Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) generate the power required to drive the vehicle via a combination of internal combustion engines and electric generators. To make HEVs as efficient as possible, proper management of the different energy elements is essential. This task is performed using the HEV control strategy. The HEV control strategy is the algorithm according to which energy is produced, used and saved. This paper describes a genetic-fuzzy control strategy for parallel HEVs. The genetic-fuzzy control strategy is a fuzzy logic controller that is tuned by a genetic algorithm. The objective is to minimize fuel consumption and emissions, while enhancing or maintaining the driving performance characteristics of the vehicle. The tuning process is performed over three different driving cycles including NEDC, FTP and TEH-CAR. Results from the computer simulation demonstrate the effectiveness of this approach in reducing fuel consumption and emissions without sacrificing vehicle performance.     

基于猴群算法的混合动力汽车能量管理策略

混合动力汽车通常由两种不同的动力源驱动,对于驾驶员需求,如何分配动力源的输出,使得整个循环的耗油量达到最小是混合动力系统控制表示法需要解决的问题。实际汽车中各部件模型是非线性的,而且受到约束,结合猴群算法理论,提出了基于猴群算法的能量管理策略。对505及NEDC等标准工况进行了仿真,结果表明,猴群算法用很小的猴群就可以很快收敛到全局最优解,寻优能力强,可以实现低油耗。     

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。     

Optimal energy management for a diesel hybrid electric vehicle considering transient PM and quasi-static NOx emissions

In this paper, optimal energy management strategies are derived to balance fuel consumption, raw particulate matter (PM) emissions, and raw nitrogen oxide (NOx) emissions for a Diesel hybrid electric vehicle. Two methods for the derivation of these strategies are compared. One method is based on dynamic programming and steady-state engine maps only. The second method is based on dynamic programming, steady-state engine maps, and a validated transient PM emission model. As a result, only the second method allows for the generation of smooth engine set point trajectories to reduce transient PM emissions without compromising fuel consumption and NOx emissions.     

混合动力电动汽车中利用决策树CART算法的能源管理方案

针对混合动力电动汽车(HEV)氮氧化物( )排放的问题,提出了一种基于决策树CART算法的柴油混合动力能源管理策略。首先,提出了一种结合决策树与回归树的分类算法(Classification and Regression Tress,CART),针对类别和变量特征,从一个或多个预测变量中预测出个例的趋势变化关系;然后,通过控制发动机和电动机之间的扭矩分配,引入了额外的自由度以调整从纯燃料经济性情况到纯 限制情况的优化权衡;最后,采用基于软件在环路和硬件在环仿真的方法,从而根据动力系统配置了解系统性能,并调整所提出的能源管理策略。实验结果表明,提出的柴油混合动力能源管理策略中, 的减少对燃料消耗的影响,且可以通过选择最佳工作点和限制发动机动力来限制 排放的潜力。相比其他几种较新的同类方案,提出的方案在同等燃料消耗的情况下 排放量更小,在燃料消耗略有下降的情况下,可以显着降低 。     

基于强度Pareto进化算法的有约束并联混合动力汽车多目标优化

将混合动力系统多目标优化问题转化为单目标优化问题进行求解需要设置权系数。为避免设置权系数,研究基于强度Pareto进化算法（SPEA2）的有约束并联式混合动力电动汽车(PHEV)参数优化方法。该方法基于Pareto支配性原理判定候选方案的优劣,采用ADVISOR仿真PHEV,并将仿真所得的燃油消耗量与污染物排量作为候选方案的目标值。实验结果表明,该方法所获得的控制策略与传动系统参数,在提高PHEV工作效率、整车性能及降低燃油消耗与污染物排放等方面效果显著。     

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Matlab/Simulink的混联式混合动力电动汽车建模与仿真

在Matlab/Simulink环境下建立混联式混合动力电动汽车(Parallel Series Hybrid Electric Vehicle,PSHEV)模型,并用Stateflow建立整车控制器的模式逻辑模型。在欧洲城市道路循环工况(European Urban Road Driving Cycle,CYC_ECE_EUDC)下对整车动力性能、燃油经济性能与排放性能进行仿真分析。仿真结果表明：与Advisor/Prius在相同工况下的仿真结果相比,所建立模型符合混联式混合动力电动汽车的整车动力学要求,模型具有正确性和可行性,且采用所建立的能量分配控制策略,百公里油耗为5.056L,较Advisor/Prius模型同比下降2.8%,CO排放量和NOx排放量分别降低16.9%和2.7%,实现控制策略的有效性,达到节能减排的目的。     

Predictive energy management for hybrid vehicle

Hybrid vehicle control strategies are algorithms devoted to the energy management. At each sampling time they choose the powertrain operating point in order to minimize a criterion, usually the fuel consumption. In simulation, an optimization algorithm can be derived from the minimum principle. For real time control, a Model Predictive Control scheme can be used but it requires the prediction of the future driving conditions. Their time ordered prediction is very difficult. Moreover, if the optimal costate is constant, only the prediction of their distribution is sufficient and allows deriving a real time control strategy. Experimental results are provided to illustrate the benefits of this approach.     

A genetic-based methodology for hybrid electric vehicles sizing

 As private transport concerns, the global challenge of this millennium is the reduction of carbon dioxide emissions from passenger cars by improving fuel economy without sacrificing the vehicle performance. Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can improve effectively the vehicle performance and fuel economy, reducing at the same time the effects of the use of private cars on the air quality of the cities. These advantages can be achieved only if the design of the powertrain is inspired to the minimisation of the main figures of merit holding in consideration many general aspects and variables. As supporting methodology in developing this difficult activity, a genetic-based sizing methodology will be presented. It will be aimed to minimise a function objective which takes into account not only technical specifications but also environmental, social, and economic aspects. Some interesting simulation results will be reported to prove the validity of the methodology, which will contribute to a substantial reduction of the pollutant emissions from hybrid electric vehicles.     

An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment

In South Korea, school buildings require significant energy inputs for heating and air-conditioning, and the majority of the occupants are adolescent students, whose health and cognitive performance are vulnerable to poor indoor air quality (IAQ) and thermal discomfort. Using field measurements, some previous studies have reported that some Korean schools have poor IAQ and thermal conditions. Thus, it is necessary to develop effective heating, ventilation, and air-conditioning (HVAC) control strategies to improve the indoor environment and reduce energy consumption. Therefore, this study proposes an intelligent HVAC integrated control strategy that can improve indoor environmental quality (IEQ) and reduce energy consumption in school buildings. The proposed strategy utilizes an integrated neural network prediction model for IEQ and a heuristic method that can optimize control objectives (i.e., the predicted mean vote [PMV], carbon dioxide [CO₂], particulate matter with diameters of 10 and 2.5 μm [PM₁₀ and PM_2.5, respectively], and HVAC energy consumption). To evaluate the control performance of the proposed strategy, the present study employs two base algorithms (i.e., a rule-based and a non-adaptive control approach) under non-disturbance and forcing disturbance scenarios. The control failure period for PMV is found to be 1.6420% and 9.4773% of the total occupancy period under the non-disturbance and forcing disturbance scenarios, respectively, while CO₂ control failure does not occur under either scenario. The control failure periods for PM₁₀ and PM_2.5 were 5.1676%, and 7.1844%, respectively, under forcing disturbance. Under the non-disturbance scenario, the proposed strategy consumed 2,467.07 kWh and 870,26 kWh for heating and cooling, respectively, representing 91.1% and 84.08% of that for the rule-based algorithm. The proposed strategy can thus effectively improve the IEQ of a building and has the potential for use in the development of integrated environmental management solutions for buildings.     

机动车油耗模型研究综述

随着交通运输业的蓬勃发展,机动车保有量急剧增长,从而导致燃油过度消耗与排放,引发了能源短缺与环境污染等问题。国内外学者致力于机动车燃油消耗模型的研究,旨在提高模型的准确率,降低车辆的燃油消耗,响应可持续发展战略。对此,依据不同的视角,将机动车的燃油消耗模型分为基于汽车动力学原理的传统油耗模型和基于机器学习方法的数据驱动油耗模型两大类。将这两大类的燃油消耗模型又分为各小类分别进行介绍,内容包含各类模型的发展历程、优缺点与对比分析,并对各类模型的应用现状进行概述,对未来应用发展方向进行探讨。最后对机动车的燃油消耗模型进行总结与展望。     

Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP‐SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite‐horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time‐invariant causal state‐feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite‐horizon case, and for the same level of complexity as a discounted future‐cost controller, the shortest‐path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck (FCT) power system more efficient, so as to improve vehicle fuel economy. When the structure of power source system and the torque distribution strategy are determined, the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources. The paper simulates the assistance of the intelligent transport system (ITS) and carries out the eco-velocity planning using the traffic signal light. On this basis, in order to further improve the energy efficiency of FCT, a model predictive control (MPC)-based energy source optimization management strategy is innovatively developed, which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption. Under the scenarios of signalized intersections, based on the planned eco-velocity, the off-line simulation results show that the proposed MPC-based energy source management strategy (ESMS) can reduce hydrogen consumption of fuel cell up to 7\% compared with the existing rule-based ESMS. Finally, the Hardware-in-the-Loop (HiL) simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.     

Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks

Hybrid electric vehicles require an algorithm that controls the power split between the internal combustion engine and electric machine(s), and the opening and closing of the clutch. Optimal control theory is applied to derive a methodology for a real-time optimal-control-based power split algorithm. The presented strategy is adaptive for vehicle mass and road elevation, and is implemented on a standard Electronic Control Unit of a parallel hybrid electric truck. The implemented strategy is experimentally validated on a chassis dynamo meter. The fuel consumption is measured on 12 different trajectories and compared with a heuristic and a non-hybrid strategy. The optimal control strategy has a fuel consumption lower (up to 3%) than the heuristic strategy on all trajectories that are evaluated, except one. Compared to the non-hybrid strategy the fuel consumption reduction ranged from 7% to 16%.     

Eco-friendly 3D-Routing: A GIS based 3D-Routing-Model to estimate and reduce CO2-emissions of distribution transports

Road freight transportation accounts for a significant share of the worldwide CO₂-Emissions, indicating that respective operations are not sustainable. Regarding the forecasted increase in CO₂-Emissions from this sector, undertaking responsibilities for its environmental impact are needed. Although technical and strategic solutions to reduce emissions have been introduced, or are in development, these rarely yield instant emission reduction potentials. A strategic approach to reducing them instantly, based on the given infrastructure and existing vehicle fleet, may be achieved through route optimization. Route optimization is a well-researched topic in the transportation domain. However, it is mainly used to reduce transportation times and expenses. Rising expectations towards sustainability by authorities and consumers led to an increased interest in route optimization in which environmental externalities, such as fuel consumption and CO₂-Emissions are minimized. This paper introduces a Geographic Information System (GIS) based 3D-Routing-Model, which incorporates models to estimate vehicle fuel consumption while taking effects, such as road inclination and varying velocities into account. The proposed model utilizes a Digital Elevation Model (DEM) to enrich a road network with elevation data. The 3D-Routing-Model is applied in different distribution scenarios within the framework of an artificial company in the Lisbon Metropolitan Area, Portugal to evaluate the effects of road inclination on vehicles fuel consumption and its proportional CO₂-Emissions. Results indicate that eco-friendly routes can yield significant fuel and emission saving potentials of up to 20% in the tested scenarios. However, eco-friendly routes are characterized by longer distances as well as operation times, which leads to increased expenses. The question remains if companies within the transportation sector are more interested in maximizing their profits, or investing in a sustainable future.     

并联式混合动力汽车双模糊控制能量管理策略研究

针对单模糊控制策略下再生制动能量不能得到充分回收,提出了一种并联式混合动力汽车的双模糊控制能量管理策略.分别设计了驱动和制动工况下相应的模糊控制器,简化了控制系统设计的复杂度.最后,在Matlab中利用Advisor软件对所设计的双模糊控制策略进行了仿真.结果表明,与电辅助控制策略和单模糊控制策略相比,所设计的双模糊控制能量管理策略,在有效提高燃油经济性和制动工作效率的同时,极大地降低了废气排放.     

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。     

MFB50 on-board estimation methodology for combustion control

Due to the increasing request for pollutant emissions reduction, modern closed-loop combustion control strategies require the on-board evaluation of the center of combustion (MFB50), i.e. the angular position where 50% of the injected fuel mass is burned. This work presents an MFB50 estimation algorithm based on engine speed measurement, that can be performed using the same toothed wheel already present on-board. Therefore, this approach is compatible with on-board application and requires no additional hardware cost. The complete methodology has been applied to a Diesel engine mounted on-board a vehicle and the accuracy of the obtained results is compatible with on-board requirements.     

动力驱动系统匹配与控制策略研究

能源、有害气体和温室气体排放是影响今后汽车技术发展的三大问题.可外接充电式混合动力电动汽车是指可以使用电力网对动力电池进行充电的混合动力电动汽车,是向最终的清洁能源汽车过渡的最佳方案之一.为实现新一代燃料电池城市客车原理样车的动力性能指标,样车动力驱动系统采用串联式能量混合型结构,基于可外接充电技术,对动力驱动系统的主要部件性能参数进行了合理的匹配理论研究.基于ADVISOR软件建立动力驱动系统仿真模型并提出了不同工作模式和控制策略.分别从纯电动工况、中国典型城市客车车况下对纯电动里程、SOC、动力电池和燃料电池的输出功率进行了仿真研究、动力性能满足性能指标要求,仿真结果验证了匹配理论、控制策略和模型的准确性.