Intelligent Hybrid Cooling for EV Batteries:
A Dynamic Thermal Management System
K. Sai Krishna (N200793)
M. Guru Dinesh Reddy (N200949)
K.P.S.Pavan kumar (N200325)
M.Suresh (N201039)
J.Sukesh (N200940)
June 2025
Abstract
Electric vehicle (EV) systems face major thermal challenges due to high power
densities in motors and batteries. Effective cooling is essential to prevent over-
heating and ensure longevity. This project presents an Intelligent Hybrid Cooling
System that combines free convection, forced convection, and liquid cooling. The
system dynamically selects the appropriate cooling mode based on real-time temper-
ature data. Simulated using MATLAB Simulink, the proposed solution maintains
optimal temperature conditions while optimizing energy consumption. Results in-
dicate improved thermal regulation, higher energy efficiency, and enhanced system
safety compared to traditional cooling systems.
1 Introduction
With the rise of electric mobility, managing the excessive heat generated by EV com-
ponents, especially motors and battery packs, is more critical than ever. High thermal
loads during acceleration, fast charging, or steep climbs can degrade cells, reduce charge
retention, and cause permanent damage.
Traditional systems like fixed air or liquid cooling struggle to handle varying environ-
mental and load conditions efficiently. Hence, the need for an intelligent, adaptive, and
energy-conscious cooling solution.
This project proposes a hybrid system that leverages real-time thermal data, enabling
dynamic switching between three cooling modes to meet changing thermal demands while
conserving power.
2 System Architecture
2.1 Overview
The Intelligent Hybrid Cooling System includes:
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• Sensors: For temperature (motor, ambient), and optionally for load or torque.
• Controller: Logic-based (PI or threshold logic) with real-time decision-making.
• Cooling Mechanisms: Natural convection, fan-based forced convection, and
pump-based liquid cooling.
• Feedback Loop: For continual correction and energy saving.
2.2 Block Diagram
Figure 1: Block Diagram of Intelligent Hybrid Cooling System
2.3 Working Logic
• Below 30◦ C: Minimal cooling, free convection mode.
• Between 30◦ C and 45◦ C: Fan-based forced convection activated.
• Above 45◦ C: Liquid cooling (pump + radiator) initiated.
• Once temperature drops, system reverts to energy-saving modes.
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3 Simulation in MATLAB Simulink
3.1 Objective
To validate the proposed thermal control mechanism under fluctuating thermal loads and
environmental conditions through simulation.
3.2 Setup
• Software: MATLAB Simulink
• Heat Source: Time-varying load ⇒ Q(t) = k · Load(t)
• Cooling Modes: Modeled using gain and switch blocks with rates R1 , R2 , R3
• Thermal Response: Modeled via integrators for temperature accumulation
• Logic: Thresholds and conditional switches control cooling transitions
3.3 Design Elements
• Integrator: Calculates motor temperature over time.
• Scope: To visualize temperature, energy use, and mode transitions.
• Switch Logic: Implements dynamic switching between modes.
3.4 Additional Considerations
• Thermal Inertia: Delays added to simulate real heat absorption/release.
• Controller Delay: Models real-world lag in actuator response.
• Adaptive Gain: Could be extended to include AI-based gain adjustment.
4 Simulation Blocks and Graph Analysis
To validate the thermal behavior and the dynamic performance of the hybrid cooling
system, the simulation was developed in MATLAB Simulink. It comprises key blocks
for modeling heat generation, cooling control logic, and the output response scope. The
following images show the system architecture and results.
This block simulates heat generated by a motor under a time-varying load. The
input load signal is modeled using a sine wave to simulate fluctuating torque/power. The
equation used is:
Q(t) = k · Load(t)
where k is a thermal gain constant. The output heat is fed into the integrator to determine
the rise in temperature over time.
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4.1 Cooling Control Logic
The control logic uses threshold values and switch blocks:
• Below 30°C: Activates free convection (low cooling gain).
• 30°C – 45°C: Activates fan (moderate cooling).
• Above 45°C: Activates liquid cooling (maximum gain).
Switches and comparators determine which cooling mode to activate depending on tem-
perature input.
4.2 Cooling Mode Transitions
This illustrates how the system dynamically transitions between different cooling modes.
As temperature increases:
• The system starts with no cooling (free convection).
• Switches to forced convection as the motor heats.
• Activates liquid cooling when a high thermal load is detected.
• Reverts to lower modes after stabilization.
4.3 Temperature Regulation and Energy Consumption
Figure 2: Graph: Motor Temperature and Energy Usage
This dual-axis graph shows:
• The motor temperature stabilizing between 30–45°C
• Minimal energy consumption in free convection
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• A spike in energy during liquid cooling, followed by a drop as the system cools
The curve flattens once the desired thermal range is maintained.
4.4 Cooling Efficiency Comparison
Figure 3: Graph: Hybrid vs. Traditional Cooling Efficiency
This comparison plot illustrates:
• Hybrid cooling maintains higher efficiency throughout operation.
• Traditional systems exhibit constant but lower performance.
• Adaptive switching helps reduce energy waste and improves thermal response.
5 Results and Discussion
5.1 Temperature Profile
Motor temperature stabilized within a safe range under all tested load conditions. Max
temperature stayed below 47◦ C.
5.2 Energy Consumption
Intelligent switching reduced unnecessary use of high-energy liquid cooling. Energy use
was 30–50% lower compared to always-on cooling.
5.3 Cooling Efficiency
• Hybrid system maintained average cooling efficiency of 88–90%.
• Traditional fixed systems varied from 70–80%, depending on load.
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5.4 Comparison Table
Parameter Traditional Cooling Hybrid Cooling
Cooling Method Fixed Mode Adaptive Switching
Avg. Efficiency 75–80% 88–90%
Energy Use 15–30W 5–15W
Temperature Range Maintained 25–50◦ C 30–45◦ C
Battery Life Impact Moderate Positive
6 Applications
The proposed system can be applied in:
• Passenger electric vehicles (2W, 3W, 4W)
• Electric buses and commercial trucks
• Electric forklifts or industrial mobility units
• Battery Energy Storage Systems (BESS)
• Aerospace electric propulsion systems
7 Conclusion
This project demonstrates a scalable, adaptable thermal management solution for EV
batteries. The intelligent hybrid system significantly improves efficiency and safety while
reducing energy consumption.
Simulations validate that temperature thresholds are well-maintained with minimal
overhead, and the smart switching algorithm ensures optimal use of available cooling
resources.
8 Future Scope
• Integration of AI/ML for predictive cooling based on driving patterns.
• Use of IoT for real-time diagnostics and predictive maintenance.
• Adaptation for heavy-duty EVs and fast-charging scenarios.
• Solar or regenerative energy to power cooling subsystems.
• Full prototype development and on-road testing.
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References
[1] Huang et al., “An Integrated Cooling System for Hybrid Electric Vehicle Motors –
Design and Simulation,” SAE Paper 2018-01-1108.
[2] Lee, K. et al., “Development of an Interior Permanent Magnet Motor Through
Rotor Cooling,” Applied Thermal Engineering, 2016.
[3] Shoai-Naini, S. et al., “A Thermal Bus for Vehicle Cooling Applications – Design
and Analysis,” SAE Journal of Commercial Vehicles.
[4] Zhang, X. et al., “Optimization of Series-HEV Control Considering Battery Cooling
Losses,” SAE J. Alt. Powertrains, 2014.
[5] Putra, N., “Electric Motor Thermal Management Using Flat Heat Pipes,” Applied
Thermal Engineering, 2017.
