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基于动力表传感器数据的发动机性能ANN建模.
Constantin Lucian Aldea1, Razvan Bocu1, Rares Lucian Chiriac1
1Department of Mathematics and Computer Science, Transilvania University of Brasov, 500036 Brașov, Romania.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究开发了一种人工神经网络 (ANN) 模型,使用传感器数据准确预测内燃机负载. 该ANN模型实现了高精度,有助于发动机效率和排放控制.
科学领域:
- 汽车工程 汽车工程
- 人工智能的人工智能
- 发动机性能分析分析
背景情况:
- 准确预测内燃机性能对于提高效率和满足排放标准至关重要.
- 现有的方法可能无法完全捕捉到传感器参数和发动机负载之间的复杂关系.
研究的目的:
- 开发和验证一个人工神经网络 (ANN) 模型,用于使用传感器衍生参数预测发动机负载.
- 为了在现实汽车应用中证明模型的准确性和稳定性.
主要方法:
- 从动力计和硬件在循环 (HiL) 设置中利用的传感器数据 (设计功率,轮功率,扭矩,旋转速度).
- 开发了一个人工神经网络 (ANN) 架构,用于多类分类和回归任务.
- 通过5倍分层交叉验证验证模型性能.
主要成果:
- 该ANN模型在多类分类中实现了99%的准确性,回归R2约为0.98.8.
- 交叉验证结果的平均准确度为0.988±0.011和宏F1得分为0.984±0.011.
- 该模型在预测复杂的发动机负载关系方面表现出强大的概括性和稳定性.
结论:
- 拟议的ANN模型准确地预测了在正常运行条件下发动机负载.
- 该型号的高性能和强度使其适用于汽车和工业应用.
- 该模型可扩展用于额外的传感器输入,并可适应其他动力系统.
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