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首页> 外文期刊>International journal of sustainable transportation >Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization
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Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization

机译：传统和混合动力电动车辆中各种驾驶风格的燃料消耗：与燃料消耗优化集成驾驶循环预测

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摘要

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.
机译：提高燃油经济性和降低排放是关键的社会目标。由美国环境保护局（EPA）预先设计的标准驾驶循环长期以来一直用于估算实验室控制条件下的车辆燃料经济性。它们还被用来测试和调整混合动力电动车（HEV）的不同能源管理策略。本文旨在估算传统车辆的燃料消耗和使用从真实数据中提取的个性化驾驶循环的燃料消耗，以研究与基于标准驱动周期的估计相比的不同驾驶风格和车辆类型对燃料消耗的影响。为此，我们从大型美国的调查中提取了传统车辆和HEV的驾驶循环，该调查包含基于世界GPS的驾驶记录。接下来，将驱动周期分配给三类中的一个：挥发性，正常或平静。然后，使用驱动循环以及驾驶员 - 车辆模拟，其捕获驾驶员决策（行程期间的车辆速度），动力总成和车辆动力学，以估计传统车辆和HEV的燃料消耗和具有电力分配动力总成的燃料消耗。为了进一步优化HEV的燃料消耗，应用了等效的消耗最小化策略（ECM）。结果表明，根据驾驶风格和驾驶场景，传统的车辆燃料消耗与标准的EPA驱动循环相比可以随着广泛的而变化。具体而言，与标准的EPA驱动周期相比，传统的车辆燃料消耗降低了13％，但挥发性公路驾驶几乎高出34％。有趣的是，当基于基于壳体的推理的应用并用于调整混合动力电动车辆中的功率分布时预测驾驶循环时，城市驾驶中的燃料消耗可以减少高达12％。讨论了调查结果的影响和局限性。

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