重度混合动力汽车燃油经济性和排放多目标优化

考虑重度混合动力汽车运行模式切换时发动机频繁起停对油耗的影响,建立了整车动态仿真模型和综合考虑燃油消耗和排放的多目标优化模型,采用简化控制变量的动态规划算法并结合自动变速器经济性换挡规律,在NEDC工况下对多目标优化模型进行仿真.结果表明,发动机的频繁起停对整车燃油消耗有显著影响,采用动态规划算法可全局优化整车燃油经济...     

PHEV复合储能系统设计及基于MODA的参数优化

为了提高插电式混合动力汽车的燃油经济性、降低污染物的排放,并解决插电式混合动力汽车单一动力电池低比功率、无法响应暂态功率需求的问题,设计蓄电池和超级电容并联的复合储能系统,采用带有滑动窗口的实时小波功率分配策略,并对滑动窗口长度进行选择。该功率分配策略将复合储能系统的需求功率分解成高频和低频两部分,超级电容接收高频分量,蓄电池接收低频分量,避免了高频分量对于蓄电池的冲击,提高了蓄电池的耐久性和可靠性。制定基于规则的控制策略,以整车燃油消耗量和污染物排放量为优化目标,利用多目标蜻蜓算法对相关控制参数进行优化。基于ADVISOR搭建含有复合储能系统的插电式混合动力汽车整车仿真模型,采用新欧洲行驶循环工况进行测试,并通过与带精英策略的非支配排序遗传算法进行对比,验证算法的有效性。研究结果表明：利用多目标蜻蜓算法优化后的车辆百公里燃油消耗平均降低了12.71%,污染物综合排放性能平均下降了10.05%;相对于优化前,发动机输出功率减少,电机输出功率增加,发动机和电机的工作效率均得到了显著提升;Pareto最优解的收敛性和覆盖范围优于带精英策略的非支配排序遗传算法,同时得到的多组Pareto最优解为整车设计和优化提供了更多选择。     

基于改进粒子群优化算法的多目标自适应巡航控制

跟驰过程中,在保证安全性的前提下为了提升自适应巡航控制(ACC)系统的舒适性和燃油经济性,研究了多目标自适应巡航控制算法.在建立车间纵向运动学模型的基础上,根据模型预测控制理论,设计综合考虑安全性、舒适性、燃油经济性以及车辆自身限制等因素的目标函数和约束条件,并引入松弛因子向量软化硬约束边界解决无可行解问题.进一步在滚...     

轻型车动力总成综合性能评价体系的构建

介绍了与整车动力性、经济性和排放性能相关的国家标准和主要指标,建立了一种包含整车动力性、经济性和排放指标的动力总成综合性能评价体系。该体系的核心思想是将各指标量纲归一化,并通过加权平均得出一个评价值,从而使得综合评价成为可能。通过计算和分析表明,采用该综合评价体系及优化算法,可以有效的对汽车动力传动系进行优化匹配,发挥整车的最佳综合性能。     

基于多目标遗传算法的混合电动汽车参数优化

动力系统和控制器参数的同时优化是提高混合电动汽车(HEV)燃油经济性并降低排放的关键。这类优化问题涉及多个相互冲突的优化目标和非线性约束,是典型的多目标优化问题。文中采用多目标遗传算法求解该优化问题的Pareto最优解集,并应用ADVISOR对实际算例的优化结果进行比较分析。结果表明,应用该方法可找到多组可行解,在满足原车动力性要求的前提下能有效提高燃油经济性,降低排放。     

基于自适应网格粒子群算法的多目标配送优化模型

对配送方案的选择提出多目标优化,在满足客户需求的前提下,力求成本最低和各配送中心负荷均衡,建立多目标规划模型。运用粒子群算法对解空间粒子进行局部和全局的搜索,再运用自适应网格算法对非劣解外部集进行更新和维护,保持其规模。实证表明,采用基于自适应网格的多目标粒子群算法对该模型进行求解能够得到均匀分布于解空间的Pareto前沿。结果表明两目标具有一定的悖反关系,据此选择满意解。     

考虑稳健性的汽车结构耐撞性优化设计

本文中基于C-NCAP中40%重叠度的偏置碰撞工况对某轿车进行结构耐撞性优化。为提高输出响应的预测精度,使用基于粒子群算法优化的支持向量回归模型来拟合设计变量与输出响应之间的关系,并利用非支配排序多目标遗传算法Ⅱ获得该优化问题的Pareto前沿。在确定性优化的基础上,并考虑产品性能在不确定因素影响下的波动,对其进行稳健性优化设计。最后,对优化结果进行有限元仿真验证。结果表明:优化后,结构质量减轻,耐撞性能明显提升,同时保障了稳定的产品性能。     

Cooperative regenerative braking control for front-wheel-drive hybrid electric vehicle based on adaptive regenerative brake torque optimization using under-steer index

In this study, cooperative regenerative braking control of front-wheel-drive hybrid electric vehicle is proposed to recover optimal braking energy while guaranteeing the vehicle lateral stability. In front-wheel-drive hybrid electric vehicle, excessive regenerative braking for recuperation of the maximum braking energy can cause under-steer problem. This is due to the fact that the resultant lateral force on front tire saturates and starts to decrease. Therefore, cost function with constraints is newly defined to determine optimum distribution of brake torques including the regenerative brake torque for improving the braking energy recovery as well as the vehicle lateral stability. This cost function includes trade-off relation of two objectives. The physical meaning of first objective of cost function is to maximize the regenerative brake torque for improving the fuel economy and that of second objective is to increase the mechanical-friction brake torques at rear wheels rather than regenerative brake torque at front wheels for preventing front tire saturation. And weighting factor in cost function is also proposed as a function of under-steer index representing current state of the vehicle lateral motion in order to generalize the constrained optimization problem including both normal and severe cornering situation. For example, as the vehicle approaches its handling limits, adaptation of weighting factor is possible to prioritize front tire saturation over increasing the recuperation of braking energy for driver safety and vehicle lateral stability. Finally, computer simulation of closed loop driver-vehicle system based on Carsim? performed to verify the effectiveness of adaptation method in proposed controller and the vehicle performance of the proposed controller in comparison with the conventional controller for only considering the vehicle lateral stability. Simulation results indicate that the proposed controller improved the performance of braking energy recovery as well as guaranteed the vehicle lateral stability similar to the conventional controller.     

Wear/comfort Pareto optimisation of bogie suspension

Pareto optimisation of bogie suspension components is considered for a 50 degrees of freedom railway vehicle model to reduce wheel/rail contact wear and improve passenger ride comfort. Several operational scenarios including tracks with different curve radii ranging from very small radii up to straight tracks are considered for the analysis. In each case, the maximum admissible speed is applied to the vehicle. Design parameters are categorised into two levels and the wear/comfort Pareto optimisation is accordingly accomplished in a multistep manner to improve the computational efficiency. The genetic algorithm (GA) is employed to perform the multi-objective optimisation. Two suspension system configurations are considered, a symmetric and an asymmetric in which the primary or secondary suspension elements on the right- and left-hand sides of the vehicle are not the same. It is shown that the vehicle performance on curves can be significantly improved using the asymmetric suspension configuration. The Pareto-optimised values of the design parameters achieved here guarantee wear reduction and comfort improvement for railway vehicles and can also be utilised in developing the reference vehicle models for design of bogie active suspension systems.     

基于烟花算法的PHEV能量管理策略参数优化

为改善插电式混合动力汽车(PHEV)的燃油经济性,提出一种基于规则的能量管理策略.结合智能网联汽车技术,利用烟花算法(F WA)结合系统约束条件,对能量管理策略参数进行优化,以求使车辆在变化的路况下能耗最低.为减轻沉重运算负荷,设计了一种事件触发机制来控制优化操作的启停.当车辆油耗超过预设上限则开始优化,一旦油耗满足预...     

考虑气动阻力和横风稳定的汽车车身多目标优化设计

综合考虑了气动阻力特性和横风稳定性,对车身外形参数进行了多目标自动优化设计。综合利用参数化建模技术、计算流体力学(CFD)仿真、试验设计方法、响应面模型和智能优化算法,集成Pro/Engineer参数化建模和ICEM网格划分工具以及Fluent仿真软件,在多学科优化平台modeFRONTIER上,搭建了一种自动优化设计流程。利用该流程,基于遗传算法(GA)对MIRA快背式模型车身几何外形进行了改型设计,得到了考虑车身气动阻力特性和横风稳定性的最优权衡设计解集。该结果使得气动阻力因数降低了5.2%,侧向力因数降低了5.8%。因而,实现了车身气动阻力和横风稳定性的多目标优化。     

基于粒子群算法的Plug-in混合动力汽车能量管理策略优化研究

基于对混合动力汽车能量管理策略优化的目的,建立了丰田Prius Plug-in混合动力汽车的MATLAB/Simulink数学模型,用数学公式描述了系统优化控制问题,采用粒子群优化算法对该包含众多约束条件的非线性优化问题进行了求解,利用PSAT专业软件对比分析了基本型优化控制算法、改进型优化控制算法和规则控制算法等的控制效果及燃油经济性。结果表明,经过优化后的Plug-in混合动力汽车在不牺牲汽车各项性能的前提下能提高动力系统工作效率。     

基于粒子群算法的Plug—in混合动力汽车能量管理策略优化研究

基于对混合动力汽车能量管理策略优化的目的,建立了丰田PnusPlug-in混合动力汽车的MATLAB／Simulink数学模型,用数学公式描述了系统优化控制问题,采用粒子群优化算法对该包含众多约束条件的非线性优化问题进行了求解,利用PSAT专业软件对比分析了基本型优化控制算法、改进型优化控制算法和规则控制算法等的控制效果及燃油经济性。结果表明,经过优化后的Plug-in混合动力汽车在不牺牲汽车各项性能的前提下能提高动力系统工作效率。     

Multi-objective optimization of quarter-car models with a passive or semi-active suspension system

A systematic methodology is applied in an effort to select optimum values for the suspension damping and stiffness parameters of two degrees of freedom quarter-car models, subjected to road excitation. First, models involving passive suspension dampers with constant or dual rate characteristics are considered. In addition, models with semi-active suspensions are also examined. Moreover, special emphasis is put in modeling possible temporary separations of the wheel from the ground. For all these models, appropriate methodologies are employed for capturing the motions of the vehicle resulting from passing with a constant horizontal speed over roads involving an isolated or a distributed geometric irregularity. The optimization process is based on three suitable performance criteria, related to ride comfort, suspension travel and road holding of the vehicle and yielding the most important suspension stiffness and damping parameters. As these criteria are conflicting, a suitable multi-objective optimization methodology is set up and applied. As a result, a series of diagrams with typical numerical results are presented and compared in both the corresponding objective spaces (in the form of classical Pareto fronts) and parameter spaces.     

Multi-objective optimization of quarter-car models with a passive or semi-active suspension system

A systematic methodology is applied in an effort to select optimum values for the suspension damping and stiffness parameters of two degrees of freedom quarter-car models, subjected to road excitation. First, models involving passive suspension dampers with constant or dual rate characteristics are considered. In addition, models with semi-active suspensions are also examined. Moreover, special emphasis is put in modeling possible temporary separations of the wheel from the ground. For all these models, appropriate methodologies are employed for capturing the motions of the vehicle resulting from passing with a constant horizontal speed over roads involving an isolated or a distributed geometric irregularity. The optimization process is based on three suitable performance criteria, related to ride comfort, suspension travel and road holding of the vehicle and yielding the most important suspension stiffness and damping parameters. As these criteria are conflicting, a suitable multi-objective optimization methodology is set up and applied. As a result, a series of diagrams with typical numerical results are presented and compared in both the corresponding objective spaces (in the form of classical Pareto fronts) and parameter spaces.     

轮毂电机驱动电动汽车双横臂前悬架运动学优化

基于传统汽车底盘平台进行电动轮驱动改型时,轮毂电机的布置将导致悬架硬点坐标的改变,从而严重影响悬架运动学特性,为此须对电动轮驱动改型车悬架系统进行优化设计。以某传统车底盘平台的双横臂前悬架运动学特性为优化目标,根据参数灵敏度分析结果,提出两步优化方案,即首先进行主销定位参数的优化,而后再进行前轮外倾角和前轮前束角的优化。利用ISIGHT软件和全局非归一化的多目标遗传优化算法NSGA-II得到的悬架参数优化解集在ADAMS/Car平台下进行了验证。结果表明,悬架运动学特性得到较大幅度的改善,特性曲线与原型车悬架K特性实验结果基本一致。证明了该优化方法的可行性,确保了改型后电动汽车的操纵稳定性受安放轮毂电机的影响较小。     

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.     

基于燃油经济性的汽车动力传动系匹配优化

传统的汽车传动系匹配研究方法,都是以汽车的动力性或经济性指标为优化目标的单目标优化。为了实现真正意义上的多目标优化,文章利用modeFRONTIER软件和遗传算法的组合优化策略,结合某5挡手动变速车传动系匹配,进行了基于燃油经济性的传动系参数优化设计,达到了降低汽车燃油消耗的效果,为汽车开发设计中传动系匹配优化提供了参考。     

PSO algorithm-based parameter optimization for HEV powertrain and its control strategy

The coordination between the powertrain and control strategy has significant impacts on the operating performance of hybrid electric vehicles (HEVs). A comprehensive methodology based on Particle Swarm Optimization (PSO) is presented in this paper to achieve parameter optimization for both the powertrain and the control strategy, with the aim of reducing fuel consumption, exhaust emissions, and manufacturing costs of the HEV. The original multi-objective optimization problem is converted into a single-objective problem with a goal-attainment method, and the principal parameters of powertrain and control strategy are set as the optimized variables by PSO, with the dynamic performance index of HEVs being defined as the constraint condition. Computer simulations were carried out, which showed that the PSO scheme gives preferable results in comparison to the ADVISOR method. Therefore, fuel consumption and exhaust emissions of HEVs can be effectively reduced without sacrificing dynamic performance of HEVs.     

Multi-objective optimal predictive control of signals in urban traffic network

Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network models are considered, i.e., simple network which captures basic macroscopic traffic characteristics and advanced network that further considers vehicle turning and different traveling routes between origins and destinations. A network signal predictive control algorithm is developed for online multi-objective optimization. A variety of objectives are considered such as system throughput, vehicle delay, intersection crossing volume, and spillbacks. The genetic algorithm (GA) is applied to solve the optimization problem. Three example networks with different complexities are studied. It is observed that the optimal traffic performance can be achieved by the dynamic control in different situations. The influence of the objective selection on short-term and long-term network benefits is studied. With the help of parallel computing, the proposed method can be implemented in real time and is promising to improve the performance of real traffic network.