Simulation for prediction of vehicle efficiency, performance, range and lifetime: A review of current techniques and their applicability to current and future testing standards
Simulation for prediction of vehicle efficiency, performance, range and lifetime: A review of current techniques and their applicability to current and future testing standards
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预测车辆效率、性能、里程和寿命的仿真：回顾当前技术及其对当前和未来测试标准的适用性
DOI：
10.1049/cp.2014.0959
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Fotouhi A
中科院分区：
文献类型：
--
作者：
Fotouhi A
计算机模拟工具可以提供关键车辆特性的早期指标。在传统混合动力汽车中，这对于优化燃油消耗的设计非常重要。在插电式混合动力汽车和纯电动汽车中，准确预测续航里程至关重要，这是一个关键的市场资格。有多种技术，通常在不同的保真度级别上运行并采用不同的建模原理。本文是在早期工作的基础上发展起来的，在当前基于 NEDC 的 UNECE 车辆测试标准和基于世界灯光测试程序的拟议替代标准的背景下探索传统和“落后”技术。 A、C 和 D 段车辆的模型敏感性被量化，这用于探索准确模型是关键的方面以及低保真度代表性模型适用的方面。本文还探讨了预测对“PID 控制”驱动器模型的敏感性，并讨论了循环跟踪容差对预测的影响。最后，本文提出了适用于模拟或实际测试的新标准，用于对使用中的电池寿命进行通用量化。以案例研究的形式展示了这些技术和敏感性分析方法在代表性仿真模型上的使用，并探讨了对电池管理策略设计的影响。
Computer simulation tools can give early indicators of key vehicle characteristics. In traditional hybrid vehicles, this is important in designing for optimal fuel consumption; in plug-in hybrids and pure electric vehicles, it is critical for accurate prediction of range, a key market qualifier. There are a variety of techniques, typically operating at different levels of fidelity and employing different modelling philosophies. This paper develops on earlier work, exploring conventional and `backward' techniques in the context of current NEDC-based UNECE vehicle testing standards and the proposed replacements based on the World Light Test Procedure. Model sensitivities for A, C and D-segment vehicles are quantified and this is used to explore aspects where accurate models are key and where lower-fidelity representative models are appropriate. The paper also explores the sensitivity of predictions to `PID control' driver models, and discusses the effect of cycle-following tolerance on predictions. Finally, the paper proposes new standards - suitable for simulation or real-world testing - for a common quantification of in-use battery lifetime. The use of these techniques and the sensitivity analysis methods on a representative simulation model is demonstrated as a case study, and the impacts on battery management strategy design are explored.