ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

Synthetical Efficiency-based Optimization for the Power Distribution of Power-split Hybrid Electric Vehicles

Now the optimization strategies for power distribution are researched widely, and most of them are aiming to the optimal fuel economy and the driving cycle must be preknown. Thus if the actual driving condition deviates from the scheduled driving cycle, the effect of optimal results will be declined greatly. Therefore, the instantaneous optimization strategy carried out on-line is studied in this paper. The power split path and the transmission efficiency are analyzed based on a special power-split scheme and the efficiency models of the power transmitting components are established. The synthetical efficiency optimization model is established for enhancing the transmission efficiency and the fuel economy. The identification of the synthetical efficiency as the optimization objective and the constrain group are discussed emphatically. The optimization is calculated by the adaptive simulated annealing （ASA） algorithm and realized on-line by the radial basis function （RBF）-based similar models. The optimization for power distribution of the hybrid vehicle in an actual driving condition is carried out and the road test results are presented. The test results indicate that the synthetical efficiency optimization method can enhance the transmission efficiency and the fuel economy of the power-split hybrid electric vehicle （HEV） observably. Compared to the rules-based strategy the optimization strategy is optimal and achieves the approximate global optimization solution for the power distribution. The synthetical efficiency optimization solved by ASA algorithm can give attentions to both optimization quality and calculation efficiency, thus it has good application foreground for the power distribution of power-split HEV.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

Modeling and optimal energy management of a power split hybrid electric vehicle

With the combination modes of engine and two electric machines,the power split device allows higher efficiency of the engine.The operation and of a power split HEV are analyzed,and the system dynamic model HEV is established event-driven for HEV forward system simulation dynamics controller design.Considering the mode,the fact the mode that the operation modes of is the are and the the is continuous theory.time-driven this for each structure selection of the controller built and the described finite with hybrid automaton control In control structure,process is depicted by the state mode machine(FSM).The multi-mode switch controller is designed to realize power distribution.Furthermore,vehicle operations programming are optimized,and finite the prediction nonlinear model horizon.predictive control(NMPC)strategy is applied by that implementing the dynamic(DP)and in the Comparative simulation The results optimal demonstrate strategy hybrid in control structure is effective feasible for HEV energy management design.NMPC is superior improving fuel economy.

Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life

The present study investigates an energy management strategy based on reinforcement learning for seriesparallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.

Powertrain control of a solar photovoltaic-battery powered hybrid electric vehicle

This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation, and maximum power extraction whenever possible from the photovoltaic panels. The powertrain controller consists of two levels of controllers named lower level controllers and a high-level control algorithm. The lower level controllers are designed to perform individual tasks such as maximum power point tracking, battery charging, and load regulation. The perturb and observe based maximum power point tracking algorithm is used for extracting maximum power from solar photovoltaic panels while the battery charging controller is designed using a PI controller. A high-level control algorithm is then designed to switch between the lower level controllers based on different operating conditions such as high state of charge, low state of charge, maximum battery current, and heavy load by respecting the constraints formulated. The developed algorithm is evaluated using theoretical simula-tion and experimental studies. The simulation and experimental results are presented to validate the proposed technique.

Concept, configuration, and application of on-board nanogrids for use in electric vehicles

Against the backdrop of increasingly serious climate change, researchers are attempting to extend macroscale carbon reduction research to smaller scales. With the rapid development and widespread application of electric vehicles(EVs), an effective approach for carbon reduction based on EVs has the potential to be developed. To coordinate and manage the EV platform in diverse application scenarios, the concept of an on-board nanogrid(OBNG), in which a nanogrid is combined with the EV, is proposed and defined, and the characteristics summarized. A configuration that includes a physical layer with the on-board hardware system;an information layer for logical control, energy management, and communication coordination;and an application layer that can cope with different working environments is proposed. A detailed introduction to the basic architecture and management mode of each layer is provided along with information concerning the relevant technologies for coordinated operation. New ideas and approaches to improve the existing performance are proposed, and finally, combined with a background of smart and low-carbon cities, major application scenarios are envisioned.

基于LabVIEW的电动汽车动力电池管理系统测试平台设计

为更直观地监测动力电池管理系统的实时数据,设计一款基于LabVIEW的电动汽车动力电池管理系统测试平台,可以展示动力电池管理系统的工作状态。该系统采用主流新能源磷酸铁锂动力电池包,总容量80V50Ah一体式电池管理,具有主从通信、外部通信、状态估算、安全管理、充放电管理、控制输出、控制输入、总压检测、绝缘检测、单体电压采集、温度采集等功能。

混动汽车机电耦合系统效率影响因素分析及优化研究

研究了机电耦合系统传动效率主要影响因素,分析了机电耦合系统机械损耗、电控损耗和电机损耗3部分损耗的组成及影响因子,分析得出机械损耗与摩擦条件密切相关,电控损耗包括IGBT损耗和FWD损耗,二者均受开关频率影响,电机损耗与磁场强度和电流大小有关。提出通过改善摩擦条件和调整IGBT开关频率来优化机电耦合系统综合效率,开发了机电耦合系统传动效率测试台架,依托自主开发的测试台架分别测得了纯电模式下机械损耗、电控损耗和电机损耗在不同工况下的结果,分析了机械损耗、电控损耗和电机损耗随工况条件变化的规律。结果表明:采用低摩擦轴承和低黏度油品改善摩擦条件后可以有效降低机械损耗,最高效率提升了0.9%;IGBT开关频率从10 kHz调整为5 kHz,在低速段传动效率有明显提升。因此,提出了基于传动效率的IGBT开关频率动态调整策略。

串联式混合动力汽车发电机恒压发电性能试验研究

针对串联式混合动力汽车发电机恒压发电性能试验存在的问题,提出一种实际电机负载试验方案,并对发电机进行恒压发电性能试验。结果表明,实际电机负载试验方案切实可行,可为动力单元联合调试提供参考。

新的PHEV动力匹配控制方法设计与应用

设计了基于CD-ECMS的动力系统控制策略,优化车辆的参数,提升车辆的综合性能。新的动力匹配模块根据路况、车辆实时状态等信息科学匹配电机和发动机的动力输出,提升发动机和电机的工作协调能力,改善车辆动力性能和燃油经济性,新的控制算法嵌入到插电式混合动力的动力控制系统中,通过系统仿真和实验验证,结果表明:新的动力匹配算法避免能量的二次转换,对比ECMS和CD-CS控制策略,百公里油耗分别降低了0.31L和0.11L,电池转换效率分别提升了1.2%和11.2%,SOC分别下降了3%和7%,综合效率分别提升了1%和19%。

基于实车试验大数据分析的插电式混合动力汽车能量管理策略解析

以某先进的串并联混合动力系统为研究对象,提出一种插电式混合动力汽车能量管理策略的逆向解析方法。基于功率流和能量分析,设计试验解析流程,通过实车试验,分别完成对整车动力性、经济性测试及其性能影响分析,在此基础上制定插电式混合动力汽车的能量管理策略。最后基于MATLAB/Simiulink平台及其Simscape模型库,开发了基于实车试验大数据的串并联插电式混合动力汽车仿真平台,通过仿真与实车试验的对比分析,验证了解析策略的正确性。

混动车辆能量管理模块化ECMS框架

文中提出一种用于混合动力车辆能量管理实时控制的模块化等效消耗最小化策略(ECMS)框架,可实现能量管理实时控制的分布式开发。该框架采用Pontryagin极小值原理求解ECMS最优控制问题。首先,将最优控制问题分解为与各个子系统相关的优化问题;其次,通过Hamiltonian函数将与每个子系统有关的优化问题协调到全局最优;最后,在某款DHT混合动力车辆验证模块化ECMS,此车辆模型支持4种操作模式:2种电动模式、并联模式和串联模式。测试结果表明,模块化ECMS只需修改能量管理系统中功率平衡方程的连接矩阵,即可解决上述每种操作模式的最优控制问题。此框架不需要迭代过程,更适合实时控制的分布式开发。

电动汽车复合储能系统的能量管理策略研究进展

当前电动汽车已成为行业的重要发展方向。在单一能量源不能满足发展需求的情况下,复合储能系统对于改善电动汽车动力电池性能、延长续驶里程、提高整车动力性和经济性的优势更为突出。合理进行功率分配成为复合储能系统研究发展的重心之一,文章对复合储能系统的能量管理策略进行分析概述,总结不同能量管理策略的研究进展,重点分析三种经典控制策略下的功率分配案例,最后对电动汽车复合储能系统的能量管理策略进行总结并讨论未来的发展方向。

PHEV电池包热管理系统设计优化与仿真分析

为实现电池包热管理系统低能耗和高效率散热的目的,文章通过流体动力学(CFD)仿真及实验对某插电式混合动力汽车(PHEV)乘用车电池包热管理系统进行优化研究。电池包热管理系统采用液冷散热,流场压力损失设计目标值为27kPa。初始方案中,流场压力损失实测值约为60 kPa,CFD仿真分析表明,液冷系统流场进出口是产生压力损失的主要部件;采用增大进出口管径的方法对液冷系统进行优化,仿真和实验结果表明,优化后的液冷系统压力损失减小至26 kPa左右;液冷系统流场优化后,对电池包散热特性进行仿真和实验分析,结果表明,在67.6 kW工况下电池包最高温度为53.2℃,低于目标值55℃。综合分析可以得出结论,优化后的电池包液冷系统各项指标达到目标状态。

混合动力电动汽车关键技术

混合动力电动汽车(ＨＥＶ)的核心是混合动力驱动系统。ＨＥＶ系统具有高度的复杂性,混合动力系统设计的关键是系统结构的选择、整车能量管理策略的开发和系统参数的确定。功率分配是系统能量管理策略研究的关键,随着研究的深入,自适应控制、模糊逻辑控制、神经元网络控制等方法也得到了有效的运用。文中对子系统的关键技术及整车试验方法和评价体系的建立等方面进行了讨论。

混合动力电动汽车结构分析

混合动力电动汽车HEV是指以蓄电池与辅助动力单元APU共同作为动力源的汽车。由于混合动力电动汽车在节能和降低排放污染方面的明显优势,因而受到很大的重视,研制开发和产业化的进程相当快。目前混合动力电动汽车主要有两种混合驱动结构:串联式和并联式。对这两种混合动力系统结构和特点进行了分析,并重点对并联式结构中的不同结构进行了分析介绍。

采用模拟退火算法的电动汽车复合电源能量管理系统优化

针对电动汽车的混合式复合电源工作模式切换复杂、不利于不同工作模式的功率最优分配问题,提出基于模拟退火算法的复合电源能量管理优化方法。对混合式复合电源的工作模式进行分层讨论,建立能量管理系统的各部件损耗模型,基于不同层次设计能量管理策略。在分层能量管理策略的基础上,采用模拟退火算法降低系统的损耗。搭建混合式复合电源仿真模型和实验台进行仿真和实验。仿真和实验结果表明:在NYCC和EUDC路况下,混合式复合电源能量管理系统采用模拟退火算法优化比滞环逻辑控制的总损耗降低0.8%和1.1%。混合式复合电源能量管理系统采用模拟退火算法不仅能有效降低系统损耗,实现功率最优分配,而且能及时跟随功率需求,由超级电容提供或吸收峰值功率,保证电池安全。

基于多工况优化算法的混合电动汽车参数优化

协调优化混合动力电动汽车动力系统与能量控制策略的参数是提高燃油经济性的关键。以汽车的动力性能为约束条件,把电池SOC平衡油耗引入目标函数,提出一种采用模拟退火粒子群算法的基于多工况优化混合动力汽车动力系统与控制策略参数的方法。利用MATLAB/Simulink仿真软件进行建模仿真,结果表明,利用该方法可以获取一组参数,在保持汽车动力性、电池SOC平衡的基础上,相对于优化前,在综合工况下油耗降低了5.49%,分工况HWFET、FTP、LA92、US06_HWY、UDDS与SC03下油耗分别降低了8.46%、4.62%、3.84%、8.43%、5.56%、4.31%。为了证明本方法优化出的参数在其他工况的通用性,选取NEDC工况进行仿真验证,结果发现,在NEDC工况下油耗降低了8.36%。并进行了实车道路试验,试验结果较好的验证了所提出的基本控制策略的有效性。