A new solution for two-bag air suspension system with leaf spring for heavy-duty vehicle

The design problem of a two-bag air suspension system for heavy-duty vehicles is formulated as a two-level (suspension system level and component level) optimization problem. At the suspension system level, optimal stiffness matrix of leaf spring, characteristics of damper and upper rod layout are determined by solving a multi-objective constrained optimization problem with response surface. At the component level, shape and thickness of the leaf spring are formed using cubic-spline curves to make the stiffness matrix as close to the target values cascaded from suspension system level as possible. Simulations using a vehicle model described by multi-body model and FEM of the novel leaf spring validate the suspension system thus derived.     

基于闭环力反馈原理的非线性液压主动阻尼悬架模型的研究及优化设计

本文在理论上建立了一套基于主动力反馈原理的新型主动阻尼悬架的设计和优化方法。首先提出了该主动阻尼悬架的实现模型，该模型是在传统的液力减振器的基础上，应用半主动控制的思想，结合力反馈的原理建立起来的内部液压反馈阻尼网络模型。理论优化分析和计算机仿真表明，通过该液阻网络模型，即可以实现电控的主动阻尼悬架的功能，它可以根据汽车行驶路况的好坏，自适应地调节悬架阻尼大小以实现主动阻尼悬架的最优控制。     

用动态规划方法优化设计船舶车辆甲板结构

基于规范的车辆甲板结构优化设计在数学上是一个较大的非线性规划问题，而且为了满足实际需要，其中一些变量只能离散变化。本文将该问题处理为三阶段决策问题，因而能运用动态规划方法有效地求解。通过一个实例详细说明求解过程，从中看出应用动态规划方法解决基于规范的船舶结构优化设计问题的特色。     

防抱制动系统基于模型的最佳滑移率计算方法

提出了一种基于μ-λ曲线的近似数学模型来实现最佳滑移率的递推最小二乘算法(RLS)。应用累积求和统计控制法(CUSUM)，解决了RLS算法在检测跃变路面时响应滞后的问题。通过计算机仿真，验证了算法在车辆防抱制动系统中的可行性和有效性。     

轿车悬架阻尼二阶最优控制设计方法研究

本文以半动悬架与二阶最优控制理论为基础，通过被动悬架阻尼的主动力表示，结合对最优控制力的分解以及最优评价指标，振动加速功率谱密函数与系统振动阻尼之间的关系的研究，建立了多自由度汽车振动系统在随机输入下以振动加速度功率谱密度函数极小为目标的悬架最优减振器阻尼参数的方法，并给出了７自由汽车振动最优减振器阻尼设计实例。     

汽车主动悬架的最优预见控制

本文针对1/2车辆模型,应用最优预见控制理论对汽车主动悬架进行控制系统的设计和研究。计算机仿真结果表明,所提出的系统能有效改善汽车乘坐舒适性。     

Dynamic stride length adaptation according to utility and personal space

Pedestrians adjust both speed and stride length when they navigate difficult situations such as tight corners or dense crowds. They try to avoid collisions and to preserve their personal space. State-of-the-art pedestrian motion models automatically reduce speed in dense crowds simply because there is no space where the pedestrians could go. The stride length and its correct adaptation, however, are rarely considered. This leads to artefacts that impact macroscopic observation parameters such as densities in front of bottlenecks and, through this, flow. Hence modelling stride adaptation is important to increase the predictive power of pedestrian models. To achieve this we reformulate the problem as an optimisation problem on a disk around the pedestrian. Each pedestrian seeks the position that is most attractive in a sense of balanced goals between the search for targets, the need for individual space and the need to keep a distance from obstacles. The need for space is modelled according to findings from psychology defining zones around a person that, when invaded, cause unease. The result is a fully automatic adjustment that allows calibration through meaningful social parameters and that gives visually natural results with an excellent fit to measured experimental data.     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.     

A cost-competitiveness analysis of charging infrastructure for electric bus operations

This study investigates the cost competitiveness of different types of charging infrastructure, including charging stations, charging lanes (via charging-while-driving technologies) and battery swapping stations, in support of an electric public transit system. To this end, we first establish mathematical models to investigate the optimal deployment of various charging facilities along the transit line and determine the optimal size of the electric bus fleet, as well as their batteries, to minimize total infrastructure and fleet costs while guaranteeing service frequency and satisfying the charging needs of the transit system. We then conduct an empirical analysis utilizing available real-world data. The results suggest that: (1) the service frequency, circulation length, and operating speed of a transit system may have a great impact on the cost competitiveness of different charging infrastructure; (2) charging lanes enabled by currently available inductive wireless charging technology are cost competitive for most of the existing bus rapid transit corridors; (3) swapping stations can yield a lower total cost than charging lanes and charging stations for transit systems with high operating speed and low service frequency; (4) charging stations are cost competitive only for transit systems with very low service frequency and short circulation; and (5) the key to making charging lanes more competitive for transit systems with low service frequency and high operating speed is to reduce their unit-length construction cost or enhance their charging power.