Optimal Transient Real-Time Engine-Generator Control in the Series-Hybrid Vehicle

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-8964

Jonathan Lock, Rickard Arvidsson, T. McKelvey

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引用次数: 0

Abstract

We study the dynamic engine-generator optimal control problem with a goal of minimizing fuel consumption while delivering a requested average electrical power. By using an infinite-horizon formulation and explicitly minimizing fuel consumption, we avoid issues inherent with penalty-based and finite-horizon problems. The solution to the optimal control problem, found using dynamic programming and the successive approximation method, can be expressed as instantaneous non-linear state-feedback. This allows for trivial real-time control, typically requiring 10–20 CPU instructions per control period, a few bytes of RAM, and 5–20 KiB of nonvolatile memory. Simulation results for a passenger vehicle indicate a fuel consumption improvement in the region of 5–7% during the transient phase when compared with the class of controllers found in the industry. Bench-tests, where the optimal controller is executed in native hardware, show an improvement of 3.7%, primarily limited by unmodeled dynamics. Our specific choice of problem formulation, a guaranteed globally optimal solution, and trivial real-time control resolve many of the limitations with the current state of optimal engine-generator controllers.

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串联式混合动力汽车瞬态发电机最优实时控制

本文研究了动态发动机-发电机最优控制问题，其目标是在提供要求的平均电功率的同时最小化燃油消耗。通过使用无限地平线公式并明确地最小化燃料消耗，我们避免了基于惩罚和有限地平线问题固有的问题。用动态规划和逐次逼近方法求解的最优控制问题可表示为瞬时非线性状态反馈。这允许进行简单的实时控制，通常每个控制周期需要10-20个CPU指令，几个字节的RAM和5 - 20kib的非易失性内存。对乘用车的仿真结果表明，与行业中发现的一类控制器相比，该控制器在瞬态阶段的燃油消耗改善了5-7%。在原生硬件中执行最优控制器的台架测试显示，主要受未建模动态的限制，性能提高了3.7%。我们对问题表述的特定选择、保证的全局最优解和琐碎的实时控制解决了当前最优发动机-发电机控制器状态的许多限制。

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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来源期刊

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.