A prediction-based thermal management strategy for stationary battery energy storage systems

Thermal management is critical for stationary battery energy storage systems (BESSs). This study proposes a prediction-based thermal management strategy for stationary BESSs, leveraging the operational predictability of BESSs to enable preemptive cooling regulation. Numerical investigations across four seasons demonstrate that, compared with the 60 s ON/60 s OFF intermittent control, the proposed prediction-based thermal management strategy reduces the overtemperature duration by approximately 80% under extreme summer conditions, lowers the peak battery temperature by 4.07 K, and eliminates temperature-difference exceedance. Continuous airflow modulation reduces the average of the seasonal maximum rates of temperature rise and drop by 38.6% and 75.1%, respectively, and decreases the standard deviation of temperature-difference change rate by 54.3%. Moreover, the proposed strategy achieves 67.1%-99.9% fan energy savings across all conditions. By preempting thermal responses and avoiding excessive cooling, the proposed strategy simultaneously enhances battery thermal safety and longevity without consuming excessive energy.