Optimal parameter selection of a Model Predictive Control algorithm for energy efficient driving of heavy duty vehicles

Michael Henzler, M. Buchholz, K. Dietmayer
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引用次数: 12

Abstract

This paper presents an improved approach to the problem of energy efficient driving of heavy duty vehicles. The proposed model for a map-based Model Predictive Control (MPC) leads to an underlying Quadratic Programming (QP) optimization problem, allowing computationally efficient and robust solutions. A parameter estimation procedure is developed for a vehicle- and optimization-independent parametrization of the tradeoff between saving energy and keeping a desired vehicle velocity. Extensive simulations on a highway scenario for different optimization parameters give further insight to optimization properties, which can be utilized to enhance control performance. Compared to previous literature, we demonstrate a significant improvement of the computation time to under one-fifth of a millisecond, while maintaining (or even increasing) the fuel consumption reduction, which is 8.1 percent with the proposed approach compared to a standard cruise controller, without a decrease in the average cruising speed.
重型车辆节能驾驶模型预测控制算法的最优参数选择
本文提出了一种解决重型车辆节能驾驶问题的改进方法。提出的基于映射的模型预测控制(MPC)模型导致一个潜在的二次规划(QP)优化问题,允许计算效率和鲁棒性的解决方案。针对节能与保持理想车速之间的权衡问题,提出了一种独立于车辆与优化的参数化估计方法。对不同优化参数的高速公路场景进行了广泛的模拟,进一步了解了优化特性,可以利用这些特性来提高控制性能。与之前的文献相比,我们证明了计算时间的显着改进到五分之一毫秒以下,同时保持(甚至增加)燃油消耗降低,与标准巡航控制器相比,该方法的燃油消耗降低了8.1%,而平均巡航速度没有降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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