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A control strategy for cabin temperature of electric vehicle considering health ventilation for lowering virus infection

机译:考虑健康通风降低病毒感染的电动汽车车厢温度控制策略

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A cooperative control strategy is proposed for the air conditioning (AC) system and ventilation system to reduce the risk of COVID-19 infection and save the energy of the AC system. This strategy integrates the dynamic model of the AC-cabin system, infection risk assessment, model predictive control (MPC) of the thermal environment inside the cabin, and ventilation control that considers passengers' sneezing. Unlike other existing AC system models, the thermal-health model established can describe not only the system performance but also the virus concentration and risk of COVID-19 infection using the Wells-Riley assessment model. Experiments are conducted to verify the prediction accuracy of the AC-cabin model. The results prove that the proposed model can accurately predict the evolution of cabin temperature under different cases. The cooperative control strategy of the AC system integrates the MPC-based refrigeration algorithm for the cabin temperature and intermittent ventilation strategy to reduce the risk of COVID-19 infection. This strategy well balances the control accuracy, energy consumption of the AC system, and the risk of COVID-19 infection, and greatly reduces the infection risk at the expense of a little rise in the energy consumption.
机译:提出了空调(AC)系统和通风系统的协同控制策略,以降低COVID-19感染的风险并节省空调系统的能源。该策略集成了空调客舱系统的动态模型、感染风险评估、客舱内热环境的模型预测控制 (MPC) 以及考虑乘客打喷嚏的通风控制。与其他现有的空调系统模型不同,建立的热健康模型不仅可以描述系统性能,还可以使用 Wells-Riley 评估模型描述病毒浓度和 COVID-19 感染风险。通过实验验证了空调舱模型的预测精度。结果表明,所提模型能够准确预测不同工况下机舱温度的演变。空调系统的协同控制策略集成了基于MPC的制冷算法,用于机舱温度和间歇通风策略,以降低COVID-19感染的风险。该策略很好地平衡了空调系统的控制精度、能耗和 COVID-19 感染风险,并以能耗略有增加为代价大大降低了感染风险。

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