Abstract: As the core component of new energy vehicles, the thermal performance of lithium-ion batteries directly affects the overall operational efficiency and safety of the vehicle. Enhancing battery thermal performance is therefore of critical importance. An Online Robust Parameter Design (ORPD) method based on observable noise information is proposed, which integrates offline design with online adjustment to optimize thermal performance design parameters, improving both optimality and robustness. To address the dynamic behavior of noise factors in actual production processes, a time series-based noise factor model is developed. Control variables are classified into online controllable and offline controllable categories, and a response surface model of the quality characteristic is constructed, with analytical expressions for its mean and variance derived. A two-stage online adjustment strategy is introduced, aiming to minimize quality loss. After determining offline control variables, online control variables are dynamically adjusted based on real-time observations of noise factors, enabling compensation for variations in both performance optimality and stability. The effectiveness and robustness of the proposed ORPD approach are demonstrated through a case study involving the liquid cooling performance of lithium-ion batteries in new energy vehicles. The results provide methodological support for robust optimization of battery thermal performance.