混合动力电动汽车能量管理策略研究综述

能量管理对于提高混合动力电动汽车(Hybrid electric vehicles, HEVs)的燃油经济性、驾驶性能及减少排放具有至关重要的作用.本文对混合动力电动汽车能量管理问题的研究进展及现状进行了全面总结, 从不同角度对混合动力电动汽车的能量管理问题进行描述, 并对主要能量管理策略进行了分析和对比研究, 指出各种控制方法的优点及其存在的问题与不足, 最后对混合动力电动汽车能量管理策略研究的未来发展方向进行了展望.     

混合动力电动汽车能量管理控制策略及仿真

研究电动汽车能量管理优化控制问题,混合动力电动汽车(HEV)能量管理系统,由于动力效率决定于发动机性能控制。针对传统方法燃油利用率低,车辆驾驶控制方式影响了优化。为了提高能源优化效率及优化驾驶控制,提出了一种燃油经济性和驾驶性能全局优化的能量管理控制策略。首先在系统中加入驾驶性能变量,并在代价函数中加入驾驶性能限制,然后把HEV能量管理问题建模为多步决策过程问题,运用动态规划(DP)原理,得到了全局优化的能量管理控制器。将该控制器模型嵌入高级车辆仿真器ADVISOR的并联车辆模型中,与传统规则的控制策略进行仿真对比。仿真结果表明,新的控制策略使燃油经济性提高了约16%,并且使驾驶性能控制在良好的范围内,能有效提高HEV能量管理的效率和实用性,为优化设计提供了依据。     

车速动态特性优化在HEV多能源动力总成系统控制中的运用

根据车辆控制工程理论,建立混合动力电动汽车（HEV）动力驱动模型及汽车闲环控制系统仿真模型,并以此进行计算机动态仿真.通过对设定不同汽车车速以及控制器参数下汽车实际车速响应情况以及响应与系统输入力矩之间关系的分析,发现现有混合动力电动汽车控制策略难以在保证汽车良好动态特性的基础上实现环保与节能.为解决上述矛盾,提出车速动态特性优化在HEV多能源动力总成系统控制中的运用,并论述其原理及实现方法.比较在两种控制策略下特定工况段的发动机油耗,说明车速动态特性优化的现实意义.     

基于DSP的混合动力车辆综合控制系统设计

混合动力车辆已经成为汽车技术研究的热点,而总线通讯技术和分布式控制网络在汽车电子领域应用日益广泛。为实现混合动力车辆的能量管理和运动控制,设计开发了基于TI公司TMS320LF2407型号DSP的综合控制系统。在系统拓扑结构设计的基础上,实现了DSP片外存储单元的扩展,实现了通过CAN总线与整车分布式控制网络的通讯。实车实验表明,该综合控制系统能够实现对混合动力车辆实时性的驱动控制,硬件系统工作可靠,软件系统具有移植性;CAN总线通讯可以满足车辆实时性控制的要求。     

混合电动汽车再生制动能量控制系统设计

混合电动汽车是目前技术条件下解决能源危机和环境保护的最佳汽车技术解决方案,而再生制动是电动汽车独有的节能环保技术;利用自主开发的CAN总线智能模块和嵌入式微计算机主控模块,组成了混合动力电动汽车的再生制动能量回收控制系统,运用相应的控制策略设计能量系统控制程序,实现了电动汽车上再生制动能量控制;经过上车的实际使用表明,控制系统运行良好,并能大大提高能量利用率,配备该系统后可比未配置该系统前节能达20％以上。     

基于队列行驶的混合动力汽车节能预测控制方法研究

针对传统混合动力汽车控制方法不考虑队列行驶对车辆能量管理影响的问题,本文提出了基于队列行驶的混合动力汽车节能预测控制智能优化策略。通过建立混合动力汽车系统的降阶模型,并采用连续广义最小残量方法求解模型预测控制问题。运用计算机进行仿真,仿真结果验证了系统模型的有效性,以及所设计的模型预测控制算法大幅度提高混合动力汽车的燃油经济性的能力和实时控制性能。     

混合动力汽车辅助动力控制器设计与实现

本控制器针对串联式混合动力汽车进行设计,根据整车工作状态及工作模式对辅助动力单元进行控制,以调节辅助动力单元工作状态,协调混合动力电动汽车能量分配。整车采用串联式混合动力结构,由蓄电池组和发电机组提供能量,以三相异步电机构成驱动系统。本控制器实现了基于TMS320F2812的辅助动力单元控制器的设计。采用了由发电机组的放电电流和发动机转速构成的双闭环控制结构,在实际应用中使辅助动力单元的能量输出达到了良好的控制效果。     

UA302/H数据卡在辅助混合动力汽车实验中的应用

本文辅助混合动力电动汽车,是指在电动汽车上配置小型汽油机和发电机组成发电机组,用以带动空调压缩机等电气系统,将蓄电池的能量全部用于驱动汽车.本文应用UA302/H数据采集卡,对电动汽车蓄电池性能的道路试验数据进行了采集分析处理,准确高效的测定出辅助混合动力能有效地缓解电池电压下降过快,延长电动汽车续驶里程的结论.     

计算机控制汽车电池实验系统

动力电池管理是实现混合动力电动汽车的节能与环保目标的关键技术之一,重点研究动力电池电容量实时计量方法和电池均衡充放电控制方法.提出基于电功率的电池组充放电均衡控制策略,以HEV动力电池组为对象,研制了基于电功率的动力电池均衡控制实验系统.     

电动汽车动力学控制研究进展

近年来随着全球资源、环境问题日益严峻,节能、环保的电动汽车得到快速发展。电动汽车采用电机驱动系统,具有转矩快速响应、易于精确测量、可实现动力分散控制、可实现制动能量回收等优点。充分挖掘并利用这些优点可显著提升车辆动力学控制性能。文中从电动汽车动力学控制运行参数的识别、动力学控制结构与方法两个角度综述了十多年来的研究成果,重点介绍了轮胎-路面接触条件识别方法、驱动防滑控制方法等。对车辆横向动力学控制,包括电子差速控制、直接横摆控制、底盘集成控制等研究现状也做了总结。最后对未来电动车辆动力学控制的发展方向作了几点展望。     

Real-time energy management strategy based on predictive cruise control for hybrid electric vehicles

With the help of traffic information of the connected environment, an energy management strategy (EMS) is proposed based on preceding vehicle speed prediction, host vehicle speed planning, and dynamic programming (DP) with PI correction to improve the fuel economy of connected hybrid electric vehicles (HEVs). A conditional linear Gaussian (CLG) model for estimating the future speed of the preceding vehicle is established and trained by utilizing historical data. Based on the predicted information of the preceding vehicle and traffic light status, the speed curve of the host vehicle can ensure that the vehicle follows safety and complies with traffic rules simultaneously as planned. The real-time power allocation is composed of offline optimization results of DP and the real-time PI correction items according to the actual operation of the engine. The effectiveness of the control strategy is verified by the simulation system of HEVs in the interconnected environment established by E-COSM 2021 on the MATLAB/Simulink and CarMaker platforms.     

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-toinfrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industriallevel HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.     

Hardware-in-the-loop simulation for the design and verification of the control system of a series–parallel hybrid electric city-bus

Hybrid electric buses have been a promising technology to dramatically lower fuel consumption and carbon dioxide (CO₂) emission, while energy management strategy (EMS) is a critical technology to the improvements in fuel economy for hybrid electric vehicles (HEVs). In this paper, a suboptimal EMS is developed for the real-time control of a series–parallel hybrid electric bus. It is then investigated and verified in a hardware-in-the-loop (HIL) simulation system constructed on PT-LABCAR, a commercial real-time simulator. First, an optimal EMS is obtained via iterative dynamic programming (IDP) by defining a cost function over a specific drive cycle to minimize fuel consumption, as well as to achieve zero battery state-of-charge (SOC) change and to avoid frequent clutch operation. The IDP method can lower the computational burden and improve the accuracy. Second, the suboptimal EMS for real-time control is developed by constructing an Elman neural network (NN) based on the aforementioned optimal EMS, so the real-time suboptimal EMS can be used in the vehicle control unit (VCU) of the hybrid bus. The real VCU is investigated and verified utilizing a HIL simulator in a virtual forward-facing HEV environment consisting of vehicle, driver and driving environment. The simulation results demonstrate that the proposed real-time suboptimal EMS by the neural network can coordinate the overall hybrid powertrain of the hybrid bus to optimize fuel economy over different drive cycles, and the given drive cycles can be tracked while sustaining the battery SOC level.     

基于PMP算法的HEV能量优化控制策略

针对常用混合动力汽车（Hybrid electric vehicle,HEV）中锂离子电池在功率波动较大时难以满足需求,以及单个驱动周期内HEV燃油能耗大且能量不能很好回收等问题,研究采用锂离子电池和超级电容器混合储能系统（Lithium-ion battery and super-capacitor hybrid energy storage system,Li-SC HESS）与内燃机共同驱动HEV运行.结合比例积分粒子群优化算法（Particle swarm optimization-proportion integration,PSO-PI）控制器和Li-SC HESS内部功率限制管理办法,提出一种改进的基于庞特里亚金极小值原理（Pontryagin's minimum principle,PMP）算法的HEV能量优化控制策略,通过ADVISOR软件建立HEV整车仿真模型,验证该方法的有效性与可行性.仿真结果表明,该能量优化控制策略提高了HEV跟踪整车燃油能耗最小轨迹的实时性,节能减排比改进前提高了1.6%~2%,功率波动时减少了锂离子电池的出力,进而改善了混合储能系统性能,对电动汽车关键技术的后续研究意义重大.     

The Combination of Two Control Strategies for Series Hybrid Electric Vehicles

With most countries paying attention to the environment protection, hybrid electric vehicles have become a focus of automobile research and development due to the characteristics of energy saving and low emission. Power follower control strategy (PFCS) and DC-link voltage control strategy are two sorts of control strategies for series hybrid electric vehicles (HEVs). Combining those two control strategies is a new idea for control strategy of series hybrid electric vehicles. By tuning essential parameters which are the defined constants under DC-link voltage control and under PFCS, the points of minimum mass of equivalent fuel consumption (EFC) corresponding to a series of variables are marked for worldwide harmonized light vehicles test procedure (WLTP). The fuel economy of series HEVs with the combination control schemes performs better compared with individual control scheme. The results show the effects of the combination control schemes for series HEVs driving in an urban environment.      

新能源汽车节能减排技术研究进展（1）——发动机系统

综述了电动力汽车（EV）、混合动力汽车（HEV）和燃料电池汽车（FCV）节能减排技术研究进展,主要从以下三方面论述：发动机系统、能量管理系统、传感器系统,并指出了存在问题和发展方向。本文主要介绍了发动机系统,包括：EV、HEV和FCV的关键技术、电机设计和驱动技术、控制技术。     

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.     

Smart procurement of naturally generated energy (SPONGE) for PHEVs

In this paper, we propose a new engine management system for hybrid vehicles to enable energy providers and car manufacturers to provide new services. Energy forecasts are used to collaboratively orchestrate the behaviour of engine management systems of a fleet of plug-in hybrid electric vehicle (PHEVs) to absorb oncoming energy in a smart manner. Cooperative algorithms are suggested to manage the energy absorption in an optimal manner for a fleet of vehicles, and the mobility simulator SUMO (Simulation of Urban MObility) is used to demonstrate the efficacy of the proposed idea.     

混合动力系统能量管理策略的实时优化控制算法

依据最优控制理论得到的混合动力汽车能量管理策略与未来的驾驶需求相关联,无法解决算法的实时性问题.本文另辟蹊径,结合规则构造二次型性能指标来限制发动机功率的大幅度频繁波动,间接地降低油耗.为此,在对混合动力系统近似线性处理的基础上,利用二次型最优跟踪理论推导出定常的反馈控制律,将发动机和电机功率表示成系统当前状态和车速指令的线性函数并应用于非线性实车系统.仿真结果表明,本文提出的能量管理实时控制算法可以达到良好的节油效果, 对不同的道路工况和电池初始荷电状态有良好的适应性.