Description:TECHNICAL FIELD
[001] The present disclosure relates, in general, to a battery monitoring system, and more specifically, relates to a system and method for accurately and rapidly of monitoring the state of health (SoH) of energy storage devices, such as battery modules for electric vehicles.

BACKGROUND
[002] Batteries have become an integral part of our lives over the past couple of decades. Due to their high energy density and low self-discharge rates, Lithium-ion batteries (LIBs) are now predominantly used in consumer electronics and electric vehicles. The degradation of LIBs depends on a number of factors such as traffic, road type, temperature, driving and charging habits and is necessarily path dependent. The state of health (SoH) of the LIB is a measure of its degradation and a quick and accurate determination of the SoH is of utmost importance towards planning maintenance and continued safe operation.
[003] The challenge arises in accurately determining the state of health (SoH) of LIBs, which is crucial for planning maintenance and ensuring continued safe operation. Existing methods for assessing SoH fall into two main categories: online and offline. Online methods use empirical or data-driven approaches but face limitations in computational power and accuracy over time. Offline methods, although more accurate, are time-consuming. Thus, the existing methods suffer from limitations, as they either lack the required accuracy or demand excessive time for execution, thereby diminishing their practical usability.
[004] Therefore, it is desired to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a device that provides a quick and accurate determination of SoH, overcoming the drawbacks of existing methods by offering a more efficient and reliable approach to assess the state-of-health of Lithium-ion batteries.

OBJECTS OF THE PRESENT DISCLOSURE
[005] An object of the present disclosure relates, in general, to a battery monitoring system, and more specifically, relates to a system and method of monitoring state of health (SoH) of power storage devices, such as battery modules for electric vehicles.
[006] Another object of the present disclosure is to provide a system that enables precise monitoring of the state of health (SoH) by capturing both voltage and current data.
[007] Another object of the present disclosure is to provide a system that provides a thorough assessment of the cells, allowing for a comprehensive analysis of discharge characteristics crucial for accurate health estimation.
[008] Another object of the present disclosure is to provide a system that provides extraction of decay constants during the CV discharge phase ensuring efficiency in characterizing the cell's behavior, and contributing to a robust and accurate health assessment.
[009] Another object of the present disclosure is to provide a system that facilitates the prediction of the state of health for untested cells, offering a valuable feature for proactive maintenance and replacement strategies.
[0010] Another object of the present disclosure is to provide a system that enables rapid and accurate monitoring SOH of the energy storage devices.
[0011] Yet another object of the present disclosure is to provide a system that aids in minimizing downtime and reducing costs associated with unexpected failures, enabling timely and cost-effective maintenance interventions.

SUMMARY
[0012] The present disclosure relates to a system and method of monitoring the state of health (SoH) of power storage devices, such as battery modules for electric vehicles. The main objective of the present disclosure is to overcome the drawbacks, limitations, and shortcomings of the existing system and solution, by providing a system that monitors the state of health (SoH) of one or more cells using a battery health estimation device. The device includes a voltage sensing unit, a current sensing unit, and a microcontroller. The microcontroller receives voltage data from the voltage sensing unit, discharges cells with a fixed voltage using constant current, performs constant voltage discharge until a predefined current threshold, measures current and time in the constant voltage phase, extracts decay constants, trains a computational model using these constants, and predicts SoH for untested cells based on the trained model.
[0013] The present disclosure relates to a system to monitor the state of health (SoH) of one or more cells. The system includes the battery health estimation device coupled to one or more cells to monitor the state of health (SoH). The battery health estimation device includes a voltage sensing unit configured to detect voltage of the one or more cells. A current sensing unit configured to measure and modulate current drawn or provided by the one or more cells and a microcontroller coupled to the voltage sensing unit and the current sensing unit.
[0014] The microcontroller is configured to receive, from the voltage sensing unit, the detected voltage of one or more cells. Discharge one or more cells by a fixed voltage value using constant current (CC) discharge. Perform constant voltage (CV) discharge until the current reaches a predefined threshold. Measure the current and time in the CV discharge phase. Extract decay constants from the measured current and time in the CV discharge phase. Train a computational model by correlating the SoH of the one or more cells with the extracted decay constants and predict the SoH of untested cells using the trained model.
[0015] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0017] FIG. 1A illustrates an exemplary view of the system to monitor state of health (SoH) of power storage devices, in accordance with an embodiment of the present disclosure.
[0018] FIG. 1B is a high-level flow chart illustrating the working of the system, in accordance with an embodiment of the present disclosure.
[0019] FIG. 1C illustrates an exemplary flow chart of the system, in accordance with an embodiment of the present disclosure.
[0020] FIG. 1D is a graphical representation depicting the relationship between current and time employed for the purpose of extracting decay constants, in accordance with an embodiment of the present disclosure.
[0021] FIG. 1E a graphical representation is disclosed, visually illustrating the application of a trained regression model that establishes a correlation between the state of health (SoH) and the decay constants, in accordance with an embodiment of the present disclosure.
[0022] FIG. 2A illustrates a graphical view of the database used for characterizing current decay during constant voltage (CV) discharge, in accordance with an embodiment of the present disclosure.
[0023] FIG. 2B illustrates an exemplary graphical view used to determine the current decay constants from the experimental current decay curve, in accordance with an embodiment of the present disclosure.
[0024] FIG. 2C illustrates an exemplary graphical view plot of the sum of decay constants and SoH obtained from the database, in accordance with an embodiment of the present disclosure.
[0025] FIG. 2D illustrates an exemplary graphical view of testing a new cell under CCCV discharge conditions, in accordance with an embodiment of the present disclosure.
[0026] FIG. 2E illustrates an exemplary graphical view of measurements for three new cells, in accordance with an embodiment of the present disclosure.
[0027] FIG. 3 illustrates an exemplary flow chart of the method for monitoring the state of health (SoH) of one or more cells, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION
[0028] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0029] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0030] The present disclosure relates, in general, to a battery monitoring system, and more specifically, relates to a system and method of monitoring the state of health (SoH) of power storage devices, such as battery modules for electric vehicles. The system monitors the state of health (SoH) of one or more cells using a battery health estimation device. The device includes a voltage sensing unit, a current sensing unit, and a microcontroller. The microcontroller receives voltage data from the voltage sensing unit, discharges cells with a fixed voltage using constant current, performs constant voltage discharge until a predefined current threshold, measures current and time in the constant voltage phase, extracts decay constants, trains a computational model using these constants, and predicts SoH for untested cells based on the trained model.The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0031] The term “CCCV discharge” as used herein refers to constant current constant voltage discharge, which is a charging or discharging method commonly used in battery management systems.
[0032] The term “constant current (CC) phase” as used herein refers to a constant current applied to the battery. This means that a fixed amount of electric current is supplied or drawn from the battery until a predefined voltage level is reached.
[0033] The term “constant voltage (CV) phase” as used herein refers to the voltage across the battery terminals being held constant, and the charging or discharging current gradually decreases.
[0034] The advantages achieved by the system of the present disclosure can be clear from the embodiments provided herein. The system is configured for precise monitoring of the state of health (SoH) through the capture of both voltage and current data. This system offers a thorough assessment of cells, enabling a comprehensive analysis of discharge characteristics crucial for accurate health estimation. Additionally, the system provides the extraction of decay constants during the CV discharge phase, ensuring efficiency in characterizing the cell's behavior and contributing to a robust and accurate health assessment. With a focus on proactive maintenance, the present disclosure facilitates the prediction of the state of health for untested cells, offering a valuable feature for strategic maintenance. This proactive approach aids in minimizing downtime and reducing costs associated with unexpected failures, thereby enabling timely and cost-effective maintenance interventions. The description of terms and features related to the present disclosure shall be clear from the embodiments that are illustrated and described; however, the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents of the embodiments are possible within the scope of the present disclosure. Additionally, the invention can include other embodiments that are within the scope of the claims but are not described in detail with respect to the following description.
[0035] FIG. 1A illustrates an exemplary view of the system to monitor the state of health (SoH) of power storage devices, in accordance with an embodiment of the present disclosure.
[0036] Referring to FIG.1A, a system 100 to monitor the state of health (SoH) of power storage devices. The power storage devices can be batteries or cells for electric vehicles, electronic devices and the like. The system 100 can include a battery health estimation device 102 coupled to the battery 104. The battery health estimation device 102 can be coupled to terminals of the battery 104 to monitor the state of health (SoH). The battery health estimation device 102 can include a voltage sensing unit 106, a load and current sensing unit 108 (also referred to as current sensing unit 108, herein), a microcontroller unit 110 and a machine learning engine 112.
[0037] In an exemplary embodiment, the battery pack or cells 104 can be Lithium-ion batteries (LIB). As can be appreciated, the present disclosure may not be limited to this lithium-ion batteries but may be extended to other batteries. The voltage sensing unit 106 is configured to detect voltage of the one or more cells 104 (also referred to as cells 104, herein) or battery module. The detected voltage by the voltage sensing unit 106 is used to ensure that the CV phase is maintained. The current sensing unit 108 is configured to measure and modulate current drawn or provided by the one or more cells. The microcontroller 110 is coupled to the voltage sensing unit 106 and the current sensing unit 108.
[0038] The microcontroller 110 is configured for communicating and controlling the behavior of the voltage and current sensing units (106, 108) using standard algorithms to make decisions on whether the current is drawn or added to the battery 104. The microcontroller 110 is configured to receive the detected voltage of the one or more cells from the voltage sensing unit 106. Discharge the one or more cells 104 by a fixed voltage value using constant current (CC) discharge. Perform constant voltage (CV) discharge until the current reaches a predefined threshold. The microcontroller 110 can measure the current and time in the CV discharge phase. Extract decay constants from the measured current and time in the CV discharge phase. Train a computational model by correlating the SoH of the one or more cells with the extracted decay constants and predict the SoH of untested cells using the trained model.
[0039] In an embodiment, the cells discharge in a constant voltage phase until the current decays to a predefined threshold of 5% of initial current. The microcontroller 110 is operatively coupled to the machine learning engine 112 that receives current, and time information as input from the CV discharge phase and calculates the decay constants. Further, the machine learning engine 112 receives as input the decay constants and estimates the SoH of the one or more cells 104.
[0040] The SoH is expressed as a function of decay constants, denoted as B1, B2, and B3. The SoH is expressed as a function of decay constants expressed as SoH = f(B1, B2, B3,..., Bn). The microcontroller 110 is operatively coupled to the machine learning engine 112 that autonomously learns the function f, utilizing decay constants values B1, B2, B3, along with corresponding measured SoH data. The trained regression model establishes a correlation between SoH and decay constants, thereby facilitating accurate predictions of the SoH based on the decay constants. The machine learning engine 112 is configured to determine current decay constants from a current decay curve depicted in FIG. 1D, thereby enhancing the accuracy of current decay analysis.
[0041] Further, the machine learning engine 112 functions as the computational model. In an exemplary embodiment, the computational model is a linear regression model. Moreover, the SoH of untested cells is predicted solely by performing the CCCV discharge condition.
[0042] For example, the state of health (SoH) of the cells can be monitored by connecting the battery health estimation device 102 to the positive and negative terminals of the battery pack or cells 104. The voltage sensing unit 106 determines the battery voltage, detecting, for instance, 50 volts. The microcontroller 110 initiates a discharge process, drawing a constant current until the voltage reaches a specified value, such as 48 volts. Subsequently, a Constant Current Constant Voltage (CCCV) discharge phase maintains the voltage at 48 volts while the current decreases from an initial value, such as 10 amps, to a final value, such as 1 amp, over time. The current decay profile during CCCV discharge is measured by the current sensing unit 108, and machine learning techniques e.g., linear regression model are applied to determine decay constants. The linear regression model is trained using previously collected data, correlating state of health (SoH) with decay constants. This trained model is then utilized to predict the SoH of a new battery cell based on its observed decay constants.
[0043] Thus, the present invention overcomes the drawbacks, shortcomings, and limitations associated with existing solutions, and provides a system configured for precise monitoring of the state of health (SoH) through the capture of both voltage and current data. This system offers a thorough assessment of cells, enabling a comprehensive analysis of discharge characteristics crucial for accurate health estimation. Additionally, the system provides the extraction of decay constants during the CV discharge phase, ensuring efficiency in characterizing the cell's behavior and contributing to a robust and accurate health assessment. The present disclosure facilitates the prediction of the state of health for untested cells. This proactive approach aids in minimizing downtime and reducing costs associated with unexpected failures, thereby enabling timely and cost-effective maintenance interventions.
[0044] FIG. 1B is a high-level flow chart illustrating the working of the system, in accordance with an embodiment of the present disclosure.
[0045] The method includes at block 114 sensing the voltage of the battery pack or cell. At block 116, initiating a Constant Current (CC) discharge to reduce the pack voltage by a defined ?V, followed by performing a Constant Voltage (CV) discharge until the current reaches a predetermined threshold.
[0046] At block 118, measuring voltage, current, and time with high precision during the CV discharge phase. This measurement ensures the maintenance of the CV phase, with a specific focus on voltage to guarantee stability.
[0047] At block 120, computing the current decay constants using a regression model based on the collected data.
[0048] At block 122, training a machine learning regression model. This model takes the computed decay constants as input and predicts the State of Health (SoH) of the cell/battery pack. Training data for the regression model is shown in table 1 below.

Cell DisCap SoH f(B1,B2,B3,...,Bn)
1 4.4744 93.21667 0.06652
2 4.538 94.54167 0.0639777
3 4.547 94.72917 0.0638161
4 4.642 96.70833 0.0612751
Table 1 Training data for the regression model

[0049] At block 124, employing the trained model to predict the SoH of untested cells/battery packs. This prediction is achieved solely through the implementation of the CCCV discharge test, leveraging the insights gained from the trained machine learning regression model. SoH prediction using the regression model shown in Table 2.

Cell DisCap SoH f(B1,B2,B3,...,Bn)
1 4.565 95.1065 0.063479
Table 2 SoH prediction using the regression model

[0050] FIG. 1C illustrates an exemplary flow chart of the system, in accordance with an embodiment of the present disclosure. In the present disclosure, at block 126, the current decay during constant voltage (CV) discharge, expressed as a function of time, allows for the extraction of current decay constants (B1, B2, B3, ..., Bn) at block 128.
[0051] Subsequently, at block 130, a linear regression model is constructed, denoted as SoH = f(B1, B2, B3,..., Bn), trained on the extracted decay constants and measured State of Health (SoH). FIG. 1D is a graphical representation depicting the relationship between current and time employed for the purpose of extracting decay constants.
[0052] At block 132, this trained model facilitates the accurate prediction of the State of Health for a new battery pack or cell based on the extracted decay constants. FIG. 1E a graphical representation is disclosed, visually illustrating the application of a trained regression model that establishes a correlation between the state of health (SoH) and the decay constants, thereby enhancing the comprehensibility and interpretability of the relationship
EXPERIMENTAL RESULTS
[0053] FIG. 2A illustrates a graphical view of the database used for characterizing current decay during constant voltage (CV) discharge, in accordance with the disclosed embodiment. In an implementation, the method for constructing an experimental database for characterizing current decay during constant voltage (CV) discharge, correlating with discharge capacity or state of health (SoH) of cells or battery packs is disclosed. The method involves measuring current and voltage at intervals of 50 milliseconds until the current decays to C/50 of the capacity of the cell or battery pack, thereby generating data for analysis and assessment of battery performance and health.
[0054] FIG. 2B illustrates an exemplary graphical view used to determine the current decay constants from the experimental current decay curve, in accordance with an embodiment of the present disclosure. A machine learning regression algorithm or analytical approach for determining current decay constants from an experimental current decay curve is disclosed, wherein the algorithm employs a trained model to analyze the decay curve data and extract relevant parameters, facilitating accurate determination of current decay constants, thereby enhancing efficiency and precision in assessing battery performance in various applications.
[0055] FIG. 2C illustrates an exemplary graphical view plot of the sum of decay constants and SoH obtained from the database, in accordance with an embodiment of the present disclosure. A database can include computed decay constants (represented as B1, B2, B3, .., Bn) and the state of health (SoH) of cells or battery packs is generated through CCCV discharge experiments. The database includes information from 4 cells depicted in table 3 below, wherein CCCV discharge has been conducted, and their SoH has been experimentally determined, providing valuable insights into battery performance and health.
[0056] The plot of the sum of decay constants and SoH obtained from the database is shown below.
Table 3 plot of the function of decay constants and SoH

[0057] FIG. 2D illustrates an exemplary graphical view of testing a new cell under CCCV discharge conditions, in accordance with an embodiment of the present disclosure. In the evaluation of a new cell, a solitary CCCV discharge is executed, during which the current decay is measured at 50 ms intervals specifically during the constant voltage (CV) discharge phase. The experimental data is represented by red dots, denoting the measured points, and a fitted curve in black is employed to model the observed behavior, providing a concise and accurate representation of the discharge characteristics of the new cell.
[0058] Function of decay constants (B1, B2, B3,..., Bn), the present disclosure enables the precise determination of the state of health (SoH) for the given cell or battery pack, providing an effective method for assessing and quantifying the health status of the battery pack.
[0059] FIG. 2E illustrates an exemplary graphical view of measurements for three new cells, in accordance with an embodiment of the present disclosure. The present disclosure provides measurements from three newly assessed cells, with concurrent experimental determination of their state of health (SoH), serving as a validation mechanism to affirm the accuracy and reliability of the proposed model for evaluating and predicting the health status of cells shown in table 4 below.

Cell DisCap SoH f(B1,B2,B3,...,Bn) predicted SoH based on trendline % error
1 4.565 95.1065 0.063479 94.75398 -0.37
Table 4 Evaluating and predicting the health status of cells.

[0060] Here, FIG. 3 illustrates an exemplary flow chart of method for monitoring the state of health (SoH) of one or more cells, in accordance with an embodiment of the present disclosure.
[0061] Referring to FIG. 3, the method 300 for monitoring the state of health (SoH) of one or more cells includes at block 302, detecting, by a voltage sensing unit of a battery health estimation device, voltage of the one or more cells, wherein the battery health estimation device coupled to one or more cells.
[0062] At block 304, measuring, by the current sensing unit of the battery health estimation device, current drawn or provided by the one or more cells. At block 306, receiving, at the microcontroller of the battery health estimation device, detected voltage information from the voltage sensing unit.
[0063] At block 308, discharging the one or more cells by a fixed voltage value using constant current (CC) discharge. At block 310, performing constant voltage (CV) discharge until the current reaches a predefined threshold. At block 312, measuring the current and time during the CV discharge phase.
[0064] At block 314, extracting decay constants from the measured current and time in the CV discharge phase. At block 316, training a computational model to estimate the SoH of the one or more cells using the extracted decay constants and at block 318 predicting the SoH of untested cells using the trained model.
[0065] It will be apparent to those skilled in the art that the system 100 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT INVENTION
[0066] The present invention provides a system that enables precise monitoring of the state of health (SoH) by capturing both voltage and current data.
[0067] The present invention provides a system that provides a thorough assessment of the cells, allowing for a comprehensive analysis of discharge characteristics crucial for accurate health estimation.
[0068] The present invention provides a system that provides extraction of decay constants during the CV discharge phase ensuring efficiency in characterizing the cell's behavior, contributing to a robust and accurate health assessment.
[0069] The present invention provides a system that facilitates the prediction of the state of health for untested cells, offering a valuable feature for proactive maintenance and replacement strategies.
[0070] The present invention provides a system that enables rapid and accurate monitoring SOH of the energy storage devices.
[0071] The present invention provides a system that aids in minimizing downtime and reducing costs associated with unexpected failures, enabling timely and cost-effective maintenance interventions.
, Claims:1. A system (100) to monitor state of health (SoH) of one or more cells, the system comprising:
a battery health estimation device (102) coupled to one or more cells (104) to monitor the state of health (SoH), the battery health estimation device (102) comprising:
a voltage sensing unit (106) configured to detect voltage of the one or more cells;
a current sensing unit (108) configured to measure and modulate current drawn or provided by the one or more cells; and
a microcontroller (110) coupled to the voltage sensing unit and the current sensing unit, the microcontroller configured to:
receive, from the voltage sensing unit, the detected voltage of the one or more cells;
discharge the one or more cells by a fixed voltage value using constant current (CC) discharge;
perform constant voltage (CV) discharge until the current reaches a predefined threshold;
measure the current and time in the CV discharge phase;
extract decay constants from the measured current and time in the CV discharge phase;
train a computational model by correlating the SoH of the one or more cells with the extracted decay constants; and
predict the SoH of untested cells using the trained model.
2. The system as claimed in claim 1, wherein the detected voltage by the voltage sensing unit (106) is used to ensure that the CV phase is maintained.
3. The system as claimed in claim 1, wherein the microcontroller (110) is operatively coupled to a machine learning engine (112) that receives current, and time information as input from the CV discharge phase and calculates the decay constants.
4. The system as claimed in claim 1, wherein the machine learning engine (112) receives as input the decay constants and estimates the SoH of the one or more cells.
5. The system as claimed in claim 1, wherein the machine learning engine (112) functions as the computational model, wherein the computational model is a linear regression model.
6. The system as claimed in claim 1, wherein the SoH is expressed as a function of decay constants denoted as SoH = f(B1, B2, B3,..., Bn).
7. The system as claimed in claim 1, wherein the SoH of the untested cells is predicted solely by performing a CCCV discharge condition.
8. The system as claimed in claim 1, wherein the microcontroller (110) discharges the one or more cells in the CV phase until the current decays to the predefined threshold of 5% of initial current.
9. The system as claimed in claim 1, wherein the battery health estimation device (102) coupled to terminals of the one or more cells (104), wherein the one or more cells are lithium-ion batteries (LIBs).
10. A method (300) for monitoring state of health (SoH) of one or more cells, the method comprising:
detecting (302), by a voltage sensing unit of a battery health estimation device, voltage of the one or more cells, wherein the battery health estimation device (102) coupled to one or more cells (104);
measuring (304), by a current sensing unit of the battery health estimation device, current drawn or provided by the one or more cells;
receiving (306), at a microcontroller of the battery health estimation device, detected voltage information from the voltage sensing unit;
discharging (308) the one or more cells by a fixed voltage value using constant current (CC) discharge;
performing (310) constant voltage (CV) discharge until the current reaches a predefined threshold;
measuring (312) the current and time during the CV discharge phase;
extracting (314) decay constants from the measured current and time in the CV discharge phase;
training (316) a computational model by correlating the SOH of the one or more cells with the extracted decay constants; and
predicting (318) the SoH of untested cells using the trained model.