METHODS AND SYSTEMS FOR ACCESSING VEHICLE HEALTH, SAFETY AND PROVIDING PERFORMANCE OF AN ELECTRIC VEHICLE

Abstract

Embodiments discloses methods and systems for for accessing a vehicle health, safety, and providing performance prediction of an electric vehicle (EV) 104, the method comprising: collecting, by a sensing unit (304) embedded within the EV, real-time data related to vehicle parameter comprising at least one of battery health, powertrain performance, vehicle usage, distance, crash, service undergone, speed, load, braking efficiency, and safety indicators; transmitting, by a telematics control unit (TCU) (302), the collected data of the vehicle parameter to an electronic device (102); analyzing, by the learning module (408), the received vehicle data to assess the health, safety, and performance of the EV by generating a health scan summary; transmitting, by the electronic device (102), the health scan summary to enable the user to view the vehicle's condition and recommended actions.

Specification

Description:TECHNICAL FIELD

[001] The present disclosure relates to an electric vehicle (EV), and more particularly accessing vehicle health, safety and providing performance prediction of the EV using various parameters such as battery health, powertrain performance, overall energy consumption, to ensure vehicle safety, optimize performance, and predict necessary maintenance using a learning module.

BACKGROUND

[002] The growing adoption of electric vehicles (EVs) and the increasing need for advanced systems to monitor vehicle health, predict performance, and ensure safety. As EVs become more prevalent, they bring unique challenges that demand innovative approaches to maintain optimal functionality, safety, and efficiency over time. Traditional internal combustion engine (ICE) vehicles rely on well-established maintenance protocols. However, EVs introduce components like high-capacity batteries, complex powertrains, and regenerative braking systems, which require different monitoring and maintenance considerations.
[003] ICE vehicles rely on well-established maintenance practices, including periodic oil changes and mechanical inspections, the needs of EVs are distinct. Battery health, for example, is critical, as battery degradation directly impacts the range, efficiency, and overall lifespan of the vehicle. Battery packs, often the most expensive component of an EV, degrade over time due to factors such as charging cycles, environmental conditions, and driving patterns. This degradation leads to diminished range and, potentially, unexpected failures if not carefully monitored. Additionally, electric powertrains require different diagnostic methods to ensure optimal performance, as wear and tear on electrical systems can occur differently compared to mechanical counterparts.
[004] The regenerative braking systems in EVs, while extending the life of brake pads, introduce unique operational demands. Variations in the regenerative and friction braking systems engage can affect driving experience, brake wear, and, ultimately, safety. Currently, many EV systems rely on traditional onboard diagnostics, which may alert drivers to an issue after it has occurred, lacking the predictive capability to address issues before they lead to failures. This limitation highlights a pressing need for more advanced health monitoring, especially as EV technology and usage expand globally.
[005] Current EV health and safety monitoring systems offer only limited predictive capabilities, relying on periodic diagnostic checks or user-driven monitoring. However, real-time and predictive analysis in areas such as battery health, powertrain condition, and performance degradation are crucial for preventing sudden breakdowns and ensuring user safety. Additionally, without intelligent predictive maintenance, there can be increased operational costs, reduced vehicle lifespan, and compromised safety.
[006] As such, advancements in artificial intelligence (AI) and machine learning (ML) provide a promising avenue for addressing these needs. AI-driven vehicle health and performance systems can predict and mitigate potential issues before they impact functionality. For instance, AI can analyze vehicle data to recognize patterns in battery health, predict powertrain wear, and provide real-time feedback to drivers and fleet managers. Such predictive capabilities are essential for ensuring that EVs operate reliably, safely, and efficiently, especially as the technology continues to evolve.
[007] Hence, there is a need in the art for solutions which will overcome the above-mentioned drawback(s), among others.

OBJECTS

[008] The primary object of the embodiments herein is to disclose methods and systems for providing vehicle health, safety and performance prediction of an electric vehicle (EV) by monitoring battery, powertrain, and braking systems.
[009] Another object of the embodiments herein is to disclose methods and systems for enable predictive maintenance by leveraging artificial intelligence (AI) and machine learning (ML) to analyze real-time and historical data, allowing the system to forecast maintenance needs before issues affect vehicle performance
[0010] Another object of the embodiments herein is to disclose methods and systems by optimizing battery management and extend battery life by monitoring key battery parameters (such as voltage, current, temperature) and advising on optimal usage and charging practices based on real-time insights, thereby reducing degradation rates.
[0011] Another object of the embodiments herein is to disclose methods and systems for providing a user-friendly interface for drivers to easily access diagnostics, performance insights, and maintenance recommendations, facilitating informed decision-making and proactive vehicle management.
[0012] Further object of the embodiments herein is to disclose methods and systems for providing robust vehicle health scan system that continuously monitors critical parameters such as motor temperature, battery charge profile, motor current consumption, tire pressure, and brake system health. This system aims to identify potential issues in the powertrain and battery systems before they lead to failures, ensuring preventive rather than reactive maintenance.

[0013] A further object of the invention is to provide an AI-driven recommendation engine that integrates sensory data from the vehicle with environmental and operational factors such as terrain type, vehicle payload, traffic conditions, and weather. This integration enables dynamic analysis, allowing for real-time optimization of battery utilization, thermal management, and overall vehicle efficiency.

BRIEF DESCRIPTION OF FIGURES

[0014] Embodiments herein are illustrated in the accompanying drawings, throughout which reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:

[0015] FIG. 1 illustrates an environment for accessing vehicle health, safety and providing performance of an electric vehicle (EV), according to embodiments as disclosed herein;

[0016] FIG. 2 depicts a block diagram illustrating various units of an electronic device, which is used for accessing vehicle health, safety and providing performance of the EV, according to embodiments as disclosed herein;

[0017] FIG. 3 depicts a block diagram illustrating various units of the electric vehicle, which is used for providing prediction of the EV by accessing various parameters, according to embodiments as disclosed herein;

[0018] FIG. 4 depicts a block diagram illustrating various modules for predicting the vehicle health using a vehicle health scan module, vehicle health prediction module and learning module, according to embodiments as disclosed herein.

[0019] FIG. 5 is an example diagram illustrating the various steps involved in accessing vehicle health, safety and providing performance of the EV, according to embodiments as disclosed herein; and

[0020] FIG. 6 is an example diagram illustrating the generated health scan summary using various sensors and parameters for accessing the vehicle health, safety and performance of the EV, according to embodiments as disclosed herein.


DETAILED DESCRIPTION

[0021] The example embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The description herein is intended merely to facilitate an understanding of ways in which the example embodiments herein can be practiced and to further enable those of skill in the art to practice the example embodiments herein. Accordingly, this disclosure should not be construed as limiting the scope of the example embodiments herein.

[0022] The embodiments herein disclose methods and systems for accessing vehicle health, safety and providing performance of the EV. Referring to the drawings, and more particularly to FIGS. 1 through 6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown example embodiments.

[0023] FIG. 1 illustrates an environment for accessing vehicle health, safety and providing performance of an electric vehicle (EV). As illustrated in FIG. 1, environment 100 comprises the EV 104, a charging mechanism 110, and the electronic device 102 to a cloud server 108 through a communication network 106. The electronic device 102 may be connected to the communication network 106 connected to the cloud server 108. The electronic device 102 may be connected to the cloud server 108 through the communication network 106 and/or at least one other communication network (not shown).
[0024] As illustrated in FIG. 1, the EV 104 may be connected to the cloud server 108 using a Controller Area Network (CAN) protocol, through the communication network 106. Therefore, connecting the EV 104 to the cloud server 108 using the CAN, helps in accessing health of the vehicle. The health of the vehicle can be analyzed based on the vehicle parameters, which may include, but are not limited to tracking vehicle location, tracking performance of the vehicle (using parameters such as acceleration, maximum speed driven by the rider, energy consumption of the battery, temperature levels of the battery, and so on), vehicle usage tracking, distance covered by the vehicle, vehicle-mode used by the rider, tracking of the vehicle charging, crash, fall tracking, service history (services undergone by the vehicle, such as number of times the vehicle has been serviced, replacement of the vehicle spare parts, and so on), remote lock and recovery of the vehicle.
[0025] In the connected EV, the CAN serves as the backbone for onboard communication, while an additional gateway module interfaces the CAN bus with external networks, such as Wi-Fi, LTE, or 5G, to connect to the cloud. CAN transmit critical vehicle data and sensor information, which may include, but are not limited to battery voltage, current, temperature, state-of-charge (SoC), motor torque, tyre pressure, and speed.
[0026] The communication network 106 may include at least one of, but is not limited to, a wired network, a value-added network, a wireless network, a satellite network, or a combination thereof. Examples of the wired network may be but are not limited to, a Local Area Network (LAN), a Wide Area Network (WAN), the CAN, an Ethernet, and so on. Examples of the wireless network may be, but are not limited to, a cellular network, a wireless LAN (Wi-Fi), Bluetooth, Bluetooth low energy, Zigbee, Wi-Fi Direct (WFD), Ultra-wideband (UWB), infrared data association (IrDA), near field communication (NFC), and so on.
[0027] In an example, the EV 104 is configured to the Telematics Control Unit (TCU) which provide, may include, but are not limited to vehicle-to-cloud communication, remote monitoring, and control capabilities. TCU acts as the central gateway for telematics functions, integrating data from various Electronic Control Units (ECUs) and transmitting it to external servers over cellular networks. TCUs play a pivotal role in enabling connected vehicle features such as vehicle health scan, tracking performance of the vehicle, providing safety and predicting the health of the vehicle, real-time diagnostics, over-the-air updates, and remote vehicle management.

[0028] The TCU configured on the EVs may perform several functions, may include but are not limited to data aggregation and transmission, real-time monitoring and diagnostics, remote control and command execution, Over-the Air (OTA) update, connectivity management, and so on.

[0029] The TCU collects data from various ECUs (e.g., Battery Management System (BMS), Motor Control Unit (MCU), Inverter Control, etc.) through the CAN or other in-vehicle communication networks. The TCU aggregates and pre-processes this data before transmitting it to cloud servers for analysis and storage using protocols such as MQTT, HTTP, or CoAP. The TCU monitors the status of critical vehicle systems by predicting the health of the vehicle by analyzing the vehicle parameters which may include, but not limited to location of the vehicle, performance of the vehicle, usage of the vehicle, charging history, crash and fall tracking of the vehicle, number of services undergone by the vehicle, battery health, motor temperature, charging status, and more. It provides real-time vehicle health, fault diagnostics, vehicle safety, generating alerts and fault codes that can be used for predictive maintenance.

[0030] Various ECUs in the EV transmit data through the CAN bus, including vehicle condition, performance of the vehicle, engine condition, battery status, motor performance, vehicle health and diagnostic information. The TCU aggregates data from all the ECUs and applies pre-processing (e.g., filtering, compression) to reduce data size. The data is transmitted to the cloud using secure cellular networks. The cloud servers can then analyze the data for real-time monitoring, diagnostics, and predictive analytics.

[0031] Hence, TCU in the EV is a sophisticated component that integrates real-time monitoring, cloud connectivity, and control functionalities. It serves as the brain of the connected vehicle, enabling features such as remote diagnostics, OTA updates, and predictive maintenance. A well-configured TCU ensures the safety, reliability, and efficiency of modern electric vehicles.

[0032] In another example, EV 104, the electronic device 102, and the databases may be connected with each other directly and/or indirectly (for example, via direct communication, via an access point, and so on). In another example, the electronic device 102, and the databases may be connected with each other via a relay, a hub, and a gateway. It is understood that the electronic device 102, and the databases may be connected to each other in any of various manners (including those described above) and may be connected to each other in two or more of various manners (including those described above) at the same time.

[0033] The electronic device 102 referred to herein may be a device that access the health of the vehicle, enables safety to the rider of the vehicle, by vehicle health scan summary and recommendation, and vehicle information. The electronic device 102 may also be a user device that is being used by the user/rider to connect, and/or interact, and/or control the operations of the plurality of EV. Examples of the electronic device 102 maybe, but are not limited to, a smartphone, a mobile phone, a video phone, a computer, a tablet personal computer (PC), a laptop, a wearable device, a personal digital assistant (PDA), an IoT device, or any other device that may be portable.

[0034] The cloud server 108 hosts the core AI and machine learning (ML) modules, which analyze vehicle data, generate predictions, and provide actionable recommendations. The cloud server 108 is integrated using an architecture to pull external Application Programming Interface/cloud information, such as vehicle location tracking, vehicle performance tracking, vehicle usage tracking, battery charging information, vehicle condition based on the usage of the rider/user, weather updates, traffic conditions, and charging station availability, and synchronize it with the vehicle's health scan. The learning module 408 within the cloud server 108 utilizes deep learning (DL) algorithms to identify patterns in historical data and predict vehicle health, safety to the rider, potential component failures or maintenance needs.

[0035] In an embodiment as disclosed herein, the location of the vehicle can be tracked real-time using integrated telematics and GPS data, providing essential data for ensuring vehicle security. It allows users/riders to monitor vehicle location continuously, enhancing recovery and response in case of theft or an emergency.

[0036] The vehicle performance can be tracked for prediction, which may include, monitoring acceleration, maximum speed, energy consumption, and temperature levels. By analyzing these metrics, the system can provide valuable insights into the performance of key vehicle systems, predicting trends or potential performance issues. It also assists in optimizing driving behavior, enhancing energy efficiency, and ensuring the longevity of the EV's components.

[0037] Tracking vehicle usage data, including distance traveled and mode of operation, enables precise analytics for health estimation. This data forms the basis for predictive maintenance and health forecasts by assessing the wear on components and usage patterns, ultimately aiding in reducing unscheduled downtimes and maintaining vehicle efficiency.

[0038] The system monitors charging events to assess battery health, tracking metrics like charge cycles, charging speed, and charging duration. This allows the system to predict battery life, diagnose issues early, and recommend optimal charging behaviors that improve the battery's performance and longevity.

[0039] The system monitors crash and fall events to gauge the physical integrity of the vehicle. Using accelerometers and gyroscopes, it detects impacts and sudden drops, which helps in determining the need for mechanical inspections or repairs following incidents that might compromise vehicle safety and performance.

[0040] By maintaining a record of service events, parts replacements, and maintenance activities, the system builds a history of the vehicle's condition. This data is crucial for predicting future maintenance needs, forecasting service intervals, and managing component lifecycles, contributing to a well-maintained and reliable vehicle.

[0041] Enabling users to run self-scans on the vehicle via a mobile app empowers proactive health checks. By scanning key systems such as battery status and diagnostics, the app can provide the user with a real-time overview of the vehicle's health, alerting them to any immediate concerns.

[0042] Remote lock and recovery for safety feature allows vehicle operators to lock and disable the vehicle remotely if needed, such as during unauthorized access or theft situations. By remotely securing the EV, this feature ensures enhanced vehicle security and recovery support, particularly useful for fleet operators managing multiple vehicles.

[0043] The charging mechanism 110 may be equipped with smart charging features that optimize the battery's charging profile based on its current vehicle health condition and safety to the users. The system supports OCPP (Open Charge Point Protocol) and OCPI (Open Charge Point Interface) protocols, ensuring compatibility with a wide range of charging stations and enabling features like slot reservation and dynamic charge rate adjustment.

[0044] In an embodiment, the EV 104 may be equipped with various sensors, actuators, and control modules that work in synchronization with the electronic device 102 to provide vehicle health, safety and performance. The health of the vehicle can be analyzed based on the vehicle parameters, which may include, but are not limited to tracking vehicle location, tracking performance of the vehicle (using parameters such as acceleration, maximum speed driven by the rider, energy consumption of the battery, temperature levels of the battery, and so on), vehicle usage tracking, distance covered by the vehicle, vehicle-mode used by the rider, tracking of the vehicle charging, crash, fall tracking, service history (services undergone by the vehicle, such as number of times the vehicle has been serviced, replacement of the vehicle spare parts, and so on), remote lock and recovery of the vehicle.
[0045] The EV 104 sends the sensor data to the electronic device 102, enabling it to analyze the vehicle health and provide the rider with relevant recommendation, warnings and suggestions.
[0046] The EV 104 may be equipped with sensors, may include, but are not limited to powertrain sensors, battery health sensors and forward and reverse sensors, speed and load detection sensor, wheels and brake detection sensor, current consumption/ friction sensor, sensor to identify battery residual capacity, and so on. The powertrain sensors provide real-time data that enables the vehicle's control systems to optimize performance, efficiency, and safety, making them integral to modern vehicle powertrain management systems. The battery health sensors provide a granular view of battery conditions and enabling intelligent battery management to ensure optimal performance and safety. The forward and reverse sensors monitor the forward movement and reverse movement of the EV 104 respectively.
[0047] Sensors on the EV continuously collect data related to various parameters such as powertrain performance, battery health, speed, load, wheel conditions, braking efficiency, current consumption, and battery residual capacity. These sensors monitor different aspects of the vehicle and generate data points in real time.
[0048] The gathered data is transmitted to an onboard electronic device (e.g., a vehicle control unit or telematics system). This device acts as an interface between the EV and the cloud infrastructure.
[0049] AI algorithms analyze powertrain sensor data to determine optimal operating conditions. For instance, AI models can predict when to adjust motor torque or power output based on driving patterns, load conditions, and temperature readings to ensure peak efficiency. Battery Health Monitoring: Machine learning (ML) models continuously evaluate battery health data, analyzing metrics like voltage, current, temperature, and charge cycles. These models can predict potential issues such as battery degradation, need for recalibration, or preventive maintenance. The AI can also provide insights on charging behaviors to optimize battery life. Predictive Maintenance: By analyzing sensor data related to wheels, brakes, and other mechanical components, the AI can detect wear and tear. It predicts when components are likely to fail, helping to schedule maintenance before a breakdown occurs. This reduces downtime and maintenance costs. Vehicle Safety Monitoring: AI systems analyze data from forward and reverse sensors, as well as speed and load sensors, to detect unsafe conditions. For example, the AI can identify excessive acceleration, abnormal braking patterns, or high load scenarios that could lead to potential accidents. Alerts can be generated for immediate action. Efficiency Enhancements: The AI algorithms can identify patterns in energy consumption based on speed, acceleration, load, and road conditions. By learning from historical data, the AI can make recommendations on driving practices to enhance energy efficiency, extend battery range, and minimize wear on vehicle components. Battery Residual Capacity Analysis: AI processes data on battery residual capacity to provide accurate estimates of the remaining driving range. By analyzing charge and discharge cycles, AI models can predict when the battery may run out of power, allowing drivers to plan charging stops.
[0050] Embodiment as disclosed herein, with the integration of sensor data, electronic devices, cloud-based AI, and real-time communication enables comprehensive monitoring and optimization of the EV's health, performance, and safety. By leveraging AI, the system can predict potential issues, optimize efficiency, and enhance the overall driving experience.
[0051] FIG. 2 depicts a block diagram illustrating various units of an electronic device, which is used for accessing vehicle health, safety and providing performance of the EV. The electronic device 102 comprises an input unit 206, a memory 202, a processor 210, an output unit 208, a communication interface 204, and a database 212.
[0052] The memory 202 referred herein include at least one type of storage medium, from among a flash memory type storage medium, a hard disk type storage medium, a multi-media card micro type storage medium, a card type memory (for example, an SD or an XD memory), random-access memory (RAM), static RAM (SRAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), programmable ROM (PROM), a magnetic memory, a magnetic disk, or an optical disk.
[0053] The memory 202 may store at least one of, but is not limited to, vehicle health scan summary and recommendation, and vehicle location, vehicle usage tracking, battery charge of the vehicle service history and so. The memory 202 may store the learning module, neural network, vehicle health scan module 404, vehicle health prediction module 406. The learning module 408 of the neural network can be processed by processor 210 to obtain the input, i.e., sensory parameters. The learning module can be provided with the vehicle health scan summary and recommendation, and vehicle information, previous alerts/ notification provided to the user.
[0054] The memory 202 stores pre-trained AI models that are periodically updated through cloud-based learning. This includes model parameters, neural network weights, and other elements essential for accurate decision-making.
[0055] Edge AI Models with some AI models, which operate directly on the electronic device, are stored locally to enable edge computing capabilities. This setup ensures that the device can perform basic AI operations even without cloud connectivity.

[0056] The learning module (408) of the neural network can be processed by processor (210) to obtain the vehicle health scan summary and recommendation, and other vehicle information.
[0057] Examples of the neural network, but are not limited to, an Artificial Intelligence (AI) model, a multi-class Support Vector Machine (SVM) model, a Convolutional Neural Network (CNN) model, a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann Machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), a regression-based neural network, a deep reinforcement model (with ReLU activation), a deep Q-network, and so on. The neural network may include a plurality of nodes, which may be arranged in layers. Examples of the layers may be but are not limited to, a convolutional layer, an activation layer, an average pool layer, a max pool layer, a concatenated layer, a dropout layer, a fully connected layer, a SoftMax layer, and so on. Each layer has a plurality of weight values and performs a layer operation through calculation of a previous layer and an operation of a plurality of weights/coefficients. A topology of the layers of the neural network may vary based on the type of the respective network. In an example, the neural network may include an input layer, an output layer, and a hidden layer. The input layer receives a layer input and forwards the received layer input to the hidden layer. The hidden layer transforms the layer input received from the input layer into a representation, which may be used for generating the output in the output layer. The hidden layers extract useful/low-level features from the input, introduce non-linearity in the network and reduce a feature dimension to make the features equivalent to scale and translation. The nodes of the layers may be fully connected via edges to the nodes in adjacent layers. The input received at the nodes of the input layer may be propagated to the nodes of the output layer via an activation function that calculates the states of the nodes of each successive layer in the network based on coefficients/weights respectively associated with each of the edges connecting the layers.
[0058] The vehicle health scan module 404, vehicle health prediction module 406 may be trained using at least one learning method. Examples of the learning method may be, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, regression-based learning, and so on. The modules may be neural network models in which several layers, a sequence for processing the layers, and parameters related to each layer may be known and fixed for performing the intended functions. Examples of the parameters related to each layer may be, but are not limited to, activation functions, biases, input weights, output weights, and so on, related to the layers.
[0059] A function associated with the learning method may be performed through the non-volatile memory, the volatile memory, and/or the processor 210. The processor 210 may include one or a plurality of processors. At the time, one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an Artificial Intelligence (AI)-dedicated processor such as a neural processing unit (NPU).
[0060] Here, being provided through learning means that, by applying the learning method to a plurality of learning data, a predefined operating rule, or the neural network, of the desired characteristic is made. Functions of the neural network, the modules may be performed in the electronic device 102 itself in which the learning according to an embodiment is performed, and/or maybe implemented through a separate server/system.
[0061] The communication interface 204 may include one or more components, which enable the electronic device 102 to communicate with another device (for example, the EV 104) using the communication methods that have been supported by the communication network 106. The communication interface (204) may include the components such as a wired communicator, a short-range communicator, a mobile/wireless communicator, and a broadcasting receiver.
[0062] The wired communicator may enable the electronic device 102 to communicate with the other devices using the communication methods such as, but are not limited to, wired LAN, Ethernet, and so on. The short-range communicator may enable the electronic device (102) to communicate with the other devices using the communication methods such as, but are not limited to, Bluetooth low energy (BLE), near field communicator (NFC), WLAN (or Wi-fi), Zigbee, infrared data association (IrDA), Wi-Fi Direct (WFD), UWB communication, Ant+ (interoperable wireless transfer capability) communication, shared wireless access protocol (SWAP), wireless broadband internet (Wibro), wireless gigabit alliance (WiGiG), and so on. The mobile communicator may transmit/receive wireless signals with at least one of a base station, an external terminal, or a server on a mobile communication network/cellular network. For example, the wireless signal may include a speech call signal, a video telephone call signal, or various types of data, according to transmitting/receiving of text/multimedia messages. The broadcasting receiver may receive a broadcasting signal and/or broadcasting-related information from the outside through broadcasting channels. The broadcasting channels may include satellite channels and ground wave channels. In an embodiment, the electronic device 102 may or may not include the broadcasting receiver.
[0063] The output unit (208) may be configured to assist the rider with vehicle health, safety, and prediction of the vehicle through vehicle health scan summary and vehicle information. The output unit (208) may include at least one of, for example, but is not limited to, a display, a User Interface (UI) module, a light-emitting device, and so on. The UI module may provide a specialized UI or graphical user interface (GUI), or the like, synchronized to the electronic device 102, according to the applications.
[0064] The processor 210 may include one or a plurality of processors. The one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an Artificial Intelligence (AI)-dedicated processor such as a neural processing unit (NPU).
[0065] In another example, the electronic device 102, and the database 212 may be connected with each other directly (for example: via direct communication, via an access point, and so on). In another example, the electronic device 102, and the database 212 may be connected with each other via a relay, a hub, and a gateway. It is understood that the electronic device 102, and the database 212 may be connected to each other in any of various manners (including those described above) and may be connected to each other in two or more of various manners (including those described above) at the same time.
[0066] FIG. 3 depicts a block diagram illustrating various units of the electric vehicle, which is used for providing prediction of the EV by accessing various parameters for accessing vehicle using various vehicle parameters. The electric vehicle 104 comprises a sensing unit 304, a telematics control unit 302, ECU 308 and an output unit 306.
[0067] The TCU 302 acts as the central processing hub, aggregating data from various sources across the EV 104. It collects and processes real-time data from sensors and other vehicle components, transmitting it securely to cloud servers for further analysis and diagnostics. The TCU is responsible for data aggregation, pre-processing techniques like data filtering and compression to ensure efficient data transmission to the cloud, and facilitating seamless cloud connectivity for real-time monitoring, diagnostics, and predictive analytics. Through real-time monitoring and historical data analysis, the TCU helps predict potential issues, enabling timely maintenance and reducing the likelihood of vehicle breakdowns. This ensures the safety and reliability of the EV.
[0068] The Sensing Unit 304 consists of a network of sensors distributed throughout the EV to collect real-time data about various vehicle parameters. These sensors are critical for providing the TCU with accurate and up-to-date information regarding the vehicle's condition. Key sensors may include powertrain sensors to monitor the performance of the engine, motor, and transmission systems, battery health sensors to track parameters like voltage, current, temperature, and charge cycles, speed and load sensors to monitor performance metrics and energy consumption, forward and reverse sensors to monitor the vehicle's movement, wheels and brake sensors to monitor wear and predict when maintenance is needed, current consumption/friction sensors to measure the current consumption and detect any unusual resistance, and battery residual capacity sensors to provide precise measurements of the battery's remaining capacity. These sensors help in optimizing performance, improving energy efficiency, and ensuring the battery's life.
[0069] The ECU 308 is responsible for controlling various sub-systems within the EV. It operates autonomously to manage individual systems like braking, steering, and power management, and communicates with the TCU to provide data on the status of these systems. ECUs are tasked with real-time control and monitoring of specific vehicle components, such as brakes and batteries, and play an essential role in implementing control functions like adaptive cruise control, anti-lock braking systems, and electronic stability control. They communicate essential data through the CAN bus, allowing the TCU to collect, analyze, and use this information for overall vehicle management and predictive maintenance.
[0070] The Output Unit 306 provides users with insights into the vehicle's health, safety, and performance. This unit is equipped with displays and user interface modules that allow drivers to receive real-time updates, warnings, and recommendations. The Output Unit can display a comprehensive health summary of the EV, issue safety alerts and notifications about excessive speed, high temperature, or low battery levels, and provide actionable recommendations based on data analysis, such as when to schedule a service, replace worn-out parts, or recharge the battery.
[0071] In terms of processing flow, the sensing unit 304 collects real-time data from various sensors distributed across the EV. This data is transmitted to the TCU via the CAN bus, where it is aggregated and pre-processed to ensure it is ready for transmission. The TCU removes redundant data, compresses the data packets, and transmits them securely to the cloud, where AI algorithms analyze it to generate insights, predictions, and recommendations. The processed data is then sent back to the EV, where it is displayed on the Output Unit, allowing drivers to receive real-time updates and actionable insights to improve the vehicle's safety, performance, and efficiency.
[0072] FIG. 3 demonstrates how the EV can monitor and diagnose its systems in real time, ensuring safety and reliability through the integration of various sensors, ECUs, and the TCU. The TCU and ECUs enable predictive maintenance by analyzing sensor data to predict when parts need to be serviced or replaced, preventing breakdowns and ensuring the vehicle's longevity. With the data collected, the EV can adjust its systems in real time, optimizing performance based on current conditions. For instance, powertrain adjustments can enhance energy efficiency, while battery management ensures optimal charging.
[0073] Overall, FIG. 3 represents an integrated architecture for the EV that utilizes real-time data collection, intelligent processing, and cloud-based analytics to enhance vehicle health, safety, and performance. This comprehensive approach ensures the EV remains efficient, reliable, and safe for the user.
[0074] FIG. 4 depicts a block diagram illustrating various modules for predicting the vehicle health using a vehicle health scan module, vehicle health prediction module and learning module. FIG. 4 provides an overview of a system architecture designed to predict the health, safety, and performance of an electric vehicle (EV) using advanced data analytics and machine learning techniques. The vehicle health, safety and performance prediction system 400 comprises a vehicle health scan module 404, vehicle prediction module 406, and a learning module 408. Together, these modules facilitate comprehensive monitoring, diagnosis, and prediction of vehicle health, enabling proactive maintenance and safety measures.
[0075] The vehicle health scan module 404 is responsible for performing regular health checks on the EV. It collects data from various sensors embedded within the vehicle, which monitor different aspects such as battery status, motor performance, brake efficiency, and overall system functionality. This module performs a diagnostic scan, identifying any anomalies or deviations from normal operating parameters. By continuously scanning the vehicle's systems, it can provide real-time insights into the current health status, helping drivers and operators to address any immediate issues.
[0076] The vehicle health prediction module 406 utilizes the data gathered from the health scans to predict potential future issues. By analyzing trends and patterns in the data, this module can forecast when specific components might fail or require maintenance. For example, it can predict battery degradation based on charge cycles, temperature variations, and energy consumption patterns, or it can anticipate wear on the braking system by examining usage data. This predictive capability allows for proactive maintenance, reducing the likelihood of unexpected breakdowns and ensuring that the vehicle remains operational and safe.
[0077] The learning module 408 is an integral part of the system that continuously improves the accuracy and effectiveness of the predictions. This module employs machine learning (ML) and artificial intelligence (AI) algorithms to learn from historical data, refine its models, and adapt to new information. As more data is processed through the system, the Learning Module updates its predictive models to account for different driving conditions, usage patterns, and external factors like weather. By continuously learning, it enhances the system's ability to provide accurate predictions, making the vehicle health and performance management more reliable and efficient over time.
[0078] The overall system works by integrating these modules into a seamless workflow. Data is first collected through the vehicle health scan module 404, which sends diagnostic information to the vehicle health prediction module 406. The prediction module analyzes this data using algorithms that have been fine-tuned by the learning module. The output of the analysis is then used to predict potential issues and generate recommendations for maintenance or operational adjustments. For instance, if the system detects that the battery is approaching a critical wear level, it may recommend a battery replacement before it fails. Similarly, if the brakes show signs of degradation, the system may suggest a service appointment to ensure safety.
[0079] Additionally, the learning module 408 allows the system to remain effective across different types of vehicles and operational conditions. By processing diverse datasets from multiple EVs, it can build more generalized models that apply to various scenarios. This adaptability ensures that the system remains useful for fleet operators managing multiple vehicles, as well as individual EV owners.
[0080] FIG. 4 demonstrates how this integrated approach allows the vehicle health, safety, and performance prediction system to not only diagnose current issues but also anticipate future problems. This proactive approach ensures that EVs can operate safely and efficiently while minimizing downtime due to unexpected failures. The system's ability to learn from new data means it can continuously improve, providing more accurate predictions and recommendations as it processes more information. This makes it a critical component in maintaining the reliability and safety of modern electric vehicles.
[0081] FIG. 5 is an example diagram illustrating the various steps involved in accessing vehicle health, safety and providing performance of the EV. FIG. 5 showcases a systematic approach that combines data analytics, machine learning, and user-friendly interfaces to provide comprehensive insights into the vehicle's condition. Each step utilizes specific data points and predictive techniques to ensure the vehicle operates efficiently and safely.
[0082] The first step involves initiating a vehicle health scan through a mobile application. This app is designed to connect with the EV's onboard systems and gather data from various sensors. By integrating AI algorithms, the application can analyze the collected data in real time, providing users with immediate feedback on the vehicle's health status. This makes it convenient for users to conduct self-checks on their EVs, ensuring that any potential issues are detected early.
[0083] The next step focuses on assessing the battery's health. The system analyzes key metrics such as charge cycles, voltage levels, temperature, and current to determine the overall condition of the battery. By evaluating these parameters, the system can estimate the residual capacity of the battery, which indicates how much charge it can still hold. This information is critical for predicting the remaining driving range and planning charging stops, thus preventing unexpected battery depletion.
[0084] This step involves analyzing the powertrain components, including the motor, transmission, and associated systems, to predict their lifespan. The system uses historical data from service records and real-time usage patterns to identify signs of wear and tear. By leveraging machine learning models, the system can forecast when components might fail, allowing for timely maintenance scheduling. This predictive maintenance approach helps to minimize downtime and extend the life of the powertrain.
[0085] The system evaluates the overall performance of the vehicle by analyzing various operational metrics such as acceleration, speed, energy consumption, and temperature levels. By understanding how the vehicle performs under different conditions, the system can predict energy consumption patterns and identify ways to optimize performance. For instance, it can recommend adjustments to driving habits to improve energy efficiency and extend the battery's range.
[0086] This step involves monitoring the vehicle's charging behavior. The system tracks how often and how long the EV has been charged, along with the charging speeds. By creating a detailed charge profile, the system can detect irregularities that may indicate underlying issues with the battery or charging system. Additionally, this information helps optimize charging practices, ensuring the battery remains in good condition over time.
[0087] Finally, the system reviews the vehicle's crash and fall history. Sensors in the EV can detect sudden impacts or drops, which may compromise the vehicle's mechanical integrity. By maintaining a log of such events, the system can recommend inspections or repairs as needed. This step is crucial for ensuring that the vehicle remains safe to operate, especially after an accident or a significant impact.
[0088] Overall, FIG. 5 represents a comprehensive framework for accessing and predicting the health, safety, and performance of an electric vehicle. By integrating real-time data analysis, historical records, and machine learning algorithms, the system can provide actionable insights, enabling users to maintain their EVs proactively. This not only ensures the safety of the vehicle but also enhances its performance and longevity.
[0089] FIG. 6 is an example diagram illustrating the generated health scan summary using various sensors and parameters for accessing the vehicle health, safety and performance of the EV.
[0090] FIG. 6 illustrates the generation of a health scan summary for an electric vehicle (EV) by utilizing various sensors and parameters to assess the overall health, safety, and performance of the vehicle. This diagram represents a comprehensive system where different sensors embedded within the EV collect critical data points, which are then processed to provide a detailed evaluation of the vehicle's condition.
[0091] FIG. 6 shows the EV equipped with sensors strategically positioned across key components such as the powertrain, battery, wheels, and other mechanical parts. These sensors continuously monitor various parameters, including battery health, motor efficiency, braking system performance, speed, load conditions, and more. The collected data is transmitted to a central processing unit or telematics control unit (TCU), which acts as the brain of the vehicle.
[0092] Once the data is aggregated by the TCU, it undergoes pre-processing to filter out noise and compress the information. The pre-processed data is then sent to cloud servers for deeper analysis using artificial intelligence (AI) and machine learning (ML) algorithms. The cloud-based systems analyze the data to generate real-time insights about the vehicle's performance, identify potential issues, and predict maintenance needs.
[0093] The generated health scan summary provides a comprehensive report covering the following aspects: Battery Health and Residual Capacity: Sensors monitor battery voltage, current, temperature, and charge cycles to assess the battery's condition. The summary includes insights on battery efficiency, predicted range, and any indicators of battery degradation.
[0094] Powertrain Performance: The system evaluates the performance of the motor, transmission, and associated components. Data such as motor torque, efficiency, and load handling are analyzed to ensure optimal functioning.
[0095] Safety Indicators: Safety-related parameters, including braking efficiency, crash detection, and stability controls, are monitored. Alerts are generated if any abnormal patterns are detected, ensuring the safety of the rider.
[0096] Overall Performance: The health scan summary includes metrics related to the EV's overall performance, such as acceleration, energy consumption, maximum speed, and driving efficiency. This allows for insights into the driving behavior and the impact on energy usage.
[0097] Maintenance Predictions: Based on historical data and current sensor readings, the system predicts when specific components may need maintenance or replacement. This proactive approach reduces unexpected breakdowns and extends the lifespan of the vehicle.
[0098] The various actions, acts, blocks, steps, or the like in the method and the flow diagram may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.
[0099] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
, Claims:1. A method for accessing a vehicle health, safety, and providing performance prediction of an electric vehicle (EV) 104, the method comprising:
collecting, by a sensing unit (304) embedded within the EV, real-time data related to vehicle parameter comprising at least one of battery health, powertrain performance, vehicle usage, distance, crash, service undergone, speed, load, braking efficiency, and safety indicators;
transmitting, by a telematics control unit (TCU) (302), the collected data of the vehicle parameter to an electronic device (102);
analyzing, by the learning module (408), the received vehicle data to assess the health, safety, and performance of the EV by generating a health scan summary;
transmitting, by the electronic device (102), the health scan summary to enable the user to view the vehicle's condition and recommended actions.

2. The method as claimed in claim 1, wherein the generated health scan summary comprises vehicle health, battery health, powertrain performance, safety indicators, overall vehicle performance, and maintenance predictions.

3. The method as claimed in claim 1, wherein the battery health comprises information related to voltage, current, temperature, charge cycles, and battery residual capacity.

4. The method as claimed in claim 1, wherein the powertrain performance comprises information related to motor torque, efficiency, energy consumption, and load handling capacity.

5. The method as claimed in claim 1, wherein the safety indicators are generated by monitoring braking efficiency, stability controls, crash detection, and other safety-related events.

6. The method as claimed in claim 1, wherein alert notification are generated to indicate abnormal patterns of the rider while driving the vehicle as the safety indicators to ensure safety.

7. The method as claimed in claim 1, wherein overall vehicle performance comprises acceleration, maximum speed, energy consumption, and driving efficiency.

8. The method as claimed in claim 1, comprising the step of providing predictive maintenance recommendations based on the analysis of historical data and current sensor readings to prevent unexpected breakdowns and extend the lifespan of vehicle components.

9. The method as claimed in claim 1, wherein the health scan summary is provided by the electronic device 102, showing detailed reports and alerts related to the vehicle's condition, energy consumption, and performance metrics.

10. A system (400) for accessing a vehicle health, safety, and providing performance prediction of an electric vehicle (EV) 104, the system comprising:
the electric vehicle (104);
an electronic device (102) with a processor (210), and memory (202);
a charging mechanism (110);
a cloud server (108);
wherein a sensing unit (304) embedded within the EV, collets real-time data related to vehicle parameter comprising at least one of battery health, powertrain performance, vehicle usage, distance, crash, service undergone, speed, load, braking efficiency, and safety indicators;
a telematics control unit (TCU) (302) transmits the collected data of the vehicle parameter to an electronic device (102);
a learning module (408) analyze the received vehicle data to assess the health, safety, and performance of the EV by generating a health scan summary; and
the electronic device (102) transmits the health scan summary to enable the user to view the vehicle's condition and recommended actions.

11. The system as claimed in claim 10, wherein the generated health scan summary comprises vehicle health, battery health, powertrain performance, safety indicators, overall vehicle performance, and maintenance predictions.

12. The system as claimed in claim 10, wherein the battery health comprises information related to voltage, current, temperature, charge cycles, and battery residual capacity.

13. The system as claimed in claim 10, wherein the powertrain performance comprises information related to motor torque, efficiency, energy consumption, and load handling capacity.

14. The system as claimed in claim 10, wherein the safety indicators are generated by monitoring braking efficiency, stability controls, crash detection, and other safety-related events.

15. The system as claimed in claim 10, wherein alert notification are generated to indicate abnormal patterns of the rider while driving the vehicle as the safety indicators to ensure safety.

16. The system as claimed in claim 10, wherein overall vehicle performance comprises acceleration, maximum speed, energy consumption, and driving efficiency.

17. The system as claimed in claim 10, comprising the step of providing predictive maintenance recommendations based on the analysis of historical data and current sensor readings to prevent unexpected breakdowns and extend the lifespan of vehicle components.

18. The system as claimed in claim 10, wherein the health scan summary is provided by the electronic device 102, showing detailed reports and alerts related to the vehicle's condition, energy consumption, and performance metrics.

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