数据驱动的电动汽车能耗评估,将标准测试推广到实际驾驶中

IF 6.7 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Patterns Pub Date : 2024-03-08 DOI:10.1016/j.patter.2024.100950
Xinmei Yuan, Jiangbiao He, Yutong Li, Yu Liu, Yifan Ma, Bo Bao, Leqi Gu, Lili Li, Hui Zhang, Yucheng Jin, Long Sun
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引用次数: 0

摘要

标准能耗测试是电池电动汽车(BEV)能耗的唯一公开可量化测量方法,对于促进电气化汽车行业的透明度和问责制至关重要;然而,标准测试与实际驾驶之间的巨大差异阻碍了对电池电动汽车的能源和环境评估及其更广泛的应用。在本研究中,我们提出了一种数据驱动的标准测试评估方法,用于描述 BEV 的能耗。通过分离驾驶环境的影响,我们的评估方法可适用于各种驾驶条件。在使用我们的方法估算能耗的实验中,我们在 13 个不同的多区域标准化测试周期中实现了 3.84% 的估算误差,在 106 个不同的实际行程中实现了 7.12% 的估算误差。我们的结果凸显了所提方法的巨大潜力,即通过标准测试提高公众对电动汽车能耗的认识,同时提供可靠的电动汽车基本模型。
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Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving
Standard energy-consumption testing, providing the only publicly available quantifiable measure of battery electric vehicle (BEV) energy consumption, is crucial for promoting transparency and accountability in the electrified automotive industry; however, significant discrepancies between standard testing and real-world driving have hindered energy and environmental assessments of BEVs and their broader adoption. In this study, we propose a data-driven evaluation method for standard testing to characterize BEV energy consumption. By decoupling the impact of the driving profile, our evaluation approach is generalizable to various driving conditions. In experiments with our approach for estimating energy consumption, we achieve a 3.84% estimation error for 13 different multiregional standardized test cycles and a 7.12% estimation error for 106 diverse real-world trips. Our results highlight the great potential of the proposed approach for promoting public awareness of BEV energy consumption through standard testing while also providing a reliable fundamental model of BEVs.
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来源期刊
Patterns
Patterns Decision Sciences-Decision Sciences (all)
CiteScore
10.60
自引率
4.60%
发文量
153
审稿时长
19 weeks
期刊介绍:
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