基于改进的自动编码器构建和优化具有代表性的实际驾驶周期
Zhichao Zhao1,2, Xilei Sun3, Xun Wang4
1China Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China.
Scientific reports
|January 29, 2024
概括
研究人员开发了一种创建精确电动汽车驾驶周期的新方法. 这种方法提炼了现实数据,提高了稳定性,减少了错误,以便更好地分析电动汽车的性能.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 运输科学 运输科学
背景情况:
- 开发具有代表性的驾驶周期对于电动汽车 (EV) 研究至关重要.
- 现有的方法可能无法有效地捕捉局部驾驶模式.
研究的目的:
- 提出一种系统方法,从原始的EV道路测试数据中构建具有代表性的实际驾驶周期.
- 提高驾驶周期开发的准确性和效率.
主要方法:
- 使用手动驾驶方法收集EV道路测试数据.
- 应用了五级波纹分析以减少噪声和数据平滑.
- 使用高斯核心主要组件分析 (KPCA) 来降低维度.
- 开发了长沙驾驶周期构造 (CS-DCC) 方法.
主要成果:
- 降低噪音导致更稳定,更顺的驾驶数据.
- KPCA选择了5个主要组成部分,达到85.99%的累计贡献率.
- 特性参数的平均误差从13.6%降至6.1% (减少了55.1%).
- 与标准循环相比,构建的CS-DCC循环表现出明显的本地特征.
结论:
- CS-DCC 方法有效地创建了反映本地驾驶模式的代表性驾驶周期.
- 该研究强调了局部驾驶周期构造的必要性,以进行准确的电动汽车分析.
- 人工智能技术在推进交通工程方面提供了强大的应用.
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