基于Python的N2类商用车制动距离计算的线性回归优化

Linear Regression Optimization for Braking Distance Calculation of N2 Commercial Vehicles Based on Python

制动距离是评价车辆制动性能的重要指标之一,文章深入研究了制动距离的计算方法,分析其误差来源,并用试验数据验证了测试过程中由于驾驶员不能精确控制制动力而导致测试结果误差大。为此运用Python语言的机器学习库用线性回归的优化方法对制动距离计算公式进行了优化,可减小计算误差,提升计算结果的有效性。经过对比发现,优化后的制动距离计算公式理论计算结果相对优化前对试验数据的拟合程度明显更高,在车辆开发过程中可在设计阶段提升计算的准确性,缩短开发周期。

Braking distance is one of the important indicators to evaluate the braking performance of a vehicle. This paper deeply studies the calculation method of braking distance, analyzes the source of its error, and it is verified by the test data that the test result has a large error due to the driver’s inability to precisely control the braking force during the test. Therefore, the machine learning library of python language is used to optimize the braking distance calculation formula with the optimization method of linear regression, which can reduce the calculation error and improve the validity of the calculation result. After comparison, it is found that the theoretical calculation results of the optimized braking distance calculation formula have a significantly higher degree of fitting to the test data than before the optimization. During the vehicle development process, the accuracy of the calculation can be improved in the design stage and the development cycle can be shortened.