基于多范围车速规划和预测的互联混合动力汽车发动机和后处理协同优化
Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction
摘要：
联网车辆（CV）具有态势感知能力，可用于控制和优化动力系统。虽然已经进行了广泛的研究，通过生态驾驶和规划的CV的能源效率的改善，这些技术对CV的热响应的影响还没有得到充分的研究。利用连接性进行基于优化的CV热管理的关键挑战之一是相对缓慢的热动态，这需要使用较长的预测范围来实现最佳性能。与基于V2 V/V2 I的短期预测不同，CV速度的长期预测是困难且容易出错的。电力和热力系统固有的多个时间尺度要求可变时间尺度优化框架，可访问短期和长期车辆速度预览。为此，本文提出了一种具有多范围速度预览的模型预测控制器（MPC），用于连接式混合动力电动汽车（HEV）的集成功率和热管理（iPTM）。MPC被制定为管理发动机和电池之间的功率分配，同时实施功率和热（发动机冷却剂和催化转化器温度）约束。MPC利用预测和优化在一个较短的后退地平线和较长的收缩地平线。在较长的收缩范围内，车辆速度估计基于从与自我车辆在相同路线上行驶的连接车辆收集的数据。模拟结果应用MPC在真实世界的城市驾驶循环在安阿伯，MI的有效性和节油潜力的建议iPTM策略下的不确定性与CV的速度的长期预测。
英文摘要：
Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the V2V/V2I-based short-range prediction, is difficult and error-prone. The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short- and long-term vehicle speed preview. To this end, a model predictive controller (MPC) with a multi-range speed preview for integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC is formulated to manage the power-split between the engine and the battery while enforcing the power and thermal (engine coolant and catalytic converter temperatures) constraints. The MPC exploits prediction and optimization over a shorter receding horizon and longer shrinking horizon. Over the longer shrinking horizon, the vehicle speed estimation is based on the data collected from the connected vehicles traveling on the same route as the ego-vehicle. Simulation results of applying the MPC over real-world urban driving cycles in Ann Arbor, MI are presented to demonstrate the effectiveness and fuel-saving potentials of the proposed iPTM strategy under the uncertainty associated with long-term predictions of the CV's speed.
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