基于遗传算法的ISG混合动力汽车参数优化

利用仿真软件CRUISE建立某一轻度混合动力汽车的仿真模型。以加速时间和100km油耗为优化目标,采用带精英策略的快速非支配排序遗传算法对其动力传动系统的速比参数进行优化。结果表明,优化后在满足汽车动力性设计指标的前提下,汽车在NEDC工况下的油耗降低了7.05%。     

并联混合动力汽车自适应控制策略

提出了以电池电量平衡为目标的基于行驶循环识别的并联混合动力汽车自适应控制策略.其核心是一个负责发动机和电机之间转矩分配的两参数模糊控制单元.自适应调整模块由行驶循环识别和参数调整两部分组成,后者的目标是维持混合动力汽车在行驶过程中的电量平衡.这样,既可使电池尽可能保持在高效率区间内工作,又能合理控制电池的充放电,提高整车燃油经济性.仿真结果表明,对于多数行驶循环,该控制策略能够明显地提高混合动力汽车的燃油经济性.     

基于遗传算法的串联混合动力汽车参数优化

控制策略参数优化是提高混合动力汽车燃油经济性和降低排放的关键,这类优化问题涉及多个相互冲突目标,而且属于非线性约束问题。文中采用遗传算法,基于MATLAB编程,调用ADVISOR对混合动力汽车参数进行优化,结果表明,该方法可找到多组可行解,在保证车辆动力性前提下提高燃油经济性和降低排放。     

功率分流混合动力汽车参数匹配与优化研究

在分析了功率分流混合动力汽车动力输出特性与系统匹配参数关系的基础上,提出了汽车参数匹配与优化的方法与流程,建立了多目标优化模型,并对某型混合动力汽车进行了参数匹配与优化,得到了合理的匹配结果,验证了该方法的有效性。     

混合动力汽车复合电源参数优化与试验研究

提出了混合动力汽车用复合电源系统的构型及其控制策略,分析确定了满足整车动力性能要求和工程约束下复合电源参数的范围.以动力性指标为约束,能耗最小、电源成本最低和电池峰值电流最小为目标函数,采用NSGA-Ⅱ算法对复合电源参数进行了多目标优化.仿真结果验证了优化的有效性.对复合电源和单一电池的功能样机进行了循环工况试验.结果...     

Simulation of HEV Multi-objective Optimization Based on Variable Logic Threshold

为实现混合动力汽车的动力性、燃油经济性以及模式切换平顺性,以基于CVT的插电式混合动力汽车为研究对象,针对混合动力汽车的多目标优化问题,提出一种可变逻辑门限控制策略.整车控制过程中根据功率需求进行模式切换,其中行驶需求功率根据车速和节气门开度计算得到,切换门限根据整车参数实时计算.以发动机和电机的输出特性、电池的SOC为主要依据计算工作模式切换的可变逻揖门限值.通过电机助力保证整车具有较好的动力性能,通过不同模式下驱动功率的一致性实现模式切换平顺,通过小功率需求时仅可能多用纯电动来提高燃油经济性.在MATLAB/Simulink平台下建立混合动力汽车前向仿真模型,并在NEDC工况下进行仿真.仿真结果表明:整车动力性能得到了提高,实现了模式切换平顺性,节油40％.     

ISG型混合动力汽车粒子群优化模糊控制研究

针对ISG型混合动力汽车能量分配的控制过程,应用传统的模糊控制存在精度不高、自适应能力有限等问题.提出一种粒子群优化模糊控制的方法.在应用传统模糊逻辑建立控制模型基础上,利用粒子群算法对模糊控制中的隶属度函数进行优化,实现了优化的隶属度函数随环境变化以及负载变化实时跟踪模糊控制器的参数变化.仿真结果表明,与Insigh...     

汽车悬架优化设计中的近似模型方法及其应用

针对目前汽车悬架设计中存在的问题,提出了一种基于近似模型和遗传算法的高效全局优化设计方法.使用Kriging方法重构目标函数,建立了悬架运动学分析的近似模型;采用了CVT试验设计以确保参数空间中样本点分布的均匀性;在重构出的目标函数基础上采用遗传算法进行寻优.以双横臂式前独立悬架为例,以车轮接地点侧向最大滑移量为优化目标进行了优化设计.结果表明,采用该设计方法可缩短设计周期及降低设计成本.     

装备CVT的中度混合动力轿车驱动工况下的能量优化策略

针对装备CVT的混合动力汽车,提出了一种以混合动力系统效率最高为优化目标,以车速、加速踏板行程和电池SOC为状态变量,以电机转矩和CVT速比为控制变量的中度混合动力汽车能量优化策略。该策略综合考虑了驾驶员的实际操作和驱动需求以及各个关键部件的效率,确定了驱动工况各工作模式下的最优电机转矩和最优CVT速比,保证了混合动力系统的效率最高。采用自行搭建的前向仿真模型对所提出的能量优化策略进行了验证,结果表明:在NEDC循环工况下该车等效100km油耗比原型车降低了26.4%。     

考虑电驱动系统成本的混合动力汽车参数综合优化

提出了一种并联式混合动力汽车(HEV)参数综合优化算法,以解决其能量管理与动力系统匹配经常各自独立进行的问题。该方法考虑电驱动系统成本,用改进型模糊能量管理策略,以能量管理策略参数、动力系统匹配参数为决策变量,以等效综合油耗、电机与电池组总成本为目标函数,在ADVISOR仿真环境下,用多目标遗传算法优化求解。结果表明:在保证整车动力性的前提下优化后,等效油耗降低23.0%,电机和电池组总成本降低41.9%;一氧化碳CO的100 km排放质量降低10.8%,碳氢化合物HC的排放降低22.2%,氮氧化物NOx的排放降低27.0%,改善了发动机效率与电机效率;验证了该方法的有效性。     

基于捕食搜索策略的混合动力汽车参数优化

混合动力汽车模型是一个较复杂的非线性系统,且设计参数较多,为一种处理燃油经济性和排放的多目标问题。文章以一辆实例样车的动力系统和逻辑门限值控制策略为例,分析并建立了以动力性能为控制约束,以最小化油耗和排放为控制目标的非线性规划模型。采用捕食搜索遗传算法,对模型进行了仿真。结果表明,该方法相对于简单遗传算法更能有效地改善车辆燃油经济性和排放。     

Plug-in HEV with CVT: configuration,control, and its concurrent multi-objective optimization by evolutionary algorithm

Plug-in Hybrid Electric Vehicle (Plug-in HEV) has dramatic improvements in fuel economy and emission reduction. It is most important to decide its optimal configuration, energy management strategy, powertrain sizes, and control logic parameters. For multi-objective optimization, we present a concurrent optimization methodology based on an optimal Plug-in HEV powertrain configuration with continuous variable transmission (CVT). The novelty is using evolutionary algorithm in conjunction with an instantaneous optimal energy management strategy. Simulation results indicate the proposed method can significantly reduce fuel consumption and emissions by simultaneously optimizing the propulsion system parameters as well as the energy control parameters.     

Optimization of power management among an engine,battery and ultra-capacitor for a series HEV: A dynamic programming application

In a hybrid electric vehicle (HEV) system, it is an important issue on how to distribute the output power from multiple power generating components to operate a vehicle more efficiently. Many studies have been conducted on how to manage multiple power sources of a vehicle based on various optimization theories. In this study, an algorithm to calculate the optimization of a series HEV that has three power generating components, engine, battery and ultra-capacitor, is developed based on dynamic programming. Normally dynamic programming is applied to the optimization of power management and components sizing by estimating potential fuel economy for electrified vehicle such as HEV, Plug-in HEV or Fuelcell HEV. In contrast with most objective systems that have only two power generating components, the system in this study has three power sources. Since the system has three power sources, the number of state and control variables of optimization problem increases. Therefore the number of calculations increases unreasonably. To decrease the number and time of calculations, a new electric model that contains the both characteristics of battery and ultra-capacitor is developed with some assumptions. In comparison with the optimization algorithm which follows the theory of DP with no assumptions, the results from the newly developed algorithm has 1.04 % discrepancy in terms of fuel economy, even though the calculation time decreases to 4400 times less.     

Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles

This paper presents the optimization of key component sizes and control strategy for parallel hybrid electric vehicles (parallel HEVs) using the bees algorithm (BA). The BA is an intelligent optimization tool that mimics the food foraging behavior of honey bees. Parallel HEV configuration and electric assist control strategy were used to conduct the research. The values of the key component size and the control strategy parameters were adjusted according to the BA to minimize the weighted sum of fuel consumption (FC) and emissions, while the vehicle performance satisfies the PNGV constraints. In this research, the software ADVISOR was used as the simulation tool, and the driving cycles FTP, ECE-EUDC and UDDS were employed to evaluate FC, emission and dynamic performance. The results demonstrate that the BA is a powerful tool in parallel HEV optimization to determine the optimal parameters of component sizes and control strategy, resulting in the improvement of FC and emissions without sacrificing vehicle performance. In addition, the BA is able to define a global solution with a high rate of convergence.     

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.     

混合动力汽车及其动力匹配策略

为应对石油危机及大气污染,混合动力汽车（HEV）成为汽车行业的研究重点。从动力来源、动力混合程度及动力系统布置及组合方式3方面对HEV进行分类,介绍了串联式、并联式及混联式HEV的结构形式及优缺点。将当前的控制策略归为基于规则的控制策略和基于系统优化的控制策略,指出当前的控制策略无法自适应复杂路况的需求,不够智能,不利于整车性能提高。提出兼顾整车性能的多智能体集成控制是未来HEV控制策略的研究重点。     

并联式混合动力电动汽车电池参数优选

通过研究双轴并联混合动力电动汽车控制策略,分析电池参数和整车油耗的关系,确定电池电压、容量和最大充放电功率的变化范围。基于CRUISE的仿真平台,以整车循环工况油耗最省为目的,优选电池的各个参数。并将选定的电池参数代入模型中,进行动力性分析计算。计算结果表明,在满足整车动力性的要求下,通过对电池参数的优化,可提高混合动力电动汽车的燃油经济性和动力电池组的性价比。     

混合动力电动汽车多能源动力总成优化研究

研究并提出了串联式混合动力电动汽车的发动机、发电机、电动机、蓄电池等多能源动力总成系统的优化方法及评价指标,建立了优化数学模型,并与传统的优化方法进行了对比。分析表明,所提出的优化方法是可行的,可作为制定混合动力电动汽车控制策略的依据。     

ISG型中度混合动力AMT汽车换挡综合控制

在对ISG型中度混合动力AMT汽车系统效率进行优化的基础上,提出整车工作模式切换控制策略.以各工作模式下系统效率最高为目标建立ISG型中度混合动力AMT系统的经济性换挡规律.针对不同工作模式的运行特点,分别采用ISG电机或电子节气门参与动力源转速/转矩调节控制以优化AMT换挡品质.采用Matlab/Simulink仿真...     

混合动力电动汽车模糊逻辑控制策略的研究与仿真

以四川汽车工业集团野马混合动力电动汽车设计要求为基础,提出了一种混合动力电动汽车模糊逻辑控制策略。这种策略通过对油耗和各排放参数动态地分配权重值确定出发动机的最佳转矩,然后再根据模糊控制原理,以电池SOC值、汽车驱动需求的输出转矩和电动机转速为模糊输入确定出发动机的实际输出转矩,最终实现整车油耗和排放的综合优化。通过在S imu link软件中搭建该控制策略的仿真模型并与基础的电力辅助控制策略相比较,证明了这种控制策略有利于整车运行经济性和环保性的提高。