| 张冰战,边博乾,杨梓恒,康谷峰,邱明明.考虑工况、驾驶员与道路信息的再生制动策略[J].电子测量与仪器学报,2025,39(4):181-192 |
| 考虑工况、驾驶员与道路信息的再生制动策略 |
| Regenerative braking strategies considering drivingcycles, drivers and road information |
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| DOI： |
| 中文关键词: 再生制动 工况 驾驶风格 附着条件 电动汽车 |
| 英文关键词:regenerative braking driving cycle driving style adhesion conditions electric vehicle |
| 基金项目:芜湖市科技计划（科技局）(2023jc-04)、国家自然科学基金（52472404）、中央高校基本科研业务费专项（PA2023GDSK0065)资助 |
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| Author | Institution |
| Zhang Bingzhan | 1.School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei 230009，China;
2.Anhui Key Laboratory of Digit Design and Manufacture, Hefei 230001，China |
| Bian Boqian | School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei 230009，China |
| Yang Ziheng | School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei 230009，China |
| Kang Gufeng | School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei 230009，China |
| Qiu Mingming | National and Local Joint
Engineering Research Center of Automotive Technology and Equipment, Hefei 230009,China |
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| 中文摘要: |
| 再生制动策略的设计需要综合考虑多种因素,其中车辆行驶工况、驾驶员特性与车辆所行驶路面对再生制动过程有显著影响。为了制定适应各种驾驶条件的电动汽车再生制动策略,提高车辆制动能量回收率和保持制动稳定性,提出了一种综合考虑工况、驾驶员与道路信息影响的再生制动策略。首先,搭建模拟驾驶平台,进行驾驶员在环实验并采集不同驾驶员的驾驶数据,从而提取工况与驾驶风格特征参数,然后基于支持向量机(SVM)训练工况与驾驶风格辨识模型;其次,建立道路图像数据集并使用语义分割网络进行道路图像预处理,去除图像复杂背景信息从而提高识别效率,然后采用轻量级卷积神经网络MobileNet V3训练道路识别模型;最后,在此基础上制定再生制动策略,考虑路面附着条件进行前后制动力配比优化,并提出了一种考虑工况、驾驶风格与道路信息作为权重因子的再生制动力修正方法。仿真结果表明,所提出的再生制动策略可以兼顾不同工况、驾驶风格与道路状况,车辆能量回收率与制动稳定性进一步提高。 |
| 英文摘要: |
| The design of regenerative braking strategies requires a comprehensive consideration of multiple factors, among which vehicle driving conditions, driver characteristics and the road surface on which the vehicle is traveling have a significant impact on the regenerative braking process. In order to formulate regenerative braking strategies for electric vehicles that are adaptable to various driving conditions, improve the vehicle braking energy recovery rate and maintain braking stability, a regenerative braking strategy that comprehensively considers the influences of driving cycles, drivers and road information is proposed. Firstly, a simulation driving platform is set up to conduct driver-in-the-loop experiments and collect driving data from different drivers, thereby extracting feature parameters of driving conditions and driving styles. Then, a support vector machine (SVM) is used to train the models for identifying driving conditions and driving styles. Secondly, a road image dataset is established and a semantic segmentation network is used for road image preprocessing to remove the complex background information of the image and thereby improve the recognition efficiency. Then, a lightweight conuolutional neural network, MobileNet V3, is adopted to train the road recognition model. Finally, the regenerative braking strategy base on this is formulated. The front and rear braking force distribution is optimized considering the road adhesion conditions, and a regenerative braking force correction method that takes driving cycles, driver and road information as weight factors is put forward. The simulation results show that the proposed regenerative braking strategy can take into account different driving cycles, drivers and road conditions, and further improve the vehicle energy recovery rate and braking stability. |
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