基于综合燃油消耗率的混合动力汽车能量管理策略研究

通过对单轴并联式混合动力汽车综合燃油消耗率的建模计算,以综合燃油消耗率(Integrated Specific Fuel Consumption,ISFC)最低为目标确定了该混合动力系统各工作模式的工作范围,并制定了相应的整车能量管理策略。在此基础上,利用Matlab/Simulink仿真平台,建立了混合动力系统能量管理策略的仿真模型,结果表明采用控制策略使该混合动力汽车的燃油经济性比传统汽车和采用基线控制策略的相同混合动力汽车有显著提高。     

并联式混合动力汽车控制策略及其发动机的优化

对并联式混合动力汽车的控制策略和节油机理进行了分析。分析了混合动力汽车发动机的工作特性,并针对混合动力汽车发动机的快速起动／停机特性分析综述了其对混合动力汽车经济性、排放性能、驾驶性和舒适性的影响。介绍了Atkinson燃烧循环在混合动力汽车上的应用,并针对混合动力汽车发动机本身的效率区域优化进行了分析综述。     

集成起动电机混合动力汽车能量管理试验研究

利用Matlab/Simulink软件建立了基于简单规则的混合动力汽车能量管理策略模型,通过对试验数据的分析,证明了能量管理策略的有效性,同时通过对整车在进行市区加市郊综合行驶循环试验工况(NEDC)中获得的油耗数据的分析,表明该系统与相同动力普通汽车相比在油耗方面降低了10%左右。     

混合动力汽车能量管理系统研究及展望

介绍了混合动力汽车能量管理系统的技术特点,同时阐述了其当前的技术现状及面临的主要技术问题,重点针对其未来发展趋势进行了展望。能量管理系统在改善混合动力汽车燃油经济性、降低排放、延长零部件寿命等方面起着重要作用,尽管目前依然存在一定的技术难点,但随着相关技术的不断完善,其必定会有着美好的应用前景。     

插电式并联混合动力汽车仿真与能量管理策略

以某插电式并联混合动力汽车为研究对象,基于AMESim仿真平台搭建由驾驶员模块、发动机模块、电池模块、车辆控制单元、变速器模块、电机模块等组成仿真模型,并设计能量管理策略。对全球轻型车辆测试循环(worldwide harmonized light vehicles test cycle, WLTC)和JC08 2种不同工况下混动汽车的动力性和经济性进行仿真。结果表明：仿真车速与实际控制车速的匹配度几乎完全重合,仿真模型准确可靠;纯电模式下车速由0加速到100 km/h的加速时间为8.53 s; WLTC工况下油耗为5.789 L/(100 km),比传统燃油车油耗节约28.73%,JC08工况下油耗5.077 L/(100 km),比传统燃油车油耗节约27.69%。     

混合动力汽车整车控制策略比较分析

对于串联式、并联式和混联式三种混合动力系统的常用结构配置特点和应用分别进行了阐述和分析,重点对三类混合动力系统中实际常用的整车控制策略进行了分析。可以得出混合动力系统越复杂整车控制策略也就越复杂,并从整车性能、开发成本等方面比较和分析各自的优缺点,从而总结出各种控制策略的适用范围和场合。     

基于模糊逻辑的混合动力汽车节气门快速控制原型

分析了混合动力汽车多能源动力总成控制系统对汽油机节气门的响应要求及电子节气门系统的研发现状,提出了基于模糊逻辑的电子节气门PWM控制算法,并以英飞凌TLE6209R芯片为核心设计了电子节气门的电路硬件部分.在此基础上,建立了电子节气门直流电机控制系统的仿真模型.采用dSPACE快速控制原型系统,完成了被控电子节气门不同工况下的实时仿真.仿真结果验证了控制算法及控制系统的有效性,为产品级的电子节气门控制器的开发提供依据.     

混合动力汽车模糊逻辑控制策略仿真

本文分析了混合动力汽车的能量管理方法，并应用模糊逻辑建立了控制策略模型然后利用ADVISOR提供的仿真环境模拟模糊逻辑控制策略的仿真效果。     

浅析混合动力汽车总成结构与能量控制策略

通过浅析插电式混合动力汽车（PHEV）动力总成结构与能量控制策略,阐述了新能源汽车对提高燃油经济性,促进节能减排的深刻意义。     

混合动力公交车深度强化学习能量管理策略研究

以某款双行星排混合动力公交车为样车,针对控制变量柴油机转速的离散控制和连续控制分别提出基于双深度Q网络(double deep Q-learning, DDQN)和基于双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradients, TD3)的能量管理策略,并使用优先级经验回放对策略进行优化。运用仿真试验,研究样车在C-WTVC工况下的能量管理特性。通过与动态规划策略(dynamic programming, DP)进行对比发现：DDQN和TD3策略收敛速度快,具有较强的自适应能力;与DP策略相似,DDQN和TD3策略在控制逻辑上均表现为低速和较低转矩时纯电驱动,高速和较高转矩时混合驱动;三种策略下柴油机均主要工作于中低转速区间,且TD3策略可以对柴油机转速进行连续控制;DDQN和TD3策略的百公里油耗分别为19.51L和19.48L,燃油经济性均达到DP策略的93%,研究证明了DDQN和TD3策略的有效性。     

Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles

The energy management strategy (EMS) is a key to reduce the equivalent hydrogen consumption and slow down fuel cell performance degradation of the plug-in fuel cell hybrid electric vehicles. Global optimal EMS based on the whole trip information can achieve the minimum hydrogen consumption, but it is difficult to apply in real driving. This paper tries to solve this problem with a novel hierarchical EMS proposed to realize the real-time application and approximate global optimization. The long-term average speed in each future trip segment is predicted by KNN, and the short-term speed series is predicted by a new model averaging method. The approximate global optimization is realized by introducing hierarchical reinforcement learning (HRL), and the strategy within the speed forecast window is optimized by introducing upper confidence tree search (UCTS). The vehicle speed prediction and the proposed EMS have been verified using the collected real driving cycles. The results show that the proposed strategy can adapt to driving style changes through self-learning. Compared with the widely used rule-based strategy, it can evidently reduce hydrogen consumption by 6.14% and fuel cell start-stop times by 21.7% on average to suppress the aging of fuel cell. Moreover, its computation time is less than 0.447 s at each step, and combined with rolling optimization, it can be used for real-time application.     

Study on the control strategy for a boost converter used in hybrid energy storage system of electric vehicles

升压型直流变换器采用滑模变结构控制策略存在收敛速度较慢、抖振剧烈等导致的动态响应品质差问题。本文提出一种双幂次滑模趋近滞环控制策略,在电流跟踪误差估计值的基础上定义滑模面以实现电流跟踪控制,依据系统的未知扰动和负载变化建立自适应状态观测器,结合李雅普诺夫函数设计自适应律,并计算自适应占空比。提出一种双幂次趋近律,根据系统不同趋近过程的特点制定参数选择标准,对系统的动态响应品质进行目的性调节,并设计滑模滞环控制器以削弱由符号函数项所引起的抖振。对以上方法进行了仿真验证,结果显示可有效改善系统的动态特性和电流控制鲁棒性。     

并联混合动力汽车能量管理与转矩协调控制策略

研究并联混合动力汽车的控制策略。基于发动机输出转矩最优的能量管理策略,对并联混合动力汽车在工作模式切换中的相互配合问题,提出发动机动态转矩控制+动力电池荷电状态(state of charge,SOC)干预+电机转矩补偿控制的转矩协调控制方法;在Matlab/Simulink/Stateflow平台搭建整车能量管理控制策略模型,控制发动机工作在高效率区,保证发动机输出最优转矩;根据电池的SOC干预电机的运行状态,协同发动机提供整车需求转矩。在Cruise平台下建立整车模型,以新欧洲驾驶周期作为循环工况进行离线仿真。结果表明,能量管理与转矩协调控制策略能够有效分配电机和发动机的转矩输出,满足混合动力汽车多模式切换的要求。     

A robust online energy management strategy for fuel cell/battery hybrid electric vehicles

Traditional optimization-based energy management strategies (EMSs) do not consider the uncertainty of driving cycle induced by the change of traffic conditions, this paper proposes a robust online EMS (ROEMS) for fuel cell hybrid electric vehicles (FCHEV) to handle the uncertain driving cycles. The energy consumption model of the FCHEV is built by considering the power loss of fuel cell, battery, electric motor, and brake. An offline linear programming-based method is proposed to produce the benchmark solution. The ROEMS instantaneously minimizes the equivalent power of fuel cell and battery, where an equivalent efficiency of battery is defined as the efficiency of hydrogen energy transforming to battery energy. To control the state of charge of battery, two control coefficients are introduced to adjust the power of battery in objective function. Another penalty coefficient is used to amend the power of fuel cell, which reduces the load change of fuel cell so as to slow the degradation of fuel cell. The simulation results indicate that ROEMS has good performance in both fuel economy and load change control of fuel cell. The most important advantage of ROEMS is its robustness and adaptivity, because it almost produces the optimal solution without changing the control parameters when driving cycles are changed.     

Efficient model predictive control for real-time energy optimization of battery-supercapacitors in electric vehicles

Integration of batteries and supercapacitors (B-SCs) is widely used to improve performance of electric vehicles (EVs). In this article, we consider the energy optimization problem of B-SCs in EVs and propose an efficient model predictive control (MPC) algorithm for real-time energy optimization of the hybrid energy storage system of EVs. Back propagation neural network is firstly adopted to learn the velocity prediction ability over a finite horizon by standard driving cycles. Then real-time energy optimization of B-SCs in EVs is formulated as the finite horizon optimal control problem by taking into account the constraints, the cost function on battery current, and the predicted velocity of the EV. Moreover, to lessen the computational burden of online solving the problem, the Pontryagin's Minimum Principle is used in a fashion of receding horizon. Compared with traditional nonlinear MPC, simulation results verify the effectiveness of the proposed MPC algorithm for real-time energy optimization of B-SCs in EVs.     

An improved energy management strategy for FC/UC hybrid electric vehicles propelled by motor-wheels

The hybridization of the fuel-cell electric-vehicle (FCEV) by a second energy source has the advantage of improving the system's dynamic response and efficiency. Indeed, an ultra-capacitor (UC) system used as an energy storage device fulfills the FC slowest dynamics during fast power transitions and recovers the braking energy. In FC/UC hybrid vehicles, the search for a suitable power management approach is one of the main objectives. In this paper, an improved control strategy managing the active power distribution between the two energy sources is proposed. The UC reference power is calculated through the DC link voltage regulation. For the FC power demand, an algorithm with five operating modes is developed. This algorithm, depending on the UC state of charge (SOC) and the vehicle speed level, minimizes the FC power demand transitions and therefore ameliorates its durability. The traction power is provided using two permanent magnetic synchronous motor-wheels to free more space in the vehicle. The models of the FC/UC vehicle system parts and the control strategy are developed using MATLAB software. Simulation results show the effectiveness of the proposed energy management strategy.     

Component sizing optimization of plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance.     

A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.     

一种基于模糊控制的混合储能系统能量管理策略研究

混合储能系统中,锂离子电池循环寿命短和超级电容能量不足是制约混合储能发展的两大因素。文章提出了一种新型混合储能系统的能量管理策略,在综合考虑锂离子电池温度及荷电状态和超级电容荷电状态的基础上,通过模糊控制动态调节低通滤波器的时间常数,实现对混合储能的功率分配。在保证锂离子电池平滑输出的同时,减少了锂离子电池热量的产生;运用能量转移的方式对超级电容进行越限保护,提高超级电容在两种极端情况下的响应能力。算例分析表明,文章提出的功率分配策略和超级电容越限保护方法,可以有效地改善两种储能介质的出力。     

Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems

In this paper, an observer-based type-2 fuzzy method is proposed for control and energy management strategy (EMS) of the hybrid energy storage system (HESS) which can be composed of the fuel cell (FC), battery (BA), and supercapacitor (SC). The objective and main contribution of the suggested strategy is to provide: 1) Appropriate tracking performance of power sources by an observer-based control method in the presence of noise and signal ripples. 2) An observer-based composite adaptive type-2 fuzzy (OCAT2F) to approximate the voltage of power sources. 3) A dynamical model of DC-bus to guarantee the stability of closed-loop system. 4) An intelligent EMS. To have a high-power supply, the proposed EMS includes two parts; a type-2 fuzzy logic control rule table (T2FLCRT), and an observer-based robust adaptive fuzzy type-2 fuzzy (ORAT2F). Furthermore, stability analyses of the closed-loop system are provided by the input-output linearization (I-OL) approach and based on the Lyapunov theorem. The simulation results of the proposed control scheme under MATLAB/Simulink indicate that the suggested strategy can provide a suitable control performance, and stability of the whole system is achieved.