锂离子电池组散热设计及送风策略

通过实验研究了锂离子电池1C倍率放电,20℃自然对流情况下的温升特性。测得了20℃环境温度下电池的充放电内阻特性,并根据某品牌18650型锂离子电池的物性参数以及实验测得的内阻数据建立了电池单体仿真模型,仿真计算了与实验同工况下的温度分布情况,最大误差4.9%。设计了一种包含480节电池的并行通风空气冷却散热结构,并通过正交试验进行了优化,得到了进出风孔距电池的最小距离1mm,上挡板距离电池的最小距离1mm,下挡板距离电池的最小距离1mm的最优结构,使电池组的最大温升下降了5.71℃,最大温差降低了5.06℃。并基于最优结构给出了120s后每60s改变送风方向的往复送风策略,使电池组即使在40℃、2C放电的恶劣工况下也能够工作在25℃-40℃,电池单体温差5℃以下的工作环境中。     

统一通信平台的优化策略研究

统一通信平台为企业内部提供了可靠的语音、视频及传输功能.针对呼和浩特铁路局科研所建立的统一通信平台,对平台的可靠性、安全性、扩展性、标准化等方面提出策略和建议,以便今后的优化处理.     

Lane assignment problem using a genetic algorithm in the Automated Highway Systems

This paper deals with the lane assignment problem in the Automated Highway Systems (AHS). The purpose of lane assignment strategies is to increase throughput on multi-lane highways with many entry and exit points, based on the itineraries of vehicles using the highway (point of entry, time of entry and exit point) and the effects of maneuvers. Using partitioned lane assignment strategies, the lane assignment problem is formulated as an optimization problem to find the proper positions of partitions. Then the optimal problem is solved using a Genetic Algorithm (GA). The proposed algorithm has been validated by comparing its solution with that of an all-cases algorithm for a simple case. Moreover, a traffic simulator with a realistic car-following model as well as lane change logic has been developed. The simulation results show the efficiency of the proposed genetic algorithm, compared with random assignment algorithms.     

An improved headway-based holding strategy for bus transit

Abstract

This paper presents an improved headway-based holding strategy integrating bus transit travel and dwelling time prediction. A support vector machine-based (SVM) model is developed to predict the baseline travel and dwell times of buses based on recent data. In order to reduce prediction errors, an adaptive algorithm is used together with real-time bus operational information and estimated baseline times from SVM models. The objective of the improved holding strategy is to minimize the total waiting times of passengers at the current stop and at successive stops. Considering the time-varying features of bus running, a ‘forgetting factor’ is introduced to weight the most recent data and reduce the disturbance from unexpected incidents. Finally, the improved holding strategy proposed in this study is illustrated using the microscopic simulation model Paramics and some conclusions are drawn.     

一种提高外显子预测的改进周期3消噪策略

为提高外显子的预测精度，文中用到了一种改进的算法-PPA算法（ post-processing algorithms ）．PPA算法通过比较滤波前后DNA信号N/3处的频谱分量的方法来去除噪声．将PPA算法分别应用到处理离散傅立叶变换（DFT），成对的频谱分析（ PSC）和改进的Gabor小波变换（ MGWT）的周期3系数来实现周期3的去噪．算法的性能在HMR195，Burset/Gui-go570，Asp673个数据库中进行了评估，并把受体工作特征（ ROC）、特异性、灵敏度曲线3个方面作为评测标准．通过实验分析，验证了PPA算法能够有效地提高外显子的预测精度，同时基于小波变换的策略确保了模型的独立性．     

What are the value and implications of two-car households for the electric car?

The major barriers to a more widespread introduction of battery electric vehicles (BEVs) beyond early adopters are the limited range, charging limitations, and costly batteries. An important question is therefore where these effects can be most effectively mitigated. An optimization model is developed to estimate the potential for BEVs to replace one of the conventional cars in two-car households and to viably contribute to the households’ driving demand. It uses data from 1 to 3 months of simultaneous GPS logging of the movement patterns for both cars in 64 commuting Swedish two-car households in the Gothenburg region.The results show that, for home charging only, a flexible vehicle use strategy can considerably increase BEV driving and nearly eliminate the unfulfilled driving in the household due to the range and charging limitations with a small battery. The present value of this flexibility is estimated to be on average $6000–$7000 but varies considerably between households. With possible near-future prices for BEVs based on mass production cost estimates, this flexibility makes the total cost of ownership (TCO) for a BEV advantageous in almost all the investigated households compared to a conventional vehicle or a hybrid electric vehicle. Because of the ubiquity of multi-car households in developed economies, these families could be ideal candidates for the initial efforts to enhance BEV adoptions beyond the early adopters. The results of this research can inform the design and marketing of cheaper BEVs with small but enough range and contribute to increased knowledge and awareness of the suitability of BEVs in such households.     

自适应交互式多模型算法改进探讨

交互式多模型算法是一种较为有效的机动目标跟踪算法.介绍交互式多模型算法的设计思想和算法结构,归纳几种自适应交互式多模型算法的特点及相互关系.通过分析自适应交互式多模型算法的不足,探讨自适应交互式多模型算法的改进问题.     

不可行集蚁群算法在铁路放射性物质载运中的应用

研究铁路货物运输中放射性物质的载运问题,目的是把安全运输与车辆的合理利用有机结合.以货物的客户要求期限与铁路运到期限之差的数学期望、铁路车辆的载重量利用率、装载货物的轻重搭配合理程度作为研究问题的3个目标;将装载货物运输指数限制、车辆标记载重限制、车辆载货容积限制、装载加固限制作为研究问题的约束条件,建立多目标规划模型.依据目标的重要性确定权向量,根据运输实际情况确定理想点,利用理想点法将多目标规划问题转化为单目标规划问题.运用基于不可行集的蚁群算法求得多目标规划问题的有效解.本文给出铁路放射性物质载运问题的计算实例,编程计算的结果证明蚁群算法具有较高的效率和可靠性.     

LDPC码量化译码仿真性能分析

主要分析LDPC码在具体实现过程中,改用最小和译码算法,并考虑硬件实现数据的量化级别、迭代过程中的限幅等因素,要求译码器在满足译码误码率的要求下,硬件的复杂度最低.     

基于自然驾驶数据的分心驾驶行为识别方法

大量证据表明，驾驶人分心是导致交通事故的主要原因之一。当前基于侵入式（如脑电波等）或半侵入式（如视频等）检测驾驶人分心的方法，不仅对驾驶任务造成一定干扰，且受多种环境因素的制约，误报率较高。基于此，只考虑非侵入式车辆运动特征，提出一种基于深度学习的驾驶人分心状态识别方法：首先，从自然驾驶数据集中获得大量的跟驰片段，采用态势感知方法，提取典型的分心驾驶片段，并建立仅包含车辆运动学特征的分心判别指标集；其次，利用梯度提升决策树-递归特征消除算法（GBDT-RFE）和随机森林-递归特征消除算法（RF-RFE）对特征进行重要度排序，得到重要度较高的分心监测指标；最后，采用长短时记忆神经网络（LSTM-NN）实现分心驾驶的分类识别，并与支持向量机和AdaBoost的模型结果进行对比。研究结果表明：LSTM-NN在判别分心或正常状态时F₁分别为89%、91%，高于SVM和AdaBoost对应二分类结果；进行多分类任务时，判别分心情景的平均F₁较SVM和AdaBoost分别提升了12%和7%，不同类别分心识别的误报率在15%以下，说明LSTM-NN能够有效学习分心序列的前后信息，有利于准确估计驾驶人的状态。研究结果可为车辆分心预警系统和驾驶风险倾向性评估提供方法基础。