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Energy consumption and modelling of the climate control system in the electric vehicle

机译:电动汽车的气候控制系统的能耗和建模

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With the introduction of electric vehicles in the automobile market, limited information is available on how the battery's energy consumption is distributed. This paper focuses on the energy consumption of the vehicle when the heating and cooling system is in operation. On average, 18 and 14% for the battery's energy capacity is allocated to heating and cooling requirements, respectively. The conventional internal combustion engine vehicle uses waste heat from its engine to provide for passenger thermal requirements at no cost to the vehicle's propulsion energy demands. However, the electric vehicle cannot avail of this luxury to recycle waste heat. In order to reduce the energy consumed by the climate control system, an analysis of the temperature profile of a vehicle's cabin space under various weather conditions is required. The present study presents a temperature predicting algorithm to predict temperature under various weather conditions. Previous studies have limited consideration to the fluctuation of solar radiation space heating to a vehicle's cabin space. This model predicts solar space heating with a mean bias error and root mean square error of 0.26 and 0.57 degrees C, respectively. This temperature predicting model can potentially be developed with further research to predict the energy required by the vehicle's primary lithium-ion battery to heat and cool the vehicle's cabin space. Thus, this model may be used in a route planning application to reduce range anxiety when drivers undertake a journey under various ambient weather conditions while optimising the energy consumption of the electric vehicle.
机译:随着电动汽车在汽车市场中的引入,关于电池能量分配方式的信息很少。本文重点研究加热和冷却系统运行时的车辆能耗。平均而言,电池能量的18%和14%分别分配给加热和冷却需求。常规的内燃机车辆利用来自其发动机的废热来满足乘客的热量需求,而不会增加车辆的推进能量需求。然而,电动汽车不能利用这种奢侈来回收废热。为了减少气候控制系统消耗的能量,需要分析各种天气条件下的车厢空间的温度分布。本研究提出了一种温度预测算法来预测各种天气条件下的温度。先前的研究有限地考虑了太阳辐射空间加热到车厢空间的波动。该模型预测太阳空间加热的平均偏差误差和均方根误差分别为0.26和0.57摄氏度。可以通过进一步研究来开发这种温度预测模型,以预测车辆的一次锂离子电池加热和冷却车厢空间所需的能量。因此,该模型可用于路线规划应用中,以减少驾驶员在各种环境天气条件下进行旅程时的范围焦虑,同时优化电动汽车的能耗。

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