Enclosed parking garages require mechanical ventilation fans to dilute concentrations of pollutants emitted from vehicles, which contributes to energy use and peak electricity demand. This study develops and applies a simulation framework combining multi-zone airflow and contaminant transport modeling, fan affinity laws, and realistic assumptions for vehicle traffic patterns and carbon monoxide (CO) emissions to improve our ability to predict the impacts of various ventilation control strategies on indoor air quality and fan energy use in parking garages. The simulation approach is validated using measured data from a parking garage case study and then applied to investigate fan energy use, peak power demand, and resulting CO concentrations for four different ventilation control strategies in a model underground parking garage under a variety of assumptions for model inputs. The four ventilation control strategies evaluated include one simplistic schedule (i.e., Always-On) and three demand-based strategies in which fan speed is a function of CO concentrations in the spaces, including Linear-Demand Control Ventilation (DCV), Standardized Variable Flow (SVF), and a simple On-Off strategy. The estimated annual average fan energy consumption was consistently lowest with the Linear-DCV strategy, resulting in average (± standard deviation) energy savings across all modeled scenarios of 843%±0.4%, 72.8%±3.6%, and 97.9%±0.1% compared to SVF, On-Off, and Always-On strategies, respectively. The utility of the framework described herein is that it can be used to model energy and indoor air quality impacts of other parking garage configurations and control scenarios.

封闭的停车场需要机械通风风扇来稀释车辆排放的污染物浓度，这会导致能源消耗和用电高峰。这项研究开发并应用了一个模拟框架，该框架结合了多区域气流和污染物迁移模型，风扇亲和力定律以及车辆交通模式和一氧化碳（CO）排放的现实假设，以提高我们预测各种通风控制策略对环境的影响的能力。停车场内的室内空气质量和风扇能源消耗。该仿真方法通过使用来自停车场案例研究的测量数据进行了验证，然后用于调查风扇的能耗，峰值功率需求，以及在模型输入的各种假设下，模型地下停车场中四种不同通风控制策略的最终CO浓度。评估的四种通风控制策略包括一个简单的时间表（即始终在线）和三种基于需求的策略，其中风扇速度是空间中CO浓度的函数，包括线性需求控制通风（DCV），标准可变流量（SVF）和简单的开关策略。使用Linear-DCV策略时，估计的年度平均风扇能耗始终是最低的，因此在所有建模方案中平均节省的能源（±标准偏差）为843％±0.4％，72.8％±3.6％和97.9％±0.1％分别与SVF，On-Off和Always-On策略进行了比较。