METHODS AND SYSTEMS FOR TRIP ASSURANCE OF AN ELECTRIC VEHICLE

Abstract

The present invention discloses methods and systems for providing trip assurance of an electric vehicle (EV) using artificial intelligence (AI). The system comprises an electric vehicle (104) equipped with a sensing unit (304) to monitor sensory parameters, an electronic device (102) for processing the sensory data, and a cloud server (108) hosting AI-based learning modules for real-time diagnostics and recommendations. The method includes sensing powertrain and battery health parameters, processing the data to generate a health scan summary, and transmitting it to the cloud for advanced analysis. The system offers predictive maintenance, real-time trip recommendations, optimal charging profiles, and route planning based on vehicle status, environmental factors, and rider inputs. It is designed to enhance safety, reliability, and performance for EV riders, particularly for two-wheelers, through continuous monitoring and intelligent data analytics. This proactive approach enables efficient energy management and ensures a safer, more reliable travel experience.

Specification

Description:TECHNICAL FIELD

[001] The present disclosure relates to an electric vehicle, and more particularly assisting riders of an electric vehicle in trip assurance using a learning module.

BACKGROUND

[002] Electric vehicles (EVs) are increasingly becoming a popular alternative to traditional internal combustion engine (ICE) vehicles due to their eco-friendly nature, low operating costs, and minimal maintenance requirements. EVs are characterized by their absence of tailpipe emissions, smoother operation, and convenience in terms of at-home charging options. As these vehicles gain traction, it becomes imperative to address several challenges associated with their performance, safety, and reliability, especially in critical components like the powertrain, battery, and other electronic systems.

[003] The core components of an EV include the electric powertrain (motor, battery, motor control unit), various electronic modules (infotainment, telematics unit), LED lighting systems, chargers, chassis, and body panels. Despite their inherent advantages, EVs, particularly two-wheelers, have unique maintenance and performance challenges. One key concern is ensuring optimal vehicle health, which directly impacts safety, battery longevity, and the overall driving experience.


[004] In the context of EVs, maintaining a robust vehicle health monitoring system is crucial, as the performance of critical components such as the battery and motor is significantly influenced by environmental conditions, temperature variations, and operational duty cycles. For instance, batteries in EVs are susceptible to performance degradation due to excessive heating or sub-optimal charging profiles. Likewise, the motor and powertrain components require continuous monitoring to prevent failures and ensure smooth functioning. Therefore, traditional EV systems, which often focus solely on providing alerts based on reactive maintenance strategies, fail to adequately address these concerns.

[005] To address these challenges, the present invention provides an innovative method and system for performing a comprehensive vehicle health scan and trip assurance using artificial intelligence (AI). This solution enables proactive and predictive maintenance, thereby ensuring that EV riders have a reliable and safe driving experience. The system is designed to offer real-time recommendations and diagnostics for thermal management, powertrain health, battery status, and trip planning. These insights not only enhance safety but also optimize vehicle performance based on multiple sensory inputs such as motor temperature, battery energy content, terrain type, vehicle payload, and traffic conditions.

[006] One of the key aspects of this invention is the integration of AI to analyze sensory data, predict potential failures, and recommend corrective actions. This approach marks a departure from conventional EV health management systems, which typically rely on pre-set thresholds and simple alert mechanisms. The inclusion of AI allows for dynamic analysis of complex parameters, enabling the system to provide more nuanced insights into vehicle health. Furthermore, the system offers trip assurance through features such as route and weather analysis, charging station location identification, charge profile optimization, and parking slot identification, all of which contribute to a safer and more efficient ride.

[007] Another novel aspect of the invention is its ability to incorporate environmental and usage-based factors into its diagnostics. For instance, the AI engine can consider factors like terrain, traffic conditions, and vehicle ride modes when generating recommendations for optimal battery utilization and route planning. This ensures that the system adapts to real-world conditions, providing more accurate and context-sensitive recommendations to the rider.

[008] In comparison to traditional systems that provide basic vehicle diagnostics, this invention introduces a comprehensive health scan using AI and data analytics. The solution not only monitors key parameters such as motor temperature, current consumption, tire pressure, and brake health but also includes advanced features like real-time data analysis and automated trip route recommendations based on battery health and travel distance estimates. Moreover, the invention facilitates predictive maintenance by preemptively identifying potential issues and suggesting remedial actions before they escalate into serious problems.

[009] This invention is particularly significant for EV two-wheelers, which have been gaining popularity for urban transportation due to their maneuverability and cost-effectiveness. However, these vehicles are often more susceptible to performance fluctuations due to their smaller battery sizes and higher sensitivity to load and environmental factors. By integrating a sophisticated health monitoring and recommendation system, the invention aims to enhance the safety and reliability of EV two-wheelers, making them a more viable and attractive option for riders.

[0010] In summary, the present invention offers an advanced trip assurance system using AI, which is capable of providing a holistic view of vehicle health, predictive diagnostics, and personalized recommendations. It aims to transform the way EVs are monitored and maintained, ensuring a safer and more enjoyable experience for users while extending the operational life of critical components. This proactive approach to vehicle health management represents a significant advancement over existing solutions, setting a new benchmark for EV performance and safety.

OBJECTS

[0011] The primary object of the present invention is to provide a method and system for trip assurance of electric vehicles (EVs) using artificial intelligence (AI), which ensures comprehensive vehicle health monitoring, predictive diagnostics, and real-time trip planning, thereby enhancing the safety, reliability, and performance of the vehicle.

[0012] Another object of the invention is to implement a 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.

[0014] An additional object is to optimize battery health and performance through AI-based assessments of charging profiles, energy content, and temperature management, thereby extending battery life and improving travel distance estimation. This invention aims to address key challenges in EV performance, particularly in varying operational conditions, by providing precise energy management strategies.

[0015] Yet another object of the present invention is to enhance trip assurance by incorporating features such as route planning, weather alerts, and charging station identification. The system not only suggests optimal travel routes based on vehicle health and environmental factors but also identifies the nearest charging stations, reserves charging slots, and optimizes the charging profile to minimize travel disruptions.

[0016] It is also an object of the invention to support driver safety and convenience through comprehensive vehicle information and alerts, including vehicle location, parking slot identification, and route mapping. This provides the rider with a complete overview of the vehicle's health and travel status, reducing the likelihood of unexpected issues during the trip.

[0017] A further object is to distinguish the present invention from existing solutions by focusing on two-wheeler electric vehicles, which are more susceptible to performance degradation due to their smaller size and higher sensitivity to environmental conditions. The invention addresses these challenges by providing a tailored solution that takes into account the unique requirements of EV two-wheelers.

[0018] Additionally, the invention aims to provide a proactive and user-friendly interface for riders, offering detailed vehicle health summaries, actionable recommendations, and trip planning features through an intuitive display, thereby enhancing the user experience and promoting wider adoption of electric vehicles.


BRIEF DESCRIPTION OF FIGURES

[0019] 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:

[0020] FIG. 1 illustrates an environment for trip assurance of an electric vehicle (EV) for a rider, according to embodiments as disclosed herein;

[0021] FIG. 2 depicts a block diagram illustrating various units of the electronic device, which is used for trip assurance of an electric vehicle (EV) for a rider, according to embodiments as disclosed herein;

[0022] FIG. 3 depicts a block diagram illustrating various units of the electric vehicle, which is used for trip assurance of an electric vehicle (EV) for a rider, according to embodiments as disclosed herein;

[0023] FIG. 4 depicts a Trip assurance system sensing module, vehicle health scan module, vehicle information module and a learning module, according to embodiments as disclosed herein.

[0024] FIG. 5 is an example diagram illustrating the various steps involved in the trip assurance of an electric vehicle (EV) for a rider, according to embodiments as disclosed herein;

[0025] FIG. 6 is an example diagram illustrating the output of the trip assurance system comprising vehicle health scan summary and recommendation, and vehicle information, according to embodiments as disclosed herein;


DETAILED DESCRIPTION

[0026] 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.

[0027] The embodiments herein disclose methods and systems for assisting rider and road user safety by identifying the obstacles, and pot holes while riding the vehicle. 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.

[0028] FIG. 1 illustrates a comprehensive environment for trip assurance of an electric vehicle (EV), according to embodiments of the present invention. The system integrates multiple hardware and software components that interact through various communication protocols to provide real-time monitoring, predictive diagnostics, and optimized trip planning for EV riders. This environment is designed to ensure the safety, efficiency, and reliability of the vehicle using advanced artificial intelligence (AI) and cloud-based data processing.

[0029] 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 CAN enables, which may include, but are not limited to real-time diagnostics, remote monitoring, over-the-air updates, and enhanced data analytics. This integration is a significant step toward building intelligent, connected vehicles that can provide valuable insights to manufacturers, fleet operators, and users. In the connected EV (104), 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 can transmit critical vehicle data and sensor information, which may include, but are not limited to motor temperature, motor current consumption, tyre pressure, brake system, charge profile and energy content.

[0030] 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.

[0031] 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 real-time diagnostics, over-the-air updates, and remote vehicle management.

[0032] 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.

[0033] 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 such as battery health, motor temperature, charging status, and more. It provides real-time fault diagnostics, generating alerts and fault codes that can be used for predictive maintenance.

[0034] Various ECUs in the EV transmit data through the CAN bus, including battery status, motor performance, 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.

[0035] 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.

[0036] 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.

[0037] The electronic device (102) referred to herein may be a device that enables trip assurance 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 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.

[0038] 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 weather updates, traffic conditions, and charging station availability, and synchronize it with the vehicle's status. The learning module (408) within the cloud server (108) utilizes deep learning (DL) algorithms to identify patterns in historical data and predict potential component failures or maintenance needs.

[0039] The analyzed vehicle data may include but is not limited to motor temperature, motor current consumption, tyre pressure, brake system, charge profile and energy content. The generated predictions and actionable recommendations may include but is not limited to powertrain, battery health, speed and load, wheels and brake, current consumption, forward and reverse movement, battery speed of charging, charging station and trip route recommendation, Vehicle location identification, Parking slot identification and route map.

[0040] The charging mechanism (110) may be equipped with smart charging features that optimize the battery's charging profile based on its current health and the trip's requirements. 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.

[0041] 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 trip assurance. It includes sensors and mechanisms for monitoring various vehicle parameters, such as motor temperature, motor current consumption, tyre pressure, brake system, charge profile and energy content. 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 warnings and suggestions.

[0042] 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. 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.

[0043] In an embodiment, the electronic device (102) can process the sensory parameters comprising powertrain and vehicle health, and battery health using an Artificial Intelligence (AI) on the cloud server (108). The AI can train the electronic device (102) to provide trip assurance to the rider of the EV (104). The AI enhances trip assurance recommendations by processing real-time data, and providing actionable recommendations for riders. The system utilizes Artificial Intelligence (AI) models, specifically Machine Learning (ML) and Deep Learning (DL), to analyze complex scenarios and enable intelligent decision-making.

[0044] The embodiments can provide trip assurance to the rider of the EV (104) by generating and providing a vehicle health scan summary and recommendation, and vehicle information.

[0045] FIG. 2 depicts a block diagram illustrating various units of the electronic device (102), which is used for trip assurance of the EV (104). The electronic device (102) includes a memory (202), a communication interface (204), an input unit (206), an output unit (208), a processor (210), and a database (212).

[0046] 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.

[0047] The memory (202) may store at least one of, but is not limited to, vehicle health scan summary and recommendation, and vehicle information.
The memory (202) may also store the learning module, neural network, vehicle health scan module (404) and vehicle information module (406). The learning module 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.

[0048] 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.

[0049] 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.

[0050] The learning module (408) of the neural network can be processed by processor (210) to obtain the vehicle health scan summary and vehicle information.

[0051] 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.

[0052] The vehicle health scan module (404) and vehicle information 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. 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).

[0053] 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.

[0054] 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.
[0055] 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.

[0056] The output unit (208) may be configured to assist the rider with trip assurance 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.



[0057] 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).

[0058] 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 (212), 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.

[0059] FIG. 3 depicts a block diagram illustrating various units of the electric vehicle (104), which is used for trip assurance of the EV (104). The electric vehicle (104) comprises a sensing unit (304), a telematics control unit (302), ECU (308) and an output unit (306).

[0060] The sensing unit (304) may include, but are not limited to powertrain sensors, battery health sensors and forward and reverse sensors. 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.

[0061] The Telematics Control Unit (TCU) (302) may include, but are not limited to vehicle-to-cloud communication, remote monitoring, and control capabilities. TCU (302) 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 (302) play a pivotal role in enabling connected vehicle features such as real-time diagnostics, over-the-air updates, and remote vehicle management. The TCU (302) 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. The TCU (302) collects data from various ECUs (308) (e.g., Battery Management System (BMS), Motor Control Unit (MCU), Inverter Control, etc.) through the CAN or other in-vehicle communication networks. The TCU (302) 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 such as battery health, motor temperature, charging status, and more. It provides real-time fault diagnostics, generating alerts and fault codes that can be used for predictive maintenance. Various ECUs (308) in the EV transmit data through the CAN bus, including battery status, motor performance, and diagnostic information. The TCU (302) 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. Hence, TCU (302) 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 (302) ensures the safety, reliability, and efficiency of modern electric vehicles.

[0062] FIG. 4 depicts a block diagram illustrating various units of a trip assurance system based on the vehicle health scan summary and vehicle information using a learning module. As depicted in FIG. 4, the trip assurance system (300) comprises a vehicle health scan module (404), vehicle information module (406) and a learning module (408).

[0063] The vehicle health scan module (404) can be configured for capturing sensory data from the electric vehicle (104) using sensing unit (304). The captured sensory data serves as the main input for the subsequent modules, enabling real-time processing and analysis. The captured content is critical for features like vehicle health scan summary and recommendations. In some configurations, the module may incorporate edge AI models for pre-processing and filtering the sensory data before it is sent to the main processing unit. The vehicle health scan summary and recommendations may include but is not limited to powertrain, battery health, speed and load, wheels and brake, current consumption, forward and reverse movement, battery speed of charging, charging station and trip route recommendation.

[0064] The vehicle information module (406) can be configured for capturing sensory data from the electric vehicle (104) using sensing unit (304). The captured sensory data serves as the main input for the subsequent modules, enabling real-time processing and analysis. The captured content is critical for features like vehicle information and recommendations. In some configurations, the module may incorporate edge AI models for pre-processing and filtering the sensory data before it is sent to the main processing unit. The vehicle information and recommendations may include but is not limited to Vehicle location identification, Parking slot identification and route map.

[0065] The learning module (310) is configured for continuous system improvement through machine learning and deep learning techniques. It records historical data from various driving scenarios, including detected obstacles, lane change patterns, and rider interactions. This data is used to train new models and update existing ones, allowing the system to learn from real-world experiences and refine its performance. The learning module personalizes the system's responses based on vehicle health scan and vehicle information. It can receive over-the-air (OTA) updates to incorporate new AI models and safety algorithms, ensuring that the system stays up-to-date with the latest advancements. By aggregating data, the learning module enables the system to generalize new patterns and improve its overall capabilities for all users.

[0066] The system (400) begins with the sensing unit (304) by gathering sensory data from the electric vehicle (104). The received sensory data, is processed by the vehicle health scan module (404) and vehicle information module (406) to generate a vehicle health scan summary report and recommendations. If the vehicle health scan summary report is positive, a 'ready to start' recommendation is provided. The learning module continuously updates its internal models based on these interactions, improving the system's decision-making capabilities over time. This modular architecture ensures that the trip assurance system can provide comprehensive assurance of a trip to the rider of the EV (104).

[0067] FIG. 5 illustrates the steps involved in conducting a vehicle health scan using a mobile application and artificial intelligence (AI) to ensure the electric vehicle (EV) is ready for operation. This health scan is part of a trip assurance system that provides real-time recommendations and diagnostics to the rider. The sequence of steps depicted in FIG. 5 ensures that all critical vehicle systems are functioning correctly and that the EV is safe to use.

[0068] FIG. 6 provides a detailed overview of the key parameters and sensor inputs used by the trip assurance system to generate vehicle health scans and recommendations. The figure outlines the various sensors and modules that collect data on the EV's health, performance, and operational status. The information gathered by these sensors is used to provide real-time insights into the vehicle's condition, enabling predictive maintenance and optimized trip planning.

[0069] 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.

[0070] 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 providing a trip assurance of an electric vehicle (EV) (104), the method comprising:
sensing, by a sensing unit (304) configured on the EV (104), at least one sensory parameter comprising powertrain and vehicle health, and battery health;
processing, by a processor (210) configured on an electronic device (102), at least one received sensory parameter comprising powertrain and vehicle health, battery health;
transmitting, by the communication interface (204) configured on the electronic device (102), at least one processed sensory parameter comprising powertrain and vehicle health, battery health to a learning module (308); and
providing, by an output unit (208) configured on the electronic device (102), the vehicle health scan summary and recommendation, and vehicle information to provide assurance of a trip for the rider of the EV.

2. The method as claimed in claim 1, wherein the powertrain and vehicle health parameters comprise motor temperature, motor current consumption, tyre pressure, and brake system.

3. The method as claimed in claim 1, wherein the battery health parameters comprise charge profile and energy content.

4. The method as claimed in claim 1, wherein the vehicle health scan summary and recommendation comprises one of a powertrain, battery health, speed and load, wheels and brake, current consumption, forward and reverse movement, battery speed of charging, charging station and trip route recommendation.

5. The method as claimed in claim 1, wherein the vehicle information comprises one of Vehicle location identification, Parking slot identification and route map.

6. A system (300) for trip assurance of an electric vehicle (EV), the system comprising:
an electric vehicle (104);
an electronic device (102);
a charging mechanism (110);
a cloud server (108);
wherein a sensing unit (304) is configured on the EV (104), to sense at least one sensory parameters comprising Powertrain and vehicle health, Battery health; a processor (210) is configured on the electronic device (102), to process at least one received sensory parameters comprising Powertrain and vehicle health, Battery health; a communication interface (204) is configured on the electronic device (102), to transmit at least one processed sensory parameters comprising Powertrain and vehicle health, Battery health to a learning module (308); an output unit (208) is configured on the electronic device (102), to provide a vehicle health scan summary and recommendation, vehicle information to provide assurance of a trip for the rider of the EV.

7. The system (300) as claimed in claim 6, wherein the powertrain and vehicle health parameters comprise motor temperature, motor current consumption, tyre pressure and brake system.

8. The system as claimed in claim 6, wherein the battery health parameters comprise charge profile and energy content.

9. The system as claimed in claim 6, wherein the vehicle health scan summary and recommendation comprises powertrain, battery health, speed and load, wheels and brake, current consumption, forward and reverse movement, battery speed of charging, charging station and trip route recommendation.

10. The system as claimed in claim 6, wherein the vehicle information comprises one of Vehicle location identification, Parking slot identification and route map.

11. The system as claimed in claim 6, wherein the sensing unit (304) comprises powertrain sensors, battery health sensors and forward and reverse sensors.

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