Internet of Things intelligent and artificial intelligence-based system for charging and maintaining electric vehicles

Abstract

An intelligent system for charging and maintaining an electric vehicle using IoT and AI, consisting of: a charging management unit configured to monitor and control the electric vehicle's charging cycles based on real-time power availability and vehicle battery status, with the charging management unit connected to a power grid; an Internet of Things (IoT) communication module operationally coupled with the charging management unit and configured to establish a network connection between the charging management unit and external servers, the electric vehicle, and various sensors distributed throughout the vehicle, with the IoT communication module enabling real-time data transmission;a sensor network comprising a variety of sensors integrated into the electric vehicle, including temperature, voltage, and current sensors, each configured to continuously monitor critical parameters of the battery and vehicle components and transmit data to the IoT communication module; an AI-based predictive unit connected to the IoT communication module, configured to analyze historical charging data, the current battery status, and environmental factors to optimize charging times and predict the electric vehicle's maintenance needs; a control processing unit operationally coupled with the charging management unit and the AI-based predictive unit, configured to dynamically adjust charging cycles based on AI-generated optimization recommendations;a maintenance planning unit integrated into the AI-based prediction unit and configured to automatically schedule maintenance operations based on predictive analyses of vehicle component wear and battery condition, with the schedule communicated to the vehicle user via a user interface; and a data encryption unit connected to the IoT communication module, the data encryption unit being configured to encrypt all real-time communication between the electric vehicle, external servers, and maintenance systems to ensure the secure transmission of sensitive vehicle information.

Description

Field of the InventionThe present invention relates to the field of electric vehicle (EV) infrastructure, and more particularly to systems and methods for optimizing electric vehicle charging and maintenance using Internet of Things (IoT) and Artificial Intelligence (AI) technologies.BACKGROUND OF THE INVENTIONAs the world-wide demand for electric vehicles (EVs) is increasing, efficient and intelligent systems for charging and servicing these vehicles have become a key prerequisite. Conventional charging systems are often inflexible and do not optimize energy consumption, which leads to inefficiencies, higher operating costs and possible power grid loading. Moreover, these systems lack the ability to monitor the state of the vehicle battery and other critical components, which can result in unexpected downtime or shortened battery life. To address these challenges, it is necessary to introduce a system that integrates IoT and artificial intelligence (AI) to enable smart charging, predictive maintenance, and performance optimization of electric vehicles in real-time. The use of IoT facilitates remote monitoring and control, while AI allows the system to make smart decisions based on real-time data analyses.The ascent of electric vehicles (EVs) is a significant advance in the world-wide effort to reduce fossil fuel dependence and reduce greenhouse gas emissions. With the increasing spread of electric vehicles, the need for efficient, reliable and smart charging systems has increased in parallel. A well-designed charging infrastructure is essential to support the growing fleet of electric vehicles on the road. However, the existing solutions for charging and servicing electric vehicles are faced with several technical challenges that impair optimal performance and user-friendliness. These challenges relate primarily to inefficient power management, limited real-time monitoring capabilities, poor battery condition monitoring, and lack of predictive maintenance mechanisms. Moreover, most of the current systems are not scalable and do not exploit the potential of advanced technologies such as the Internet of Things (IoT) and artificial intelligence (AI), which could otherwise significantly increase the efficiency of the charging infrastructure for electric vehicles.One of the earliest and most widely used solutions for charging electric vehicles is the use of conventional, grid-coupled charging stations. These stations normally operate in two modes: Stage 1 charging, which uses a standard household socket, and Stage 2 charging, which uses a higher power socket or a special charger. These systems, while widely used and provide basic charging functions, are often inefficient in terms of power consumption and cannot accommodate different power requirements. These conventional systems lack intelligence and operate uniformly regardless of the actual network load or specific requirements of the EV battery at any particular time. This not only leads to suboptimal energy utilization, but also contributes to grid instability, especially during peak load times. Over time, this can result in higher costs for both power supplies and consumers.Another problem with conventional electric vehicle charging systems is their inability to monitor and maintain the state of the vehicle battery. Battery wear is a significant problem for electric vehicle owners as it directly affects the range and overall performance of the vehicle. The performance of lithium ion batteries, which are frequently used in electric vehicles, deteriorates over time due to factors such as repeated charging cycles, temperature fluctuations, and overcharge. Current charging systems often lack the ability to dynamically monitor the state of the battery and adjust the charging patterns accordingly. As a result, these systems may inadvertently accelerate battery wear due to over- or under-charging, resulting in a shortened battery life. This drawback presents a great problem to electric vehicle owners, as battery replacement is costly and may compromise the economic and ecological advantages of electric vehicles.In recent years, some attempts have been made to address the limitations of conventional charging systems by the introduction of smart chargers. These charging devices generally have extended communication protocols that enable connection to the power grid and enable functions such as demand response. Demand Response is a mechanism by which power suppliers can control the grid load by adjusting the power supply to the charging stations during peak load times. This approach, while contributing to improving network stability, is still limited in improving charging efficiency for individual electric vehicles. Moreover, these systems generally do not have real-time monitoring of the vehicle's battery state and do not provide insight into the long-term effects of charging patterns on battery degradation.Multiple manufacturers have also integrated basic IoT functions into charging stations for electric vehicles that enable remote monitoring and control of charging operations via mobile applications. While these solutions are useful for providing the owners of electric vehicles more control over their charging schedules, they still do not provide a fully automatic and intelligent charging experience. IoT-enabled chargers typically provide little to no integration with extended analysis or machine learning techniques that could optimize the charging process based on the specific usage patterns or environmental conditions of the vehicle. Moreover, while these systems allow users to remotely start or end a charging operation, they generally do not provide comprehensive data on the state of the vehicle battery or its need for maintenance.Another major drawback is the lack of predictive maintenance functions in existing electric vehicle charging systems. Electric vehicles, like all mechanical systems, must be regularly maintained in order to ensure optimum performance and durability. Most maintenance work is, however, carried out reactively, i.e. problems are not solved until they have already caused a problem. This approach may result in longer downtime and higher repair costs because smaller problems may develop into larger problems if they are not immediately solved. While some high-end electric vehicles have onboard diagnostics systems that can alert owners of potential issues, these systems are often limited in scope and do not provide predictive insights based on long-term data analyses. Moreover, these onboard systems are vehicle specific and not integrated into the charging infrastructure, which limits their effectiveness in optimizing both charging efficiency and vehicle maintenance.In response to these challenges, various research and development efforts have focused on improving the efficiency and intelligence of electric vehicle charging systems. Some of the latest advances in this field include the use of artificial intelligence (AI) and machine learning techniques to optimize the charging process. These AI-controlled systems analyze historical and real-time data of the vehicle battery, charging station, and power grid to make smart decisions about when and how to charge the vehicle. For example, AI may be used to predict the optimal charging time based on the usage patterns of the vehicle, the current state of the network, and environmental conditions such as temperature. By optimizing charging in this manner, these systems can reduce power consumption, reduce operating costs, and extend the life of the vehicle battery. Although AI-based charging systems are very promising, their introduction has been limited due to the high implementation cost and complexity of their integration into the existing infrastructure.Another new solution is the integration of bidirectional charging functions, which allow electric vehicles not only to draw current from the grid, but also to feed current into the grid during times of high demand. This vehicle-to-grid technology (V2G) has the potential to significantly improve the grid stability by having electric vehicles function as mobile energy stores. However, the full-area implementation of V2G technology is still in children's shoes, and considerable technical and regulatory challenges must still be overcome before it can be used full-area. Moreover, V2G systems require a high degree of coordination between the vehicle, the charging station, and the power grid, making the system more complex and increasing the need for smart control mechanisms.Despite these advances, the current generation of electric vehicle charging systems is still unable to address all challenges faced by electric vehicle owners and power suppliers. The lack of fully integrated IoT and AI solutions, as well as the limited availability of functions for predictive maintenance and bidirectional charging, means that there is still a considerable need for improvement in this area. A truly smart system for charging and servicing electric vehicles would need to combine the strengths of IoT and AI technologies to provide a seamless, smart, and efficient charging experience that optimizes power consumption, extends battery life, and minimizes maintenance costs. Such a system would benefit not only from individual owners of electric vehicles, but also contribute to the overall sustainability of the ecosystem of the electric vehicles by improving grid stability and reducing environmental effects of battery wear and waste of energy.SUMMARY OF THE INVENTIONThe present invention relates to an intelligent electric vehicle charging and servicing system that uses the Internet of Things (IoT) and AI-based techniques to optimize the charging operation, monitor the state of the vehicle, and predict need for service. The system includes a network of IoT-enabled sensors installed in the electric vehicle and the charging station that continuously acquire data on battery power, vehicle status, and environmental factors. The collected data is transmitted to a central processing unit where AI techniques analyze the information to adjust charging patterns, predict battery degradation, and plan preventative maintenance. The system is configured to conform energy efficiency to the requirements of the vehicle, ensure optimum charge levels, and prevent over- or under-charging. It also integrates real-time communication with power supplies to optimize network load during peak and sub-times. In addition, the system includes remote monitoring functions via a user interface that allows vehicle owners to track performance, plan maintenance, and obtain notifications on their mobile devices. This invention increases the life of the vehicle, reduces operating costs and improves general user comfort.The main object of the present invention is to provide an intelligent electric vehicle charging and maintenance system that uses advanced IoT and AI technologies to optimize power consumption and improve overall charging efficiency. The system aims to extend the life of the battery of the electric vehicle by dynamically adjusting charging patterns based on real-time data collected by the battery management system of the vehicle, environmental factors, and network conditions. By incorporating AI techniques, the system may predict battery degradation and adjust the charge cycles accordingly to ensure that the battery remains in its optimal operating conditions, thus prolonging its useful life.A further object of the invention is to enable predictive maintenance of electric vehicles. By continuously monitoring battery state and other critical components of the vehicle through IoT sensors, the system may detect potential issues before resulting in significant failures. The AI-controlled analysis of the data allows the system to warn early and propose maintenance measures, thereby reducing the risk of unexpected leaks and minimizing vehicle downtime. This proactive maintenance approach increases the reliability and safety of the vehicle while decreasing the long term maintenance costs.Moreover, the invention is intended to provide a solution for optimizing the energy consumption by coordinating with the power grid. The system aims to balance the energy load during the charging process by scheduling off-peak charging times or dynamically adapting the power consumption to the real-time conditions of the power grid. This reduces the load on the power grid and helps avoid grid overloads, contributing to general energy efficiency. In addition, the system helps to integrate renewable energy sources so that electric vehicle owners can charge their vehicles with solar or wind energy, thereby reducing environmental impact by charging the vehicles.Another important object of the invention is to provide vehicle owners with a user-friendly interface. The system is designed to provide remote monitoring and control functions so that users can display the status of their vehicle, monitor charging progress, and obtain real-time notifications of the state of the battery and need maintenance. The intuitive interface facilitates users planning maintenance work, monitoring vehicle performance, and receiving updates for charging efficiency-all from a smartphone or other connected devices.Finally, the invention is intended to contribute to the more comprehensive sustainability goals of the ecosystem of the electric vehicles by reducing battery degradation, optimizing power consumption, and improving grid stability. By improving the efficiency of charging systems and minimizing the environmental effects of battery wear, the invention supports long term electrical vehicle portability as a key solution to reduce greenhouse gas emissions and to transition to cleaner energy sources.BRIEF DESCRIPTION OF THE FIGURESThese and other features, aspects, and advantages of the present invention will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: FIG. 1 is a block diagram of an intelligent system for charging and servicing electric vehicles using IoT and KI.Moreover, those skilled in the art will appreciate that elements are shown in the drawing for simplicity and need not necessarily be drawn to scale. For example, the flowcharts illustrate the method using the major steps to improve understanding of aspects of the present disclosure. Moreover, with respect to the construction of the apparatus, one or more components of the apparatus may have been represented in the drawing by conventional symbols, and the drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art having the benefit of the description herein.DETAILED DESCRIPTION OF THE INVENTIONIn order to aid in the understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation of the scope of the invention is intended, since such changes and further modifications to the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated, as would normally occur to one skilled in the art to which the invention pertains.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrase "in one embodiment," "in another embodiment," and similar language in this specification may or may not all refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps not only comprises those steps, but may also comprise other steps not expressly listed or inherent in such a process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" do not exclude, without further limitations, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The system, methods, and examples provided herein are illustrative only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.Referring now to FIG. 1, a block diagram of an intelligent system for charging and servicing electric vehicles using IoT and KI is shown. The system 100 includes: a charge management unit 102 configured to monitor and regulate charging cycles of the electric vehicle based on real-time power availability and vehicle battery status, the charge management unit connected to a power grid; an Internet of Things (IoT) communication module 104 operatively coupled to the charge management unit and configured to establish a network connection between the charge management unit and external servers, the electric vehicle, and various sensors distributed throughout the vehicle, the IoT communication module enabling real-time data transmission; a sensor network 106 comprising a plurality of sensors integrated into the electric vehicle, including temperature, voltage, and current sensors, each sensor configured to continuously monitor critical parameters of the battery and vehicle components and transmit data to the IoT communication module; an AI-based prediction unit 108 connected to the IoT communication module, the AI-based prediction unit configured to analyze historical charge data, current battery status, and environmental factors to optimize charging times and predict need for maintenance of the electric vehicle; a control processing unit 110 operatively connected to the charge management unit and the AI-based prediction unit, the control processing unit configured to dynamically adjust charge cycles based on the AI-generated optimization recommendations; a maintenance planning unit 112 integrated with the AI-based prediction unit and configured to automatically plan maintenance operations based on predictive analytics of wear of vehicle components and battery condition, the planning being communicated to the vehicle user via a user interface; and a data encryption unit 114 connected to the IoT communication module, the data encryption unit configured to encrypt all real-time communication between the electric vehicle, external servers, and maintenance systems to ensure secure transmission of confidential vehicle information.In one embodiment, the charge management unit 102 also includes a polyphase power regulator configured to adaptively switch between AC and DC sources based on the vehicle charging requirements and the availability of power from the grid.In one embodiment, the sensor network 106 also includes a battery impedance sensor positioned at multiple locations within the vehicle battery cells and configured to detect and report cell-level impedance fluctuations in real time, and wherein the impedance data is used by the AI-based prediction unit to detect early battery degradation.In one embodiment, the AI-based prediction unit 108 is also configured to use machine learning models trained from historical vehicle operating data including temperature, charging cycles, and road conditions to provide real-time prediction of battery performance and adaptive charging control to extend battery life.In one embodiment, the IoT communication module 104 is also equipped with a 5G network interface to enable extremely low latency communication between the electric vehicle, the charging stations, and the remote diagnostic servers, where the network interface supports real-time adjustments to the charging protocols based on grid variations.In one embodiment, the maintenance planning unit 112 also includes a diagnostic feedback loop that communicates with vehicle diagnostics and external service providers, where the feedback loop ensures that real-time assessments of the vehicle condition are continuously updated and incorporated into the predictive maintenance plan.In one embodiment, the control processing unit 110 is also configured to optimize charging priority based on network requirements and regional power consumption data, while communicating with other electric vehicles in a vehicle-to-grid (V2G) communication system to coordinate charging schedules and prevent network congestion.The present invention relates to an intelligent electric vehicle charging and maintenance (EV) system that integrates Internet of Things (IoT) technology and artificial intelligence (AI) to optimize charging, extend battery life, and predict maintenance needs. The system is designed to address important challenges in electric vehicle management by utilizing advanced predictive analyses, real-time data monitoring, and secure communication protocols. The invention uses a combination of hardware units, communication modules, and AI-based techniques to ensure effective operation of the system.The charge management unit is responsible for monitoring and controlling the charging cycles of the electric vehicle. This unit is connected to the power grid and can regulate the charging process based on the real-time availability of power and the current status of the vehicle battery. The charge management unit may dynamically adjust charging parameters using a polyphase power regulator that adaptively switches between alternating current (AC) and direct current (DC) sources. The unit determines the optimum power source based on the vehicle charging requirements and the grid conditions, thus providing efficient power consumption.To achieve real-time data transmission and networking, the invention includes an IoT communication module. This module establishes a communication network between the charge management unit, the electric vehicle, external servers, and various sensors distributed throughout the vehicle. The IoT module supports real-time monitoring and data exchange and provides continuous updates to important vehicle parameters. The sensor network includes voltage, current and temperature sensors that monitor the state of critical vehicle components, particularly the battery. The data collected by these sensors is continuously transmitted to the IoT communication module so that the system maintains a comprehensive understanding of the operating state of the vehicle.The AI-based prediction unit plays a decisive role in the optimization of loading and maintenance processes. This unit uses advanced machine learning techniques that analyze historical data from previous charge cycles, current battery performance measurements, and external factors such as environmental conditions. Based on these inputs, the AI prediction unit generates optimization strategies to maximize charging efficiency and extends the life of the battery by minimizing unnecessary charge-discharge cycles. The unit may also predict future degradation of the battery by analyzing data from battery impedance sensors that detect cell level variations in battery impedance. This early detection of degradation allows the system to adapt charging strategies before significant damage occurs, thereby reducing long term maintenance costs and preserving battery health.In addition, the AI-based prediction unit integrates machine learning models trained from large data sets including various operating conditions such as temperature variations, road conditions, and charging behavior. These models continuously learn and conform to new patterns, thereby providing highly accurate, real-time predictions of battery power and charge demand. The machine learning models are embedded in the system architecture to ensure adaptive functionality and make the system robust to changes in driving conditions and vehicle usage.The control processing unit is connected to both the load management unit and the AI-based prediction unit. This control unit dynamically adjusts the charging cycles on the basis of the recommendations of the AI-based prediction unit and thus optimizes the energy flow between the network and the vehicle. The system may also prioritize charging based on network requirements, minimize power consumption during peak times, and communicate with a vehicle-to-grid (V2G) system to coordinate charging with other surrounding electric vehicles. In this way, the system prevents overloading of the network and at the same time ensures that the vehicle receives sufficient current for optimum performance.With respect to vehicle maintenance, the system includes a maintenance scheduler that automatically schedules service tasks based on predictive analyses of the AI-based predictor. The scheduler is connected to the vehicle diagnostic systems and external service providers and ensures that service deadlines are agreed upon before significant wear or failure occurs. This unit utilizes a diagnostic feedback loop that continuously communicates with the onboard diagnostics of the vehicle and updates service plans in real time to reflect the current state of the vehicle components.Security is a critical aspect of the invention, particularly in view of the sensitive nature of real-time vehicle data transmission. To accommodate this, the system includes a data encryption unit connected to the IoT communication module. This encryption unit ensures all communication between the electric vehicle, external servers, and remote service systems through the use of advanced encryption protocols and ensures that the vehicle data remains protected from unauthorized access.The technique used by the AI-based prediction unit is of central importance to the intelligence of the system. It combines supervised learning models with reinforcement learning strategies to continuously optimize charging cycles and predict maintenance needs. The technique takes place in two different phases: data acquisition and predictive analysis. During the data acquisition phase, real-time data from the sensor network is processed to update the knowledge of the system about the operating state of the vehicle. This data is fed into the machine learning model where it is compared to historical data sets. In the predictive analysis phase, this data is evaluated to generate recommendations for the control processing unit. These recommendations are based on an AI model that weights factors such as charging efficiency, electrical grid availability, and predicted battery cell life. The model uses a reward function to refine its predictions and learns from both successful and failed load results to improve future predictions.Moreover, the prediction capabilities of the system extend to maintenance planning through the use of regression models trained from data on wear of electric vehicle components. These models predict the likelihood of component failure based on real-time sensor data, including temperature and current variations, and feed this data to the maintenance scheduler. The system can then issue warnings and plan maintenance deadlines early, thereby avoiding costly repairs and extending the operating life of the vehicle.In summary, the intelligent electric vehicle charging and servicing system integrates IoT communication, real-time sensor data, and AI-controlled predictive analyses to optimize both charging and servicing operations. By employing advanced machine learning models and secure communication protocols, the system improves performance of electric vehicles, extends battery life, and minimizes operating disturbances through predictive maintenance.The smart electric vehicle charging and maintenance system is designed to utilize IoT and AI technologies for optimal performance. The system consists of the following main components: a series of IoT sensors, a central processing unit, a power management unit, a battery management system, an AI-based control unit, a communication interface and a user interface. The IoT sensors are strategicly installed in the battery compartment, the engine system, and the charge station of the vehicle to continuously acquire real-time data such as battery temperature, state of charge, voltage, current, and general vehicle condition. The central processing unit is connected to the AI-based control unit that uses machine learning techniques to analyze historical and real-time data. Based on the detected patterns, the AI unit may predict battery degradation, optimize charging cycles to extend battery life, and determine when maintenance is required. The AI system takes into account various factors including vehicle drivability, battery age, environmental conditions, and grid load to intelligently adjust the charging operation.The energy management unit communicates with the battery management system in order to regulate the energy flow during the charging process. It ensures that the battery is charged in the optimum range and prevents over- or under-charging, which can shorten the life of the battery. The energy management unit may also communicate with the power grid to perform smart load balancing. This ensures that the charging process takes place outside the peak times when the energy requirement is low, or the power consumption is dynamically adapted during times with high demand.In addition to charging optimization, the system monitors the state of the battery and motor systems of the electric vehicle by continuous diagnosis. Predictive maintenance techniques analyze the collected data to predict potential problems before resulting in failures. When anomalies are detected, the system notifies the vehicle owner via the user interface that can be accessed via a smartphone application. The user interface provides detailed insight into the state of the vehicle, including battery status, charging history, and maintenance schedules. The user may remotely initiate diagnostic tests or agree on a service appointment via the application.The communication interface enables seamless interaction between the smart system, the vehicle owner, and the power suppliers. It supports wireless communication standards such as 5G or LoRa for long-range connectivity and provides real-time data transmission even when the vehicle is not near the charging station. This connectivity allows power supplies to remotely manage and monitor charging stations, thus ensuring the stability and performance of the network.Through the use of the smart electric vehicle charging and servicing system, users benefit from improved charging efficiency, longer battery life, and reduced downtime through predictive service. The system can be integrated seamlessly into existing charging infrastructures and supports future advances in electric vehicle technology.The present invention relates to the field of electric vehicle technology and is particularly focused on systems for managing charging and predictive maintenance using IoT and AI-based solutions. It addresses the technical challenges of optimizing energy consumption, extending battery life, and ensuring timely maintenance of electric vehicles through real-time data analysis and predictive techniques. The invention integrates advanced sensor networks, secure data communication protocols, and AI-controlled optimization techniques to provide a smart, automated solution for electric vehicle charging and maintenance management.The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow chart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimension, and material usage, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages, and solutions to problems have been described above with respect to certain embodiments. However, the advantages, benefits, solutions to problems, and any components that may result in an advantage, solution occurring or becoming clearer are not to be construed as a critical, required, or essential feature or component of any or all claims.REFERENCES100 Smart System for Charging and Servicing Electric Vehicles with IoT and KI. 102 Charging management unit 104 Internet of things (iot) communication module 106 A sensor network 108 Ki-based prediction unit 110 Control processing unit 112 Maintenance planning unit 114 A data encryption unit

Claims

An intelligent system for charging and servicing an electric vehicle using IoT and AI, comprising: a charge management unit configured to monitor and regulate charging cycles of the electric vehicle based on real-time power availability and vehicle battery status, wherein the charge management unit is connected to a power grid; an Internet of Things (IoT) communication module operatively coupled to the charge management unit and configured to establish a network connection between the charge management unit and external servers, the electric vehicle, and various sensors distributed in the vehicle, wherein the IoT communication module enables real-time data transmission; a sensor network comprising a plurality of sensors integrated with the electric vehicle, including temperature, voltage, and current sensors, each sensor configured to continuously monitor critical parameters of the battery and vehicle components and transmit data to the IoT communication module; an AI-based prediction unit connected to the IoT communication module, the AI-based prediction unit configured to analyze historical charge data, current battery status, and environmental factors to optimize charging times and predict maintenance requirements of the electric vehicle; a control processing unit operatively coupled to the charge management unit and the AI-based prediction unit, the control processing unit configured to dynamically adjust charging cycles based on the AI-generated optimization recommendations; a maintenance planning unit integrated with the AI-based prediction unit and configured to automatically plan maintenance operations based on predictive analyses of wear of vehicle components and battery condition, the planning being notified to the vehicle user via a user interface; and a data encryption unit connected to the IoT communication module, the data encryption unit configured to encrypt all real-time communication between the electric vehicle, external servers, and maintenance systems to ensure secure transmission of sensitive vehicle information.The system of claim 1, wherein the charge management unit further comprises a polyphase power regulator configured to adaptively switch between AC and DC sources based on the vehicle charging requirements and the availability of power from the grid.The system of claim 1, wherein the sensor network further comprises a battery impedance sensor positioned at multiple locations within the vehicle battery cells and configured to detect and report cell-level impedance fluctuations in real time, and wherein the impedance data is used by the AI-based prediction unit to detect early degradation of the battery.The system of claim 1, wherein the AI-based prediction unit is further configured to use machine learning trained models based on historical operating data of the vehicle including temperature, charging cycles, and road conditions to provide real-time prediction of battery power and adaptive charging control to extend battery life.The system of claim 1, wherein the IoT communication module is additionally equipped with a 5G network interface to enable extremely low latency communication between the electric vehicle, the charging stations, and the remote diagnostic servers.The system of claim 1, wherein the maintenance scheduler further comprises a diagnostic feedback loop that communicates with vehicle diagnostic systems and external service providers.

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