Description:FIELD OF THE INVENTION
The present invention relates to battery management systems, particularly in electric vehicles, and more specifically to a novel method that employs quantum computing for the precise estimation of a battery’s State-of-Charge (SoC). This invention enhances SoC prediction accuracy using quantum algorithms, optimizing energy management, battery longevity, and real-time adaptability.
BACKGROUND OF THE INVENTION
Accurately estimating a battery’s State-of-Charge (SoC) is crucial for energy storage systems. Traditional methods face challenges in handling nonlinear dynamics and computational complexity. This patent proposes a quantum computing-based approach to enhance SoC estimation accuracy, leveraging quantum algorithms for faster optimization, improved data processing, and real-time adaptability in battery management systems.
Currently, there are no commercially available products that utilize quantum computing for battery State-of-Charge (SoC) estimation. The present commercial practice relies on classical methods, including:
(a) Coulomb Counting: This method calculates SoC by integrating the battery's current over time. While straightforward, it can accumulate errors due to current measurement inaccuracies and requires periodic recalibration.
(b) Voltage-Based Methods: By measuring the open-circuit voltage (OCV) of a battery, SoC can be inferred using known OCV-SoC relationships. This method is sensitive to temperature changes and may not be accurate during dynamic loads.
(c) Kalman Filter Method: Latest techniques like the Extended Kalman Filter (EKF) combine battery models with real-time measurements to estimate SoC. EKF can accommodate changing battery conditions and provide more accurate estimations compared to simpler methods.
Commercial Practice:
At present, there are no public patents that specifying the estimation of battery State-of-Charge (SoC) using quantum computing techniques. The intersection of quantum computing within this field remains a remarkable invention.
2. In what way(s) do the presently available solutions fall short of fully solving the problem?
The present available techniques for SoC estimation faces various constraints:
(a) Computational Complexity
(b) Accuracy and Error Accumulation
(c) Unable to handle Nonlinear and Stochastic Battery Behaviour
(d) Real-Time Adaptability Challenges
Potential Advantages of Quantum Computing
• Increased optimization: Quantum algorithms are able to speedup the computational work compared to traditional techniques.
• Augmented pattern recognition: Quantum machine learning can enhance SoC estimation by uncovering the hidden correlations.
• Better handling of uncertainty: Quantum probabilistic models can improve accuracy in SoC prediction by better modelling stochastic battery behaviour.
Conclusion
Current methods do not fully solve the problem due to computational inefficiencies, error accumulation, and challenges in real-time adaptability. Quantum computing offers a promising alternative by enabling faster, more accurate, and more adaptive SoC estimation.
Feature/Aspect Previous Solutions Proposed Solution
Computational
Efficiency Classical methods (Kalman filtering, neural networks) require high computational resources, making real-time estimation challenging. Quantum computing provides exponential speedup in solving complex nonlinear SoC estimation problems.
Accuracy Coulomb counting accumulates errors; voltage-based methods are affected by temperature and load variations. Quantum Machine Learning (QML) models offer higher accuracy by processing complex battery data more efficiently.
Handling
Nonlinearity &
Stochastic Behavior Classical model require approximations, leading to inaccuracies in real world conditions. Quantum models efficiently capture nonlinear and stochastic battery behaviors, improving estimation reliability.
Adaptability to Aging
& Environmental
Variations Traditional methods require frequent recalibration to adjust for battery degradation. Quantum algorithms dynamically adapt to battery aging, temperature fluctuations, and charge-discharge patterns without recalibration.
Error Reduction Traditional approaches suffer from cumulative errors (e.g., drift in Coulomb counting). Quantum probabilistic models reduce estimation errors by leveraging quantum state superposition and entanglement.
Scalability Classical methods become inefficient as battery pack size and complexity increase (e.g., EV applications). Quantum algorithms scale efficiently with large battery packs and multi-battery systems (EVs, grid storage, aerospace).

Battery state of charge (SoC) is of paramount importance in many different applications, among them electric vehicle applications, storage systems for renewable energy, and consumer electronics. These traditional techniques such as Coulomb counting and OCV-based models and especially those based on machine learning lack in terms of precision, in computing power consumption, and have very limited applicability in presence of battery ageing and changing operating environment. The battery system's complex and unpredictable dynamics confront timehonored algorithms, causing significant error accumulation and a decrease in real-time efficiency. Quantum computing initiates a new generation of estimating SoC at much faster speed and accuracy levels. QML in combination with quantum-boosted optimization approaches can potentially go through large sets of battery datasets while being in an adaptive setting to changing circumstances. This latest invention brings on a new perspective of SoC estimation based on quantum computing methodology that is unique, more real-time effective and more adaptive to changing situations in comparison to what was available beforehand.
OBJECTS OF INVENTION:
The main objectives of this invention are as follows:
1. A quantum computing-based approach to accurate SoC estimation of the battery.
2. To improve the computational speed of SoC forecasting by employing quantum methods.
3. Enhance real-time adaptability; dynamically refine the models of SoC estimate.
4. Reduce the errors induced due to aging of the battery, temperature variation, and nonlinear behavior.
5. Design a hybrid quantum-classical architecture that is compatible with current BMS.
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.
The proposed invention integrates quantum computing techniques into battery SoC estimation to address the limitations of traditional methods. It employs quantum machine learning (QML) algorithms such as Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Support Vector Machines (QSVM) to optimize battery data analysis and improve prediction accuracy.
A hybrid quantum-classical model is implemented, wherein quantum computing performs high-dimensional computations while classical computing processes real-time sensor data. This approach enhances compatibility with existing BMS while leveraging the advantages of quantum computation.
The quantum model continuously updates based on real-time battery parameters, dynamically adapting to factors such as temperature variations, battery aging, and different chemistries. Unlike conventional machine learning models, this method efficiently processes large datasets, reducing computational overhead and improving real-time adaptability.
Additionally, the proposed invention applies quantum probabilistic models to better handle nonlinear and stochastic battery behaviors. The quantum-enhanced approach ensures robustness in SoC estimation, minimizing errors and enhancing battery longevity.
This novel technology is applicable to lithium-ion, solid-state, lead-acid, and other emerging battery chemistries. It supports integration into EVs, renewable energy systems, smart grids, and aerospace applications, making it a versatile and scalable solution for advanced energy storage management.
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.
This innovation involves the introduction of a quantum computing-based mechanism to estimate the SoC of a battery, thereby using advanced machine learning techniques in the form of QNNs, VQC, and QSVM. Real-time monitoring is facilitated by monitoring of key parameters like voltage, current, temperature, and charge-discharge cycles.
Using the hybrid approach based on quantum computing and classical computation, where sensors' readings data are captured at once in classically performed while quantum performs hard algorithms on calculating SoC accuracy, thus provides better efficiency by real-time adjustment to changing conditions of batteries rather than using mere traditional ways. This design is scalable and can be developed for various battery types, and because it easily integrates into existing Battery Management Systems (BMS), this solution is more suitable for electric vehicles, grid storage, and portable electronic devices.
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 best method of working for the present quantum computing-based SoC estimation system involves a combination of real-time data acquisition, quantum-enhanced processing, and a hybrid computational framework. The system continuously collects battery parameters such as voltage, current, temperature, and charge-discharge cycles using high-precision sensors integrated into the battery management system (BMS). This data is then preprocessed and structured for further analysis.
The hybrid quantum-classical computational model processes the collected data efficiently. Classical computing handles real-time sensor data acquisition and initial preprocessing, ensuring compatibility with existing BMS. Meanwhile, quantum computing is employed for high-dimensional optimization and pattern recognition, allowing for more accurate SoC estimation. Quantum machine learning algorithms, including Variational Quantum Circuits (VQC) and Quantum Neural Networks (QNN), process the data to predict battery behavior and dynamically update the SoC values.
Once the quantum processing stage is complete, the refined SoC estimations are transmitted back to the BMS, enabling real-time monitoring and adaptive control of the battery system. This continuous feedback loop ensures that battery performance is optimized under varying conditions such as temperature fluctuations, aging effects, and different charging/discharging rates. The integration of quantum probabilistic models further enhances prediction accuracy by accounting for uncertainties in battery behavior.
The system is designed to be scalable across different battery chemistries, including lithium-ion, solid-state, and lead-acid batteries, making it suitable for a wide range of applications, including electric vehicles, renewable energy storage, and aerospace. By leveraging quantum computing’s superior computational power, the proposed method significantly reduces estimation errors, improves battery longevity, and enhances overall energy efficiency.
This innovative approach ensures that the SoC estimation system operates with high accuracy, adaptability, and efficiency. The hybrid integration of quantum and classical computing enables seamless deployment within current battery management infrastructures, paving the way for the next generation of energy storage solutions.
The present invention brings forward a novel approach of using quantum computing for precise estimation of the battery State-of-Charge (SoC). With its improved computational powers, quantum systems would help to better estimate the accuracy, efficiency, and reliability in the SoC. The new technology is envisioned to be widely used in all applications of electric vehicles, renewable energy storage, and portable electronic devices for ensuring the best battery performance and management.
Key Features of the Present Invention
1. Quantum Algorithm for SoC Estimation
• This invention employs cutting-edge quantum algorithms like Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Support Vector Machines (QSVM).
• Quantum optimization techniques improve the accuracy of SoC estimation by efficiently solving complex nonlinear battery models.
2. Quantum-Classical Hybrid Model
• A hybrid approach is implemented, where quantum computing is used to perform high-dimensional computations while classical systems handle real-time sensor data acquisition and preprocessing.
• This ensures compatibility with existing Battery Management Systems (BMS) while leveraging the advantages of quantum computation.
3. Enhanced Prediction and Adaptability
• The quantum model dynamically updates its parameters based on real-time battery data, adapting to factors like temperature fluctuations, aging effects, and different battery chemistries.
• Unlike conventional machine learning models, the quantum system can process vast datasets with reduced computational overhead.
4. Application to Various Battery Technologies
• The proposed invention is applicable to lithium-ion batteries, solid-state batteries, lead-acid batteries, and other emerging battery chemistries.
• It is suitable for integration into EVs, smart grids, portable electronic devices, and aerospace applications.
Benefits Over existing Methods
Higher Accuracy: Quantum machine learning models improve the accuracy of SoC estimation over conventional methods.
High-performance computation: it allows quantum computation to solve for complex battery dynamics much more swiftly than its equivalents.
Better Real-time Performance: With this strategy, real-time observation is much stronger and highly advisable for a battery management system (BMS).
Better Handling of Nonlinearities: Quantum-based methods can capture intricate electrochemical reactions in a battery more accurately than classical models.
This is one of the significant breakthroughs for using quantum computers in battery SoC estimation. It has been able to eliminate the traditional problems associated with earlier methods and enhances accuracy, efficiency, and adaptability in modern energy storage applications.
The proposed invention reveals a quantum computing-centric approach to estimating the State-of Charge (SoC) of batteries, which successfully overcomes the limitations of traditional estimation methods. The major novel aspects include:
(a) Quantum Computing for SoC Estimation
• This breakthrough uses quantum algorithms to interpret battery state information, unlike existing SoC estimate techniques that use deterministic mathematical models (e.g., Coulomb, Kalman filtering, neural networks).
• QML models, including QNN and QSVM, gain accuracy due to pattern recognition and approximation of non-linear functions.
Hybrid Quantum-Classical Battery Management System
• The invention suggests a hybrid architecture wherein quantum computing tackles intricate SoC estimations while classical processors oversee real-time sensor data collection.
• This synergy guarantees practical viability, as near-term quantum hardware (NISQ devices) can seamlessly integrate into current battery management systems.
(c) Enhanced Computational Performance and Scalability
• Quantum algorithms provide classical machine learning models in processing high-dimensional battery data.
• The system can handle large datasets and multi-parameter optimizations, thus being applicable for electric vehicles, grid storage, and portable electronics.
(d) Robustness to Battery Degradation and Environmental Changes
• Legacy methods of SoC estimation take into account battery degradation and environmental changes.
• The new invention utilizes quantum probabilistic modeling to enhance long-term accuracy through dynamic responses to battery health degradation, temperature changes, and load shifts.
(e) New Method for Managing Nonlinear and Stochastic Battery Behavior
• Traditional methods are based on approximations and simplifications that may cause inaccuracies in SoC estimation.
• This invention applies quantum-enhanced optimization to capture the stochastic and nonlinear nature of battery activity with reduced estimation errors.
This innovative technology is the first to apply quantum computing to estimate the SoC of batteries. It is superior compared to the classical estimation methodologies currently used by taking advantage of quantum algorithms, hybrid quantum-classical systems, and more flexibilities toward real-world battery conditions. Its uniqueness lies in superior processing efficiency, accuracy, and adaptability-dynamic-that establish it as a transformative solution for next-generation battery management systems.
, Claims:1. A quantum computing-based method for estimating the State-of-Charge (SoC) of a battery, comprising the collection of battery parameters, quantum machine learning processing, and a hybrid quantum-classical computational framework;
wherein the quantum algorithm employs Variational Quantum Circuits (VQC) for solving nonlinear battery models;
wherein Quantum Neural Networks (QNN) are utilized for pattern recognition and predictive analysis;
Quantum Support Vector Machines (QSVM) are applied for battery anomaly detection and classification.
2. The method as claimed in claim 1, wherein a hybrid quantum-classical computational approach is used to optimize real-time battery monitoring.
3. The method as claimed in claim 1, wherein real-time sensor data is continuously collected and processed to refine the quantum-enhanced SoC estimation model.
4. The method as claimed in claim 1, wherein quantum probabilistic models dynamically adjust to battery aging and environmental conditions.
5. The method as claimed in claim 1, wherein the system is designed for integration into electric vehicle battery management systems.
6. The method as claimed in claim 1, wherein the system is applicable to various battery chemistries, including lithium-ion, solid-state, and lead-acid.
7. The method as claimed in claim 1, wherein the quantum computing-based SoC estimation method improves battery longevity and energy efficiency.
8. A system for estimating the State-of-Charge (SoC) of a battery, comprising a quantum computing module, real-time sensor data acquisition, and a hybrid computational processing unit for SoC estimation;
wherein the quantum computing module includes quantum machine learning algorithms for processing battery data and optimizing estimation accuracy;
wherein the hybrid computational processing unit integrates quantum and classical computing elements to enhance real-time adaptability and efficiency.