Description:Title of the invention :
Adaptive Telematics-Based Range Prediction System for Electric Vehicles.
Background Of The Invention:
The field of electric vehicles (EVs) has been a focal point of technological advancement and innovation for the last two decades. One of the most critical challenges in this area is accurate prediction of the vehicle's range based on various factors such as battery charge, terrain, and traffic conditions. Traditional systems rely on simple State of Charge (SoC) indicators, often offering inaccurate predictions that are not adaptive to real-time conditions.
Current State of Technology:Battery Management Systems (BMS): Standard in many electric vehicles for monitoring battery parameters. However, they typically lack real-time adaptiveness to varying conditions.
Developments:-
Adaptive Systems: There is a trend towards systems that adapt to real-time conditions, an area in which "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" seems to innovate.
IoT Integration: Increasing usage of IoT sensors for real-time data feeding into battery management systems.
API Usage: Use of open-source APIs for real-time traffic and terrain data is emerging, which "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" capitalizes upon.
The "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" system appears to fill a significant gap by providing an adaptive, real-time range prediction system for electric vehicles that takes into account a multitude of factors including terrain, traffic, and real-time state of the vehicle's battery. Its integration of telematics, API-based data retrieval for real-time conditions, and advanced algorithms places it at the forefront of current technology.
Field Of Invention And Use Of Invention:
This invention relates to the field of electric vehicle (EV) management systems, specifically focusing on adaptive, real-time range prediction. The invention leverages integrated telematics, machine learning algorithms, and application programming interface (API)-based data retrieval to dynamically adjust the predicted range of an electric vehicle based on various parameters such as battery status, terrain, and traffic conditions. In this manner, the "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" system enhances the accuracy and reliability of range predictions, thereby augmenting user experience and mitigating range anxiety often associated with electric vehicles.
The "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" invention finds application in various scenarios involving electric vehicles (EVs) that require accurate, real-time range prediction to enhance the user experience and management of electric vehicle fleets. Below are some illustrative examples:
Example 1: Long-Distance Travel in Personal EV
An individual plans to undertake a long-distance journey in their electric vehicle. Utilizing the "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" system, the car assesses its battery status, pulls real-time traffic data, and considers the terrain on the planned route. As the vehicle traverses different types of terrain and encounters varied traffic conditions, "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" dynamically updates the estimated range, thereby enabling the driver to make informed decisions about charging stops, route changes, or speed adjustments.
Example 2: Electric Bus Fleets in Urban Areas
A public transportation authority incorporates the "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" system in their electric bus fleet. The system assesses the state of charge (SoC) of each bus and uses this information to provide real-time range estimations that take into account variables like traffic congestion and varied road types in urban settings. This assists in optimizing bus routes and schedules, thereby improving the efficiency of the fleet.
Example 3: Logistics and Commercial Transport
A logistics company employing electric trucks uses the "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" system to optimize its operations. The system helps in pre-trip planning by considering the cargo weight, route terrain, and real-time traffic conditions to provide a more accurate range estimation. This would help the company in planning charging stops and ensuring timely deliveries.
Description Of Related Art :
Patent(1) No: - US9834111B2
Titled: - Range prediction in electric vehicles
Disclosure/Abstract: - A first method of predicting the range of an electric vehicle comprises, determining a range value during a current vehicle operating cycle using a first range model, wherein the first range model is dependent on an energy consumption rate value recorded during a previous vehicle operating cycle. A second method of predicting the range of an electric vehicle comprises, monitoring a trailer detecting means of the vehicle; and determining a first range value if the trailer detecting means detects that a trailer is attached to the vehicle.
Our invention "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" offers distinct advancements in the realm of electric vehicle (EV) battery management systems. Unlike the approach described in "Patent(1)", which focuses on a singular aspect of battery management or utilizes a limited set of parameters for assessing battery state, our invention integrates a comprehensive range of dynamic factors including driving habits, terrain, and weather conditions. This holistic approach, underpinned by a sophisticated adaptive telematics-based system, not only predicts the State of Charge (SoC) more accurately but also enhances the predictive capabilities for the vehicle's range. The inclusion of a multi-parameter sensor, a high-speed controller applying algorithms, a cloud-based GIS for real-time data processing, and EEPROM for machine learning significantly elevates our invention by offering a more adaptable, efficient, and reliable solution for EV battery management. This indicates a broader scope of innovation, potentially offering superior performance and utility over the system described in "Patent(1)".
Critical scientific and technical differences between Our invention and “Patent(1)”:-
Multi-Parameter Sensor: Our invention utilizes a sensor that measures multiple parameters including voltage, current, temperature, and impedance, offering a more comprehensive real-time analysis of the battery's state.
Algorithms: Our invention applies algorithms to calculate the State of Charge (SoC), enhancing accuracy over the methods disclosed in "Patent(1)".
Cloud-Based GIS Integration: The telematics device in our invention leverages a cloud-based Geographic Information System (GIS) for real-time data on terrain and traffic, which is not mentioned in "Patent(1)".
EEPROM for Machine Learning: Our invention includes EEPROM to store historical data, enabling machine learning for predictive analytics, a feature that offers advanced adaptability not specified in "Patent(1)".
Comprehensive Predictive Analytics: The integration of driving habits, terrain, and weather conditions into our invention’s predictive model offers a more sophisticated and adaptable system for predicting future SoC and vehicle range, surpassing the capabilities described in "Patent(1)".
Scientific and technical differences between Our invention and “Patent(1)” in a tabular format :-
Feature
Our invention
Patent(1)
Advantages of our invention
Sensing Technology
Utilizes multi-parameter sensors for comprehensive real-time battery state analysis.
Rely on simpler or single-parameter sensing technology.
Comprehensive battery state analysis enhancing prediction accuracy.
Algorithm
algorithms for SoC calculation.
Standard or generic algorithms.
Superior accuracy in SoC prediction, tailored to specific EV needs.
Data Integration
Cloud-based GIS for real-time terrain, traffic, and weather data.
No mention of external data integration.
Enhances predictive analytics by considering external factors.
Machine Learning Capability
Incorporates EEPROM for historical data storage, enabling adaptive predictions through machine learning.
Lacks emphasis on machine learning for predictive improvement.
Improves prediction over time, adapting to user habits.
Predictive Analytics
Comprehensive predictive analytics considering driving habits, terrain, and weather.
Focuses narrowly on battery condition.
Offers a more dynamic range prediction, improving EV usability.

Patent(2) No: - US20120109408A1
Titled: - Electrical vehicle range prediction
Disclosure/Abstract: - A method for predicting the remaining travel distance of an electric vehicle. The method includes determining a useable battery energy value based on battery state-of-charge and battery capacity and a power value needed to heat or cool a vehicle cabin. The method determines an available battery energy value based on the useable battery energy value and an estimated energy value to provide the vehicle cabin heating or cooling, where the estimated energy value is determined using the power value. The method determines a recent energy used value based on an actual recent HVAC energy used value, a recent energy used value with no HVAC system loads and a recent energy used value with maximum HVAC system loads. The method determines a recent distance traveled value and determines the range by dividing the recent distance traveled value by the recent energy used value and multiplying by the available battery energy value.
Our invention "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" presents a more advanced approach to battery management in electric vehicles (EVs) by incorporating adaptive telematics-based range prediction. This system leverages real-time data on driving habits, terrain, and weather conditions, alongside in-house developed algorithms, to offer a highly accurate State of Charge (SoC) and range predictions. Unlike "Patent(2)," which focuses on a singular aspect of battery management or employs a less dynamic method, our invention integrates multiple data sources and predictive analytics to enhance the accuracy and reliability of SoC estimations. This comprehensive approach, especially the use of machine learning capabilities through historical data storage, positions our invention as a significant improvement over existing technology described in "Patent(2)."
Critical scientific and technical differences between Our invention and “Patent(2)”:-
Adaptive Predictive Analytics: Our invention utilizes real-time predictive analytics that incorporate driving habits, terrain, and weather conditions, which is a more comprehensive approach than the system described in "Patent(2)."
Dual working: ‘Patent(2)’ only predicts the remaining travel distance of an electric vehicle, while our invention "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" also monitors the battery's health and suggests maintenance routines.
Multi-Parameter Sensing: The battery state measuring sensor in our invention is capable of assessing multiple parameters (voltage, current, temperature, impedance) in real-time, possibly offering a more detailed analysis than the sensing technology mentioned in "Patent(2)."
Algorithm used: ‘Patent(2)’ uses a simple algorithm to predict the remaining travel distance, while our invention "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" uses a more sophisticated algorithm that takes into account a wider range of factors.
Telematics-Based System Integration: Our invention features a telematics device that integrates the vehicle with a cloud-based GIS for enhanced data communication, which provides a more dynamic and responsive system than the technology employed in "Patent(2)."
These differences highlight our invention’s advanced capabilities in offering a more accurate, efficient, and adaptive system for electric vehicle battery management over "Patent(2)."
Scientific and technical differences between Our invention and “Patent(2)” in a tabular format :-
Feature
Our invention
Patent(2)
Advantages of our invention
Predictive Analytics
Utilizes real-time data, considering driving habits, terrain, and weather.
Use static or less comprehensive data.
Enhances prediction accuracy for SoC and vehicle range.
Machine Learning
Employs EEPROM for historical data to refine predictions.
Limited or no machine learning capabilities.
Improves over time, offering personalized and adaptive predictions.
Sensing Technology
Multi-parameter sensor for detailed battery state analysis.
Focuses on fewer parameters.
Offers a more nuanced understanding of battery conditions.
Data Processing
High-speed microprocessor for efficient algorithm application.
Have slower or less sophisticated processing.
Ensures rapid, real-time analytics and response.
System Integration
Integrates with cloud-based GIS via telematics for dynamic data exchange.
Less dynamic or no integration with external data sources.
Provides a more responsive and informed system, improving user experience.

In conclusion, our invention distinguishes itself from "Patent(2)" through its advanced real-time predictive analytics, machine learning enhancements, comprehensive multi-parameter sensing, high-speed data processing, and superior system integration. These technological advancements not only underscore our invention's innovative edge but also its potential to significantly improve electric vehicle battery management, offering a more accurate, efficient, and user-adaptive solution.
Patent(3) No: - US9776528B2
Titled: - Electric vehicle range prediction
Disclosure/Abstract: - Electric vehicle range prediction may include identifying vehicle transportation network information representing a vehicle transportation network, identifying expected departure temporal information, identifying a route from a first location to a second location in the vehicle transportation network using the vehicle transportation network information, identifying a predicted ambient temperature based on the first location and the expected departure temporal information, identifying vehicle state information for an electric vehicle, identifying an expected efficiency value for the electric vehicle based on the predicted ambient temperature, determining an expected operational range, such that, on a condition that the electric vehicle traverses the vehicle transportation network from the first location to the second location in accordance with the expected departure temporal information and the route, the expected operational range indicates an estimated operational range from the second location, and outputting the expected operational range for presentation at a portable electronic computing and communication device.
Our invention employs advanced real-time predictive analytics that incorporate dynamic factors such as driving habits, terrain, and weather conditions, offering a more accurate State of Charge (SoC) prediction compared to "Patent(3)”. Our invention’s adaptive telematics-based system utilizes cloud-based GIS data for real-time adjustments, enhancing vehicle range predictions beyond the capabilities of "Patent(3)." Additionally, our invention provides a comprehensive driver interface for enhanced interaction and efficiency, whereas "Patent(3)" does not offer such advanced user engagement.
Critical scientific and technical differences between Our invention and “Patent(3)”:-
Real-Time Data Integration and Predictive Analytics :- Our invention integrates a sophisticated multi-parameter sensor system for measuring various battery parameters in real-time and incorporates dynamic factors such as driving habits, terrain, and weather conditions. This allows for highly accurate State of Charge (SoC) predictions. In contrast, Patent(3) is likely to rely on more traditional methods of battery monitoring without the comprehensive integration of such diverse real-time data, resulting in less precise SoC predictions.
Adaptive Telematics-Based System :- Our invention employs a telematics device that connects with a cloud-based Geographic Information System (GIS), enabling it to adapt predictions based on real-time traffic, terrain, and weather data. This level of adaptability and external data integration is a significant advancement over Patent(3), which does not utilize telematics to enhance predictive accuracy regarding battery management.
Enhanced Predictive Accuracy :- By incorporating dynamic external factors and machine learning, our invention offers significantly improved predictive accuracy for the SoC and vehicle range compared to "Patent(3)," which rely on more static or less comprehensive data.
Machine Learning for Enhanced Prediction :- Our invention utilizes machine learning algorithms to analyze historical data stored in EEPROM, which allows the system to refine its predictions over time. This self-improving feature of our invention likely surpasses the capabilities of Patent(3), which does not incorporate machine learning to dynamically improve prediction accuracy based on past performance.
Comprehensive Driver Interface :- Our invention features an I/O device that provides drivers with predictive SoC levels, range estimations, and alerts or notifications. This interface enhances driver awareness and vehicle efficiency. Patent(3), by comparison, offers a less comprehensive driver interface, with limited focus on predictive information or real-time updates, thus offering a less interactive experience.
In summary, our invention distinguishes itself from "Patent(3)" through its advanced real-time data integration, adaptive telematics-based analytics, utilization of machine learning for prediction refinement, a comprehensive and informative driver interface, and the application of algorithms for enhanced SoC calculation. These technical and scientific advancements position our invention as a superior solution in the domain of EV battery management systems, offering significant improvements in prediction accuracy, adaptability to changing conditions, and overall user engagement.
Scientific and technical differences between Our invention and “Patent 3” in a tabular format :
Feature
Our invention
Patent(3)
Advantages of our invention
Real-Time Data Integration
Utilizes multi-parameter sensors for real-time monitoring of various battery parameters and external factors.
Rely on basic monitoring of battery parameters without integrating external factors.
Offers comprehensive and dynamic monitoring, enhancing SoC prediction accuracy.
Adaptive Predictive Analytics
Employs algorithms that factor in driving habits, terrain, and weather conditions.
Uses simpler algorithms without considering such a wide range of dynamic factors.
Provides more accurate and context-aware predictions for battery management.
Telematics-Based Connectivity
Uses a telematics device for real-time communication with a cloud-based GIS database.
Does not leverage telematics for real-time external data integration.
Enhances the system’s adaptability to real-world conditions, improving range estimation.
Driver Interface and Interaction
Features an advanced I/O device displaying predictive SoC levels, range estimations, and alerts.
Offers a less interactive or informative driver interface.
Enhances user experience and vehicle efficiency through detailed, actionable insights.

This tabular comparison succinctly captures the innovative aspects of our invention, demonstrating its advanced capabilities in real-time data integration, adaptive analytics, telematics-based connectivity, machine learning, and user interaction. These features collectively position our invention as a superior solution in the domain of EV battery management systems, offering significant advantages in terms of prediction accuracy, adaptability, and user engagement over what is presumed to be available in "Patent(3)."
Objects Of The Invention:-
The primary objective of our invention is to revolutionize electric vehicle (EV) range prediction by incorporating real-time data on multiple variables, including traffic conditions, terrain types, and battery state, thereby providing more accurate and dynamic range estimates.
Another objective of this invention is to mitigate the prevalent issue of 'range anxiety' among electric vehicle users by offering a trustworthy, adaptive algorithm that continually refines its predictions based on a multitude of real-time variables.
A further objective of this invention is to create a system that is easily integrable with existing EV infrastructure, including charging stations and in-vehicle displays, to provide seamless, user-friendly interactions and facilitate widespread adoption.
Another objective of this invention is to generate a database of real-world driving conditions and their effects on EV battery consumption, which serves as a valuable resource for further academic research and development in the field of sustainable mobility.
A further objective of this invention is to design an algorithmic framework that is customized and scaled for different vehicle models, driving conditions, and geographical locations, thereby creating a universally applicable system for range prediction in electric vehicles.
Brief Summary Of The Invention :
The invention "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" is an integrated system designed to provide enhanced and accurate range predictions for electric vehicles (EVs) with non-combustion propulsion systems. The system incorporates several components including a battery state measuring sensor, a controller, a telematics device, an EEPROM, and an I/O device. These components work in harmony through various stages to calculate the vehicle's real-time range with high precision.
In the first stage, the battery state measuring sensor feeds relevant parameters like residual power consumption, minimum power consumed by the powertrain, and other variables into the EEPROM. In the second stage, the controller evaluates the State of Charge (SoC) of the battery using this data. The telematics device then calculates a derived value of the vehicle’s heading using a real-time range algorithm in the third stage. In the fourth stage, data from GIS databases and respective APIs provide the controller with details about traffic, terrain, and route conditions. In the fifth stage, the controller integrates this multifaceted information to predict the vehicle's real-time range, which is then displayed on the I/O device.
The invention has a specialized algorithm that takes into account various factors like traffic density, terrain type, and current power consumption. The algorithm uses these variables to adjust an error factor dynamically, thereby ensuring that the system's range predictions are continually refined. The parameters for traffic and terrain are categorized into different zones and types, with specific algorithms to extrapolate power consumption in each case. The system also includes a dynamic feedback loop that adjusts the error factor based on real-world data, making the range prediction increasingly reliable.
The invention solves the prevailing issue of "range anxiety" among electric vehicle users by offering a reliable range prediction based on real-time, multi-variable data. It further distinguishes itself from existing solutions by its capability to adapt to a wide range of driving conditions and its potential for easy integration into existing electric vehicle infrastructure.
"Adaptive Telematics-Based Range Prediction System for Electric Vehicles" aims to revolutionize the EV industry by providing a highly accurate, real-time range prediction system. It brings novelty in its algorithmic approach that factors in multiple variables, including traffic and terrain conditions, to dynamically adjust its range estimates. The invention offers significant advancements over existing technologies and addresses several unmet needs in the rapidly evolving electric vehicle sector.
Detailed Description Of The Invention :
Our invention relates to the domain of real-time predictive analytics for vehicular battery management, particularly focusing on electric vehicles (EVs). The system is designed to comprehensively monitor and predict the State of Charge (SoC) of an EV's battery, taking into account various dynamic factors such as driving habits, terrain, and external weather conditions.
Components of Adaptive Telematics-Based Range Prediction System for Electric Vehicles:
Battery State Measuring Sensor: This is a sophisticated multi-parameter sensor capable of measuring voltage, current, temperature, and impedance of the battery in real-time.
Controller: Equipped with a high-speed microprocessor, the controller receives raw data from the sensor and applies algorithms to calculate the SoC of the battery.
Telematics Device: This component acts as the bridge between the controller and the cloud-based GIS (Geographic Information System) database. It sends vehicle location and other relevant data to the database and receives terrain and traffic data in return.
EEPROM (Electrically Erasable Programmable Read-Only Memory): This is used for storing the historical data concerning the SoC levels and other parameters, thereby enabling machine learning capabilities.
I/O Device: This is the interface through which the driver interacts with the system. It displays predictive SoC levels, range estimations, and any alerts or notifications.
Functional Description:
Upon startup, the Battery State Measuring Sensor commences its data collection process. The controller receives this data and calculates the current SoC using algorithms developed in-house. This SoC is displayed on the I/O Device for the driver's awareness. Concurrently, the Telematics Device communicates with the GIS database to fetch relevant data about upcoming terrains, traffic conditions, and weather. This data is factored into another layer of predictive algorithms that estimate future SoC and the range of the vehicle. Historical data stored in EEPROM contribute to the machine learning component, allowing the system to refine its predictions over time.
The invention "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" serves as a landmark development in the domain of battery management systems for electric vehicles. Its incorporation of multiple parameters and real-time analytics makes it highly adaptable to a range of driving conditions. It not only addresses the limitations in existing technologies but also offers the advantage of predictive capabilities, thereby enhancing the overall efficiency and reliability of electric vehicles.
Embodiments:
The following text elucidates various embodiments of the invention "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" thereby showcasing its diverse applicability and inherent advantages:
Embodiment 1: Urban Commuter Model
Numerical Range:
Battery Voltage: 48V to 60V
Measurable Current: 10A to 50A
Temperature Range: 10°C to 50°C
Specific Examples:
In this embodiment, the “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” system is configured to prioritize frequent short-distance travel typically encountered in urban commuting. The algorithm is fine-tuned to work with smaller-capacity batteries ranging from 48V to 60V. The system handles current measurements between 10A to 50A and is conditioned to perform optimally at temperature ranges commonly encountered in urban areas, i.e., 10°C to 50°C.
Function in Practice:
For urban commuters, the system would offer a rapid update of the SoC, adjusting to stop-and-go traffic, and frequent short halts. By analyzing the past data, Adaptive Telematics-Based Range Prediction System for Electric Vehicles. also intelligently predicts when the next charging station should be reached based on current traffic conditions and driving habits.
Embodiment 2: Long-Distance Model
Numerical Range:
Battery Voltage: 72V to 120V
Measurable Current: 20A to 100A
Temperature Range: -10°C to 40°C
Specific Examples:
This embodiment is optimized for long-distance travel with higher capacity batteries, typically in the range of 72V to 120V. It is designed to handle higher currents from 20A to 100A and works efficiently in temperatures as low as -10°C, making it suitable for colder climates.
Function in Practice:
For long-haul drivers, “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” provides a more gradual update of SoC and range predictions, accounting for changes in terrain and driving speed. This model would also factor in weather conditions to provide more accurate SoC estimates.
Embodiment 3: Heavy-Duty Commercial Vehicles
Numerical Range:
Battery Voltage: 120V to 240V
Measurable Current: 50A to 200A
Temperature Range: -20°C to 60°C
Specific Examples:
This embodiment is specifically tailored for heavy-duty electric trucks and buses. The battery voltage goes as high as 240V, and the system measures currents up to 200A. It is also designed to operate in extreme temperature conditions from -20°C to 60°C.
Function in Practice:
In a commercial setup, “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” offers fleet management capabilities, integrating with existing systems to provide comprehensive SoC data and predictive analytics to maximize vehicle efficiency and route planning.
The different embodiments of "Adaptive Telematics-Based Range Prediction System for Electric Vehicles" provide comprehensive solutions that cater to various end-user requirements. These embodiments clearly establish the invention's versatility, making it adaptable for different scenarios without compromising its core functionality. Each embodiment inherently demonstrates inventive step, non-obviousness, and industrial applicability.
Flowchart
Flowchart 1 :
Battery: The battery has three sensors attached to it, each measuring different parameters:
Current sensor
Voltage sensor
Temperature sensor (Temp. sensor)
Motor Controller: The motor controller has three parameters that it monitors:
Revolutions per minute (RPM)
Current and voltage (Current and Volts)
Temperature
Data Compilation: Data from the sensors on the battery and the parameters monitored by the motor controller are compiled together in a data compilation step.
EEPROM (Electrically Erasable Programmable Read-Only Memory): This storage component interacts with the data compilation step. It suggests that EEPROM may be used for storing compiled data, and there is also a feedback arrow from EEPROM to the data compilation step, indicating that stored data may be retrieved and used in the compilation process.
Display Params: The compiled data is then sent to a display, labeled as "Display Params," where the parameters are presumably shown for the user or system to interpret.
Flowchart 2:

Vehicle Parameters: A set of vehicle parameters are collected, which include:
Throttle response
Overall acceleration
Mode switching
Brake response
Data Filtering: The collected vehicle parameters undergo data filtering.
Algorithm Processing: The filtered data is then processed by an algorithm.
User Driving Pattern: A decision point or analysis occurs here, identifying the user's driving pattern.
Display / Log: The outcome of the driving pattern analysis is then either displayed to the user or logged for record-keeping.
Flowchart 3:

Inputs: Three types of data inputs are fed into the system:
Cell voltage
Cell current
Cell temperatures
SOC Estimation & Range Prediction Algorithm: These inputs are processed by the SOC (State of Charge) Estimation & Range Prediction Algorithm.
EEPROM: There is an interaction between the SOC Estimation & Range Prediction Algorithm and an EEPROM (Electrically Erasable Programmable Read-Only Memory), suggesting data storage or retrieval.
Internal Feedback Loop: An internal feedback loop is indicated, which implies that the system uses previous outputs to inform current operations.
Cell Models cycle: There is a process or function labeled "Cell Models cycle" that seems to interact with the SOC Estimation & Range Prediction Algorithm.
To Display: The outcome of the algorithm is sent to a display, as indicated by the block titled "To Display".
Flowchart 4:

Gyro Sensor: This sensor measures orientation and provides data on Inclination and Altitude.
GPS: The Global Positioning System (GPS) provides data on Latitude and Longitude.
Humidity Sensor: This sensor detects the humidity level, but its outputs are not labeled in the diagram.
Accelerometer: This sensor measures acceleration forces, but similar to the humidity sensor, its outputs are not labeled.
Data Filtering & Processing: The data from all these sensors (Gyro, GPS, Humidity, Accelerometer) converge into a data filtering and processing block, indicating that the data is being refined and processed.
To Display: The processed data is then sent to be displayed, as indicated by the block titled "To Display."
Flowchart 5:

Step 1 :- Data Gathering: This is the first step, where data is collected from three sources: Battery, Motor Controller, and Other Sensors.
Step 2 :- Variable and dynamic Memory preprocessing: The gathered data then undergoes preprocessing which is variable and dynamic in nature.
Step 3 :- Comparing data to existing Model: The preprocessed data is compared to an existing model.
Step 4 :- Decision Point - Obtained values in range of calibration?: This is a decision point where a check is made to determine if the values obtained are within the range of calibration.
If 'YES', the process moves to the next step.
If 'NO', there is a feedback loop that goes back to the "Variable and dynamic Memory preprocessing" step.
Step 5 :- Discard advanced data anomalies: If the obtained values are in the calibration range, the next step is to discard advanced data anomalies.
Step 6 :- Train the Model: After discarding anomalies, the model is trained with the refined data.
Step 7 :- Decision Point - Re-Calibration: Another decision point checks if re-calibration is needed.
If 'YES', the process loops back to the "Variable and dynamic Memory preprocessing" step.
If 'NO', the process moves forward to the next step.
Step 8 :- Data declaration: This step involves declaring or finalizing the data post-processing and training.
Step 9 :- Display data: Finally, the processed and validated data is displayed.
Flowchart 6:

CAN signals: This is the input to the system, indicating that Controller Area Network (CAN) signals are being received.
CAN Transceiver: The CAN signals are fed into a CAN transceiver, which is responsible for the transmission and reception of CAN signals.
CAN module: Connected to the CAN transceiver, the CAN module is likely responsible for processing the CAN signals.
Microprocessor: This is the central component of the system, connected to several modules:
It receives input from the CAN module.
It is connected to a "Compensation network and Rectifier" block, which suggests that it may be receiving power or signals that have been conditioned by these components.
It is also connected to a "Cloud connect module / SIM", indicating that the system has the capability to connect to the cloud for data transmission or processing.
A "Supply / Power unit" is connected to the microprocessor, indicating that it provides power to the system.
"BLE" (Bluetooth Low Energy) is listed as another component connected to the microprocessor, indicating wireless communication capabilities.
A "GPS modem" is connected as well, indicating that the system has GPS capabilities, likely for location tracking.
Accomplishing Objectives:
Primary Objective:
Improve Efficiency in Battery Management :- The invention utilizes a cutting-edge predictive algorithm to continuously monitor and analyze battery parameters such as voltage, current, and temperature. By doing so, it provides real-time State-of-Charge (SoC) estimates that allow for optimized battery usage and management.
Another Objective:
Enhance Battery Longevity :- A specialized Health Monitoring Module is implemented within the “Adaptive Telematics-Based Range Prediction System for Electric Vehicles”. system, which gauges the wear and tear of the battery based on parameters like cycle count and voltage fluctuations. As a result, the system suggests timely maintenance routines, thereby prolonging battery lifespan.
Further Objective:
Enable User-Friendly Interface :- The “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” system is designed with an intuitive and easily navigable user interface. Features like a well-organized dashboard and real-time monitoring make it convenient for users to interact with the system.
Another Objective:
Ensure Safety Measures :- The system incorporates multiple safety mechanisms, including temperature monitoring and voltage regulation, which trigger protective measures in case of anomalies, thus ensuring user and device safety.
Further Objective:
Facilitate Remote Monitoring :- “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” is equipped with a state-of-the-art communication module, which allows for seamless integration with smartphones and computers. This feature enables remote monitoring of the battery system, making it easier for users to keep track of their battery health without manual intervention.
The “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” invention diligently meets its stated objectives through intricate algorithmic processing, robust hardware components, and intuitive user interface. Thus, the invention stands to make a significant contribution to the domain of battery management systems.
Best Method :
Most Effective Method for Using or Manufacturing:
Manufacturing:
Algorithm Development: The first step involves programming a predictive algorithm that effectively computes and tracks battery parameters like voltage, current, and temperature.
Hardware Integration: The next phase involves integrating a microcontroller and other sensors to collect real-time data, which will be processed by the algorithm.
Software Interface: Create an intuitive software interface to display the real-time monitoring and analytics. This should be compatible with various platforms such as Android, iOS, and Windows.
Safety Mechanisms: Install a safety protocol system that incorporates features like temperature monitoring and voltage regulation.
Quality Assurance: Conduct multiple stress tests under different conditions to validate the robustness and effectiveness of the system.
Using:
Installation: Install the hardware component onto the desired battery system.
Initialization: Boot up the “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” system and allow it to calibrate.
Monitoring: Use the software interface for ongoing monitoring and maintenance guidance.
Safety Checks: Regularly review the system’s safety reports and updates.
Remote Monitoring: Utilize the remote monitoring feature for managing the system while away from the physical location.
Potential Applications:
Automotive Industry: Battery management in electric and hybrid vehicles to extend battery life and ensure safe operation.
Renewable Energy Systems: “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” also integrated into solar power storage systems for optimizing charge and discharge cycles.
Consumer Electronics: In smartphones and laptops for enhancing battery performance and extending device lifespan.
Industrial Settings: In factories where automated machinery relies on battery systems, “Adaptive Telematics-Based Range Prediction System for Electric Vehicles” helps in predictive maintenance.
Healthcare Sector: For medical devices that are battery-operated, ensuring their reliability and safety through “Adaptive Telematics-Based Range Prediction System for Electric Vehicles”'s monitoring systems. , C , Claims:Claim 1:
An adaptive telematics-based range prediction system for electric vehicles, comprising:
a battery state measuring sensor designed to measure voltage, current, temperature, and impedance of an electric vehicle's battery in real-time;
a controller equipped with a high-speed microprocessor that receives data from the battery state measuring sensor and calculates the State of Charge (SoC) of the battery;
a telematics device acting as a bridge between the controller and a cloud-based Geographic Information System (GIS) database, configured to transmit vehicle location and other data to the database and to receive terrain, traffic, and weather data in return;
an Electrically Erasable Programmable Read-Only Memory (EEPROM) for storing historical data regarding SoC levels and other parameters of the electric vehicle;
an input/output (I/O) device serving as an interface for the driver to display predictive SoC levels, range estimations, and alerts or notifications;
characterized by the integration of the controller, telematics device, and EEPROM to employ a machine learning algorithm that refines predictive capabilities of the SoC and vehicle range estimations based on the aggregation of real-time data from the battery state measuring sensor, terrain, traffic, and weather information from the GIS database, and historical data stored in EEPROM, thereby enabling the system to adaptively predict and enhance the efficiency and reliability of the electric vehicle's battery management under various driving conditions.
Claim 2: The system of Claim 1, wherein the battery state measuring sensor is further configured to measure the battery's state of health (SoH) by evaluating degradation factors, with the controller programmed to adjust the State of Charge (SoC) calculations based on the measured SoH, thereby enhancing the accuracy of the range predictions.
Claim 3: The system of Claim 1, wherein the machine learning algorithm employed by the controller utilizes a neural network architecture capable of processing inputs from the battery state measuring sensor, the telematics device, and the EEPROM to predict the SoC and vehicle range with a predictive accuracy margin of +/- 5%.
Claim 4: The system of Claim 1, wherein the telematics device communicates with the cloud-based Geographic Information System (GIS) database at intervals of no more than 10 seconds to ensure real-time accuracy of terrain, traffic, and weather data.
Claim 5: The system of Claim 1, further comprising a regenerative braking system sensor interfaced with the controller, designed to measure energy reclaimed through braking and factor this into the SoC and range estimations, providing a more comprehensive energy recovery analysis.
Claim 6: The system of Claim 1, wherein the EEPROM is capable of storing at least 5 years’ worth of historical data regarding SoC levels and other parameters, with the controller using this extensive historical data set to enhance machine learning algorithm predictions over time.
Claim 7: The system of Claim 1, wherein the I/O device includes a touchscreen display capable of presenting predictive SoC levels, range estimations, and a graphical interface for inputting destination information, which the system uses to calculate and display optimized charging stops along the route.
Claim 8: The system of Claim 1, further characterized by the telematics device being configured to receive updates from the cloud-based GIS database regarding the location and availability of charging stations, incorporating this data into the vehicle range estimations.
Claim 9: The system of Claim 1, wherein the machine learning algorithm is further refined to account for driver behavior patterns, including acceleration and braking habits, to predictively adjust the SoC and vehicle range estimations, thereby personalizing the predictions to the driver’s unique driving style.