Predictive Analysis of EV Battery Cooling Using PCM and Hardware Implementation

Abstract

Electric vehicle (EV) batteries require proactive cooling strategies to maintain safe operating conditions and extend service life. This paper introduces a predictive analysis framework that integrates hardware implementation with computational monitoring to evaluate the effectiveness of Phase Change Material (PCM) in thermal regulation. Unlike hardware only studies, the proposed system anticipates temperature variations under dynamic load conditions, enabling timely activation of safety mechanisms. Experimental validation confirmed stable operation between 27.0°C–28.2°C under normal conditions and an average of 32.0°C–32.8°C under load, while predictive monitoring demonstrated PCM’s capacity to absorb approximately 18?kJ of heat and sustain a liquid fraction of?0.6?at?41°C. The combined approach highlights the importance of prediction in real time battery safety, offering a scalable and intelligent solution for advanced EV thermal management systems.

Introduction

The text presents a study on Phase Change Material (PCM)-based battery thermal management for electric vehicles (EVs). EV battery performance, efficiency, lifespan, and safety are strongly affected by excessive heat during charging and discharging. The study proposes combining real-time hardware monitoring with predictive thermal analysis to improve battery cooling and safety.

Literature Review

Previous research has investigated air cooling, liquid cooling, PCM cooling, and hybrid cooling systems. Studies show that PCM can effectively absorb heat through latent heat during phase change, while machine learning and predictive models can forecast temperature changes and optimize cooling. However, few studies combine experimental hardware validation with predictive monitoring, which is the main research gap addressed by this work.

Methodology

The study uses two main approaches:

• Hardware implementation: A battery prototype was encapsulated with PCM, and thermocouples were used to monitor battery temperatures. An Arduino-based controller activates cooling when the temperature exceeds 35°C.
• Predictive analysis: A computational thermal model was developed to predict heat transfer and PCM phase changes under dynamic operating conditions.
• The heat absorbed by the PCM was calculated using sensible and latent heat equations.
• Experimental data were used to calibrate the predictive model, allowing the system to anticipate potentially unsafe temperature conditions.

Results

The experimental sensors recorded battery temperatures between 27.0°C and 28.2°C, showing relatively uniform temperature distribution and no significant hotspots. The PCM absorbed approximately 18 kJ of heat, consisting of about 2.56 kJ of sensible heat and 15.4 kJ of latent heat.

At 41°C, approximately 60% of the PCM had changed into the liquid phase, demonstrating its ability to absorb and buffer heat effectively.

Conclusion

This study presented a dual methodology integrating hardware experimentation with predictive modelling for electric vehicle battery thermal management. The experimental results confirmed that Phase Change Material (PCM) effectively stabilized battery temperatures, maintaining values within safe operating limits and reducing the risk of localized hotspots. Predictive analysis further demonstrated PCM’s capacity to absorb approximately 18 kJ of heat during phase transition, with a liquid fraction of 0.6 at 41°C, validating its buffering ability under dynamic load conditions. The integration of real time sensor monitoring with predictive forecasting provided a proactive framework for safety management. Unlike hardware only approaches, this system anticipates critical temperature rise and activates control mechanisms before thresholds are exceeded. Regression analysis of operational parameters strengthened the predictive capability, ensuring reliable monitoring of temperature, humidity, motor speed, and battery performance. Overall, the results highlight that PCM combined with predictive monitoring offers a scalable, reliable, and forward looking solution for next generation EV battery cooling. This approach not only enhances safety and efficiency but also establishes a foundation for intelligent thermal management systems capable of adapting to diverse operating conditions.

References

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Copyright

Copyright © 2026 Leela Narasimha Viyyapu, Neelima Devi Chinta . 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.