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首页> 外文期刊>International Journal of Automotive Technology >IMPACT OF HILLY ROAD INFORMATION ON FUEL ECONOMY OF FCHEV BASED ON PARAMETERIZATION OF HILLY ROADS
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IMPACT OF HILLY ROAD INFORMATION ON FUEL ECONOMY OF FCHEV BASED ON PARAMETERIZATION OF HILLY ROADS

机译:基于丘陵路参数化的丘陵路信息对FCHEV燃油经济性的影响

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摘要

Under real-life driving conditions, hilly roads are prevalent. Hilly road profile substantially influences fuel economy (FE) due to large impacts (increase or decrease) on power demand profile. Thus, the utilization of future altitude profile information has large potential to improve FE. In this paper, for optimal energy management of fuel cell hybrid electric vehicles (FCHEV), we investigate how much FE could potentially be improved when future altitude profile information is available. In particular, the simulation results are analyzed to justify the reason for this potential improvement and to identify which characteristics of hilly roads leads to large FE improvements. First of all, four statistical parameters are defined to characterize hilly roads: mean value, standard deviation (STD), distance interval (DI), and total distance. Then, several types of virtual hilly roads are generated based on various parameter combinations. In order to evaluate the potential FE improvement two energy management strategies (EMSs) are utilized: the first is Dynamic Programming, which evaluates the globally optimal FE when future hilly road information is available; the other is the Equivalent Consumption Minimization Strategy (ECMS) with adaptive equivalent factor for charge-sustenance, which represents the baseline EMS when future hilly road information is not available. The results show that downhill roads have much larger potential than uphill roads do for FE improvements when the future altitude profile is properly used for EMS. Furthermore, if the battery capacity is not large enough to handle the difference in potential energy, future hilly road information is more important to prevent violations of the maximum state-of-charge bound.
机译:在现实的驾驶条件下,丘陵公路十分普遍。由于对电力需求曲线有很大的影响(增加或减少),丘陵路况对燃油经济性(FE)有很大影响。因此,利用未来的高度剖面信息具有改善FE的巨大潜力。在本文中,为了实现燃料电池混合动力汽车(FCHEV)的最佳能源管理,我们研究了在将来的高度分布信息可用时可以潜在地提高多少FE。特别是,对仿真结果进行分析,以证明进行这种潜在改进的原因,并确定丘陵道路的哪些特征可导致大幅有限元改进。首先,定义了四个统计参数来表征丘陵公路:平均值,标准差(STD),距离间隔(DI)和总距离。然后,基于各种参数组合生成几种类型的虚拟丘陵道路。为了评估潜在的有限元改善,使用了两种能源管理策略(EMS):第一个是动态规划,当未来的丘陵公路信息可用时,它评估全局最优有限元。另一种是等效电荷消耗最小化策略(ECMS),具有适用于电荷维持的自适应等效因子,它代表了将来没有丘陵公路信息时的基线EMS。结果表明,当将来的海拔高度轮廓正确用于EMS时,下坡道路比上坡道路具有更大的潜力来改善FE。此外,如果电池容量不足以处理势能差异,则将来的丘陵道路信息对于防止违反最大充电状态限制更为重要。

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