School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, GuangDong, China.
Guangdong Key Laboratory of Automotive Engineering, Guangzhou, China.
PLoS One. 2022 May 23;17(5):e0266672. doi: 10.1371/journal.pone.0266672. eCollection 2022.
The optimal initial pre-conditioning parameter is essential to properly adjust the temperature within the cabin in an effective and accurate way, especially while passengers' thermal comfort and energy-saving properties are both considered. Under the various environmental thermal loads, the pre-conditioning solutions resulting from those pre-fixed cooling parameters are unfeasible for achieving accurately passengers' comfort temperature. In addition, it is also difficult in such a narrow car space to identify a lot of local attributes due to the different material properties and sizes of a variety of structural parts that have various thermal responses to environmental conditions. This paper presents a data-driven decision model to numerically identify the degrees of the cabin thermal characteristic to determine satisfactory pre-conditioning parameter schemes. Initially, based on the thermal data within a vehicle recorded through the whole year at a selected hot climate region of the Middle East, the study levels multiple climate scenes corresponding to change in the cabin air temperature. Then three classification algorithms (Support Vector Machines, Decision Tree, and K-nearest neighbor model) are used to comparatively identify climate levels according to the input conditions. Based on the identified climate level, an appropriate parameters scheme for this level is applied. A comprehensive evaluation index (CEI) is proposed to characterize the passengers' satisfaction in numerical computation, on considering multi-satisfaction objectives including Predicted Mean Vote (PMV), local temperature, air quality, and energy efficiency; and it formulates the pre-conditioning parameter scheme for each climate scene with CEI. Several scene cases are carried out to verify the effectiveness of the proposed models. The result shows that the pre-conditioning schemes of the model can effectively satisfy passengers in multi-satisfaction objectives.
优化初始预条件参数对于有效地、准确地调节舱内温度至关重要，特别是在考虑乘客的热舒适度和节能属性的情况下。在各种环境热负荷下，这些预固定冷却参数产生的预条件解决方案无法实现乘客舒适温度的精确控制。此外，由于各种结构部件的材料特性和尺寸不同，它们对环境条件的热响应也不同，因此在如此狭窄的车内空间中很难识别出许多局部属性。本文提出了一种基于数据驱动的决策模型，用于数值识别舱室热特性的程度，以确定令人满意的预条件参数方案。首先，基于在中东选定的炎热气候地区全年通过车辆记录的热数据，研究了与舱内空气温度变化相对应的多种气候场景。然后，使用三种分类算法（支持向量机、决策树和 K 最近邻模型）根据输入条件比较识别气候水平。根据识别出的气候水平，为该水平应用适当的参数方案。提出了一个综合评价指标（CEI）来对数值计算中的乘客满意度进行特征化，考虑了多满意度目标，包括预测平均投票（PMV）、局部温度、空气质量和能源效率；并为每个气候场景制定了 CEI 的预条件参数方案。进行了几个场景案例来验证所提出模型的有效性。结果表明，模型的预条件方案可以有效地满足乘客的多满意度目标。
