Advancing Electric Vehicle Battery Safety and Efficiency through Robust Predictive Thermal Management


Due to their exceptional qualities, such as high energy density, voltage, and extended lifespan, lithium-ion batteries (LIBs) have emerged as the preferred option as electric vehicles have gained popularity. To ensure safety, optimal performance, and longevity, LIBs must operate within a specific temperature range because they are sensitive to temperature variations. For LIBs, the ideal operating temperature is typically 20°C to 30°C. The battery’s life and capacity may decline if it is operated outside of this range, posing risks of thermal runaway that endanger human safety. Therefore, keeping the battery temperature within the proper range requires the implementation of efficient thermal management systems (TMS).

Traditional TMS strategies, such as air and liquid cooling, have their respective advantages and drawbacks. Air cooling, while simple and energy-efficient, is limited in its cooling capacity and temperature control accuracy, resulting in temperature fluctuations. On the other hand, liquid cooling provides better heat transfer efficiency and temperature control accuracy but requires complex systems with high maintenance costs. In a new study published in the peer -reviewed Journal, Applied Thermal Engineering, Professor Xiangbo Cui from Hunan University of Technology and PhD candidate Tete Hu from Central South University proposed a novel thermal management system based on thermoelectric cooling (TEC) that overcomes the limitations of existing approaches, providing rapid cooling, high control accuracy, and enhanced energy optimization.

The research team proposed thermal management system based on the principles of thermoelectric cooling, which utilizes the Peltier effect to create refrigeration. This system involves forming a circuit of two P-N semiconductors with different thermoelectric effects, applying DC power, and achieving efficient cooling. The first step in the development of the new system is the construction of a heat transfer model based on thermal resistance, which takes into account the influence of heat sinks and fans. This model is crucial for accurately predicting and optimizing the cooling performance. Subsequently, a distributed battery thermal model is developed using the finite difference method, considering the heat generation features of the battery tabs and their impact on temperature distribution. This refined modeling approach ensures improved accuracy in predicting temperature profiles.

Next, the authors created a thermal management model by integrating the heat transfer model and the distributed battery thermal model. To achieve robust and precise temperature control, Professor Xiangbo Cui and Tete Hu proposed a robust nonlinear model predictive control (NMPC) strategy based on neural networks (NN). Unlike traditional control methods, this strategy accounts for system uncertainty arising from parameter diversities in battery packs due to manufacturing inconsistencies, as well as external factors like electromagnetic noise, measurement errors, weather conditions, traffic congestion, and driving style. To validate the performance of the proposed thermal management system, a series of experiments is conducted under different operating conditions. The test results demonstrate the accuracy and effectiveness of the system, with temperature errors remaining below 0.5°C compared to the desired reference value during all test cycles.

The proposed robust predictive BTM strategy offers several key benefits, firstly, enhanced Safety: By precisely regulating the battery temperature within the optimal range, the risk of thermal runaway and associated safety hazards is significantly reduced. Secondly, improved Efficiency: The use of TEC in the thermal management system allows for rapid cooling and accurate temperature control, leading to increased energy optimization and overall efficiency. Thirdly: stability and Robustness: The incorporation of robust NMPC with neural networks ensures system stability and compensates for uncertainties, providing a reliable and robust control strategy. Lastly: better Model Accuracy: The distributed battery thermal model, considering the heat generation features of battery tabs, results in improved model accuracy, further enhancing temperature prediction and control.

The new study presented the significance of thermal management in the context of electric vehicle battery safety and efficiency. The proposed robust predictive BTM strategy, based on thermoelectric cooling, offers superior cooling capabilities, precise temperature control, and energy optimization. The integration of a distributed battery thermal model and robust NMPC using neural networks enhances system accuracy and stability, while experimental verification confirms the system’s effectiveness. Implementing this advanced thermal management system in electric vehicles will undoubtedly contribute to safer and more efficient battery operation, further propelling the adoption and widespread use of electric vehicles in the future.

About the author

Dr. Xiangbo Cui is an assistant professor in the College of Railway Transportation at Hunan University of Technology. He received his bachelor’s degree in Vehicle Engineering from Xi’an University of Science and Technology in 2013 and received his Master’s degree in Vehicle Engineering at Xiamen University of Technology in 2017. In 2023, he completed his doctorate degree in mechanical engineering at Central South University under the supervision of Prof. Lu Xinjiang. Cui began his current appointment at Hunan University of Technology in 2022. He is particularly interested in machine learning, battery management system, and thermal management strategies.

About the author

Tete Hu completed his bachelor’s degree in mechanical engineering at Hefei University in 2013 and completed his Master’s degree in mechanical engineering at China University of Geosciences in 2017. In 2017, he started his doctorate degree in mechanical engineering at Central South University under the supervision of Prof. Xinjiang Lu. His research is mainly focused on machine learning, complex system modeling, bio-inspired of design and control, and their application in the field of soft and bio-inspired robotics.


Xiangbo Cui, Tete Hu, Robust predictive thermal management strategy for lithium-ion battery based on thermoelectric cooler, Applied Thermal Engineering Volume 221, 2023, 119833

Go to Applied Thermal Engineering

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