Construction and Validation of Road Test Cycle for Predictive Cruise Control Algorithm in Heavy-Duty Trucks

In recent years, the predictive cruise control (PCC) algorithm has garnered significant attention due to its ability to optimize fuel consumption by leveraging slope information. However, assessing its fuel-saving effectiveness typically involves randomly selecting real road environments or generating specific road test cycles for evaluation. This leads to a lack of standardized testing methodologies encompassing all potential road conditions. Addressing this gap, this paper presents the construction of a comprehensive road test cycle tailored for heavy-duty trucks during high-speed cruising, intending to encompass diverse road scenarios. Comparative testing reveals that the PCC algorithm achieves a fuel-saving rate of approximately 3.86% in real-world conditions and approximately 3.81 % under the constructed road test cycle, indicating a marginal difference of 0.05 %. This finding underscores the fidelity of the constructed road test cycle in representing real-world road environments, thus enhancing the validity of fuel-saving assessments for PCC algorithms.