为了对轮胎磨损程度进行准确估计，提高汽车行驶的安全性，提出了一种基于胎内应变特征和改进的鲸鱼优化算法优化支持向量回归（improved whale optimization algorithm-support vector regression, IWOA-SVR）的轮胎磨损程度估计方法。通过有限元方法建立不同磨损程度、气压、载重和速度的轮胎模型，然后提取胎内应变特征并通过相关性分析选择关键特征。基于气压、载重、速度和选择的关键特征，建立了IWOA-SVR模型进行轮胎磨损程度的估计，SVR的参数通过IWOA进行优化。最后，在不同磨损程度和不同运行工况下进行了测试，结果显示，IWOA优化SVR模型后提高了估计的准确性，估计平均绝对误差不超过0.21 mm，可以满足轮胎磨损检测的需要。

In order to accurately estimate the tire wear degree and improve the safety of vehicle operation, a tire wear degree estimation method was proposed based on the strain features inside the tire and IWOA-SVR(improved whale optimization algorithm-support vector regression). The tire models with different wear degrees, pressures, loads and speeds were established by the finite element method, and then the strain features inside the tire were extracted and the key features were selected by the correlation analysis. Based on the pressures, loads, speeds and the selected key features, the IWOA-SVR model was established to estimate the tire wear degree, and the parameters of SVR were optimized by IWOA. Finally, the test was carried out under different wear degrees and different operating conditions, and the results show that the estimation accuracy was improved with the IWOA-SVR model, and the estimation mean absolute error is not more than 0.21 mm, which can meet the needs of tire wear detection.