Description:FIELD OF THE INVENTION

[0001] The present invention relates to pertains to a battery state-of-charge (SoC) estimation system for accurate battery SoC estimation. The system employs quantum optimization to solve complex battery models, providing enhanced precision, reliability, and extended battery life for diverse applications.

BACKGROUND OF THE INVENTION

[0002] The innovative battery State-of-Charge (SoC) system combines quantum and classical computing to deliver unprecedented precision and efficiency, thereby extending battery life, enhancing reliability, and enabling more effective power management across various applications. A key aspect is its use of quantum optimization techniques to solve inherently complex battery models, providing robust and highly accurate SoC estimations. Furthermore, its capability to support diverse battery types makes it a universal and reliable solution, particularly for electric vehicle applications, ultimately promoting their widespread adoption by ensuring dependable battery performance.

[0003] Traditional battery State-of-Charge (SoC) estimation methods face several limitations. Simple techniques like Coulomb counting accumulate errors over time due to sensor inaccuracies and unmeasured self-discharge, leading to drift. Open Circuit Voltage (OCV) methods require long rest periods for accurate readings, making them impractical for real-time applications, and struggle with batteries exhibiting flat discharge curves (e.g., LiFePO4). Model-based approaches, while more sophisticated, often involve high computational complexity and are sensitive to model parameters, requiring frequent recalibration. Furthermore, these classical methods often lack the adaptability to handle complex battery degradation effects, varying operating conditions like temperature, and the diverse chemistries of modern batteries, limiting their precision and universality.

[0004] US10641830B2 discloses a battery's state of charge estimation apparatus includes: a charge and discharge current detection unit; a terminal voltage detection unit; an open circuit voltage method state of charge estimation unit for estimating an open circuit voltage of the battery and an open circuit voltage method state of charge; a current integration method state of charge estimation unit for obtaining a current integration method state of charge as a state variable; and an error correction value calculation unit for calculating an error correction value for correcting the current integration method state of charge. The current integration method state of charge estimation unit corrects the current integration method state of charge by using the error correction value.

[0005] US11575271B2 discloses a provides state-of-charge (SOC) and state-of-health (SOH) estimation methods of a battery pack. The SOC estimation method of the battery pack includes the following steps. First, a current resting time, a current battery temperature, and a current measured open circuit voltage corresponding to a current initial power-on time of the battery pack are obtained. Next, an SOC value corresponding to the current initial power-on time is determined according to the obtained current resting time, current battery temperature, current measured open circuit voltage, and a relational expression between an open circuit voltage, a resting time, a battery temperature, and an SOC value at predetermined different battery temperatures, so that the battery pack can be characterized according to the obtained SOC value.

[0006] Conventionally, many systems are available in the market for SoC estimation but existing devices lack precision and efficiency in SoC estimation, struggling with complex battery models and offering limited support for various battery types. This hinders universal reliability for electric vehicles and effective power management.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system offers unprecedented SoC estimation accuracy and efficiency via quantum-classical computing, extending battery life, enhancing reliability, and providing universal support for diverse battery types, especially for EVs.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of accurately estimate the state-of-charge (SoC) of a battery by combining quantum and classical computing methods for improved precision and efficiency, extending battery life, enhancing reliability, and enabling more effective power management in various applications.

[0010] Another object of the present invention is to develop a system that is capable of enhancing the accuracy of battery SoC estimation by using quantum optimization techniques to solve complex battery models, therefore providing a more robust and reliable SoC estimation that directly contributes to improved battery performance and longevity.

[0011] Yet another object of the present invention is to develop a system that is capable of support SoC estimation for various battery types, enabling broad use in electric vehicle applications, thus promoting the widespread adoption of electric vehicles by providing a universal and highly reliable SoC estimation solution.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a battery state-of-charge (SoC) estimation system that using quantum optimization for highly accurate, robust battery SoC estimation, supporting various battery types. This enables universal, reliable SoC solutions crucial for widespread electric vehicle adoption and improved battery longevity.

[0014] According to an embodiment of the present invention, a battery state-of-charge (SoC) estimation system, comprising, a quantum computing unit configured to process battery data using quantum protocols, including vibrational quantum circuits (VQC), quantum neural networks (QNN), and quantum support vector machines (QSVM), to estimate the SoC with high accuracy, a classical computing unit connected to the quantum computing unit, configured to collect real-time sensor data from a battery and pre-process the data for the quantum computing unit, a communication interface connecting the quantum computing unit and the classical computing unit to exchange data and enable hybrid quantum-classical processing, the quantum computing unit further includes quantum optimization techniques to solve nonlinear battery models, improving estimation accuracy over classical methods, the quantum computing unit dynamically adjusts its parameters based on real-time battery data, including temperature fluctuations and battery aging, to maintain accurate SoC estimation, the system is configured to support multiple battery chemistries, including lithium-ion, solid-state, and lead-acid batteries, for versatile application in electric vehicles, the quantum computing unit uses quantum probabilistic modeling to adapt to battery degradation and load shifts, enhancing long-term SoC estimation accuracy and the quantum computing unit processes high-dimensional battery data with reduced computational overhead compared to classical machine learning models.

[0015] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a block diagram depicting workflow of battery state-of-charge (SoC) estimation system.

DETAILED DESCRIPTION OF THE INVENTION

[0017] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0018] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0019] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0020] The present invention relates to a battery state-of-charge (SoC) estimation system for accurate and efficient battery SoC estimation. This system will support various battery types, providing a universal and reliable solution to promote widespread electric vehicle adoption and enhance power management.

[0021] Referring to Figure 1, illustrates a block diagram depicting workflow of battery state-of-charge (SoC) estimation system is illustrated, comprising a quantum computing unit, classical computing unit and communicate interface.

[0022] The present invention introduces a ground breaking battery state-of-charge (SoC) system that leverages the computational prowess of quantum computing, seamlessly integrated with classical computing, to achieve unprecedented accuracy and efficiency in monitoring battery performance. This hybrid quantum-classical system is designed to address the limitations of traditional SoC estimation methods, which often struggle with nonlinear battery dynamics, computational complexity, and adaptability to diverse battery chemistries. By combining quantum protocols with real-time sensor data processing, the system offers a versatile, high-precision solution for applications in electric vehicles (EVs), renewable energy storage, and portable electronics.

[0023] At the core of the invention is a quantum computing unit engineered to process battery data using sophisticated quantum protocols, including variation quantum circuits (VQC), quantum neural networks (QNN), and quantum support vector machines (QSVM). These quantum protocols exploit the principles of quantum mechanics, such as superposition and entanglement, to perform complex computations with greater efficiency than classical counterparts. The VQC optimizes parameterized quantum circuits to model intricate battery behaviors, enabling precise SoC predictions even under dynamic operating conditions. The QNN enhances this capability by mimicking neural network architectures within a quantum framework, offering superior pattern recognition for battery data analysis. Meanwhile, the QSVM leverages quantum kernels to classify high-dimensional battery data, distinguishing subtle variations in SoC with remarkable accuracy. Together, these protocols enable the quantum computing unit to tackle the nonlinear and stochastic nature of battery systems, outperforming traditional machine learning models.

[0024] Complementing the quantum computing unit is a classical computing unit, which serves as the system's interface with the physical battery. This unit is responsible for collecting real-time sensor data, such as voltage, current, temperature, and charge-discharge cycles, directly from the battery. The classical computing unit preprocesses this data to ensure compatibility with the quantum computing unit, performing tasks such as noise filtering, data normalization, and feature extraction. By preparing high-quality input data, the classical unit ensures that the quantum protocols operate on accurate and relevant information, maximizing the system's overall performance. The classical unit also facilitates post-processing of quantum outputs, translating quantum-derived insights into actionable SoC estimates for practical use.

[0025] A robust communication interface bridges the quantum and classical computing units, enabling seamless data exchange and hybrid quantum-classical processing. This interface ensures low-latency, high-fidelity transmission of sensor data to the quantum unit and returns processed results to the classical unit for further refinement or display. The hybrid architecture allows the system to capitalize on the strengths of both computing paradigms: the quantum unit's ability to handle high-dimensional, nonlinear problems and the classical unit's efficiency in real-time data acquisition and preprocessing. This synergy results in a system that is both computationally powerful and practically implementable.

[0026] The quantum computing unit incorporates quantum optimization techniques to solve complex, nonlinear battery models that classical methods struggle to address. By employing protocols such as the quantum approximate optimization protocols (QAOA), the system efficiently navigates the multidimensional parameter spaces of battery dynamics, yielding SoC estimates with enhanced accuracy. These optimization techniques are particularly effective in modeling battery degradation and electrochemical reactions, which are inherently nonlinear and challenging to predict using classical approaches.

[0027] To ensure adaptability, the quantum computing unit dynamically adjusts its parameters in response to real-time battery data. Factors such as temperature fluctuations and battery aging, which significantly impact SoC accuracy, are continuously monitored and integrated into the quantum protocols. This dynamic tuning enables the system to maintain high precision over the battery's lifespan, even as its electrochemical properties evolve. Additionally, the system employs quantum probabilistic modeling to adapt to battery degradation and load shifts, further enhancing long-term SoC estimation reliability.

[0028] The invention is designed for versatility, supporting multiple battery chemistries, including lithium-ion, solid-state, and lead-acid batteries. This adaptability makes it suitable for a wide range of applications, from EVs to grid-scale energy storage. By processing high-dimensional battery data with reduced computational overhead compared to classical machine learning models, the quantum computing unit ensures scalability and efficiency, even for large-scale battery systems.

[0029] The present invention work best in the manner, where the invention presents the hybrid quantum-classical battery state-of-charge (SoC) system, integrating the quantum computing unit with the classical computing unit to achieve unparalleled accuracy in monitoring battery performance across diverse chemistries like lithium-ion, solid-state, and lead-acid. The quantum computing unit employs quantum protocols variation quantum circuits (VQC), quantum neural networks (QNN), and quantum support vector machines (QSVM)—to process complex battery data. VQC optimizes parameterized circuits for precise SoC predictions under dynamic conditions, QNN enhances pattern recognition for battery data analysis, and QSVM classifies high-dimensional data for accurate SoC differentiation. Complementing this, the classical computing unit collects real-time sensor data (voltage, current, temperature, charge-discharge cycles), performing preprocessing tasks like noise filtering and feature extraction to ensure compatibility with quantum protocols. The robust communication interface enables seamless, low-latency data exchange between units, leveraging quantum strengths in handling nonlinear problems and classical efficiency in data acquisition. The quantum computing unit uses the quantum approximate optimization protocols (QAOA) to model nonlinear battery dynamics and degradation, dynamically adjusting parameters to account for temperature fluctuations and aging. This ensures reliable, long-term SoC estimates. Quantum probabilistic modeling further enhances adaptability to load shifts and degradation. Scalable and efficient, the system supports applications in electric vehicles, renewable energy storage, and portable electronics, outperforming traditional machine learning by processing high-dimensional data with reduced computational overhead.

[0030] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A battery state-of-charge (SoC) estimation system, comprising:

i) a quantum computing unit configured to process battery data using quantum protocols, including vibrational quantum circuits (VQC), quantum neural networks (QNN), and quantum support vector machines (QSVM), to estimate the SoC with high accuracy;
ii) a classical computing unit connected to the quantum computing unit, configured to collect real-time sensor data from a battery and pre-process the data for the quantum computing unit; and
iii) a communication interface connecting the quantum computing unit and the classical computing unit to exchange data and enable hybrid quantum-classical processing; wherein the system estimates the battery SoC by combining quantum protocol computations with classical data processing to improve accuracy and efficiency.

2) The system as claimed in claim 1, wherein the quantum computing unit further includes quantum optimization techniques to solve nonlinear battery models, improving estimation accuracy over classical methods.

3) The system as claimed in claim 1, wherein the quantum computing unit dynamically adjusts its parameters based on real-time battery data, including temperature fluctuations and battery aging, to maintain accurate SoC estimation.

4) The system as claimed in claim 1, wherein the system is configured to support multiple battery chemistries, including lithium-ion, solid-state, and lead-acid batteries, for versatile application in electric vehicles.

5) The system as claimed in claim 1, wherein the quantum computing unit uses quantum probabilistic modeling to adapt to battery degradation and load shifts, enhancing long-term SoC estimation accuracy.

6) The system as claimed in claim 1, wherein the quantum computing unit processes high-dimensional battery data with reduced computational overhead compared to classical machine learning models.