calculations simultaneously.
2. Higher Accuracy: Quantum algorithms detect battery degradation trends more precisely than conventional methods.
3. Scalability: The approach efficiently handles large amounts of battery data, making it ideal for electric vehicles and energy storage systems.
4. Real-Time Monitoring: Quantum parallelism enables instant SOH tracking, improving maintenance and decision-making.
5. Energy Efficiency: The method reduces computational power requirements compared to traditional machine learning techniques.
By utilizing quantum computing, the proposed invention addresses the limitations of conventional SOH estimation and delivers a faster, more accurate, and scalable solution for battery health monitoring.
, Claims:1. A technique for estimating the State of Health (SOH) of lithium-ion batteries using quantum computing, comprising: Quantum Machine Learning (QML), quantum optimization methods, and quantum-enhanced predictive modeling.
2. The technique as claimed as claim 1, wherein the technique is implemented on a hybrid quantum-classical architecture for optimal performance.
3. The technique as claimed as claim 1, wherein the QSVMs operate on a quantum processor using qubit-based parallelism to reduce training time.
4. The technique as claimed as claim 1, wherein the quantum computing platform is configured to interface with a real-time battery management system in electric vehicles.
5. The technique as claimed as claim 1, wherein the SOH estimation enables predictive alerts for maintenance scheduling and battery replacement.
6. The technique as claimed as claim 1, wherein the technique reduces computational power requirements compared to traditional machine learning techniques.