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Experimental Validation of Eco-Driving and Eco-Heating Strategies for Connected and Automated HEVs
arXiv - CS - Systems and Control Pub Date : 2021-01-15 , DOI: arxiv-2101.05944 Mohammad Reza Amini, Qiuhao Hu, Hao Wang, Yiheng Feng, Ilya Kolmanovsky, Jing Sun
arXiv - CS - Systems and Control Pub Date : 2021-01-15 , DOI: arxiv-2101.05944 Mohammad Reza Amini, Qiuhao Hu, Hao Wang, Yiheng Feng, Ilya Kolmanovsky, Jing Sun
This paper presents experimental results that validate eco-driving and
eco-heating strategies developed for connected and automated vehicles (CAVs).
By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal
timing, and queue length estimations, optimized and smoothed speed profiles for
the ego-vehicle are generated to reduce energy consumption. Next, the planned
eco-trajectories are incorporated into a real-time predictive optimization
framework that coordinates the cabin thermal load (in cold weather) with the
speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as
the only heat source for cabin heating) and the cabin air are leveraged as two
thermal energy storages. Our eco-heating strategy stores thermal energy in the
engine coolant and cabin air while the vehicle is driving at high speeds, and
releases the stored energy slowly during the vehicle stops for cabin heating
without forcing the engine to idle to provide the heating source. To test and
validate these solutions, a power-split hybrid electric vehicle (HEV) has been
instrumented for cabin thermal management, allowing to regulate heating,
ventilation, and air conditioning (HVAC) system inputs (cabin temperature
setpoint and blower flow rate) in real-time. Experiments were conducted to
demonstrate the energy-saving benefits of eco-driving and eco-heating
strategies over real-world city driving cycles at different cold ambient
temperatures. The data confirmed average fuel savings of 14.5% and 4.7%
achieved by eco-driving and eco-heating, respectively, offering a combined
energy saving of more than 19% when comparing to the baseline vehicle driven by
a human driver with a constant-heating strategy.
中文翻译:
连接和自动混合动力电动汽车的生态驱动和生态加热策略的实验验证
本文提供的实验结果验证了为联网和自动驾驶汽车(CAV)开发的生态驾驶和生态加热策略。通过利用车辆到基础设施(V2I)的通信,交通信号定时和队列长度估计,可以生成针对自我车辆的优化且平滑的速度曲线,以减少能耗。接下来,将计划的生态轨迹整合到实时预测优化框架中,该框架将机舱热负荷(在寒冷天气中)与速度预览(即生态加热)进行协调。为了实现生态加热,发动机冷却液(作为车厢加热的唯一热源)和车厢空气被用作两个热能存储装置。当汽车高速行驶时,我们的生态加热策略将热能存储在发动机冷却液和车厢空气中,并在车辆停车期间缓慢释放存储的能量以进行车厢加热,而不会强迫发动机怠速提供加热源。为了测试和验证这些解决方案,已对动力分配混合动力汽车(HEV)进行了机舱热管理的测试,从而可以调节供暖,通风和空调(HVAC)系统输入(机舱温度设定点和鼓风机流量)。即时的。进行了实验,以证明在不同的寒冷环境温度下,生态驾驶和生态供暖策略在实际城市驾驶循环中的节能优势。数据证实,通过生态驾驶和生态供暖可分别平均节省燃料14.5%和4.7%,
更新日期:2021-01-18
中文翻译:
连接和自动混合动力电动汽车的生态驱动和生态加热策略的实验验证
本文提供的实验结果验证了为联网和自动驾驶汽车(CAV)开发的生态驾驶和生态加热策略。通过利用车辆到基础设施(V2I)的通信,交通信号定时和队列长度估计,可以生成针对自我车辆的优化且平滑的速度曲线,以减少能耗。接下来,将计划的生态轨迹整合到实时预测优化框架中,该框架将机舱热负荷(在寒冷天气中)与速度预览(即生态加热)进行协调。为了实现生态加热,发动机冷却液(作为车厢加热的唯一热源)和车厢空气被用作两个热能存储装置。当汽车高速行驶时,我们的生态加热策略将热能存储在发动机冷却液和车厢空气中,并在车辆停车期间缓慢释放存储的能量以进行车厢加热,而不会强迫发动机怠速提供加热源。为了测试和验证这些解决方案,已对动力分配混合动力汽车(HEV)进行了机舱热管理的测试,从而可以调节供暖,通风和空调(HVAC)系统输入(机舱温度设定点和鼓风机流量)。即时的。进行了实验,以证明在不同的寒冷环境温度下,生态驾驶和生态供暖策略在实际城市驾驶循环中的节能优势。数据证实,通过生态驾驶和生态供暖可分别平均节省燃料14.5%和4.7%,