System robustness and performance are essential considerations in controller design to ensure reference tracking, disturbance rejection, and resilience to modeling uncertainties. However, guaranteeing that the system operates within safe bounds becomes a priority in safety-critical applications, even if performance must be compromised temporarily. One prominent example is the thermal management of lithium-ion battery packs, where temperature must be strictly controlled to prevent degradation and avoid hazardous thermal runaway events. In these systems, temperature constraints must consistently be enforced, regardless of external disturbances or control errors. Traditional strategies, such as Model Predictive Control (MPC), can explicitly handle such constraints but often require solving high-dimensional optimization problems, making real-time implementation computationally demanding. To overcome these limitations, this study investigates the use of a Constraint Enforcement strategy to manage the temperature of a safety-critical battery pack system. This approach reduces computational complexity using a single-step horizon, making it suitable for real-time applications. We applied Constraint Enforcement to a battery pack thermal system to assess this strategy’s effectiveness and practical implications in a thermal management context. We compared its performance to a conventional PID controller commonly used in industrial applications. Numerical simulations demonstrate that the Constraint Enforcement approach successfully maintains battery temperature within safe operational limits under varying load and environmental conditions, outperforming the PID controller in critical scenarios where constraint violations would occur. Furthermore, the results highlight the trade-offs between responsiveness and constraint satisfaction, offering valuable insights into the practical deployment of constraint-aware controllers in battery management systems. This study shows that Constraint Enforcement provides a promising alternative for safety-critical thermal control, balancing performance and safety with manageable computational demand, as well as demonstrating the ease of implementing it into an existing controlled system.