Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wear process under real driving conditions using a tire wear simulator and custom-designed test conditions. This study shows that tire wear PM_2.5 accounts for about 65 % of PM₁₀. The response relationship between TWP emissions (both PM_2.5 and PM_2.5-10) and factors (the radial force, the lateral force, the tangential force, speed, driving torque, tire contact area, total contour length and tire tread temperature) was obtained by machine learning (ML) method. The random forest (RF) model was developed and displayed good prediction performance with an R² of 0.84 and 0.78 for PM_2.5 and PM_2.5-10 on the test set, respectively. Model-related (similarity network graph) and model-unrelated (partial dependence plots and centered-individual conditional expectation plots) explainability methods were used to break the black box of ML. Model explainability results show that the feature parameters-emission response relationships for tire wear PM_2.5 and PM_2.5-10 are different. Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread temperature (T < 45℃), and minimizing the number of small tread patterns are feasible ways to reduce TWPs.

中文翻译：

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机器学习有助于揭示影响轮胎磨损颗粒物排放的关键因素


轮胎每次旋转都会产生轮胎磨损颗粒 （TWP）。然而，在真实驾驶条件下获得 TWP 并揭示影响 TWP 的关键因素具有挑战性。在这项研究中，我们通过使用轮胎磨损模拟器和定制设计的测试条件模拟真实驾驶条件下的轮胎磨损过程，获得了 TWPs 数据集。这项研究表明，轮胎磨损 PM _2.5 约占 PM ₁₀ 的 65%。TWP 排放（PM _2.5 和 PM _2.5-10 ）与因素（径向力、侧向力、切向力、速度、驱动扭矩、轮胎接触面积、总轮廓长度和轮胎胎面温度）之间的响应关系是通过机器学习 （ML） 方法获得的。开发了随机森林 （RF） 模型，并显示出良好的预测性能，在测试集上 PM _2.5 和 PM _2.5-10 的 R ² 分别为 0.84 和 0.78。模型相关 （相似性网络图） 和模型无关 （部分依赖图和居中个体条件期望图） 可解释性方法被用来打破 ML 的黑匣子。模型可解释性结果表明，轮胎磨损 PM _2.5 和 PM _2.5-10 的特征参数-发射响应关系不同。避免剧烈驾驶行为（TTF < 400 N，TLF < 400 N），降低胎面温度 （T < 45°C）和尽量减少小胎面花纹的数量是减少 TWP 的可行方法。