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-efficient anticipative driving control of connected and automated hybrid electric vehicles

In this paper, we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles (HEVs). Considering the inherent complexities brought about by the velocity profile optimization and energy management control, a hierarchical control architecture in the model predictive control (MPC) framework is developed for real-time implementation. In the higher level controller, a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving. The real-time control actions are derived through a computationally efficient algorithm. In the lower level controller, an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller. The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13% fuel consumption saving compared with a benchmark strategy.     

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.     

Editorial - Special issue on networked system control and connected vehicles: opportunities and challenges

CASE (Connected, Automated, Sharing, and Electrifying) is a global trend in the automotive industry due to the big potential in improving energy efficiency and reducing the air pollution from automobile exhaust. Indeed, the connectivity, connecting the vehicles with the internet, is firstly implemented in the automotive industry in the sense of large scale connection of vehicles. The connected environment has been two decades in the automotive industry which enables us to provide a much comfortable and smart telemetric service. However, the attention has not been focused on the control technology with the connectivity for efficiency and emission improvement. From the view of system control, the connected vehicles are large-scaled, multi-agent or high dimension systems that coupled and interacted but centralized control is not reasonable. How to formulate the optimization or control problem for the connected vehicles and how to solve the problem with system control theory are significant challenging issues. This special issue collected seven papers that addressed these control problems from the view of networked system and optimal control theory. The collection can be divided into three groups. The first group includes three papers that focused on vehicle control with the use of V2V and V2I information. The article by Qiuyi Guo et al., demonstrated the possibility of improving the fuel economy of fuel cell trucks using the traffic light signal. It is shown that with the V2I information, the model predictive control technology can save more than 7.43\% hydrogen consumption in a case study driving cycle. The model predictive control technology is also applied to car-following control on an urban road network by using V2V and V2I information. The paper by A. S. M. Bakibillah et al., investigated this issue and it is shown that the control with V2Vand V2I can improve traffic flow and fuel economy. The paper by Bo Zhang et al., proposed a two-stage optimization approach for speed planning and energy management of hybrid electric vehicles, where the control policy of MPC is fully applied in the two stages of design and a typical scenario of merging is targeted. The second group collected two papers that focused on automated driving. For automated vehicles, control of vehicle dynamics is the main subject, but it is an important elemental subject for driving vehicles under connected environment. Controlling an individual vehicle in the scene of parking is addressed in the paper by Dequan Zen et al. which also demonstrated real test results. The issue of driving-by-wire full is investigated in the paper by Ping Wang et al., where again MPC is exploited for developing the real-time control law. Finally, two articles are collected that discussed active fault tolerant control for connected mobile robots by M. Hussein et al., and powertrain control for electric vehicles with robust control theory by J. Buerger ad J. Anderson, respectively.     

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.     

并联混合动力汽车能量管理系统的仿真研究

对于混合动力汽车而言,节能减排是促使其发展的主要原因,而能量管理策略是节能减排的关键技术,因此针对并联混合动力汽车的能量管理策略展开研究;首先运用ADVISOR电动汽车仿真软件,选用某款并联混合动力车型,并使用标准ECE_ECDU和UDDS循环工况来评估整车燃油经济性和污染物排放效果;然后,采用门限参数优化的方法对控制策略进行优化;最后对比优化前后不同循环工况仿真结果中汽车的燃油经济性和排放性能的变化,并分析了优化后的策略对汽车性能的影响;研究表明,所提出的优化方法使汽车在ECE_ECDU和UDDS循环工况中的每百公里油耗分别降低了8.45%和10%,有害气体HC、CO和NOX含量分别减少了5.88%和5.8%、12.24%和11.54%、8.55%和7.51%,进一步验证了优化策略的有效性。     

Energy management strategies for parallel hybrid vehicles using fuzzy logic

This paper presents a fuzzy-logic-based energy management and power control strategy for parallel hybrid vehicles (PHV). The main objective is to optimize the fuel economy of the PHV, by optimizing the operational efficiency of all its components. The controller optimizes the power output of the electric motor/generator and the internal combustion engine by using vehicle speed, driver commands from accelerator and braking pedals, state of charge (SOC) of the battery, and the electric motor/generator speed. Separate controllers optimize braking and gear shifting. Simulation results show potential fuel economy improvement relative to other strategies that only maximize the efficiency of the combustion engine.     

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.     

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.     

城市交叉口车辆速度与交通信号协同优化控制

为了降低城市交通中的行车延误与燃油消耗,针对人类驾驶车辆与自动驾驶车辆混合交通环境,提出一种基于交通信息物理系统(TCPS)的车辆速度与交通信号协同优化控制方法.首先,综合考虑路口交通信号、人类驾驶车辆、自动驾驶车辆三者之间的相互影响,设计一种适用于自动驾驶车辆与人类驾驶车辆混合组队特性的过路口速度规划模型;其次,针对车辆速度规划单一应用时的局限性,即无法减少车辆路口通行延误且易出现无解情况,提出一种双目标协同优化模型,能够综合考虑车辆速度规划与路口交通信号控制,同时降低车辆燃油消耗与路口平均延误.由于双目标优化问题求解的复杂性,设计一种遗传算法-粒子群算法混合求解策略.基于SUMO的仿真实验验证了所提出方法的有效性.     

Predictive car-following scheme for improving traffic flows on urban road networks

Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections. This paper proposes a car-following scheme in a model predictive control (MPC) framework to improve the traffic flow behavior, particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle (CV) environment. Using information received through vehicle-to-vehicle (V2V) communication, the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon. The objective function is to minimize the weighted costs due to speed deviation, control input, and unsafe gaps. The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision. The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections. The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.     

基于粒子群优化的并联式混合动力汽车模糊能量管理策略研究

针对一种并联式混合动力轿车,以混合驱动系统需求转矩和电池组荷电状态(SOC)为输入,以发动机转矩为输出,构建了能量管理模糊控制器,并以总的等效燃油消耗为优化目标,利用粒子群算法对模糊隶属度函数参数和模糊控制规则进行优化.基于ADVISOR的仿真研究表明,与未优化的模糊能量管理策略相比,经过优化的模糊能量管理策略能够更有效地降低混合动力汽车的燃油消耗,更好地控制电池组SOC的变化.     

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.      

Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm

Along with the shortage of energy and the increasingly serious pollution of environment in cities, automobile industries all over the world are exploring and developing energy saving and clean automobile. Hydraulic hybrid vehicle has better potential in medium-size and large-size passenger vehicles than its electric counterparts. The key components’ sizes have remarkable influence on the vehicle performance and fuel economy, and an optimization process is needed to find the best design parameters for maximum fuel economy while satisfying the vehicle performance constraints. Multi-Objective optimization method based on adaptive simulated annealing genetic algorithm (ASAGA) is proposed to optimize the key components in HHV. In the objective function of the optimization, all the weighting factors can be set with different values according to different requirements. The optimal results show that the proposed method effectively distinguishes the key components’ optimal parameters’ position of HHV, enhances the performance and fuel consumption.     

汽车电子节能系统的设计方法研究与仿真

研究汽车节能方面的问题,降低汽车耗油量。针对在颠簸路段行驶时,车速会发生突变,耗能呈现高度的非线性,无法用数学模型描述、造成电子节能装置降低耗能的效果下降的问题。为了解决上述问题,提出了采用应激反馈响应的汽车电子节能算法。首先选取合理的车速参数和耗油量参数,然后建立数学模型,对选取的参数进行预处理,最后用应激反馈响应优化参数的调节,增加系统稳定性,降低汽车在颠簸路段运行时的耗油量。实验证明,利用基于应激反馈响应的汽车电子节能算法,能够有效降低汽车在颠簸路段运行时的耗油量,为汽车节能系统设计提供了依据。     

Plug_in电动车能量管理控制器研究

基于模糊神经网络算法研究设计Plug_in混合动力汽车整车能量管理控制器。将驾驶行为用神经网络进行建模,驾驶模式、踏板(油门和刹车)位置以及当前车轮力矩作为神经网络输入,目标力矩作为输出;将道路类型、目标力矩、电池SOC、当前车轮力矩为模糊输入变量,以满足整车动力性能、燃油经济性和极限边界极值为约束条件,对混合动力汽车的能量进行分配与管理,并在DSP硬件平台设计实现能量管理控制器。测试表明,行驶里程在40 km内时,样车等价燃油经济性最好,随着行驶里程的增加,燃油经济性下降,整个测试过程中样车动力性能以及各部件工况良好。     

考虑车辆跟驰作用和通信时延的网联车辆队列轨迹跟踪控制

针对智能网联车辆轨迹跟踪问题, 本文通过考虑车辆跟驰作用和车车通信过程中存在的通信时延, 提出了一种分布式非线性轨迹跟踪控制器. 具体来讲, 首先, 提出一种双向领导跟随通信拓扑来描述智能网联环境下车辆间的通信连接. 其次, 考虑车辆跟驰作用和通信时延, 设计一种分布式非线性轨迹跟踪控制器. 然后, 使用Lyapunov方法证明了所设计控制器的稳定性. 最后, 考虑速度干扰作用于领导者车辆, 针对无时延、同质时延和异质时延等三种场景进行数值仿真实验. 仿真结果表明: 本文所设计的控制器不仅保证了车辆位置跟踪误差收敛到原点, 而且车辆运动规律符合交通流理论, 即无负位置跟踪误差和负速度现象.     

通信延时环境下异质网联车辆队列非线性纵向控制

针对通信延时环境下的异质车辆队列控制问题, 本文提出了一种基于三阶模型的分布式非线性车辆队列纵向控制器. 首先, 基于三阶动力学模型描述了车辆的异质特性. 考虑车辆跟驰行为以及异质通信延时, 提出一种通信延时环境下的异质车辆队列非线性控制器. 所提控制器不仅可以在通信延时以及车辆异质特性的影响下实现队列中车辆的位置、速度以及加速度的一致性, 而且可以有效避免负的车辆间距和不合理的加/减速度, 保证车辆的运动行为符合交通流理论. 然后, 利用Lyapunov-Krasovskii定理对车辆队列的稳定性进行分析, 得出车辆队列的稳定性条件和通信延时上界. 最后, 所提控制器的有效性和稳定性通过数值仿真得到验证.     

插电式混合动力汽车模型预测控制策略研究

针对传统插电式混合动力汽车智能控制策略计算量大,难以实现实时最优控制的问题,提出了基于蓄电池充放电管理的插电式混合动力汽车预测控制策略.利用实测通勤插电式混合动力汽车车速信息,以蓄电池荷电状态为系统状态变量,以蓄电池充放电功率为系统控制变量,插电式混合动力汽车燃油消耗量最低为系统性能指标,设计了插电式混合动力汽车的模型预测控制智能优化算法,运用连续广义最小残量方法求解最优控制问题.在Matlab/Simulink与GT-POWER联合仿真平台上进行仿真,实验结果验证了所设计的模型预测控制算法不仅可以大幅度提高混合动力汽车的燃油经济性,而且能够满足实时控制的要求.     

负载影响下纯电动汽车配送路径规划及充电策略

由于纯电动汽车行驶里程的限制，在满足商用要求的前提下，纯电动汽车用于长途运输服务在短期内难以实现。不过，城市物流因其配送区域较小、货物的批量较小、批次较多的特点，可以考虑使用纯电动汽车来完成城市的配送任务。为满足车辆当天多次配送任务的要求以及考虑车辆负载对实时能耗的具体影响，建立了考虑车辆负载对实时能耗影响的配送模型，以及时满足客户的服务时间要求。并以城市A为例，设计了蚁群算法对模型进行求解，为纯电动汽车的配送任务进行合理的路径规划与充电策略的安排。最后，通过与使用燃油车辆运营相比较，分析未来纯电动汽车在城市配送物流中的可行性。