应用多目标粒子群算法的车辆传动系参数优化仿真研究

利用多目标粒子群算法研究了车辆传动系参数优化问题。首先根据目标车辆的结构搭建了整车模型,通过台架试验对整车模型进行验证;其次以车辆传动系参数作为设计变量,以车辆动力性和经济性作为优化目标,建立多目标优化模型;然后运用多目标粒子群算法对传动系参数进行优化计算,得到Pareto最优解集;最后运用基于信息熵的多属性决策方法,确定了最优传动系参数。研究结果表明:优化后的整车的动力性能和燃油经济性均得到提升,其中燃油经济性提高了4.83%,全负荷0~100 km/h加速性能提升了2.03%。     

基于遗传算法的商用车传动系的参数优化

研究了综合考虑动力性和燃油经济性指标的汽车动力传动系数学模型。以燃油经济性最佳为优化目标以及整车动力性作为约束条件,根据指定柴油机和整车参数,运用遗传算法对某商用货车的传动系参数进行优化计算。结果显示,优化后的传动系参数,使得整车在原地起步加速时间基本不变的情况下最高车速提高了2.98%,使得整车混合百公里油耗降低了0.67%。表明优化参数使整车的动力性和燃油经济性得到了不同程度的改善。     

基于LVQ工况识别的混合动力汽车自适应能量管理控制策略

为提高混合动力汽车的燃油经济性,选取6种典型行驶工况代表“市区”、“郊区”和“高速公路”3类主要工况,采用基于规则的模糊能量管理控制策略,以整车燃油经济性为目标,在3类主要工况下用改进型粒子群优化算法优化发动机联合工作曲线与发动机关闭曲线系数,得到相应的优化后的隶属度函数的参数;运用学习向量量化(LVQ)算法识别车辆运行工况,动态选择相应的模糊控制策略,使混合动力汽车控制策略对选定的几种代表性工况具有自适应性,从而提高整车的燃油经济性。仿真对比结果表明,相比于传统混合动力汽车,燃油经济性提高了3.4%。     

HEV传动系统多目标优化研究

针对当前混合动力研究主要集中在燃油经济性等单目标上,对多目标研究较少的情况,提出一种基于非支配排序的多目标优化算法(multi-objective evolutionary algorithm,MOEA)。以装备5档手动变速器的并联混合动力汽车为对象,研究传动系速比匹配对燃油经济性与排放性的影响。结果表明:相比优化前,优化后燃油经济性提升了3.09%,排放性综合指标提升17.92%;得到的Pareto解集具有良好的分布性与收敛性,不仅优化了目标,更体现出目标间的冲突情况,说明提出的多目标优化算法能够体现混合动力多目标优化的本质;对解集进一步挖掘,理论上能搜寻到的全局最优解集,为混合动力多目标权衡控制策略提供了理想的控制基础。     

矿用汽车动力传动系统参数匹配建模优化

在对公路汽车动力性与燃油经济性评价指标进行分析基础上,以Matlab/GUI为开发工具,开发矿用汽车动力传动系统计算仿真及参数优化平台。该平台以原地起步加速时间和等速百公里油耗线性加权为目标函数,采用线性加权法把他转化成单一目标函数;建立矿用汽车发动机和传动系统匹配方案优化数学模型。对矿用汽车动力性和燃油经济性进行计算,对传动系统匹配方案和传动系参数进行优化分析。以改进前后某车各项性能指标进行仿真,验证改进方案可行性,对改进后传动系参数进行优化,验证优化平台实用性。通过遗传算法优化模型分析,最后对具有可控性总速比和传动效率进行灵敏度分析,为新车设计和旧车改型提供参考。     

多目标遗传算法的混合动力传动系参数优化

对基于超级电容的混合动力客车(hybrid  electric bus,HEB)进行了混合动力传动系多目标参数优化设计。通过CRUISE建立HEB整车仿真模型和传动系多目标参数优化模型,以等效燃油消耗量和加速时间为优化目标,同时运用带精英策略的非支配排序遗传算法(NSGA-Ⅱ)和iSIGHT优化软件对HEB传动系参数进行多目标优化,并进行了HEB性能仿真分析。结果表明,与优化前相比,优化后的等效燃油消耗量降低了
7.8%,连续换挡加速时间减少了6.5%。
     

基于遗传算法矿用汽车动力传动系参数优化设计

在对公路汽车动力性与燃油经济性评价指标进行分析基础上,针对矿用汽车提出动力性指标及基于等速百公里燃油消耗量和矿区循环工况燃油消耗量燃油经济性评价指标。基于优化设计理论,以高效区最大爬坡度和等速百公里油耗的线性加权为目标函数,建立传动系匹配方案优化数学模型;以原地起步加速时间和等速百公里油耗线性加权为目标函数,建立矿用汽车动力传动系参数优化数学模型,并选用遗传算法进行优化求解。对改进前后矿用汽车其各项性能指标进行计算,验证可行性并对参数进行优化,验证优化模型实用性。结果可知:通过参数优化,(0～45)km/h加速时间由原来89.89s缩短为84.63s,同时等速百公里由原来125.81L降为124.78L。通过对具有可控性总速比和传动效率进行灵敏度分析,为新车设计和旧车改型提供参考。     

采用动态规划算法优化的液压混合动力车辆能源消耗仿真研究

为了降低车辆行驶的燃油消耗量,采用动态规划算法对液压混合动力车辆控制进行优化,并对发动机燃油消耗量进行仿真验证.建立了液压混合动力车辆简图模型,推导出车辆发动机功率方程式.创建发动机能源消耗目标函数,添加约束条件,引用动态规划算法优化目标函数,得出液压混合动力发动机能源消耗的最优参数.设计车辆控制流程图,利用Matlab软件对优化结果进行仿真,并与优化前仿真结果形成比较.仿真结果表明:在无起停的情况下,优化后的液压混合动力车辆燃油消耗量降低了11.71%;在有起停的情况下,优化后的液压混合动力车辆燃油消耗量降低了19.01%.采用动态规划算法优化液压混合动力车辆参数,车辆制动释放的能量被回收,从而节约车辆行驶所消耗的燃油.     

基于免疫算法的四驱插电式混合动力汽车控制策略多目标优化

为改善四驱插电式混合动力汽车的燃油经济性和排放性能,构建了基于规则的能量管理控制策略,并建立了整车仿真模型;将多种群协同进化的思想引入免疫算法,提出多种群免疫算法,并运用该算法对四驱插电式混合动力汽车的控制策略进行了多目标优化;在dSPACE实时仿真系统上对优化前后的控制策略进行了硬件在环仿真实验。结果表明：优化后的控制策略控制效果良好,且发动机的燃油消耗降低了12.71%,HC、CO以及NOx的排放分别下降了15.74%、15.92%和12.69%。     

基于动态规划的挖掘机混合动力系统优化与试验

为了提高液压挖掘机混合动力系统的效率,研究了挖掘机混合动力系统的控制策略与参数优化方法。通过分析挖掘机混合动力系统的工作原理与各元件的特性,建立挖掘机混合动力系统的仿真模型。针对挖掘机工况特点,以系统燃油经济性为目标,提出一种基于动态规划的能量管理控制策略,实现能量管理的全局优化。在前述动态规划算法基础上,对系统进行参数匹配分析,并通过MATLAB对优化结果进行仿真。最后,通过样机进行试验,对系统优化方法的可行性与有效性进行验证。     

Optimal operation strategy development for fuel cell hybrid vehicle

An overall simulation model for fuel cell hybrid vehicle (FCHV) power train in parallel configuration using MATLAB/Simulink programming is constructed in this study. The model runs on power control strategy by using logic-threshold approach, achieved by the hybrid control unit (HCU) and fuel cell stack number. Using accelerator and decelerator pedal positions deduced from the driving schedule as the primary input, the simulation implements power flow and distribution under different vehicle operating modes. The HCU control strategy also incorporates regenerative braking and recharge for battery capacity recovery. Using the D-optimality method for experiment points selection and sequential quadratic programming (SQP) algorithm for obtaining the optimal operational parameters, three control threshold variables of HCU and optimal stack cell number are selected for hydrogen fuel economy under certain driving cycles. The proposed method provides optimized configurations of the FCHV model and the fuel cell stack, which has the capability in satisfying drive power request while satisfying vehicle driving schedule and battery state of charge (SOC) recovery with lower fuel consumption.     

液驱混合动力车辆动力源储能元件匹配优化研究

以串联式液驱混合动力传动系统为对象, 从燃油经济性改善率观点出发,根据动力源运行规则假设及优化,分析蓄能器容积大小对液驱混合动力车辆燃油经济性改善率的影响,并通过城市和公路循环工况对使用不同容积蓄能器的燃油经济性进行仿真计算。仿真结果表明：在满足功率流和回收制动能量的前提下,匹配较小容积蓄能器的液驱混合动力车辆具有较好的燃油经济性改善率和节能效果,其燃油经济性改善率达25%~39%。     

Development of integrated controller for a compound hybrid excavator

This paper presents an integrated control algorithm for a compound hybrid excavator. Both conventional hydraulic excavator and compound hybrid excavator models are developed to compute fuel consumption. The excavator simulation models, which consist of engine, pump and electric components have been validated with test data. To design the hybrid system’s integrated control algorithm which determines the output power of two motor/generators and ultra capacitor, a rule-based control algorithm to improve fuel economy is proposed. Simulation studies have been conducted for a typical digging process to investigate the performance of the proposed rulebased controller. Simulation results show that 26% reduction of fuel consumption can be obtained using the proposed controller to the compound hybrid excavator compared to the conventional hydraulic excavator.     

Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus

Energy management(EM) is a core technique of hybrid electric bus(HEB) in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization(PBIO) energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus(SPHEB) dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.     

串联混合动力客车参数设计与优化

以某传统客车为原型,对串联混合动力客车动力传动系统各部件进行了参数匹配设计,重点阐述了驱动电机参数的设计方法。在保证动力性的前提下,按照均一性的原则分析了发动机功率,电池功率和驱动电机功率的变化对车辆燃油经济性的影响,并对所采用的功率跟随控制策略的参数进行了分段寻优,仿真结果表明:优化后的串联混合动力客车的燃油经济性和动力性都得到了进一步的提高,在控制参数优化后的控制策略下,电池的SOC能迅速回到0.7附近,并保持稳定。     

燃料电池有轨电车能量管理策略多目标优化

为研究一类以质子交换膜燃料电池、超级电容和动力电池为动力源的混合动力有轨电车能量管理问题,首先介绍该类有轨电车的混合动力系统结构和工作特点,提出一种基于系统工作模式的逻辑门限式能量管理策略,工作模式的切换通过多个控制参数来实现。针对该能量管理策略中控制参数的不确定性,应用多目标遗传算法,将整车超级电容和动力电池的最小配置成本以及有轨电车运行的能耗、准时性、准地点停车作为优化目标,对影响列车动力性能的主要能量管理策略控制参数进行优化。以国内某规划线路为实例,在已通过实车试验数据验证的系统仿真模型中进行优化分析,优化结果表明,在保证列车动力性能的前提下,优化后列车的总牵引能耗减少了约12.5%,回收的再生制动能量增加了约14.5%,燃料电池的平均效率提高了约0.83%;同时通过优化得到了超级电容和动力电池的最小容量配置,为整车车载储能系统的冗余配置提供参考。     

混合动力汽车能量管理策略的四步骤设计方法

以行星齿轮结构的混合动力汽车的动力系统为研究对象,提出了一套能量管理策略的四步骤设计方法。第一步,引入混杂动态系统理论来描述既有离散事件动态系统特征又有连续变量动态系统特征的混合动力系统;第二步,利用序列二次项规划算法和动态规划算法分别求解出最佳功率分配和最佳工作模式切换序列的基本规律;第三步,建立基于规则和模糊规则的能量管理策略;第四步,利用遗传算法对第三步已经建立的工作模式切换规则和功率分配的模糊控制规则中的参数进行优化,以获得更佳的控制效果。     

Performance enhancement of axial fan blade through multi-objective optimization techniques

This paper presents an axial fan blade design optimization method incorporating a hybrid multi-objective evolutionary algorithm (hybrid MOEA). In flow analyses, Reynolds-averaged Navier-Stokes (RANS) equations were solved using the shear stress transport turbulence model. The numerical results for the axial and tangential velocities were validated by comparing them with experimental data. Six design variables relating to the blade lean angle and the blade profile were selected through Latin hypercube sampling of design of experiments (DOE) to generate design points within the selected design space. Two objective functions, namely, total efficiency and torque, were employed, and multi-objective optimization was carried out, to enhance the performance. A surrogate model, Response Surface Approximation (RSA), was constructed for each objective function based on the numerical solutions obtained at the specified design points. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with local search was used for multi-objective optimization. The Pareto-optimal solutions were obtained, and a trade-off analysis was performed between the two conflicting objectives in view of the design and flow constraints. It was observed that, by the process of multi-objective optimization, the total efficiency was enhanced and the torque reduced. The mechanisms of these performance improvements were elucidated by analysis of the Pareto-optimal solutions.     

新型行星混联式混合动力系统匹配与优化

以一种新型行星混联式混合动力系统作为研究对象,通过对电机、离合器与发动机的协调控制,可方便地实现高低速状态下的纯电驱动、发动机直驱、混合驱动及制动能量回收4种工作模式的切换。为了评估其燃油经济性和动力性,参照国内某品牌混合动力客车性能参数,对发动机、ISG电机、发电机和双行星排齿轮机构的基本性能参数进行匹配。通过集成了CRUISE与MATLAB的ISIGHT的优化平台,进行行星混联式混合动力总成匹配参数的优化。优化匹配仿真结果表明:在保证整车动力性的前提下,燃油经济性较优化前提高了19.3%。     

基于近似模型的高速列车头型多目标优化设计

为改善高速列车气动性能,建立一套高效的多目标气动优化设计方法,对流线型头型进行多目标气动优化设计。建立高速列车流线型头型三维参数化模型,并提取5个优化设计变量;为减少优化设计时间,利用最优拉丁超立方设计方法在优化设计空间中进行均匀采样,利用计算流体力学方法获得对应于各个采样点的气动载荷,利用Kriging代理模型构建优化设计变量和气动载荷之间的近似模型;利用多体系统动力学方法计算气动载荷作用下的高速列车轮重减载率;以气动阻力和轮重减载率为优化目标,利用多目标遗传算法NSGA-II对高速列车流线型头型进行多目标优化。优化设计变量和优化目标均呈现收敛的趋势,采用Kriging近似模型优化计算的Pareto前沿与采用CFD（Computational fluid dynamics,CFD）优化计算的Pareto前沿较为接近。优化后高速列车的气动阻力最多可降低3.27%,轮重减载率最多可降低1.44%,气动阻力最优的头型与轮重减载率最优的头型的主要差异在于中部辅助控制线的变化,前者向内凹,后者则向外凸。