热舒适性
估计
电动汽车
汽车工程
电子设备和系统的热管理
计算机科学
模型预测控制
环境科学
工程类
气象学
人工智能
机械工程
系统工程
控制(管理)
物理
功率(物理)
量子力学
作者
Bharatkumar Hegde,İbrahim Haskara,Shailendra Kaushik,Chen-Fang Chang
出处
期刊:SAE technical paper series
日期:2024-04-09
摘要
<div class="section abstract"><div class="htmlview paragraph">Electric vehicles (EV) present distinctive challenges compared to ICE (Internal Combustion Engine) powered counterparts. Cabin heating and air-conditioning stand out among them, especially cabin heating in cold weather, owing to its outsized effect on drivable range of the vehicle. Efficient management of the cabin thermal system has the potential to improve vehicle range without compromising passenger comfort. A method to improve cabin thermal system regulation by effectively leveraging the solar load on the vehicle is proposed in this work. The methodology utilizes connectivity and mapping data to predict the solar load over a future time horizon. Typically, the solar load is treated as an unmeasured external disturbance which is compensated with control. It can however be treated as an estimated quantity with potential to enable predictive control. The solar load prediction, coupled with a passenger thermal comfort model, enables preemptive thermal system control over a route. A predictive control architecture is used to generate the HVAC control inputs for actuators of the cabin thermal system based on solar load preview information. This framework effectively leverages connectivity-enabled prediction and establishes novel means to include solar-load in the cabin thermal control. A simulation study is presented to demonstrate the effectiveness of the proposed method in improving temperature regulation performance.</div></div>
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