Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to maintenance of an electric vehicle and particularly to a digital twin-based system for predictive maintenance of Electric Vehicle (EV) powertrain components.
Description of Related Art
[002] Electric Vehicles (EVs) have emerged as a sustainable alternative to traditional internal combustion engine vehicles, addressing concerns related to fossil fuel depletion and environmental pollution. The adoption of EVs has been accelerated by advancements in battery technology, powertrain efficiency, and intelligent transportation systems. However, ensuring the reliability and safety of EV powertrains remains a critical challenge. Issues such as thermal runaway in batteries, unexpected motor failures, and inefficiencies in power management necessitate continuous monitoring and predictive maintenance solutions to enhance vehicle longevity and safety.
[003] Conventional approaches to vehicle maintenance predominantly rely on reactive and scheduled maintenance strategies. In reactive maintenance, faults are addressed only after they manifest, often leading to unexpected breakdowns and high repair costs. Scheduled maintenance, on the other hand, follows a predetermined timeline irrespective of the actual condition of vehicle components. While these methods provide a basic level of reliability, they fail to offer real-time insights into component health and do not effectively predict potential failures, leading to inefficient energy consumption and suboptimal vehicle performance.
[004] To improve EV reliability, various technological advancements have been explored, including IoT-enabled monitoring, AI-driven fault prediction, and cloud-based diagnostics. Solutions such as industrial digital twins and predictive analytics have been employed in other sectors, such as manufacturing and aviation, to monitor equipment performance and preempt failures. While some EV monitoring systems integrate basic fault detection and data analytics, they often lack the precision and real-time processing capabilities required for accurate predictive maintenance. The need for a robust, scalable, and intelligent system that provides real-time insights and proactive maintenance guidance is increasingly evident in the evolving landscape of electric mobility.
[005] There is thus a need for an improved and advanced digital twin-based system 100 for predictive maintenance of Electric Vehicle (EV) powertrain components that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a digital twin-based system for predictive maintenance of Electric Vehicle (EV) powertrain components. The system comprising a detection unit configured to collect real-time operational data of an Electric Vehicle (EV) powertrain. The real-time operational data is selected from a battery health, state of charge (SOC), temperature, motor drive parameters, voltage, current, speed, or a combination thereof. The system further comprising a cloud-based processing unit, connected to the detection unit. The cloud-based processing unit is configured to process the real-time operational data collected by the detection unit; generate a digital twin model of the Electric Vehicle (EV) powertrain based on the processed operational data; generate predictive analysis results by simulating the digital twin model for conditional variables by applying a machine learning model; and display the generated predictive analysis on an output unit.
[007] Embodiments in accordance with the present invention further provide a method for predictive maintenance of Electric Vehicle (EV) powertrain components using a digital twin-based system. The method comprising steps of collecting real-time operational data of an Electric Vehicle (EV) powertrain using a detection unit, wherein the real-time operational data is selected from a battery health, state of charge (SOC), temperature, motor drive parameters, voltage, current, speed, or a combination thereof; processing the real-time operational data collected by the detection unit; generating a digital twin model of the Electric Vehicle (EV) powertrain based on the processed operational data; generating predictive analysis results by simulating the digital twin model for conditional variables by applying a machine learning model; and displaying the generated predictive analysis on an output unit.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a digital twin-based system for predictive maintenance of Electric Vehicles (EV) powertrain components.
[009] Next, embodiments of the present application may provide a system for maintenance of electric vehicles (EV) that enables continuous monitoring of EV powertrain components using IoT sensors and AI algorithms, ensuring early detection of potential faults before they lead to critical failures.
[0010] Next, embodiments of the present application may provide a system for maintenance of Electric Vehicles (EV) that predicts battery thermal runaway, motor drive faults, and other powertrain issues, the system improves vehicle safety, reducing the risk of accidents caused by unexpected malfunctions.
[0011] Next, embodiments of the present application may provide a system for maintenance of Electric Vehicles (EV) that offers predictive maintenance insights, allowing timely servicing of EV components only when necessary, thereby minimizing downtime and maintenance expenses.
[0012] Next, embodiments of the present application may provide a system for maintenance of Electric Vehicles (EV) that provides data-driven recommendations for optimizing powertrain performance, ensuring efficient use of battery energy, and extending the driving range of electric vehicles.
[0013] Next, embodiments of the present application may provide a system for maintenance of Electric Vehicles (EV) that enables access of real-time diagnostics, receives fault alerts remotely, and takes preventive measures, enhancing user convenience and operational efficiency.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1A illustrates a digital twin-based system for predictive maintenance of Electric Vehicle (EV) powertrain components, according to an embodiment of the present invention;
[0018] FIG. 1B illustrates an exemplary implementation of the system, according to an embodiment of the present invention; and
[0019] FIG. 2 depicts a flowchart of a method for predictive maintenance of Electric Vehicle (EV) powertrain components using a digital twin-based system, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] 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 scope of the invention as defined in the claims.
[0022] 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.
[0023] 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.
[0024] FIG. 1A illustrates a digital twin-based system 100 (hereinafter referred to as the system 100) for predictive maintenance of Electric Vehicle (EV) powertrain components, according to an embodiment of the present invention. The system 100 may be adapted to monitor an overall performance of the Electric Vehicle (EV). Further, the system 100 may be adapted to inspect components of the Electric Vehicle (EV) powertrain. Further, the system 100 may generate and display a report for the conducted inspection. The system 100 may further generate predictive analysis relating to the Electric Vehicle (EV) powertrain.
[0025] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise a detection unit 102, a cloud-based processing unit 104, an output unit 106, a wireless communication network 108, and a computing application 110. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0026] In an embodiment of the present invention, the detection unit 102 may be configured to collect real-time operational data of an Electric Vehicle (EV) powertrain. The real-time operational data may be, but not limited to, a battery health, state of charge (SOC), temperature, motor drive parameters, voltage, current, speed, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the real-time operational data, including known, related art, and/or later developed technologies. In an embodiment of the present invention, the detection unit 102 may comprise Internet of Things (IoT)-enabled sensors, that may be, but not limited to, temperature sensors, current and voltage sensors, accelerometers, vibration sensors, pressure sensors, humidity sensors, thermal imaging sensors, gas sensors, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of sensors, including known, related art, and/or later developed technologies, encapsulated in the detection unit 102.
[0027] In an embodiment of the present invention, the Internet of Things (IoT) - enabled sensors may be strategically placed to ensure efficient real-time monitoring of the EV powertrain. The temperature sensors may be positioned on the battery pack, inverter, and motor to detect thermal variations. The current and voltage sensors may be placed along battery terminals, power distribution lines, and motor controllers to monitor electrical performance. The accelerometers and vibration sensors may be installed near the motor and drivetrain components to detect mechanical misalignments or excessive vibrations. The pressure and humidity sensors may be positioned within the battery enclosure to monitor environmental conditions that may impact battery performance. The thermal imaging and gas sensors may be integrated near the battery and power electronics to detect early signs of thermal runaway or hazardous gas emissions.
[0028] In an embodiment of the present invention, the cloud-based processing unit 104 may be connected to the detection unit 102. The cloud-based processing unit 104 may be configured to process the real-time operational data collected by the detection unit 102. The cloud-based processing unit 104 may be configured to generate a digital twin model of the Electric Vehicle (EV) powertrain based on the processed operational data.
[0029] The cloud-based processing unit 104 may be configured to generate predictive analysis results by simulating the digital twin model for conditional variables by applying a machine learning model. The machine learning model may include, but not limited to, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), random forest classifiers, Support Vector Machines (SVMs), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the machine learning model, including known, related art, and/or later developed technologies. The generated predictive analysis may be, but not limited to, a fault detection analysis, a remaining useful life (RUL) estimation, anomaly detection, energy efficiency optimization analysis, maintenance scheduling recommendations, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the generated predictive analysis, including known, related art, and/or later developed technologies.
[0030] The cloud-based processing unit 104 may be configured to display the generated predictive analysis on the output unit 106. the cloud-based processing unit 104 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud-based processing unit 104 including known, related art, and/or later developed technologies.
[0031] In an embodiment of the present invention, the output unit 106 may be adapted to display the predictive analysis generated by the cloud-based processing unit 104. The output unit 106 may be, but not limited to, a vehicle dashboard display, a mobile device, a computer, a smart phone, a mobile application based device, a web-based interface, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the output unit 106, including known, related art, and/or later developed technologies.
[0032] In an embodiment of the present invention, the cloud-based processing unit 104 may further be configured to transmit an alert signal to an authorized user of the Electric Vehicle (EV) upon detecting a probability of a mishap, a fire accident, a component failure, and so forth based on the predictive analysis results.
[0033] In an embodiment of the present invention, the wireless communication network 108 may be adapted to enable a communicative link between the cloud-based processing unit 104 and the detection unit 102. The wireless communication network 108 may be, but not limited to, a Wi-Fi communication unit, a Bluetooth communication unit, a millimeter waves communication unit, an Ultra-High Frequency (UHF) communication unit, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the wireless communication network 108, including known, related art, and/or later developed technologies.
[0034] In an embodiment of the present invention, the computing application 110 for enabling the authorized user to input the conditional variables for simulating the digital twin model.
[0035] FIG. 1B illustrates an exemplary implementation of the system 100, according to an embodiment of the present invention. In an exemplary embodiment of the present invention, the detection unit 102 may continuously monitor the real-time operational parameters related to the fire risk, such as battery temperature, voltage fluctuations, current spikes, overheating of power electronics, and abnormal thermal behavior. The collected data may be transmitted to the cloud-based processing unit 104, such as the digital twin model of the EV powertrain is created. By applying machine learning algorithms, the system 100 may simulate various thermal and electrical stress conditions to predict a likelihood of a thermal runaway event, short circuit, or overheating that may potentially lead to a fire accident. If the analysis detects an increased fire risk, the system 100 may generate an early warning notification and may display it on the output unit 106, such as the vehicle’s dashboard or a connected mobile application. Additionally, the system 100 may trigger automatic safety measures, such as limiting power output, shutting down high-risk components, or alerting emergency services.
[0036] In another embodiment of the present invention, the system 100 may be configured to predict failures in critical powertrain components, such as the battery pack, electric motor, inverter, and power electronics. The detection unit 102 may collect the real-time operational data, including battery degradation, unusual current or voltage drops, motor efficiency loss, and sensor-reported faults. The cloud-based processing unit 104 may utilize this data to generate a digital twin representation of each component and may simulate different operational scenarios. By analyzing deviations from normal performance metrics, the system can predict component degradation, estimate remaining useful life, and identify potential failure points. The predictive results may be displayed on the output unit 106 to allow the users or maintenance personnel to take proactive actions, such as scheduling repairs or replacing components before failure occurs. This minimizes unexpected breakdowns, enhances vehicle reliability, and improves overall safety. Through the implementation of these predictive maintenance capabilities, the system 100 may be configured to ensure early detection of potential fire hazards and component failures, thereby enhancing the safety, longevity, and operational efficiency of the Electric Vehicle (EV).
[0037] FIG. 2 depicts a flowchart of a method 200 for predictive maintenance of Electric Vehicle (EV) powertrain components using the system 100, according to an embodiment of the present invention.
[0038] At step 202, the system 100 may collect the real-time operational data of the Electric Vehicle (EV) powertrain using the detection unit 102.
[0039] At step 204, the system 100 may process the real-time operational data collected by the detection unit 102.
[0040] At step 206, the system 100 may generate the digital twin model of the Electric Vehicle (EV) powertrain based on the processed operational data.
[0041] At step 208, the system 100 may generate the predictive analysis results by simulating the digital twin model for conditional variables by applying the machine learning model.
[0042] At step 210, the system 100 may display the generated predictive analysis on the output unit 106.
[0043] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0044] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A digital twin-based system (100) for predictive maintenance of Electric Vehicle (EV) powertrain components, comprising:
a detection unit (102) configured to collect real-time operational data of an Electric Vehicle (EV) powertrain, wherein the real-time operational data is selected from a battery health, state of charge (SOC), temperature, motor drive parameters, voltage, current, speed, or a combination thereof; and
a cloud-based processing unit (104), connected to the detection unit (102), characterized in that the cloud-based processing unit (104) is configured to:
process the real-time operational data collected by the detection unit (102);
generate a digital twin model of the Electric Vehicle (EV) powertrain based on the processed operational data;
generate predictive analysis results by simulating the digital twin model for conditional variables by applying a machine learning model; and
display the generated predictive analysis on an output unit (106).
2. The system (100) as claimed in claim 1, wherein the detection unit (102) comprises Internet of Things (IoT)-enabled sensors.
3. The system (100) as claimed in claim 1, wherein the machine learning model is selected from Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), random forest classifiers, Support Vector Machines (SVMs), or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the output unit (106) is selected from a vehicle dashboard display, a mobile device, a computer, a smart phone, a mobile application based device, a web-based interface, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the generated predictive analysis is selected from a fault detection analysis, a remaining useful life (RUL) estimation, anomaly detection, energy efficiency optimization analysis, maintenance scheduling recommendations, or a combination thereof.
6. The system (100) as claimed in claim 1, wherein the cloud-based processing unit (104) is connected to the detection unit (102) through a wireless communication network (108).
7. The system (100) as claimed in claim 1, wherein the cloud-based processing unit (104) configured to transmit an alert signal to an authorized user of the Electric Vehicle (EV) upon detecting a probability of a mishap, a fire accident, or component failure based on the predictive analysis results.
8. The system (100) as claimed in claim 1, comprising a computing application (110) for enabling a user to input the conditional variables for simulating the digital twin model.
9. A method (200) for predictive maintenance of Electric Vehicle (EV) powertrain components using a digital twin-based system (100), the method (200) comprising steps of:
collecting real-time operational data of an Electric Vehicle (EV) powertrain using a detection unit (102), wherein the real-time operational data is selected from a battery health, state of charge (SOC), temperature, motor drive parameters, voltage, current, speed, or a combination thereof;
processing the real-time operational data collected by the detection unit (102);
generating a digital twin model of the Electric Vehicle (EV) powertrain based on the processed operational data;
generating predictive analysis results by simulating the digital twin model for conditional variables by applying a machine learning model; and
displaying the generated predictive analysis on an output unit (106).
10. The method (200) as claimed in claim 9, comprising a step of transmitting an alert signal to an authorized user of the Electric Vehicle (EV) upon detecting a probability of a mishap, a fire accident, or component failure based on the predictive analysis results.
Date: March 07,2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant