电动汽车排放的改进差分进化算法

文章针对增程式电动汽车增程器燃油经济性和CO、HC排放问题进行优化。首先,将问题利用归一化和加权平均的方法转化为单一目标问题,以增程器外特性、功率区间需求和其他特性参数作为约束条件,利用MATLAB软件基于map图进行建模和仿真,使用改进的差分进化算法予以实现。最后,在AVL Puma Open台架实验平台上对HWFET工况下的增程器燃油经济性和CO、HC排放运用文章所提出的策略进行实验验证。实验结果表明,以增程器的燃油经济性和CO、HC排放为目标,可以实时精确地控制发动机转速、发电机转矩,有效实现降低整车油耗和排放。     

基于多目标优化模型的电动汽车增程器油耗及排放优化

针对增程式电动汽车油耗和排放优化问题,首先综合考虑增程器的油-电转换效率特性、HC排放特性、CO排放特性以及NOx排放特性,构造了增程器油耗和排放多目标优化模型,同时结合实际增程器工作中的机械和电气约束特征,分析了多目标优化模型的3种转速、转矩约束条件.然后采用多目标粒子群算法和加权尺度法对增程器油耗和排放多目标优化模型进行了离线优化,得出了增程器的最优全局工作点和各功率值下的多目标最优工作曲线.最后,采用NEDC,FTP和HWFET3种测试工况在AVL Puma Open发动机测试台架上进行了实验,并和基于最佳制动燃油消耗率(BSFC)的油耗单目标优化模型进行了比较.结果表明,本文提出的方法能够以微弱的油耗增加为代价,有效的改善整车的HC,CO和NOx排放.     

萤火虫算法的电动汽车综合成本运行优化研究

为实现节能减排,文章以增程式电动汽车为研究对象,提出了一种基于动态综合成本的增程器运行优化方法。首先以增程器发动机外特性为研究基础,根据实际工作状况分别建立了发动机燃油消耗率及CO排放率模型,再通过归一化后将多个目标加权求和的方法建立电动汽车综合成本运行优化模型。模型建立后,在全局优化及特定功率优化这两种常见模式下以萤火虫算法进行寻优,最后在不同的权重条件下得出最佳综合成本运行曲线。实验结果表明,文章提出的方法能够在不同的运行环境下通过动态调整权重值,实现基于燃油消耗率及CO排放的综合成本运行优化。     

一种改进的无导数共轭梯度法

本文基于共轭梯度法的子空间研究,针对无约束优化问题提出了一种改进的无导数共轭梯度法。新算法不仅能有效弥补经典共轭梯度法要求线搜索为精确搜索的局限性,而且可适用于导数信息不易求得甚至完全不可得的问题。实验结果表明:相比于一次多项式插值法、有限差商共轭梯度法以及有限差商拟牛顿法,新算法的效率有很大的提高。     

增程式电动汽车自校正变结构模糊控制策略

针对增程式纯电动汽车的结构,为了提高燃油效率保证燃油经济性、限制充放电电流、延长电池使用寿命,提出了一种基于自校正变结构模糊的增程器控制策略。综合考虑电池电量SOC及其变化率、驱动电机功率需求,通过模糊控制调整增程器的输出功率进行能量分配。首先根据SOC值与驱动电机功率需求设计多输入单输出模糊控制器,输出量为增程器的功率;其次根据SOC值将电池状态分为充电模式与放电模式,对不同模式制定不同的模糊控制规则,进行模糊控制器的变结构设计;再次根据SOC值的变化率进行自校正设计,通过限制SOC变化率实现对电池充放电电流的限制,达到对电池的保护功能;最后通过Cruise仿真软件和台架测试对该控制策略进行仿真验证,结果表明燃油经济性以及电池寿命均得到有效提升。     

基于观测器的发动机转矩跟踪模型预测控制

针对如何实现发动机转矩快速精准地跟踪期望转矩的问题,提出一种基于观测器的模型预测控制策略.首先,利用均值模型对汽油发动机的进气歧管压力动态、转矩和转速动态进行建模,考虑到发动机真实转矩不可测的情况,采用Lyapunov稳定性理论和可测转速信号设计观测器对进气歧管压力进行在线估计,进而获得发动机的实时估计转矩;然后,利用基于观测器的模型预测控制算法设计转矩跟踪控制器,通过C/GMRES数值优化算法在线求解滚动时域优化问题,实现转矩的实时跟踪控制;最后,利用汽油发动机实验台进行实验验证以表明所提出算法的有效性.     

基于模糊逻辑NSGA-Ⅲ的开关磁阻发电机多目标优化算法

为了减小开关磁阻发电机S RG转矩脉动,提高发电机系统运行效率及发电机功率密度,提出一种基于模糊逻辑NSGA-Ⅲ的开关磁阻发电机多目标优化算法.搭建1 kW四相8/6极SRG多目标优化设计模型;采用响应面法RSM搭建SRG优化目标的响应面模型;基于模糊逻辑搭建了模糊推理系统,完成了在SRG优化过程中对NSGA-Ⅲ算法内...     

增程式电动汽车动力总成参数匹配设计

增程式电动汽车作为一种新型节能环保型汽车,是传统内燃机汽车向纯电动汽车过渡的一种车型;根据动力传动系统的设计原则和设计目标,基于铃木奥拓燃油汽车改装设计了增程式电动汽车的动力传动系统的参数匹配方法,根据电动汽车基本参数及其控制目标,对动力总成系统的关键零部件进行了匹配设计;匹配结果表明:电动机的额度电压为60V,最高转速为4 000r/min,额定功率为8kW,额定转矩为40.43N·m,峰值功率为11kW,最大扭矩为80.86N·m;增程器采用马勒增程器和两缸直列四冲程发动机;动力电池组采用磷酸铁锂子电池,单组额定容量为80Ah、总电压为96V;对于指导增程式电动汽车的开发、提高汽车性能和安全性,以及对于电动汽车底盘集成控制系统的开发都具有重要的工程应用意义。     

基于虚拟仪器的风力发电机效率测控系统设计

为提高垂直轴风力发电机风能利用率,在变风速情况下的发电机能量转移分析的基础上,提出一种发电机能量流向主动控制的方法.通过在发电机和负载之间添加受控能量调节网络,实现发电机在不同风速下能量的受控流动,从而优化提高变风速下风力发电效率.基于该方法,设计了一套基于虚拟仪器的风力发电机效率测控系统.系统由垂直轴风力发电机、谐振...     

发动机燃油分布器性能异常仿真分析

关于优化燃油分布器控制性能,燃油分布器在发动机台架试车中出现计量活门前后油压压差摆动,燃油流量衰减,产生发动机掉转故障.为找出故障原因所在并提出故障解决方案,以流量连续方程及力平衡方程为基础,利用基于键合图的液压机械系统建模仿真及动力学分析软件AMESim,采用图形化物理建模方式建立系统模型.从系统级的仿真研究人手,再现故障特性,找出了故障的原因.对改进燃油分布器性能进行仿真,结果表明,上述改进方案解决了故障,保证了发动机的正常工作.仿真研究还为燃油分布器的设计与改进提供了依据.     

插电式混合动力汽车车速预测及整车控制策略

本文针对插电式混合动力汽车(plug-in hybrid electric vehicle,PHEV)这一典型混杂系统,提出了一种基于车速预测的混合逻辑动态(mixed logical dynamical,MLD)模型预测控制策略.首先,通过对发动机和电动机能量消耗模型进行线性化,建立双轴并联插电式混合动力城市公交车的动力传动系统数学模型;其次,运用模糊推理进行驾驶意图分析,提出基于驾驶意图识别和历史车速数据相结合的非线性自回归(nonlinear auto-regressive models,NAR)神经网络车速预测方法进行未来行驶工况预测.然后,以最小等效燃油消耗为目标建立PHEV的混合逻辑动态模型,运用预测控制思想对车速预测时域内最优电机转矩控制序列进行求解.最后,通过仿真实验验证了本文所提出控制策略在特定的循环工况下与电动助力策略相比,能够提高燃油经济性.     

Predictive planning of optimal velocity and state of charge trajectories for hybrid electric vehicles

The combination of electric motors and internal combustion engines in hybrid electric vehicles (HEV) can considerably improve the fuel efficiency compared to conventional vehicles. In order to use its full potential, a predictive intelligent control system using information about impending driving situations has to be developed, to determine the optimal gear shifting strategy and the torque split between the combustion engine and the electric motor. To further increase fuel efficiency, the vehicle velocity can be used as an additional degree of freedom and the development of a predictive algorithm calculating good choices for all degrees of freedom over time is necessary.In this paper, an optimization-based algorithm for combined energy management and economic driving over a limited horizon is proposed. The results are compared with results from an offline calculation, which determine the overall fuel savings potential through the use of a discrete dynamic programming algorithm.     

无人飞行器纵向剖面轨迹优化

对飞行管理系统的纵向剖面轨迹优化功能进行了研究.以固定距离最省油为优化指标,用能量法动态地建立了3阶段轨迹优化模型.区别于固定推力只对速度寻优的传统的模型求解方法,把发动机推力和速度同时作为寻优变量,并结合无人飞行器飞行的物理过程,将3阶段轨迹优化模型进一步变换成非线性规划问题,利用再开始FR(Fletcher-Revees)共轭梯度法进行求解.最后以某型无人飞行器为例进行仿真验证,结果表明将发动机推力设为变量比推力固定求得的纵向剖面最优轨迹更省油,对节省燃油降低经济成本有一定的实用参考价值.     

Torque control strategy with V2X information for HEVs tominimize fuel consumption

Today, much information from traffic infrastructures and sensors of ego vehicle is available. Using such information has a potential for internal combustion engine vehicle to reduce fuel consumption in real world. In this paper, a powertrain controller for a hybrid electric vehicle aiming to reduce fuel consumption is introduced, which uses information from traffic signals, the global positioning system and sensors, and the preceding vehicle. This study was carried out as a benchmark problem of engine and powertrain control simulation and modeling 2021 (E-COSM 2021). The developed controller firstly decides reference acceleration of the ego vehicle using the traffic signal and the position information and the preceding vehicle speed. The acceleration and deceleration leading to increase in unnecessary fuel consumption is avoided. Next, the reference engine, generator, and motor torques are decided to achieve the reference acceleration and minimize fuel consumption. In addition, the reference engine, generator and motor torques were decided by the given fuel consumption map for the engine, and by the virtual fuel consumption maps for the generator and the motor. The virtual fuel consumption is derived from the efficiency maps of the generator and the motor using a given equivalent factor, which converts electricity consumption to fuel for the generator and the motor. In this study, a controller was designed through the benchmark problem of E-COSM 2021 for minimizing total fuel consumption of the engine, the generator, and the motor. The developed controller was evaluated in driving simulations. The result shows that operating the powertrain in efficient area is a key factor in reducing total fuel consumption.     

Speed planning and energymanagement strategy of hybrid electric vehicles in a car-following scenario

The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles. First, to achieve car-following in a connected environment while reducing vehicle fuel consumption, a power split hybrid electric vehicle was used as the research object, and a mathematical model including engine, motor, generator, battery and vehicle longitudinal dynamics is established. Second, with the goal of vehicle energy saving, a layered optimization framework for hybrid electric vehicles in a networked environment is proposed. The speed planning problem is established in the upper-level controller, and the optimized speed of the vehicle is obtained and input to the lower-level controller. Furthermore, after the lower-level controller reaches the optimized speed, it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy. The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy.     

基于深度强化学习的网联车辆队列纵向控制

针对车辆队列中多目标控制优化问题,研究基于强化学习的车辆队列控制方法.控制器输入为队列各车辆状态信息以及车辆间状态误差,输出为基于车辆纵向动力学的期望加速度,实现在V2X通信下的队列单车稳定行驶和队列稳定行驶.根据队列行驶场景以及采用的间距策略、通信拓扑结构等特性,建立队列马尔科夫决策过程(Markov decision process,MDP)模型.同时根据队列多输入-多输出高维样本特性,引入优先经验回放策略,提高算法收敛效率.为贴近实际车辆队列行驶工况,仿真基于PreScan构建多自由度燃油车动力学模型,联合Matlab/ Simulink搭建仿真环境,同时引入噪声对队列控制器中动作网络和评价网络进行训练.仿真结果表明基于强化学习的车辆队列控制燃油消耗更低,且控制器实时性更高,对车辆的控制更为平滑.     

Optimization of fuel consumption and NOx emission for mild HEV via hierarchical model predictive control

In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NOx emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research.     

Closed-loop diesel engine combustion phasing control based on crankshaft torque measurements

Methods for closed-loop combustion phasing control in a diesel engine, based on measurements of crankshaft torque, are developed and evaluated. A model-based method for estimation of cylinder individual torque contributions from the crankshaft torque measurements is explained and a novel approach for identification of crankshaft dynamics is proposed. The use of the combustion net torque concept for combustion phasing estimation in the torque domain is also described. Two different control schemes, one for individual cylinder control and one for average cylinder control, are studied. The proposed methods are experimentally evaluated using a light-duty diesel engine equipped with a crankshaft integrated torque sensor. The results indicate that it is possible to estimate and control on a cylinder individual basis using the measurements from the crankshaft torque sensor. Combustion phasing is estimated with bias levels of less than 0.5 crank angle degrees (CAD) and cycle-to-cycle standard deviations of less than 0.7 CAD for all cylinders and the implemented combustion phasing controllers manage to accurately counteract disturbances in both fuel injection timing and EGR fraction.