Intelligent Predictive Climate Control Systems for Electric and Autonomous Vehicles: A Novel Integration Framework for Enhanced Comfort and Energy Efficiency

Abstract

This paper presents a comprehensive patent filing framework for an advanced automotive climate control system that integrates artificial intelligence, multi-modal sensor fusion, and vehicle connectivity technologies. The proposed system addresses critical challenges in electric and autonomous vehicles by optimizing individual occupant comfort while maximizing energy efficiency and extending vehicle range. Through extensive prior art analysis of 82 patents and research papers, we identify key technological gaps and present a novel integration architecture that combines biometric sensing, emotional state detection, predictive AI algorithms, Vehicle-to-Everything (V2X) communication, and Vehicle-to-Grid (V2G) capabilities. The system employs Model Predictive Control with reinforcement learning to create dynamic micro-climate zones tailored to individual occupants. Our novelty assessment demonstrates significant inventive steps beyond existing technologies, particularly in the synergistic integration of diverse data streams for holistic thermal management. The paper provides detailed patent claims, comparative analysis with closest prior art, and strategic recommendations for intellectual property protection in the rapidly evolving automotive climate control domain.

Introduction

The automotive industry is undergoing major transformation due to electrification, autonomous driving, and personalized mobility trends. In this evolving landscape, automotive HVAC systems play a crucial role in energy efficiency, occupant comfort, and overall vehicle performance. Traditional climate control systems are ill-suited for electric and autonomous vehicles, where energy use directly affects driving range and comfort needs are more complex.

This research presents a novel Intelligent Predictive Climate Control System (IPCCS) that leverages AI, multi-modal sensors, and vehicle connectivity to deliver energy-efficient, highly personalized climate control. The system integrates:

• Multi-modal sensing (biometric, visual, environmental)

• External data sources (V2X, V2G, weather, calendar)

• AI-driven predictive control (using MPC, deep learning, and fuzzy logic)

• Integrated thermal management for cabin, battery, and powertrain

The study identifies current technological gaps in existing systems, such as fragmented integration, limited personalization, and lack of predictive control. Through analysis of 82 patents and academic sources, the researchers propose an innovative system that creates adaptive micro-climate zones and achieves holistic thermal optimization.

Key novelties include:

• Real-time fusion of sensor data to build a "digital twin" of occupants

• Predictive AI models that optimize HVAC operation before changes occur

• Integration with grid and infrastructure systems for energy efficiency

• Seamless personalization based on physiological and emotional data

The paper also outlines a framework for patent development and commercialization strategy.

Conclusion

This paper presents a comprehensive framework for an intelligent predictive climate control system that addresses critical challenges in modern automotive applications. Through extensive prior art analysis, we identify significant opportunities for innovation in the integration of AI, multi-modal sensing, and connectivity technologies. The proposed system demonstrates clear inventive steps beyond existing prior art through its holistic approach to thermal management, comprehensive external data integration, and adaptive personalization capabilities. The system\'s ability to simultaneously optimize individual comfort, vehicle energy efficiency, and grid interaction represents a significant advancement in automotive climate control technology. Our patent analysis reveals strong potential for intellectual property protection, particularly in the areas of integrated system architecture, novel AI algorithms, and unique sensor fusion approaches. The proposed patent claims provide comprehensive protection while enabling commercial flexibility. The commercial implications of this technology are substantial, addressing key market drivers including EV adoption, autonomous vehicle readiness, and grid integration opportunities. The system\'s modular architecture and standardized interfaces facilitate integration with existing vehicle platforms while providing a foundation for future enhancements. Future research directions include advanced AI techniques, emerging sensor technologies, and evolving grid integration standards. These developments will further enhance the system\'s capabilities and market value. The intelligent predictive climate control system represents a significant step forward in automotive thermal management, providing a foundation for the next generation of vehicle comfort and efficiency optimization. Through strategic patent protection and continued development, this technology has the potential to transform the automotive climate control landscape and contribute to the broader goals of sustainable transportation and grid modernization.

References

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Copyright

Copyright © 2025 Shankar R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.