A MACHINE LEARNING-BASED FORECASTING SYSTEM WITH A PORTABLE CHARGING STATION FOR ELECTRIC VEHICLE HAVING ARTIFICIAL INTELLIGENCE INTERFACE

Abstract

The present invention provides a machine learning-based forecasting system integrated with a portable charging station for electric vehicles. The system includes an artificial intelligence (AI) interface that utilizes real-time data such as battery status, driving routes, weather conditions, and traffic patterns to predict optimal charging schedules. The portable charging station is equipped with a power bank, intelligent connectors, and a solar panel for self-recharging. The AI interface communicates with the user, offering real-time suggestions on energy consumption, charging times, and route optimization. The system is modular, allowing for future updates and integration of new renewable energy technologies, enhancing the overall efficiency and convenience for electric vehicle owners.

Specification

Description:[013] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[014] Referring now to the drawings, these are illustrated in FIG. 1, the system comprises: Portable Charging Station: The portable charging station includes a power bank with a high-capacity battery, an intelligent connector for connecting to various electric vehicle types, and a solar panel for self-recharging purposes. The charging station is compact, lightweight, and easily transportable, making it suitable for roadside and remote charging needs. The portable charging unit is equipped with intelligent connectors capable of detecting the vehicle's battery type and adjusting the charging parameters accordingly. This feature ensures compatibility with various electric vehicles and provides efficient energy transfer without manual intervention.
The solar panel integrated into the portable charging station allows for renewable energy harvesting, making the system more sustainable. The solar panel continuously charges the power bank when exposed to sunlight, ensuring that the charging station is always ready for use, even in remote areas without access to grid power. Additionally, the power bank includes an onboard inverter to convert DC power from the battery to AC power for vehicle charging, if necessary.
[015] In accordance with another embodiment of the present invention, the forecasting system utilizes historical and real-time data to predict battery consumption rates and charging requirements. The machine learning model analyzes multiple data inputs, such as battery level, driving distance, weather conditions, and driving patterns, to determine the optimal time and place to initiate charging. This system enables users to effectively plan their routes, reducing the risk of battery depletion. The machine learning algorithm is based on a combination of supervised and unsupervised learning techniques, allowing it to improve accuracy over time by learning from user behavior and environmental conditions.
The forecasting system also takes into account external factors such as traffic density and elevation changes along the driving route. By considering these additional parameters, the system can provide more precise predictions about energy consumption, ensuring that the vehicle has sufficient charge to reach its destination. The system can be updated over-the-air (OTA) with new data models and improved algorithms, ensuring that users always benefit from the latest advancements in machine learning technology.
[016] In accordance with another embodiment of the present invention, the AI interface integrates natural language processing (NLP) and computer vision to allow communication with the user via voice commands and a touchscreen display. The AI provides suggestions related to energy consumption, driving behavior, and charging points based on real-time data processing. It can communicate with cloud-based services for route optimization and to identify available public charging stations. The AI interface also has the capability to learn user preferences over time, offering personalized recommendations that align with the user's driving habits and charging preferences.
The AI interface features a visual recognition system that can analyze the environment around the vehicle, such as identifying nearby charging stations or detecting potential hazards on the road. This feature enhances the safety of the driver and ensures that charging opportunities are not missed. The AI is also capable of interacting with smart home systems, allowing users to schedule charging sessions based on their home's energy usage patterns and electricity tariffs.
[017] In accordance with another embodiment of the present invention, the system is equipped with sensors and communication modules to collect data in real time. This data includes battery status, vehicle speed, route information, and weather conditions. The collected data is analyzed by the AI-powered forecasting system to make intelligent predictions about the charging needs. The communication module uses technologies such as 4G/5G, Wi-Fi, and Bluetooth to ensure continuous connectivity, enabling real-time updates and synchronization with cloud-based databases.
The system also includes GPS functionality to track the vehicle's location and provide accurate navigation instructions. The GPS data is integrated into the machine learning model to optimize route planning, ensuring that charging is conducted at the most convenient locations along the route. The data collected from sensors is stored securely in a cloud-based environment, ensuring privacy and data security while allowing for advanced analytics as shown in figure 2.
[018] In accordance with another embodiment of the present invention, the invention features a user-friendly interface that provides real-time information, such as battery status, charging suggestions, estimated time of arrival, and route guidance. The system sends notifications to the user about optimal charging schedules, power availability, and maintenance requirements. The user interface is accessible via a touchscreen display mounted inside the vehicle or through a mobile application that connects to the portable charging station via Bluetooth or Wi-Fi.
[019] The notification system is designed to be proactive, alerting the user well in advance of potential charging needs or maintenance issues. The system can send push notifications to the user's smartphone, ensuring that important information is always available, even when the user is away from the vehicle. The mobile application also allows users to remotely monitor the charging station's status, initiate charging sessions, and view detailed analytics related to energy consumption and driving patterns.
[020] The present invention provides an innovative solution for electric vehicle owners by combining a machine learning-based forecasting system with a portable charging station featuring an AI interface. This invention is designed to offer intelligent, efficient, and user-friendly solutions for managing charging needs, ultimately enhancing the overall electric vehicle experience.
[021] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[022] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention.
, Claims:1. A machine learning-based forecasting system integrated with a portable charging station for electric vehicles, comprising:
a portable charging unit with a power bank and intelligent connector capable of charging multiple types of electric vehicles;
a solar panel integrated with the charging unit for self-recharging capabilities;
a machine learning module configured to analyze data related to battery usage, route distance, traffic conditions, and weather to determine the optimal charging schedule.
a battery management unit configured to monitor battery health and regulate charging rates based on the vehicle's battery type and condition, ensuring extended battery life and optimal charging efficiency;
wherein the portable charging station is equipped with an artificial intelligence interface that communicates with the user via voice and touchscreen interactions, providing real-time suggestions for energy consumption, charging, and route planning;
wherein the portable charging station also includes a retractable handle and wheels, making it easy for users to transport the charging unit over different terrains.
2. The system as claimed in claim 1, wherein the AI interface is further capable of interacting with smart home devices and adjusting charging schedules based on electricity tariffs and home energy usage patterns.
3. The system as claimed in claim 1, wherein the machine learning module utilizes real-time data from vehicle sensors, GPS modules, and external sources to predict battery depletion and recommend optimal charging times and locations.
4. The system as claimed in claim 1, wherein the portable charging station is lightweight and easily transportable, providing roadside charging capabilities for electric vehicles in remote locations. The
5. The system as claimed in claim 1, wherein the AI interface communicates with a cloud-based database to identify nearby public charging stations and optimize route planning based on user preferences.
6. The system as claimed in claim 1, wherein the AI interface also provides personalized route suggestions that consider user preferences for charging locations, driving speed, and preferred travel times.
7. The system as claimed in claim 1, wherein the portable charging unit includes an onboard inverter to convert DC power to AC power as needed, allowing compatibility with different types of electric vehicles and household appliances.
8. The system as claimed in claim 1, wherein the machine learning module is capable of being updated over-the-air (OTA) to incorporate new data models, improve forecasting accuracy, and adapt to evolving driving conditions and charging infrastructure advancements.
9. The system as claimed in claim 1, wherein the portable charging unit is equipped with a solar tracking mechanism that optimizes the angle of the solar panel to maximize energy harvesting efficiency throughout the day.

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