AI-ENHANCED IOT SYSTEM FOR REAL-TIME BATTERY AND PMSM MOTOR EFFICIENCY MONITORING IN ELECTRIC VEHICLES

Abstract

The invention is an AI-enhanced IoT system for real-time monitoring and optimization of battery and Permanent Magnet Synchronous Motor (PMSM) efficiency in electric vehicles (EVs). This system employs IoT sensors and AI algorithms to track and analyze battery health, state of charge, temperature, motor load, and efficiency metrics. The AI component predicts potential inefficiencies, enabling dynamic adjustments to both battery usage and motor operation to maximize energy efficiency, extend battery life, and enhance motor performance. This integrated system optimizes energy consumption, contributing to sustainable EV operation by balancing component longevity and energy use efficiency. By seamlessly integrating AI with IoT, the system achieves enhanced energy efficiency, extended battery and motor life, and a reduction in EV maintenance costs. This comprehensive solution empowers drivers with real-time insights and automated optimizations, contributing to a more reliable, sustainable, and user-friendly EV experience. The system is designed to adapt to diverse vehicle models, ensuring broad applicability across the electric vehicle industry.

Specification

Description:The proposed invention consists of an AI-enhanced IoT system that provides continuous real-time monitoring and optimization for the battery and Permanent Magnet Synchronous Motor (PMSM) in electric vehicles (EVs). The system incorporates IoT sensors strategically placed within the vehicle to gather essential data on battery health, motor performance, temperature, speed, and load conditions. These sensors transmit data to a central processing unit, where AI algorithms, including machine learning (ML) and deep learning (DL), analyze the data to predict inefficiencies and potential issues. For the battery system, the AI monitors state of charge (SOC), state of health (SOH), and temperature, allowing the system to make predictive adjustments that can help avoid situations of deep discharge or excessive temperature, which may lead to rapid degradation. Simultaneously, the AI evaluates the PMSM's operating parameters to identify and optimize factors such as load distribution, cooling requirements, and energy consumption to maximize efficiency.
The IoT framework enables continuous data communication between sensors and the AI system, facilitating seamless and adaptive adjustments to both the battery and motor. The AI algorithms analyze incoming data in real time, generating insights that guide dynamic decision-making to optimize component performance and conserve energy. For instance, the system can autonomously adjust the motor's operating parameters to match current driving conditions, minimizing power loss and reducing wear. Moreover, the AI system is designed to learn from historical data, which enhances its predictive capabilities over time. By employing machine learning techniques, the system can improve its predictions and optimization strategies as it gathers more data, allowing for increasingly precise adjustments. This approach not only ensures optimal energy utilization but also contributes to prolonged battery life and enhanced motor longevity, making the EV system more sustainable and efficient.
The proposed invention introduces a robust AI-enhanced IoT system designed to monitor, analyze, and optimize both the battery and the Permanent Magnet Synchronous Motor (PMSM) in electric vehicles (EVs) through a unified platform. This system employs a network of IoT sensors and an advanced AI-driven control unit that work in tandem to collect and process real-time data, enabling dynamic, data-driven decision-making. The IoT sensors are strategically placed within the EV's powertrain and battery compartments to monitor crucial parameters, including battery state of charge (SOC), state of health (SOH), voltage, temperature, current, and energy consumption rates. For the PMSM, these sensors capture data related to rotational speed, load, torque, temperature, and power usage.
Once collected, the sensor data are transmitted to a central processing unit equipped with AI algorithms, including machine learning and deep learning techniques. This processing unit uses the data to analyze the battery's health and performance metrics, identifying trends that may indicate potential issues or areas where efficiency can be improved. For instance, the system can detect patterns associated with battery degradation, such as temperature rises during charge cycles or irregularities in discharge rates, which may signal early-stage battery wear. When such patterns are detected, the system applies predictive analytics to adjust the battery management strategy, possibly modifying charge and discharge cycles or limiting the depth of discharge to preserve battery life and maintain optimal operating conditions.
Simultaneously, the AI component monitors the PMSM's performance, analyzing data points such as load, speed, and power draw. By adjusting operational parameters in real time, the system can optimize the PMSM's efficiency based on current driving conditions. For instance, during low-load scenarios, the system may reduce the motor's energy input, effectively conserving battery power without sacrificing performance. In contrast, during high-load or acceleration phases, the system can adjust the motor's output to deliver the required torque efficiently. This dynamic control of the PMSM helps maintain an optimal power-to-efficiency ratio, contributing to extended motor life and reduced energy losses.
In addition to monitoring and optimization, the system incorporates a predictive maintenance framework that utilizes historical data to forecast maintenance needs for both the battery and the PMSM. By learning from past performance data, the AI algorithms can predict future operational states and notify the user or the EV's central management system of impending maintenance requirements. For example, the system can identify trends that suggest impending motor wear or battery inefficiencies, prompting early intervention to avoid costly repairs or breakdowns. This proactive maintenance capability is essential for extending the lifespan of both the battery and motor, reducing downtime, and minimizing the total cost of ownership.
The invention also includes a user interface, accessible via a mobile app or the vehicle's dashboard, which displays real-time information on battery and motor status, including health metrics, efficiency ratings, and predicted remaining life. This interface empowers users with insights into their vehicle's performance and provides actionable recommendations, such as optimal charging times, anticipated maintenance needs, and driving habits that can improve energy efficiency. The interface uses visual indicators, charts, and alerts to keep users informed, enabling them to make data-driven decisions about vehicle operation and maintenance.
On the backend, the AI algorithms are designed to continuously improve through machine learning, adapting to new data and refining predictions over time. As the system gathers more information on the vehicle's operational patterns, it becomes more adept at identifying subtle anomalies and predicting performance outcomes, leading to progressively more accurate and effective optimization strategies. The learning process involves analyzing vast amounts of data, which may be stored either on the vehicle's onboard computer or in a cloud-based server, depending on connectivity and storage capabilities. This flexibility allows the system to operate effectively in various network environments, ensuring consistent performance regardless of connectivity limitations.
Furthermore, the system's architecture is designed to be compatible with a range of electric vehicle models and configurations. The modular design of the IoT sensors and processing units allows for customization based on specific vehicle requirements, making the system adaptable to different battery chemistries, motor designs, and driving environments. This adaptability ensures that the system can be deployed in a broad spectrum of electric vehicles, from compact city cars to high-performance electric trucks, enhancing its potential impact on the EV industry.
In terms of security, the system employs encryption protocols to safeguard the data transmitted between sensors, processing units, and user interfaces. This ensures that sensitive information, such as battery health and vehicle performance metrics, remains secure from potential cyber threats. Additionally, the AI algorithms incorporate fail-safe mechanisms to prevent erroneous adjustments in the event of system malfunctions or data anomalies, preserving the safety and reliability of the vehicle's power systems.
Overall, the proposed AI-enhanced IoT system offers a comprehensive solution for real-time energy management in electric vehicles, combining monitoring, predictive analytics, dynamic optimization, and user empowerment. By integrating IoT and AI technologies, this invention provides a sophisticated approach to improving EV performance, reducing energy consumption, and extending the lifespan of critical components. The system's predictive and adaptive capabilities not only ensure optimal operation under varying conditions but also contribute to a sustainable and economically viable electric vehicle infrastructure by maximizing energy efficiency and minimizing wear on essential components. This approach represents a significant advancement in the field of electric vehicle technology, aligning with global efforts to promote energy conservation, reduce emissions, and enhance the overall sustainability of transportation solutions. , Claims:1.A system for real-time monitoring and optimization of battery efficiency in electric vehicles, comprising IoT sensors that gather data on state of charge, temperature, and load conditions.

2.The system of claim 1, wherein the data is transmitted to a central processing unit that utilizes machine learning algorithms to analyze and optimize battery usage.

3.A system for real-time monitoring and optimization of PMSM efficiency, comprising IoT sensors that gather data on motor load, temperature, and rotational speed.

4.The system of claim 3, wherein the central processing unit applies AI algorithms to predict and adjust motor operational parameters for optimal efficiency.

5.The system of claim 1, wherein AI-driven analytics predict battery health degradation based on usage patterns and temperature variations.

6.The system of claim 3, wherein AI-driven analytics adjust PMSM load distribution to reduce energy consumption and prevent overheating.

7.The system of claim 2, wherein battery usage predictions optimize charging cycles to prolong battery life.

8.The system of claim 4, wherein PMSM adjustments are made dynamically based on real-time driving conditions.

9.The system of claim 1 and claim 3, integrated to provide a holistic approach to energy management in electric vehicles.
10.The system of claim 1 and claim 4, configured to continuously learn from historical data to improve predictive accuracy and optimization efficiency.

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