Integrated System of Solar Cells with Hierarchical NiCo2O4 Battery-Supercapacitor Hybrid Devices for Self-Driving Light-Emitting Diodes

An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo2O4 battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo2O4 arrays as faradic electrode,with capacitive electrode of active carbon(AC),BSHs were assembled with energy density of 16.6 Wh kg-1,power density of 7285 W kg-1,long-term stability with 100% retention after 15,000 cycles,and rather low self-discharge.The NiCo2O4//AC BSH was charged to 1.6 V in 1 s by solar cells and acted as reliable sources for powering LEDs.The integrated system is rational for operation,having an overall efficiency of 8.1% with storage efficiency of 74.24%.The integrated system demonstrates a stable solar power conversion,outstanding energy storage behavior,and reliable light emitting.Our study offers a precious strategy to design a self-driven integrated system for highly efficient energy utilization.

Data Augmentation Technology Driven By Image Style Transfer in Self-Driving Car Based on End-to-End Learning

With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.

The Environmental Impacts and Economic Benefits on Comprehensively Promoting Alternative Fuel Buses in China: Life-Cycle and Scenario Analysis Based on LEAP Model

With the continuous development of urban public transportation, the harmful GHG emissions and pollutants generated by itself and the consequent issues such as the losses of residents’ health, economic value and residents’ welfare have become the focus of social attention. In order to study the impacts of promoting new energy vehicles on public transportation pollution mitigation and residents’ health benefits, this paper adopts the LEAP model to build some scenarios that fulfill different development needs to quantitatively analyze the ownership of new energy buses, the reduction of pollutants and the losses of residents’ health welfare. It is concluded that promoting new energy buses comprehensively can significantly reduce the emissions of atmospheric pollutants and the economic losses of residents’ health, but cannot fully realize the targets of greenhouse gas reduction under Life Cycle Analysis.

An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.

The Effect of Self-Driving Car on Urban Traffic

Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.

A Driving Strategy Model of Self-Driving Cars

The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.

Laws and Ethics Policy of Self-driving Cars in Taiwan

Countries have invested considerable sums of human capital and material resources in the practical application of self-driving cars demonstrating the impressive market opportunity.In light of this trend,Taiwan does not want to fall behind either.As on-road testing and technological development for self-driving cars continue to develop in different countries,the controversial issues of safety,ethics,liability,and the invasion of privacy continue to emerge.In order to resolve these issues,the government of Taiwan seeks to provide a good environment for AI(artificial intelligence)innovation and applications.This article summarizes and highlights relevant content and key points of Unmanned Vehicles Technology Innovative Experimentation Act,which was legislated in Taiwan in 2018.In addition,it points out the fundamental ethics regulation of AI,which has influenced Taiwan legal policy.

Should Self-Driving Vehicles Be Encouraged?

Late this March.China＇s Internet giant Baidu became the first self-driving car developer to obtain temporary license plates to carry out self driving tests on public roads in Beijing.

轨道交通对交通客流参数的技术效应分析

城市轨道交通与公交车在整个城市居民出行中占据主要地位,地铁的开通运营势必会对沿线公交及整个城市公交客流产生重要影响,进而需要重新进行优化配置公交线路,使其与地铁融合衔接,从而更加方便居民出行。本文以哈尔滨市作为研究对象,基于历年哈尔滨市公交车客流数据,借助MATLAB软件,运用灰色预测GM(1,1)方法,研究哈尔滨市公交车客流增长变化模型,在地铁开通后,公交实际客流与预测客流进行对比,结合常住人口的变化,可以分析地铁开通运营对公交车客流的具体影响,同时为其他城市公交车在地铁开通后的优化配置提供相关建议与借鉴。

“双碳”视角下超大城市交通结构问题及对策研究

超大城市大规模人口承载,催生机动车保有量迅速攀升致交通供需不平衡,造成道路拥堵并引发高碳排放,需要以“双碳”目标重新审视城市交通发展,以更加合理的出行结构促进交通环境改善。本文遵循物质不灭自然逻辑,探究交通运输行为的碳排放时空转移内在机理,运用波及效应分析法从“用前、用中、用后”三阶段对交通运输碳排放进行识别,在碳排放标准煤系数折算法的基础上,对城市交通工具碳排放量进行分类比较,给出更科学、更合理的城市交通碳排放量分类测度方法与思路,以不便指数和成都实例论证提升公共交通分担率是未来城市交通低碳发展的必然选择,聚焦交通结构优化给出五项助力交通减碳举措。

计及实时交通特征的电动公交车备用率与充电桩联合优化

为了提高电动公交车及充电站总投资和运营的经济性,需综合考虑公交车和充电桩的购置成本以及充电成本。以道路特征、实时交通特征、环境温度为主要影响因素,计算电动公交车的行驶时间和能耗;在满足公交车运营基本需求和保证车内温度的前提下,以日公交车购置成本、日充电桩建造成本、日充电成本之和最小为目标进行优化,建立电动公交车备用率与充电桩的联合优化模型,并采用改进的离散型自适应粒子群优化算法进行计算,得到公交车和充电桩的最优购置方案以及各季节和全年的日均投资及运营成本。以某公交线路上电动公交车年内多种运营条件为算例,验证所建优化模型的正确性。

混合动力城市客车车架结构分析及轻量化设计

车架作为混合动力城市客车的主要承载结构,其强度和刚度会影响混合动力城市客车的稳定性、舒适性和续航性等。通过对混合动力城市客车的车架静强度进行分析,得出了混合动力城市客车车架在不同工况下的应力和应变情况,以混合动力城市客车车架轻量化为目标,对混合动力城市客车车架进行结构进行了优化和验证,既有效地减少了混合动力城市客车的排放,也改善了混合动力城市客车的综合性能。

Small-scale self-driving cars:A systematic literature review

The autonomous vehicle(AV)technology has the potential to significantly improve safety and efficiency of the transportation and logistics industry.Full-scale AV testing is limited by time,space,and cost,while simulation-based testing often lacks the necessary accuracy of AV and environmental modeling.In recent years,several initiatives have emerged to test autonomous software and hardware on scaled vehicles.This systematic literature review provides an overview of the literature surrounding small-scale self-driving cars,summarizing the current autonomous platforms deployed and focusing on the software and hardware developments in this field.The studies published in English-language journals or conference papers that present small-scale testing of self-driving cars were included.Web of Science,Scopus,Springer Link,Wiley,ACM Digital Library,and TRID databases were used for the literature search.The systematic literature search found 38 eligible studies.Research gaps in the reviewed papers were identified to provide guidance for future research.Some key takeaway emerging from this manuscript are:(i)there is a need to improve the models and neural network architectures used in autonomous driving systems,as most papers present only preliminary results;(ii)increasing datasets and sharing databases can help in developing more reliable control policies and reducing bias and variance in the training process;(iii)small-scaled vehicles to ensure safety is a major benefit,and incorporating data about unsafe driving behaviors and infrastructure problems can improve the accuracy of predictive models.

面向资源约束的电动公交车充电调度策略

为减少公交运营成本、合理制定插入式充电模式下公交总站的电动公交车充电调度方案,本文基于帝国竞争算法提出了一种面向资源约束的公交车充电调度策略(RCO-CSS)。基于电动公交车运营的时空特点和充放电特性,应用多技能资源约束多项目调度问题(MSRC-MPSP)运筹规划思想对电动公交车充电问题进行抽象建模,以车队规模与充电桩数量为主要资源参数,以最小化充电成本和日均设备购置成本为目标,构建资源约束充电调度模型,进而设计一种二阶段演化帝国竞争算法(TSE-ICA)对模型进行求解,输出最佳的充电调度方案及匹配的行车运营计划。采用4个分别包含5、10、20和36条线路的公交运行实例对RCO-CSS进行了性能评估与有效性验证。在实例探讨中,首先运用Taguich法对资源参数进行了敏感性分析,发现资源越宽裕,模型输出的日充电费用越小,但车辆与充放电设备平摊至每日的购置成本越大;其次,将TSE-ICA与其他4种先进的元启发式算法进行实验数值对比,验证了所提算法的寻优性能;最后,通过与无序充电调度策略和常规有序充电调度策略进行比较,证明了RCO-CSS能够更好地降低用电成本、设备购置成本和电池充放电次数。基于MSRC-MPSP和TSE-ICA的RCO-CSS为公交运营商制定充电调度方案和行车运营计划提供了一种可行且敏捷高效的新思路。

纯电动新能源客车夏季内流场CFD仿真

与传统客车相比,纯电动新能源客车内没有明显热源,外界温度和空调系统对车厢内空气流动影响较大,为此建立了某52座纯电动新能源客车简化模型,采用标准k-ε湍流模型、利用计算流体力学理论,得出在炎热夏季高温条件下车厢内部的温度场和速度场云图,仿真获得车厢内空调系统送风温度和送风速度对车内乘员所在位置温度场和速度场的影响。研究结果对降低新能源客车能量损耗、提升乘客舒适感提供了优化依据。

基于深度学习的观光车轴承故障诊断分析

观光车作为城市观光、景区旅游的重要交通工具,其稳定性和可靠性对游客体验至关重要。轴承作为观光车的核心部件,其故障诊断对预防事故、保障安全具有重要意义。传统的轴承故障诊断方法主要依赖人工检测和经验判断,易受主观因素影响,且难以实现实时监测。对此,需要探讨基于深度学习的观光车轴承故障诊断。

客车舱门气弹簧选型设计与运动仿真

为了提高客车舱门气弹簧选型的设计质量、效率及可视性,文章采用三维软件进行客车舱门气弹簧的选型设计,通过作图法得到气弹簧的安装位置,然后理论计算确认该安装位置的舱门支撑力和关闭力满足设计要求。通过CATIA中的数字样机(DMU)模块的运用,实现气弹簧运动仿真,直观观测舱门开启角度、干涉情况等,从设计源头校核确认,保证气弹簧自锁有效。气弹簧三维选型设计与运动仿真分析的使用,提高了客车舱门气弹簧选型设计的效率与设计水平,在设计源头避免气弹簧选型不当导致舱门开启角度不对、舱门开启力度偏大或偏小、气弹簧寿命短等问题。

纯电动公交车电机超速故障的分析与处理

纯电动公交车在正常运营过程会出现电机超速问题,不仅导致车辆行驶动力不足、行驶安全性降低,还对乘客的乘坐体验造成影响。本文以某车型纯电动公交车电驱动系统为例,分析车辆行驶中电机超速故障产生的原因,并提出了解决方法。通过实车测试验证,该解决方法可以减少电动公交车电机超速故障,提升车辆运营效率和乘客乘车体验。