混合动力汽车经济型巡航的车速规划策略

选取功率分流式混合动力汽车为对象,以燃油消耗最小为目标开展巡航场景下的经济车速规划研究. 结合车辆动能管理与等效燃油最小化策略（ECMS）,提出增强型等效燃油最小化策略（R-ECMS）. 运用极小值原理推导油电等效系数,建立动能与电能间的等效关系;结合电能与燃油之间的等效关系,将车辆动能变化和电能消耗统一转化成燃油消耗. 为了兼顾电池SOC平衡以及车辆通行速度,采取非支配排序遗传算法优化R-ECMS权重系数中的参数. 仿真结果表明,与传统能量管理策略ECMS相比,R-ECMS可以降低8.06%的燃油消耗. 与采用最优算法的动态规划策略相比,R-ECMS能在实现次优的优化效果的同时大幅降低计算时间. 同时,与ECMS相比,R-ECMS在其他仿真场景下能实现6.94%的节油率,具有较好的泛化性能和应用前景.     

Fuel efficient model predictive control strategies for a group of connected vehicles incorporating vertical vibration

This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of the suspension system is integrated with the longitudinal dynamics of the vehicle. Furthermore, a MPC framework with finite time horizon is formulated to calculate the optimal velocity profile that compromises fuel economy, mobility and ride comfort for every individual vehicle with the safety and physical constraints considered. In the MPC framework, the target velocity is calculated using signal phase and timing(SPAT)information to reduce the number of stoppage at red lights, and the vertical acceleration is calculated parallel to the calculation of the fuel consumption. The MPC optimal problem is solved with fast-MPC approach which enhances the computational efficiency via exploiting the structure of the control system and approximate methods. Simulation studies are conducted over different SPATs and connectivity penetration rates and the results validate the advantages of the proposed control architecture.     

Integrated energy-oriented cruising control of electric vehicle on highway with varying slopes considering battery aging

Eco-driving strategies for vehicles with conventional powertrains have been studied for years and attempt to reduce fuel consumption by optimizing the driving velocity profile. For electric vehicles(EVs) with regenerative braking, the speed profile with the best energy efficiency should be different from conventional vehicles. This paper proposes an energy-oriented cruising control strategy for EVs with a hierarchical structure to realize eco-cruising on highways with varying slopes. The upper layer plans the energy-optimized vehicle velocity, and the lower layer calculates the torque allocation between the front and rear axles.However, the resulting speed profile with varying velocity may cause a high charge and discharge rate of the battery, resulting in rapid battery fading. To extend the battery life, we make a tradeoff between the energy consumption and wear of the battery by formulating an optimal control problem, where driving comfort and travel time are also considered. An indirect optimal control method is implemented to derive the optimal control rule. As an extension, the control rule for avoiding rear-end collisions is presented and simulated for driving in the real world.     

基于工况识别的燃料电池公交车能量管理策略

为解决燃料电池混合动力公交车中基于优化的能量管理策略难以实车应用的问题,在分析燃料电池公交车(Fuel cell hybrid bus,FCHB)行驶路线的固定性和片段性的基础上,提出了一种基于SOM-K-means(Self-organized mapping K-means)工况识别的能量管理策略。首先,根据公交车站点将行驶路线划分为多个行驶片段,在车辆停站时,运用SOM-K-means二阶聚类模型完成工况识别,获取车辆下一行驶片段的识别协态变量;当车辆在下一个行驶片段运行时,运用识别协态变量完成基于庞特里亚金极值原理(Pontryagins maximum principle,PMP)求解的能量管理策略的实时应用。其次,建立基于公交车实际运行数据的仿真实验,最后建立硬件在环实验,将所提出的策略移植入整车控制器(Vehicle control unit,VCU)中进行实验。实验结果表明,与基于规则的能量管理策略相比,本研究提出的能量管理策略降低了19.77%的平均等效氢气消耗。且该策略在VCU中每一步的计算时间大约为30 ms,计算结果与仿真结果完全一致,满足车辆对能量管理策略的时效性和准确性的要求。     

一种提高氢气平均利用效率的整车控制策略

为提高燃料电池汽车的氢气平均利用效率,提出一种基于燃料电池老化状态的改进恒温器控制策略。利用最小二乘法拟合出燃料电池不同老化阶段的极化曲线;以提高氢气平均利用效率为目标,利用遗传算法不断地优化恒温器控制策略的规则参数;根据动力电池的电池荷电状态(SOC)与遗传算法优化出的规则参数确定燃料电池的输出功率大小,并利用采集到的800 h燃料电池汽车数据进行仿真。结果表明:在不同的循环工况下,提出的改进恒温器控制策略可使不同燃料电池老化阶段的氢气平均利用效率维持在较高的水平,与普通的恒温器控制策略相比,氢气平均利用效率最大可提高1.1%。根据燃料电池的老化状态和循环工况信息,及时调整恒温器的规则参数有利于提高整车氢气平均利用效率。     

城市传统车辆与飞轮节能车辆燃油经济性能分析与仿真

针对城市用车辆的行驶条件，对城市用传统车辆与带有飞轮节能动力传动系统的车辆的燃油经济性能进行分析与仿真，指出城市用传统车辆的燃油浪费，主要是由于车辆的频繁制动、发动机负荷不合理、长时间的发动机怠速运转及循环行驶过程中非最佳速度状态造成，消除或减少这些浪费必须更新或改进传统的动力传动系统，并对其动力传递规律进行控制。ＺＫ１４１Ａ公共汽车改进前后仿真结果表明，传统车辆的燃油浪费率和改进车辆的节油率随行驶循环次数以较大梯度递增，在中国城市公共汽车行驶条件下，传统车辆的燃油浪费率达５４．３％，改进车辆的节油率达３５．１％。     

Energy and environmental life-cycle assessment of passenger car electrification based on Beijing driving patterns

This paper examines the energy and environmental benefits within the whole life cycle shifting from traditional gasoline vehicles to electrified advanced vehicles under regional real-world driving behaviors.The advance vehicles focus on family passenger cars and include battery electric vehicles(BEVs),plug-in hybrid electric vehicles(PHEVs),and hybrid electric vehicles(HEVs).The GREET(greenhouse gases,regulated emissions,and energy use in transportation)model is adopted with regional circumstances modifications,especially the UF(utility factors)of PHEVs.The results show that the electrified vehicles offer great benefits concerning energy consumption,greenhouse gas(GHG)emissions as well as urban Particulate Matter 2.5(PM2.5)emissions.Compared to conventional gasoline vehicles,the life-cycle total energy reduction for advance vehicles is 51%to57%.There is little difference on energy reduction among the HEVs,PHEVs and BEVs,with the energy mix shifting from petroleum to coal for the stronger electrification.The reductions of GHG emissions are 57%for HEV,54%to 48%for PHEVs with 10 miles to 40 miles CD range,and 40%for BEV.The life-cycle and local PM2.5 emissions are discussed separately.The life-cycle PM2.5 emissions increase with vehicle electrification and reach a maximum for the BEV which are 5%higher than the conventional vehicle(CV).However,electric vehicles can shift PM2.5 emissions from vehicle operation to upstream operations and help mitigate PM2.5 emissions in urban areas.The local emissions of PHEVs and BEVs can be reduced by 37%to81%and 100%compared with CVs.     

Velocity forecasts using a combined deep learning model in hybrid electric vehicles with V2V and V2I communication

Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle's velocity time series. The main features of the model are to combine the feature extraction capability of deep restricted Boltzmann machines(DBM) and sequence pattern predicting capability of bidirectional long short-term memory(BLSTM). Hence, the model is named as DBMBLSTM. In addition, the DRMBLSTM model utilizes the vehicle driving information and roadside infrastructure information provided respectively through vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication channels to predict vehicle velocity at various length of prediction horizon. Furthermore, the predictions results of this study are compared with the state of the art of vehicle velocity forecasts. The root mean square error(RMSE) is used as an evaluation criteria of predictions accuracy. Finally,these compared prediction model are applied in model predictive control(MPC) energy management strategy for the verifications of fuel economy improvement of a HEV. Simulation results confirm that the proposed combined deep learning model performs better than other five prediction methods. Therefore, it is a means of arriving at a reliable forecast model for HEV.     

Longitudinal Control Strategy f or Vehicle Adaptive Cruise Control Systems

A new longitudinal control strategy for vehicle adaptive cruise control (ACC) systems is presented. The running relationship between the ACC vehicle and the detected target vehicle is described by the relative velocity and the deviation between the actual headway distance and the prescribed safety distance. Based on this, two state space models are built and the linear quadratic optimal control theory is used to yield desired velocity for the ACC-equipped vehicle when with the target vehicle detected. By switching among four control modes, the desired velocity profile is designed to deal with different running situations. A velocity controller, which includes a PID controller for throttle openness and a neural network controller for brake application, is developed to achieve the desired velocity profile. The proposed control strategy is applied to a non-linear vehicle model in a simulation environment and is shown to provide the ACC vehicle comfortable ride and satisfying safety.     

车辆对行人速度障碍自主避碰的驾驶方法

针对过街行人与智能车辆之间运动协调中的安全避碰问题,设计一种CVIS环境下的基于行人避碰的车辆驾驶控制器.结合速度障碍法的基本原理提出车辆对过街行人的避碰规则,在此基础上搭建模型预测控制框架,提出车辆对行人的自主避碰算法.综合考虑车辆驾驶的操作约束,以最小化车速变化及满足驾驶员操作舒服性要求为控制目标,在车辆对行人避碰的前提下优化车辆的驾驶策略.分别设置车辆直行避碰与允许换道避碰两种控制场景,在MATLAB环境下对车辆驾驶控制效果进行仿真实验.结果表明:车辆对不同情况的过街行人,能够通过加速或减速进行避碰;通过与七次多项式换道轨迹进行对比,自主避碰驾驶的安全性更高.     

微型燃气涡轮机增程式电动汽车设计

为提高纯电动轿车的适用性,弥补其续驶里程的缺陷,提出以微型燃气涡轮机作为增程器的增程式电动汽车开发方案.对主要动力总成及零部件进行分析,并根据分析确定整车参数,对主要零部件进行计算选型,在Advisor中对整车模型的可行性和燃油经济性进行了仿真验证.仿真结果表明:所提出的设计方案在纯电动模式下可以满足大部分人的需求,在增程模式下,平均每百公里等价油耗为2.02L.该设计方案提升车辆的燃料适应性,同时充分利用了电网能量,相比于传统车,燃油经济性也有很大提升.     

增程式燃料电池车经济性与耐久性优化控制策略

为了避免车用燃料电池由于启停变化、功率波动等状态而导致的性能衰退,在能量管理控制策略的开发过程中需要在保证经济性的同时兼顾燃料电池的耐久性。针对该目标,建立了基于改进动态规划算法的增程式燃料电池车经济性与耐久性优化控制策略,将燃料电池的启停状态设为状态变量,在燃料电池的启动和关闭状态之间增加怠速过渡阶段,实现了燃料电池的自适应启停间隔控制;并使用燃料电池性能衰退指数为耐久性代价,整车能耗为经济性代价,构建了经济性与耐久性联合代价函数。进行仿真并与经典动态规划策略对比,结果表明所建立策略的整车能耗比经典动态规划策略升高5.3%,燃料电池的输出功率稳定,启停变化较少,性能衰退程度比经典动态规划策略改善65.5%,有效保证了整车经济性与燃料电池的耐久性。     

系统效率最优的功率分流式混合动力汽车非线性预测控制

针对一种基于双行星排构型的功率分流式混合动力汽车,建立系统动态模型,准确描述其转速转矩耦合关系,通过建立各部件的效率模型,分析不同模式下系统的工作效率. 设计控制器结构框架,以系统工作效率和电池充放电平衡为目标,构建基于模型预测控制的优化问题,采用一步马尔科夫链模型预测驾驶员需求转矩及车速,将有限时域内的优化问题转化为非线性规划问题,基于序列二次规划算法实现优化求解. 仿真研究表明,基于系统效率最优的预测控制器能够维持电池的充放电平衡,在美国城市驾驶循环（UDDS）下,当电池初始电池荷电状态（SOC）分别为0.50、0.55和0.60时,相较于以发动机燃油消耗最优为目标,车辆等效燃油经济性分别提高了7.17%、5.73%和10.11%,验证了控制器的有效性和优越性.     

前后轴独立驱动的增程式电动汽车整车控制策略

纯电动汽车因续驶里程短和充电速度慢等原因制约了其进一步发展,增程式电动汽车在保证较低燃油消耗率的前提下弥补了纯电动汽车的缺陷。本文以前后轴独立驱动的增程式电动汽车为例,以保证整车的动力性能和降低燃油消耗率为出发点,提出了前后轴独立驱动增程式电动汽车的整车逻辑门限控制策略,该策略控制发动机的工作点随整车需求功率的变化而工作在不同的最优燃油消耗点上。仿真分析可知发动机在不同工况下均可以工作在低燃油消耗点附近,这表明该控制策略可实现对整车燃油经济性的良好控制。     

全时四轮驱动汽车驱动轮牵引力综合控制策略

提出了全时四轮驱动汽车驱动轮牵引力综合控制策略。建立了全时四轮驱动汽车驱动动力学模型,应用MATLAB/Simulink设计了驱动轮牵引力控制软件在环仿真系统,并对四轮驱动越野汽车特种工况进行了软件在环仿真模拟。仿真结果表明:设计的控制策略可有效消除驱动轮过度滑转现象,明显改善车辆在对接路面和单轮在低附着系数路面上的牵引性能。     

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus (PHEB). Firstly, the principal component analysis (PCA) and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle, congestion driving cycle, urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly, an improved particle swarm optimization (IPSO) algorithm is proposed, and is used to optimize the control parameters of PHEB under different driving cycles, respectively. Then, the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally, for an actual running vehicle, the driving condition is identified by relevance vector machine (RVM), and the corresponding control parameters are selected to control the vehicle. The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy, and the feasibility of the variable parameter self-adaptive control strategy is verified.     

动态称重中动态轮胎力的特性

动态轮胎力是影响汽车动态称重的重要因素.基于1/4车辆模型推导了动态轮胎力方程,依据路面不平度模型,仿真计算了动态轮胎力的频率特性.考察了路面、速度、载重、轮胎和悬架等因素对动态轮胎力的影响程度,为动态称重系统的设计和称重信号的处理提供依据.     

ECVT型混合动力城市客车动力系统设计与验证

针对传统客车集成启动/发电机一体化(integrated starter/generator, ISG)混合动力系统节油效果不理想的问题,选用电控无极自动变速(electronic continuously variable transmission, ECVT)混合动力系统方案,在插电式混合动力城市客车上应用并验证。在单行星排运动学特性的等效杠杆分析基础上,对某插电式ECVT动力系统关键部件进行参数匹配与计算;根据标定试验数据,采用Matlab/Simulink软件建立发动机仿真模型、驱动电机仿真模型和发电机仿真模型,搭建整车仿真模型;在中国典型城市公交循环工况下,研究目标车型的燃油经济性、动力性、纯电最大续航特性,完成经济性、动力性等道路测试试验。结果表明,本研究设计的ECVT混合动力系统汽车相较于传统汽车节油率达到57.47%,相较于ISG混合动力系统汽车节油率提高了24.12%。因此,插电式混合动力城市客车采用ECVT混合动力系统方案是可行有效的,且节油效果明显。     

需求侧规模化电动汽车的充电负荷优化调控策略

针对电力需求侧电动汽车(electric vehicles, EV)用户充电行为的不确定性,分析电动汽车充电负荷调度与控制技术,构建电动汽车充电负荷调控优化模型,提出考虑分时电价与削峰填谷的分层多目标电动汽车充电策略,并对某区域内的1 000辆电动汽车的充电场景进行调控仿真试验。结果表明,本研究提出的优化充电策略能够有效降低充电费用和充电功率峰值,验证了所提出的优化调控策略的有效性。     

并联型气动燃油混合动力汽车控制策略

针对气动燃油混合动力汽车2种动力源的协调控制问题,在阐述了该类型车的控制策略的开发原则的基础上,提出了用于切换工作模式的门限值的控制策略,应用非支配遗传多目标优化算法获取了混合工作模式下的转矩分配比的脉谱（MAP）图,实现了混合工作模式的转矩实时优化分配.通过在ADVISOR软件基础上开发的气动燃油混合动力整车模型进行的仿真分析验证了所提出的控制策略.结果表明,控制策略能够在满足气动燃油混合动力车驱动性能的前提下,实现混合动力各种工作模式的正确切换,完成气动发动机耗气量和内燃机油耗、排放的优化控制.