Learned Optimal Control of a Range Extender in a Series Hybrid Vehicle
Learned Optimal Control of a Range Extender in a Series Hybrid Vehicle
复制标题
串联混合动力汽车增程器的学习最优控制
DOI：
10.1109/itsc.2015.420
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
H. Rottengruber
中科院分区：
文献类型：
--
作者：
A. Styler;A. Sauer;I. Nourbakhsh;H. Rottengruber
每年，混合动力汽车在道路上的车辆总数中占有更大的比例。这些车辆联合收割机结合了多种能源，如电池和汽油，它们具有不同的优点和缺点。对这些能源的主动管理可以提高车辆效率、寿命或性能。优化能量管理对车辆上即将到来的功率负载高度敏感，但传统的控制策略仅对当前状态做出反应。此外，这些策略是在设计时计算的，并且不适应单个驱动程序。廉价传感和计算的进步使以前不可能实现的板载学习和优化成为可能。在这项工作中，我们开发并实现了一个实时控制器，该控制器利用从其他驱动程序收集的数据集计算的预测。这种数据驱动的控制器管理串联气电混合动力车辆中的增程器，优化燃料使用，噪音和点火频率。该算法可扩展到大量的源数据，性能随着预测精度的提高而提高。我们测试了算法在模拟和修改后的车辆与直接编程控制的增程器。车辆上的实验结果反映了模拟中观察到的结果，实现了高达12%的燃油节省和73%的噪音成本降低。
Each year, hybrid vehicles command a larger portion of total vehicles on the road. These vehicles combine multiple sources of energy, such as batteries and gasoline, which have different strengths and weaknesses. Active management of these energy sources can increase vehicle efficiency, longevity, or performance. Optimizing energy management is highly sensitive to upcoming power loads on the vehicle, but conventional control policies only react to the present state. Furthermore, these policies are computed at design-time and do not adapt to individual drivers. Advancements in cheap sensing and computation have enabled on-board learning and optimization that was previously impossible. In this work, we developed and implemented a real-time controller that exploits predictions computed from a dataset collected from other drivers. This data-driven controller manages a range-extender in a series gas-electric hybrid vehicle, optimizing fuel use, noise, and ignition frequency. The algorithm is scalable to large amounts of source data, and performance improves with prediction accuracy. We tested the algorithm in simulation and on a modified vehicle with direct programmatic control of the range extender. The experimental results on the vehicle reflected those observed in simulation, achieving fuel savings up to 12% and a noise-cost reduction of 73%.