ELECTRIC VEHICLE BATTERY

EARLIEST PRIORITY DATE

This Application claims priority from a Complete patent application filed in India having Patent Application No. 202141061535, filed on December 29, 2021, and titled “SYSTEM AND METHOD FOR DEFECT DETECTION OF A CELL IN AN ELECTRIC VEHICLE BATTERY”.

BACKGROUND

Embodiments of the present disclosure relate to a vehicle diagnostics system and more particularly to a system and a method for defect detection of a cell in an electric vehicle battery.

In recent years, environmental protection has become an utmost concern, and new energy efficient vehicles have become more and more popular. The new energy efficient vehicle is an environment-friendly travel tool adopting unconventional vehicle fuel as a power source, and comprises a pure electric vehicle, a range-extended electric vehicle, a hybrid electric vehicle, a fuel cell electric vehicle, a hydrogen engine vehicle and the like. The new energy efficient vehicle is vigorously advocated to be used in the social aspect due to the several characteristics such as high utilization rate, zero emission of pollutants and the like. But with advancement of the new energy efficient vehicle, demand of power battery also increases therewith. Generally, the power battery such as lithium-ion battery system has become indispensable component in electric vehicles such as car or two-wheeler. Management and diagnosis of such power battery is one of the crucial factor and as a result various systems are available for defect detection of cells in the power battery of the electric vehicle.

Conventionally, the system available for defection detection of cells in the electric vehicle battery includes manually identifying defective cells by identifying faults in electrical path for current flow. However, such a conventional system is time consuming, and error prone as manual intervention is involved. Also, assembly, testing, and diagnostics of a high voltage battery pack is very complex. Furthermore, such a conventional system detects the complete battery pack as defective instead of identifying individual battery cells or cell modules which may fail, become problematic, or reach the end of their useful lives before, during, or just after assembly.

Hence there is a need for improved system and a method for defect detection of a cell in an electric vehicle battery in order to address the aforementioned issues.

BRIEF DESCRIPTION

In accordance with an embodiment of the present disclosure, a system for defect detection of a cell in an electric vehicle battery is disclosed. The system includes a processing subsystem hosted on a server, wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an image capturing module configured to receive a captured image of heat waves emitted by one or more cells of the electric vehicle battery, wherein the heat waves emitted by the one or more cells of the electric vehicle battery are captured using an image acquisition device. The processing subsystem also includes an image processing module configured to filter the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using an image filtering technique. The image processing module is also configured to extract a coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery upon filtering. The processing subsystem also includes an image analysis module configured to utilize a trained deep learning model to feed extracted coloured pattern of the image of the heat waves emitted by the one or more cells of the electric vehicle battery. The image analysis module is also configured to compare the extracted coloured pattern of the image of the heat waves emitted by the one or more cells with corresponding one or more battery cell temperature threshold values using the trained deep learning model. The image analysis module is also configured to determine a deviation in the extracted coloured pattern of the image of the one or more cells from the corresponding one or more battery cell temperature threshold values upon comparison. The image analysis module is also configured to identify a region of interest in the extracted coloured pattern of the image of the one or more cells based on the deviation determined. The image analysis module is also configured to map the region of interest identified in the extracted coloured pattern of the image of the one or more cells with a prestored grid layer of battery pack image. The image analysis module is also configured to detect one or more defective cells in the electric vehicle battery based on mapping of the region of the interest with the prestored grid layer of the battery pack image. The image analysis module is also configured to identify a position of the one or more defective cells within the electric vehicle battery based on the one or more defective cells detected. The processing subsystem also includes a battery health prediction module configured to predict an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model. The processing subsystem also includes a battery health notification module configured to notify the one or more defective cells and the operable range of temperature of the one or more defective cells for the predefined period to an operator of the electric vehicle via one or more notification means.

In accordance with another embodiment of the present disclosure, a method for defect detection of a cell in an electric vehicle battery is disclosed. The method includes receiving, by an image capturing module of a processing subsystem, a captured image of heat waves emitted by one or more cells of the electric vehicle battery, wherein the heat waves emitted by the one or more cells of the electric vehicle battery are captured using an image acquisition device. The method also includes filtering, by the image processing module of the processing subsystem, the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using an image filtering technique. The method also includes extracting, by the image processing module of the processing subsystem, a coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery upon filtering. The method also includes utilizing, by an image analysis module of the processing subsystem, a trained deep learning model to feed extracted coloured pattern of the image of the heat waves emitted by the one or more cells of the electric vehicle battery. The method also includes comparing, by the image analysis module of the processing subsystem, extracted coloured pattern of the image of the heat waves emitted by the one or more cells with corresponding one or more battery cell temperature threshold values using the trained deep learning model. The method also includes determining, by the image analysis module of the processing subsystem, a deviation in the extracted coloured pattern of the image of the one or more cells from the corresponding one or more battery cell temperature threshold values upon comparison. The method also includes identifying, by the image analysis module of the processing subsystem, a region of interest in the extracted coloured pattern of the image of the one or more cells based on the deviation determined. The method also includes mapping, by the image analysis module of the processing subsystem, the region of interest identified in the extracted coloured pattern of the image of the one or more cells with a prestored grid layer of battery pack image. The method also includes detecting, by the image analysis module of the processing subsystem, one or more defective cells in the electric vehicle battery based on mapping of the region of the interest with the prestored grid layer of the battery pack image. The method also includes identifying, by the image analysis module of the processing subsystem, a position of the one or more defective cells within the electric vehicle battery based on the one or more defective cells detected. The method also includes predicting, by a battery health prediction module of the processing subsystem, an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model. The method also includes predicting, by a battery health prediction module of the processing subsystem, an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model. The method also includes notifying, by a battery health notification module of the processing subsystem, the one or more defective cells and the operable range of temperature of the one or more defective cells for the predefined period to an operator of the electric vehicle via one or more notification means.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which: FIG. 1 is a block diagram of a system for defect detection of a cell in an electric vehicle battery in accordance with an embodiment of the present disclosure;

FIG. 2 is a schematic representation of an exemplary embodiment of a system for defect detection of a cell in an electric vehicle battery of FIG. 1 in accordance with an embodiment of the present disclosure;

FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and

FIG. 4 (a) and FIG. 4 (b) is a flow chart representing the steps involved in a method for defect detection of a cell in an electric vehicle battery in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system and a method for defect detection of a cell in an electric vehicle battery. The system includes a processing subsystem hosted on a server, wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an image capturing module configured to receive a captured image of heat waves emitted by one or more cells of the electric vehicle battery, wherein the heat waves emitted by the one or more cells of the electric vehicle battery are captured using an image acquisition device. The processing subsystem also includes an image processing module configured to filter the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using an image filtering technique. The image processing module is also configured to extract a coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery upon filtering. The processing subsystem also includes an image analysis module configured to utilize a trained deep learning model to feed extracted coloured pattern of the image of the heat waves emitted by the one or more cells of the electric vehicle battery. The image analysis module is also configured to compare the extracted coloured pattern of the image of the heat waves emitted by the one or more cells with corresponding one or more battery cell temperature threshold values using the trained deep learning model. The image analysis module is also configured to determine a deviation in the extracted coloured pattern of the image of the one or more cells from the corresponding one or more battery cell temperature threshold values upon comparison. The image analysis module is also configured to identify a region of interest in the extracted coloured pattern of the image of the one or more cells based on the deviation determined. The image analysis module is also configured to map the region of interest identified in the extracted coloured pattern of the image of the one or more cells with a prestored grid layer of battery pack image. The image analysis module is also configured to detect one or more defective cells in the electric vehicle battery based on mapping of the region of the interest with the prestored grid layer of the battery pack image. The image analysis module is also configured to identify a position of the one or more defective cells within the electric vehicle battery based on the one or more defective cells detected. The processing subsystem also includes a battery health prediction module configured to predict an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model. The processing subsystem also includes a battery health notification module configured to notify the one or more defective cells and the operable range of temperature of the one or more defective cells for the predefined period to an operator of the electric vehicle via one or more notification means.

FIG. 1 is a block diagram of a system (100) for defect detection of a cell in an electric vehicle battery in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network to control bidirectional communications among a plurality of modules. In one embodiment, the server (108) may include a cloud server. In another embodiment, the server (108) may include a local server. The electric vehicle charging station control subsystem (105) is configured to execute on a network (not shown in FIG. 1) to control bidirectional communications among a plurality of modules. In one embodiment, the network may include a wired network such as local area network (LAN). In another embodiment, the network may include a wireless network such as Wi-Fi, Bluetooth, Zigbee, near field communication (NFC), infra-red communication (RFID) or the like. The processing subsystem (105) includes an image capturing module (110) configured to receive a captured image of heat waves emitted by one or more cells of the electric vehicle battery, wherein the heat waves emitted by the one or more cells of the electric vehicle battery are captured using an image acquisition device. As used herein, the term ‘heat waves’ is defined as one or more infrared rays captured by emission from the one or more cells in the battery pack. In one embodiment, the image acquisition device may include at least one of one or more image sensors, one or more cameras or a combination thereof.

The processing subsystem (105) also includes an image processing module (120) configured to filter the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using an image filtering technique. In one embodiment, the image filtering technique may include at least one of mean filtering technique, median filtering technique, wiener filtering technique, filtering using wavelet transform, filtering using curvelet transform or a combination thereof.

The image processing module (120) is also configured to extract a coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery upon filtering. In one embodiment, the coloured pattern of the image may include a maximum temperature colour pattern image of the heatwaves emitted by the one or more cells within the electric vehicle battery.

The processing subsystem (105) also includes an image analysis module (130) configured to utilize a trained deep learning model to feed extracted coloured pattern of the image of the heat waves emitted by the one or more cells of the electric vehicle battery. The image analysis module (130) is also configured to compare the extracted coloured pattern of the image of the heat waves emitted by the one or more cells with corresponding one or more battery cell temperature threshold values using the trained deep learning model. As used herein, the term ‘battery cell temperature threshold values’ is defined as one or more values of temperature which is set as maximum withstanding values of temperature for operation of vehicle battery corresponding to industrial standard.

The image analysis module (130) is also configured to determine a deviation in the extracted coloured pattern of the image of the one or more cells from the corresponding one or more battery cell temperature threshold values upon comparison. The image analysis module (130) is also configured to identify a region of interest (RO I) in the extracted coloured pattern of the image of the one or more cells based on the deviation determined. As used herein, the term ‘ROT is defined as subsets of images identified from a large dataset and utilised for a specific purpose.

The image analysis module (130) is also configured to map the region of interest identified in the extracted coloured pattern of the image of the one or more cells with a prestored grid layer of battery pack image. The image analysis module is also configured to detect one or more defective cells in the electric vehicle battery based on mapping of the region of the interest with the prestored grid layer of the battery pack image. In one embodiment, the grid layer of the battery pack may include a blueprint of a structure of the one or more cells within the electric vehicle battery pack. The image analysis module (130) is also configured to identify a position of the one or more defective cells within the electric vehicle battery based on the one or more defective cells detected.

The processing subsystem (105) also includes a battery health prediction module (140) configured to predict an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model. In one embodiment, the predefined period may include at least one of upcoming minutes, upcoming hours, upcoming days, upcoming weeks, upcoming months or a combination thereof. In a specific embodiment, the battery health prediction module is also configured to enable a user to set up the predefined period for predicting the operable range of temperature of the one or more defective cells detected within the electric vehicle battery. In such embodiment, the user may mention predict next hours temperature, based on this user can stop the vehicle and wait for battery cooling.

The processing subsystem (105) also includes a battery health notification module (150) configured to notify the one or more defective cells and the operable range of temperature of the one or more defective cells for the predefined period to an operator of the electric vehicle via one or more notification means. In one embodiment, the one or more notification means may include atleast one of displaying a battery health notification message on a display interface, an alarm signal or a combination thereof. FIG. 2 is a schematic representation of an exemplary embodiment of a system (100) for defect detection of a cell in an electric vehicle battery of FIG. 1 in accordance with an embodiment of the present disclosure. Considering an example, wherein the system (100) is used in a society where electric vehicle is widely used for transportation. In such a scenario, for maintenance of the electric vehicle battery, monitoring of one or more cells in the electric vehicle battery is required at regular intervals. The system (100) helps in monitoring battery health of the electric vehicle (102) and detects one or more defects of the one or more cells.

For monitoring the battery health, an image capturing module (110) located on a processing subsystem (105) receives a captured image of heat waves emitted by one or more cells of the electric vehicle battery, wherein the heat waves emitted by the one or more cells of the electric vehicle battery are captured using an image acquisition device. Here, the processing subsystem is hosted on a cloud server (108), wherein the processing subsystem (105) is configured to execute on a wireless communication network (115) to control bidirectional communications among a plurality of modules. In the example used herein, the image acquisition device may include at least one of one or more image sensors, one or more cameras or a combination thereof.

Once, the captured image is received, an image processing module (120) is configured to filter the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using an image filtering technique. For example, the image filtering technique may include at least one of mean filtering technique, median filtering technique, wiener filtering technique, filtering using wavelet transform, filtering using curvelet transform or a combination thereof. Upon filtration, the image processing module (120) extracts a coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery.

Again, an image analysis module (130) utilize a trained deep learning model to feed extracted coloured pattern of the image of the heat waves emitted by the one or more cells of the electric vehicle battery. Also, by utilizing the trained deep learning model, comparison of the extracted coloured pattern of the image of the heat waves emitted by the one or more cells with corresponding one or more battery cell temperature threshold values is also performed. The image analysis module (130) is also configured to determine a deviation in the extracted coloured pattern of the image of the one or more cells from the corresponding one or more battery cell temperature threshold values upon comparison. Further, based on the deviation determined, the image analysis module (130) identifies a region of interest (ROI) in the extracted coloured pattern of the image of the one or more cells.

The image analysis module (130) is also configured to map the region of interest identified in the extracted coloured pattern of the image of the one or more cells with a prestored grid layer of battery pack image. Based on the mapping, the image analysis module (130) is also configured to detect one or more defective cells in the electric vehicle battery. For example, the grid layer of the battery pack may include a blueprint of a structure of the one or more cells within the electric vehicle battery pack. The image analysis module (130) is also configured to identify a position of the one or more defective cells within the electric vehicle battery based on the one or more defective cells detected.

Further, a battery health prediction module (140) is configured to predict an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model. In the example used herein, the predefined period may include at least one of upcoming minutes, upcoming hours, upcoming days, upcoming weeks, upcoming months or a combination thereof. In addition, the battery health prediction module is also configured to enable a user to set up the predefined period for predicting the operable range of temperature of the one or more defective cells detected within the electric vehicle battery. For example, the user may mention predict next hours temperature, based on this user can stop the vehicle and wait for battery cooling.

Again, a battery health notification module (150) is configured to notify the one or more defective cells and the operable range of temperature of the one or more defective cells for the predefined period to an operator of the electric vehicle via one or more notification means. In the example used herein, the one or more notification means may include atleast one of displaying a battery health notification message on a display interface of an electronic device associated with the user. For example, the electronic device may include a mobile phone associated with the user. Thus, the system (100) by using deep learning technique identifies the root cause of the defect in the one or more cells and detects the exact position of the one or more cells within the electric vehicle battery.

FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (200) includes processor(s) (230), and memory (210) operatively coupled to the bus (220). The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

The memory (210) includes several subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) includes a processing subsystem (105) of FIG.l. The processing subsystem (105) further has following modules: an image capturing module (110), an image processing module (120), an image analysis module (130), a battery health prediction module (140), a battery health notification module (150).

The image capturing module (110) configured to receive a captured image of heat waves emitted by one or more cells of the electric vehicle battery, wherein the heat waves emitted by the one or more cells of the electric vehicle battery are captured using an image acquisition device. The image processing module (120) is configured to filter the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using an image filtering technique. The image processing module (120) is also configured to extract a coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery upon filtering. The image analysis module (130) is configured to utilize a trained deep learning model to feed extracted coloured pattern of the image of the heat waves emitted by the one or more cells of the electric vehicle battery. The image analysis module (130) is also configured to compare the extracted coloured pattern of the image of the heat waves emitted by the one or more cells with corresponding one or more battery cell temperature threshold values using the trained deep learning model. The image analysis module (130) is also configured to determine a deviation in the extracted coloured pattern of the image of the one or more cells from the corresponding one or more battery cell temperature threshold values upon comparison. The image analysis module (130) is also configured to identify a region of interest in the extracted coloured pattern of the image of the one or more cells based on the deviation determined. The image analysis module (130) is also configured to map the region of interest identified in the extracted coloured pattern of the image of the one or more cells with a prestored grid layer of battery pack image. The image analysis module (130) is also configured to detect one or more defective cells in the electric vehicle battery based on mapping of the region of the interest with the prestored grid layer of the battery pack image. The image analysis module (130) is also configured to identify a position of the one or more defective cells within the electric vehicle battery based on the one or more defective cells detected. The battery health prediction module (140) configured to predict an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model. The battery health notification module (150) configured to notify the one or more defective cells and the operable range of temperature of the one or more defective cells for the predefined period to an operator of the electric vehicle via one or more notification means.

The bus (220) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.

FIG. 4 (a) and FIG. 4 (b) is a flow chart representing the steps involved in a method (300) for defect detection of a cell in an electric vehicle battery in accordance with an embodiment of the present disclosure. The method (300) includes receiving, by an image capturing module of a processing subsystem, a captured image of heat waves emitted by one or more cells of the electric vehicle battery, wherein the heat waves emitted by the one or more cells of the electric vehicle battery are captured using an image acquisition device in step 310. In one embodiment, receiving the captured image of heat waves emitted by the one or more cells of the electric vehicle battery may include receiving the captured image of heat waves emitted by the one or more cells of the electric vehicle battery from the image acquisition device including, but not limited to, one or more image sensors, one or more cameras or a combination thereof.

The method (300) also includes filtering, by the image processing module of the processing subsystem, the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using an image filtering technique in step 320. In one embodiment, filtering the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using the image filtering technique may include filtering the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery using at least one of mean filtering technique, median filtering technique, wiener filtering technique, filtering using wavelet transform, filtering using curvelet transform or a combination thereof.

The method (300) also includes extracting, by the image processing module of the processing subsystem, a coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle battery upon filtering in step 330. In some embodiment, extracting the coloured pattern of the captured image of the heat waves emitted by the one or more cells of the electric vehicle may include extracting the coloured pattern of the image including a maximum temperature colour pattern image of the heatwaves emitted by the one or more cells within the electric vehicle battery.

The method (300) also includes utilizing, by an image analysis module of the processing subsystem, a trained deep learning model to feed extracted coloured pattern of the image of the heat waves emitted by the one or more cells of the electric vehicle battery in step 340. The method (300) also includes comparing, by the image analysis module of the processing subsystem, extracted coloured pattern of the image of the heat waves emitted by the one or more cells with corresponding one or more battery cell temperature threshold values using the trained deep learning model in step 350.

The method (300) also includes determining, by the image analysis module of the processing subsystem, a deviation in the extracted coloured pattern of the image of the one or more cells from the corresponding one or more battery cell temperature threshold values upon comparison in step 360. The method (300) also includes identifying, by the image analysis module of the processing subsystem, a region of interest in the extracted coloured pattern of the image of the one or more cells based on the deviation determined in step 370. In one embodiment, identifying the region of interest in the extracted coloured pattern of the image of the one or more cells based on the deviation may include identifying subsets of images identified from a large dataset and utilised for a specific purpose.

The method (300) also includes mapping, by the image analysis module of the processing subsystem, the region of interest identified in the extracted coloured pattern of the image of the one or more cells with a prestored grid layer of battery pack image in step 380. The method (300) also includes detecting, by the image analysis module of the processing subsystem, one or more defective cells in the electric vehicle battery based on mapping of the region of the interest with the prestored grid layer of the battery pack image in step 390. In one embodiment, detecting the one or more defective cells in the electric vehicle battery based on mapping of the region of the interest with the prestored grid layer of the battery pack image may include mapping of the region of the interest with a blueprint of a structure of the one or more cells within the electric vehicle battery pack. The method (300) also includes identifying, by the image analysis module of the processing subsystem, a position of the one or more defective cells within the electric vehicle battery based on the one or more defective cells detected in step 400.

The method (300) also includes predicting, by a battery health prediction module of the processing subsystem, an operable range of temperature of the one or more defective cells detected within the electric vehicle battery for a predefined period using the trained deep learning model in step 410. In one embodiment, predicting the operable range of the temperature of the one or more defective cells for the predefined period may include predicting the operable range of the temperature of the one or more defective cells for at least one of upcoming minutes, upcoming hours, upcoming days, upcoming weeks, upcoming months or a combination thereof.

The method (300) also includes notifying, by a battery health notification module of the processing subsystem, the one or more defective cells and the operable range of temperature of the one or more defective cells for the predefined period to an operator of the electric vehicle via one or more notification means in step 420. In some embodiment, notifying the one or more defective cells and the operable range of the temperature of the one or more defective cells may include notifying the one or more defective cells and the operable range of temperature of the one or more defective cells via atleast one of displaying a battery health notification message on a display interface, an alarm signal or a combination thereof.

Various embodiments of the present disclosure relate to an automated system for identifying the defective or bad cell from the large set of batteries used in automotive electric vehicles.

Moreover, the present disclosed system utilises deep learning technique for defect detection of the one or more cells in the electric vehicle battery from coloured pattern images, thereby helps in accurate and faster defect detection.

Furthermore, the present disclosed system identifies the area where exactly the defective cell in the battery is located and notifies the user in real-time which further helps in maintenance of the battery as well as alerts the user to replace the battery based on requirement.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.