电动动力总成综合能量足迹基准算法
Comprehensive Energy Footprint Benchmarking Algorithm for Electrified Powertrains
摘要：
汽车控制系统的自主性和电气化使现代动力总成成为最复杂的网络物理系统之一。本文提出了一种基准算法来量化复杂的汽车系统表现出机械，电气和热的相互作用在不同的时间尺度的性能。传统的动态规划方法被用来对系统的性能进行基准测试，然而，由于维数灾难的原因，动态规划方法无法对具有更多状态和控制杠杆的系统提供结果。我们提出了“PS3”，一个三步算法的混合整数非线性最优控制问题的应用动力总成能量管理。PS3使用伪谱配置理论进行高精度的动力学建模。基于经过验证的动力系统组件模型，我们已经解决了电气（SOC），车辆（生态驾驶）和热（后处理和电池温度）动态的同时优化，以及整数（齿轮和发动机开/关）控制及其相应的（停留时间）约束。PS3用于解决具有快速和缓慢动态状态、不连续行为、不可微和线性内插的1-D和2-D映射以及组合约束的大型动力系统问题。五个案例研究动力系统控制问题的基准精度和计算工作量对动态规划。我们的分析表明，该算法不会像动态规划那样增加计算负担，并且可以处理现代动力系统中发生的高度复杂的相互作用，而不会影响非线性和复杂的工厂建模。
英文摘要：
Autonomy and electrification in automotive control systems have made modern-day powertrains one of the most complex cyber-physical systems. This paper presents a benchmark algorithm to quantify the performance of complex automotive systems exhibiting mechanical, electrical, and thermal interactions at various time-scales. Traditionally Dynamic Programming has been used for benchmarking the performance, however, it fails to deliver results for system with higher number of states and control lever due to curse of dimensionality. We propose "PS3", a three-step algorithm for mixed-integer nonlinear optimal control problems with application to powertrain energy management. PS3 uses pseudo-spectral collocation theory for highly accurate modeling of dynamics. Based on the validated powertrain component models, we have addressed simultaneous optimization of electrical (SOC), vehicular (eco-driving) and thermal (after-treatment and battery temperatures) dynamics along with an integer (gear and engine on/off) control and its corresponding (dwell-time) constraints. PS3 is used to solve such large-scale powertrain problems having fast and slow dynamic states, discontinuous behaviors, non-differentiable and linearly interpolated 1-D and 2-D maps, as well as combinatorial constraints. Five case study powertrain control problems are given to benchmark the accuracy and computational effort against Dynamic Programming. Our analysis shows that this algorithm does not scale computational burden as Dynamic Programming does, and can handle highly complex interactions that occur in modern-day powertrains, without compromising nonlinear and complex plant modeling.
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