Control and Powertrain Management for Multi-Autonomous Hybrid Vehicles
Control and Powertrain Management for Multi-Autonomous Hybrid Vehicles
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DOI：
10.1115/1.4043110
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发表时间：
2019-07
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通讯作者：
M. Ghasemi;Xingyong Song
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文献类型：
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作者：
M. Ghasemi;Xingyong Song
低油耗的需求和更高水平的自动驾驶的趋势共同推动了混合动力自动驾驶汽车的动力优化。多车协调控制和混合动力系统能量管理都需要优化，以最大限度地节省燃油。在本文中，我们打算有一个计算效率高的框架来分别优化它们，然后评估整体控制性能。优化是串联进行的。首先在多车系统层面，给出了具有非线性动力学的车辆的分布式局部最优解。其次，在混合动力系统层面进行动力系统管理优化。利用庞特里亚金最小值原理（PMP）给出了每种混合动力汽车动力系统优化的解析公式。通过将最优瞬时燃油消耗率近似为发动机转速的多项式，将优化问题转化为一组代数方程，从而实现了计算效率的实时实现。为了证明该方法在实时中的适用性，我们给出了这些代数方程的数值迭代解的方向。通过统计分析对该方法的稳定性进行了分析。最后，通过进一步的仿真验证了所提优化方法的有效性和鲁棒性。虽然所开发的框架也可以应用于公路场景，但仿真中给出了一个非公路场景的例子。
The need for less fuel consumption and the trend of higher level of autonomy together urge the power optimization in multihybrid autonomous vehicles. Both the multivehicle coordination control and the hybrid powertrain energy management should be optimized to maximize fuel savings. In this paper, we intend to have a computationally efficient framework to optimize them individually and then evaluate the overall control performance. The optimization is conducted in series. First is at the multivehicle system's level where the distributed locally optimal solution is given for vehicles with nonlinear dynamics. Second, the powertrain management optimization is conducted at the hybrid powertrain level. We provide an analytical formulation of the powertrain optimization for each hybrid vehicle by using Pontryagin's minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations, which enables the computationally efficient real-time implementation. To justify the applicability of the methodology in real-time, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach. An off-road example is given in the simulation, although the framework developed can be applied to on-road scenario as well.