Description:FIELD OF THE INVENTION
The present invention relates to an innovative method for estimating the State of Health (SOH) of lithium-ion batteries by integrating Differential Thermal Voltammetry (DTV) with a Self-Structure Adjusting Elman Neural Network (SSA-Elman NN). This approach enhances SOH prediction accuracy and battery performance monitoring.
BACKGROUND OF THE INVENTION
Lithium-ion batteries are essential for electric vehicles (EVs) and energy storage systems (ESS), but they wear out over time due to repeated charging and discharging, temperature changes, and internal chemical reactions. This aging process reduces their capacity, efficiency, and safety. To keep batteries reliable and working well, it is important to accurately estimate their State of Health (SOH). Traditional methods rely on measuring voltage, current, and impedance, but these techniques can be expensive, less accurate, or difficult to use in real-time conditions. Incorrect SOH predictions can lead to unexpected battery failures, shorter lifespan, and poor energy management. Therefore, there is a need for a more precise, flexible, and cost-effective SOH estimation method that can provide real-time data, helping with timely maintenance and better performance.
State of Health (SOH) estimation is currently done using Battery Management Systems (BMS) from companies like Tesla, LG Chem, and Panasonic, as well as AI-based tools such as AVL, Battelle, and Bosch Battery in the Cloud. Some methods also use high-precision devices like Electrochemical Impedance Spectroscopy (EIS) from Gamry and Solartron. Most commercial approaches rely on voltage, current, and machine learning models, but Differential Thermal Voltammetry (DTV) is rarely used. This opens the door for innovation by combining DTV with an Sparrow Search Algorithm -Elman Neural Network, which could provide a faster, more accurate, and adaptable way to estimate SOH.
Existing State of Health (SOH) estimation methods lack accuracy, require extensive data, and often need battery-specific calibration, making real-time prediction challenging. Additionally, advanced techniques like Electrochemical Impedance Spectroscopy (EIS) are costly and impractical.
OBJECTIVES OF THE INVENTION
Main objective of the present invention is to develop a novel SOH estimation technique for lithium-ion batteries using Differential Thermal Voltammetry (DTV).
Another objective of the present invention is to integrate a Self-Structure Adjusting Elman Neural Network (SSA-Elman NN) for enhanced prediction accuracy.
Another objective of the present invention is to improve real-time battery health diagnostics through temperature and voltage-based data interpretation.
Another objective of the present invention is to increase the reliability and lifespan estimation of lithium-ion batteries in various applications.
Another objective of the present invention is to reduce computational complexity while maintaining high precision in SOH prediction.
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.
Differential Thermal Voltammetry (DTV) is a method used to check the health of lithium-ion batteries by analysing how voltage and temperature change during charging and discharging. As the battery works, it produces heat due to internal chemical reactions. By carefully observing these temperature changes and linking them with voltage variations, DTV helps detect early signs of battery aging, capacity loss, and internal resistance buildup. Unlike traditional methods that mainly focus on voltage or current, DTV provides a deeper understanding of battery performance by capturing small thermal variations. This makes it highly useful for monitoring battery health in electric vehicles and energy storage systems, allowing for early detection of problems and better maintenance.
Herein enclosed a method for estimating the State of Health (SOH) of lithium-ion batteries, the said method comprising the steps of:
acquiring thermal and voltage data of the battery using Differential Thermal Voltammetry (DTV);
selecting significant degradation indicators through feature optimization using Sparrow Search Algorithm (SSA); and
processing the optimized features using an Elman Neural Network (Elman NN) to predict SOH in an adaptive and memory-retaining manner.
The DTV technique captures thermal-voltage behaviour to provide insights into battery degradation beyond traditional voltage, current, or impedance methods.
The SSA algorithm is configured to minimize computational complexity by selecting only the most relevant features for SOH estimation.
The Elman Neural Network processes selected features while retaining temporal information of previous battery states for enhanced real-time prediction accuracy.
The proposed integration of DTV, SSA, and Elman NN improves the precision, efficiency, and scalability of SOH estimation for use in Battery Management Systems (BMS) of electric vehicles (EVs) and energy storage systems (ESS).
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.
Differential Thermal Voltammetry (DTV)
Differential Thermal Voltammetry (DTV) is a method used to check the health of lithium-ion batteries by analysing how voltage and temperature change during charging and discharging. As the battery works, it produces heat due to internal chemical reactions. By carefully observing these temperature changes and linking them with voltage variations, DTV helps detect early signs of battery aging, capacity loss, and internal resistance buildup. Unlike traditional methods that mainly focus on voltage or current, DTV provides a deeper understanding of battery performance by capturing small thermal variations. This makes it highly useful for monitoring battery health in electric vehicles and energy storage systems, allowing for early detection of problems and better maintenance.
Sparrow Search Algorithm (SSA)
The Sparrow Search Algorithm (SSA) is a smart technique inspired by the way sparrows search for food. In a group of sparrows, some act as leaders, finding the best food spots, while others follow and adjust their movements based on the leaders’ success. If a predator appears, the sparrows quickly change direction to stay safe. This natural behaviour is used in SSA to efficiently find the best solutions for complex problems. In battery health monitoring, SSA helps identify the most important battery parameters while filtering out unnecessary data. This reduces the amount of information a machine learning model needs to process, making predictions faster and more accurate. By choosing only the most useful data, SSA improves battery health estimation, making monitoring systems more efficient.
Elman Neural Network (Elman NN)
The Elman Neural Network (Elman NN) is a type of artificial intelligence model that is especially good at understanding time-based patterns. Unlike regular neural networks that only process current data, Elman NN has a "memory" that helps it remember past information. This feature makes it perfect for predicting battery health because battery performance changes gradually over time. By analysing past and present battery data, Elman NN can detect trends in battery degradation and provide more accurate predictions about its health. When combined with DTV and SSA, Elman NN helps create a powerful system for monitoring battery health, allowing for better diagnostics and maintenance in electric vehicles and energy storage applications.
This approach using DTV for collecting data, SSA for choosing the most important features, and Elman NN for making accurate predictions creates a strong and reliable method for estimating battery health, leading to better performance and longer battery life.
Herein enclosed a method for estimating the State of Health (SOH) of lithium-ion batteries, the said method comprising the steps of:
acquiring thermal and voltage data of the battery using Differential Thermal Voltammetry (DTV);
selecting significant degradation indicators through feature optimization using Sparrow Search Algorithm (SSA); and
processing the optimized features using an Elman Neural Network (Elman NN) to predict SOH in an adaptive and memory-retaining manner.
The DTV technique captures thermal-voltage behaviour to provide insights into battery degradation beyond traditional voltage, current, or impedance methods.
The SSA algorithm is configured to minimize computational complexity by selecting only the most relevant features for SOH estimation.
The Elman Neural Network processes selected features while retaining temporal information of previous battery states for enhanced real-time prediction accuracy.
The proposed integration of DTV, SSA, and Elman NN improves the precision, efficiency, and scalability of SOH estimation for use in Battery Management Systems (BMS) of electric vehicles (EVs) and energy storage systems (ESS).
EXAMPLE 1
BEST METHOD
The proposed method integrates Differential Thermal Voltammetry (DTV) with the Sparrow Search Algorithm (SSA) and Elman Neural Network (Elman NN) to provide a more accurate, efficient, and adaptive State of Health (SOH) estimation for lithium-ion batteries. Unlike current methods that only utilize voltage, current, or impedance-based methods, DTV records thermal-voltage behaviour, providing greater understanding of battery degradation. SSA optimizes feature selection to point out the most significant degradation indicators, minimizing computational requirements while enhancing accuracy. These optimized features are then analysed through an Elman Neural Network, which, in contrast to legacy models, maintains memory of previous battery states, enabling real-time, adaptive SOH prediction. This method gets over the challenges of existing solutions by increasing accuracy, lowering computational complexity, and being scalable for practical use in Battery Management Systems (BMS) of electric vehicles and energy storage systems.
NOVELTY:
This new integration improves precision, minimizes computational expense, and offers scalability for Battery Management Systems (BMS), electric vehicles (EVs), and energy storage systems (ESS) and is better than existing methods. 
, Claims:1. A method for estimating the State of Health (SOH) of lithium-ion batteries, the said method comprising the steps of:
a) acquiring thermal and voltage data of the battery using Differential Thermal Voltammetry (DTV);
b) selecting significant degradation indicators through feature optimization using Sparrow Search Algorithm (SSA); and
c) processing the optimized features using an Elman Neural Network (Elman NN) to predict SOH in an adaptive and memory-retaining manner.
2. The method as claimed in claim 1, wherein the DTV technique captures thermal-voltage behaviour to provide insights into battery degradation beyond traditional voltage, current, or impedance methods.
3. The method as claimed in claim 1, wherein the SSA algorithm is configured to minimize computational complexity by selecting only the most relevant features for SOH estimation.
4. The method as claimed in claim 1, wherein the Elman Neural Network processes selected features while retaining temporal information of previous battery states for enhanced real-time prediction accuracy.
5. The method as claimed in claim 1, wherein the proposed integration of DTV, SSA, and Elman NN improves the precision, efficiency, and scalability of SOH estimation for use in Battery Management Systems (BMS) of electric vehicles (EVs) and energy storage systems (ESS).