DYNAMIC TEMPERATURE CONTROL IN Li-ION BATTERIES USING AN APPROXIMATE SOLUTION: A NUMERICAL STUDY

Optimizing lithium-ion battery performance, safety, and lifespan requires effective thermal management, especially in high-demand operating environments. In order to maintain ideal thermal conditions, this study presents a novel predictive temperature control strategy that dynamically modifies the charge-discharge rate and airflow velocity. A computationally effective algorithm based on the integral method and backed by an extensive mathematical and physical model forms the basis of this strategy. Through simulations using the finite element method in a variety of scenarios, the resulting approximate solution was thoroughly validated. Tests were carried out with airflow velocities ranging from 0.5 m/s to 4 m/s, discharge rates of 2C, 6C, and 12C, and ambient and initial temperatures of 298.15 K and 313.15 K, respectively. An approximate solution correctly predicted the absolute temperature evolution and the associated temperature rise under standard thermal conditions (i.e., initial and ambient temperatures were equal). For instance, the maximum temperature increase at 2C and 0.5 m/s only displayed 1.1% error during charging and 1.9% error during discharging. The prediction error was less than 0.8% at 12C and 4 m/s. Maximum temperature increase errors continuously stayed below 4.2%, and average temperature profile errors were below 3.2% across all test cases. Errors under thermal transients, heating from 298.15 K to 313.15 K and cooling in the opposite direction, remained below 7.3%, and absolute temperature deviations were continuously below 2.8 K. Then a dynamic control algorithm was implemented to adjust the flow rate and airflow speed according to the charge level. Even during high-load discharge, this technique was able to keep the battery temperature below the 333.5 K safety threshold. The efficiency of the control logic under thermal perturbation was verified by a particular test that involved a charge-discharge cycle at 12C, with both the initial and ambient temperatures set to 303.15 K and an internal heat generation of 10<sup>6</sup> W/m<sup>3</sup>. By enabling real-time prediction and thermal regulation based on airflow, discharge rate, and state of charge, the approximate solution lowers the possibility of thermal runaway and enhances electrochemical performance. This logic-based control method for forced air cooling is more computationally efficient, scalable, and especially well-suited for embedded and internet of things battery management systems than complicated phase-change or liquid-based systems. This work is a noteworthy advancement.