Combining Coordination of Motion Actuators with Driver Steering Interaction

Objective: A new method is suggested for coordination of vehicle motion actuators; where driver feedback and capabilities become natural elements in the prioritization.

Methods: The method is using a weighted least squares control allocation formulation, where driver characteristics can be added as virtual force constraints. The approach is in particular suitable for heavy commercial vehicles that in general are over actuated. The method is applied, in a specific use case, by running a simulation of a truck applying automatic braking on a split friction surface. Here the required driver steering angle, to maintain the intended direction, is limited by a constant threshold. This constant is automatically accounted for when balancing actuator usage in the method.

Results: Simulation results show that the actual required driver steering angle can be expected to match the set constant well. Furthermore, the stopping distance is very much affected by this set capability of the driver to handle the lateral disturbance, as expected.

Conclusion: In general the capability of the driver to handle disturbances should be estimated in real-time, considering driver mental state. By using the method it will then be possible to estimate e.g. stopping distance implied from this. The setup has the potential of even shortening the stopping distance, when the driver is estimated as active, this compared to currently available systems. The approach is feasible for real-time applications and requires only measurable vehicle quantities for parameterization. Examples of other suitable applications in scope of the method would be electronic stability control, lateral stability control at launch and optimal cornering arbitration.     

Research on the recycling industry development model for typical exterior plastic components of end-of-life passenger vehicle based on the SWOT method

In-depth studies on the recycling of typical automotive exterior plastic parts are significant and beneficial for environmental protection, energy conservation, and sustainable development of China. In the current study, several methods were used to analyze the recycling industry model for typical exterior parts of passenger vehicles in China. The strengths, weaknesses, opportunities, and challenges of the current recycling industry for typical exterior parts of passenger vehicles were analyzed comprehensively based on the SWOT method. The internal factor evaluation matrix and external factor evaluation matrix were used to evaluate the internal and external factors of the recycling industry. The recycling industry was found to respond well to all the factors and it was found to face good developing opportunities. Then, the cross-link strategies analysis for the typical exterior parts of the passenger car industry of China was conducted based on the SWOT analysis strategies and established SWOT matrix. Finally, based on the aforementioned research, the recycling industry model led by automobile manufacturers was promoted.     

我国机动车污染防治行业2008年发展综述

综述了我国机动车污染防治行业2008年发展状况，分析了行业发展中存在的主要问题，提出了解决对策和建议，对行业的发展进行了展望。     

夜间动态环境中空间距离驾驶员判识变化

为研究驾驶员在夜间地面动态环境中距离判识变化的规律,改进行车安全.选取32名驾驶员进行实际道路试验.被试在自车行进方向不同的深度距离、车速下,判识前方障碍物的绝对距离和相对距离.统计距离判识值,得出夜间距离判识值数据分布,取得距离判识特征值.回归分析判识距离与速度的关系,建立障碍物判识变化的数学模型.比较昼夜距离判识差异,分析其对夜间行车安伞的影响.结果表明:在车辆行驶深度方向,夜间距离判识值随速度增加而不断下降,两者之间呈负指数关系;在相同深度距离和速度下,夜间判识距离大于昼间;夜间行车时,需适当降低车速,确保有效的安伞距离.     

电动汽车用电机及其控制器振动试验方法的探讨

振动试验是汽车产品设计验证和质量检验重要试验方法。本文主要从试验标准、试验条件、振动试验中主要参数的计算推导及应用、试验工装的设计、试验中应注意的问题等方面对电动汽车电机及其控制器的振动试验进行介绍。     

Deciphering urban traffic impacts on air quality by deep learning and emission inventory

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM_2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m³ to 12.283 µg/m³ compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM_2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.     

浅谈汽车内空气污染来源及影响因素

随着中国社会经济的不断发展,人民生活水平的不断提高,汽车已逐渐成为人们日常生活中的一部分。然而在世界的范围内,汽车内的空气污染问题已越来越得到重视,但就国内的情况而言,汽车内的空气污染问题却没有随着汽车的普及而得到人们足够的认识。有见及此,本文指出了造成汽车内空气污染的几种主要污染来源及其有可能产生的危害,同时讲述了影响车内空气质量的几种主要因素,并就此给出了相关污染减轻的建议,希望籍此减少汽车内空气污染对人们健康的危害。     

交通活动对公路两侧土壤和灰尘中重金属含量的影响

为了解公路交通带来的重金属污染及其在公路两侧土壤中的分布规律,对相关研究结果的分析表明,含铅汽油、润滑油的燃烧,汽车轮胎、刹车里村的机械磨损等是公路两侧土壤和灰尘中重金属污染的重要来源.机动车辆排放的含重金属颗粒物或直接沉积在路面灰尘中,或通过干湿沉降沉积在公路两侧的土壤中,使得公路两侧土壤和灰尘中重金属出现不同程度的积累.一般地,公路两侧土壤中重金属含量随着距公路距离的增加呈指数形式下降.公路两侧土壤中重金属的含量及其分布格局因受交通流量、车辆类型、地形与路况、绿化带配置和风、降雨等气象条件的影响而异.     

废旧汽车拆解区土壤中多环芳烃垂向分布特性

研究分析了废旧汽车拆解区土壤剖面的美国EPA优控的16种多环芳烃的纵向分布.结果表明,表层土壤中16种多环芳烃总含量达到了17 323 ng·g-1,其中芘(Pyr)、苯并[a]蒽(BaA)、芴(Flu)含量最高,分别达到11 820、1 234和1 083 ng·g-1.汽车拆解区表面和土壤深度为10 cm的土壤均达到了重度污染级别;深度在50~350 cm之间的土壤为轻度至中度污染,当土壤深度超过400 cm,土壤基本未受到污染.但是,7种致癌性PAHs(Chr、BaA、BbF、BkF、BaP、DahA、IcdP)总量在土壤深度达到850 cm时仍有34.15 ng·g-1.随着土壤深度的增大,多环芳烃含量急剧降低,当土壤深度超过300 cm,三环的菲(Phe)、荧蒽(Fl)和二氢苊(Ace)成为优势组分.土壤剖面菲(Phe)/蒽(Ant)比值和荧蒽(Fla)/芘(Pyr)、Fluo/Pyr、BaA/(BaA+Chr)等参数表明,土壤表面的多环芳烃主要来源于石油污染.     

车内挥发性有机物检测方法与控制策略研究

以静止状态下车内挥发性有机化合污染物为研究对象,在分析其排放与时间、温度及通风等关系的基础上,对比了国内外车内空气中挥发性有机化合物的检测方法,重点分析了日本汽车工业协会JAMA标准,并在分析国外经验基础上,对我国车内空气挥发性有机污染物的控制策略提出了合理建议。