A forecasting system for car fuel consumption using a radial basis function neural network

A predictive system for car fuel consumption using a radial basis function (RBF) neural network is proposed in this paper. The proposed work consists of three parts: information acquisition, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors affecting the fuel consumption of a car in a practical drive procedure, in the present system the relevant factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In fuel consumption forecasting, to verify the effect of the proposed RBF neural network predictive system, an artificial neural network with a back-propagation (BP) neural network is compared with an RBF neural network for car fuel consumption prediction. The prediction results demonstrated the proposed system using the neural network is effective and the performance is satisfactory in terms of fuel consumption prediction.     

Research on the comprehensive traffic state evaluation model linked with drivers’ perception under the vehicle networking

Unified judgment standards and methods are often adopted to identify traffic state in certain road network based on traffic flow parameters. However, drivers often have different perceptions about the traffic state on different road sections, since their expectations on traffic state vary more or less from each other on different road sections. In particular, under the vehicle networking, out of considerations for safety and other relevant factors, requirements for the correlation and coordination of running vehicles have also raised significantly. Therefore, it is necessary to take driver’s perception about the driving conditions of certain road sections into consideration to adjust the release of traffic state. This paper has provided a comprehensive traffic state evaluation model linked with driver’s perception under the vehicle networking. The authors first establish an ANFIS model based on the T–S model and then conduct statistical analysis on drivers’ perceptions about certain traffic state. At last, the authors use the results of statistical analysis as regulatory factors to amend the parameters input through ANFIS. Through simulation, this paper has demonstrated that the model established has a high rate of convergence, a high identification precision and the generalization ability to conduct researches on the identification of traffic state.     

A centralized control system for ecological vehicle platooning using linear quadratic regulator theory

This paper presents an ecological vehicle platooning control system that aims in reducing overall fuel consumption of the vehicles in a platoon. A centralized linear quadratic regulator system for controlling the vehicles in the platoon has been developed considering the aerodynamic characteristics of the vehicle and the resistance due to the road slope. The proposed control system is simulated on a highway with up?Cdown slopes for high speed driving. Its fuel saving performance is compared with a conventional decentralized vehicle platooning control system. Computer simulation results reveal the significant improvement in fuel economy by the proposed control system.     

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。     

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems(ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.     

Model Predictive Control of Hybrid Electric Vehicles for Improved Fuel Economy

This brief proposes a model predictive control method using preceding vehicle information within hybrid electric vehicles' (HEVs') predictive cruise control system to improve car following performance and reduce fuel consumption. This paper adds two original contributions to the related literature. First, a real‐time optimization approach using Pontryagin's minimum principle with analytical methods rather than numerical iteration methods is proposed. Second, to compute the desired battery state of charge trajectory as a function of vehicle position, only the topographic profile of the future road segments must be known. Both the fuel economy and the driving profile are optimized using the proposed approach. Simulation results show that fuel economy using the proposed method is improved significantly.     

基于动态规划不同优化目标的HEV转矩分配策略对比研究

双动力源的结构模式使得混合动力汽车相对于传统汽车拥有更高的燃油经济性,同时也给混合动力汽车整车控制器的设计提出了更高的要求。本文采用动态规划算法,分别以油耗最低同时电池SOC波动尽可能小、以及整车效率最高为目标,对混合动力汽车在NEDC循环工况下的最优转矩分配进行求解。并将两种转矩分配结果进行对比分析,得出选择不同优化目标对控制效果的影响以及SOC参数选择的标准,为制定更加高效的控制规则提供了理论依据。     

A model to estimate and interpret the energy-efficiency of movement patterns in urban road traffic

Urban road traffic is highly dynamic. Traffic conditions vary in time and with location and so do the movement patterns of individual road users. In this article, a movement pattern is the behaviour of a car when traversing a road link in an urban road network. A movement pattern can be recorded with a global navigation satellite system (GNSS), such as the Global Positioning System (GPS). A movement pattern has a specific energy-efficiency, which is a measure of how fuel-intensively the car is moving. For example, a car driving uniformly at medium speed consumes little fuel and, therefore, is energy-efficient, whereas stop-and-go driving consumes much fuel and is energy-inefficient. In this article we introduce a model to estimate the energy-efficiency of movement patterns in urban road traffic from GNSS data. First, we derived statistical features about the car's movement along the road. Then, we compared these to fuel consumption data from the car's controller area network (CAN) bus, normalized to the car's overall range of fuel consumption. We identified the optimal feature set for prediction. With the optimal feature set we trained, tested and verified a model to estimate energy-efficiency, with the fuel consumption serving as ground truth. Existing fuel consumption models usually view movement as a snapshot. Thus, the behaviour of the car remains unknown that causes a movement pattern to be energy-efficient or energy-inefficient. Our model views movement as a process and allows to interpret this process. A movement pattern can, for example, be energy-inefficient because the car is driving in stop-and-go traffic, because it is travelling at high speed, or because it is accelerating. Our model allows to distinguish between these different types of behaviours. Thus, it can provide new insights into the dynamics of urban road traffic and its energy-efficiency.     

基于CVT的混合动力汽车建模与仿真

建立了基于无级变速器 (Continously Variable Transmission,CVT) 的前向并联式混合动力电动汽车动力系统模型,为了研究整车动力性、经济性,根据行驶动力学方程,采用极值原理和曲面拟合法对发动机台架试验得到的数据进行了多项式拟合,建立了发动机万有特性与最佳操作曲线(Optimal Operating Line,OOL) 模型,并建立了牵引用三相感应电机动力模型以及牵引蓄电池(State of Charge,SOC)模型.同时,提出了燃油消耗最低、蓄电池充放电平衡的能量分配控制策略,进行整车动力性仿真计算,仿真结果表明在保证循环结束电池充放电基本平衡的同时发动机燃油消耗最低,仿真试验对比结果验证了建立的模型的精确性.     

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.     

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。     

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。     

基于时空图卷积网络的交通事故风险预测研究

交通事故的预测是通过对过去路段发生的交通事故进行分析,在综合考虑影响交通事故的相关因素后,对未来路段的交通事故发生状态进行预测。以往的大多数研究通常采用传统机器学习方法或单一深度学习模型预测法,利用网格化确定预测空间的单位,忽略了影响交通事故的天气、路况等外部因素,导致模型的预测性能不佳。提出一种基于时空特性的城市交通事故风险预测模型,在模型中使用改进的时空图卷积网络,利用图卷积网络（GCN）提取空间相关特征,并加入批标准化层解决梯度消失爆炸问题。在时间维度上采用门控线性单元（GLU）实现一维卷积操作,提取时间相关特征,并将GCN和GLU组合成时空卷积模块提取时空相关特征,使用均方误差损失函数解决样本数据零膨胀问题。实验结果表明,与GLU、SDCAE和ConvLSTM模型相比,该模型的RMSE指标分别降低了28%、4.87%、4.19%,能有效捕获时空相关性,综合性能得到较大提升。     

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.     

基于飞行数据的民机减推力起飞耗油影响因素研究

燃油成本作为民航运营成本的重要组成部分,如何安全有效地降低油耗是航空公司关注的焦点之一。研究影响油耗的因素是实施节油的重要理论支撑。在发动机功率最高的起飞阶段使用减推力起飞可有效降低油耗。从实际飞行数据出发,分析减推力起飞过程中油耗与影响因素间的定量关系。对减推力起飞进行机理分析,获得对油耗具有潜在影响的参数。利用灰典型相关分析对飞行数据时间序列进行研究,获得影响因素与油耗之间的定性关系。并选定参数作为模型输入,基于LSTM(Long Short Term Memory)网络建立了误差约为0.48%的油耗模型。利用真实飞行数据基于网络进行灵敏度分析,获得特定影响因素与油耗之间的定量关系。结果表明,逆风情况有利于民机起飞阶段降低油耗,且在风速4.5m/s〖JP2〗附近节油效果最佳;降低总重2%〖KG-*4〗~4%能获得更高的燃油收益,可辅助计算飞机总重的收益。     

Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow?

Road transport emission and fuel consumption models are currently used extensively to predict levels of air pollution along roadway links and networks. This paper examines how, and to what extent, models which are currently used to predict emissions and fuel consumption from road traffic include the effects of congestion. A classification framework is presented in which a key factor, driving pattern, connects emissions to congestion. Prediction of the effects of different driving patterns in emission models is generally restricted to certain aspects of modelling, i.e. hot-running emissions of regulated pollutants. As a consequence, the effects of congestion are only partially incorporated in the predictions. The majority of emission models explicitly incorporate congestion in the modelling process, but for one important family of emission models, namely average speed models, this could not be determined directly. Re-examination of the (light-duty) driving patterns on which three average speed models (COPERT, MOBILE, EMFAC) are based, shows that it is likely that congestion is represented in these patterns. Since (hot-running) emission factors are based on these patterns, this implies that the emission factors used in these emission models also reflect different levels of congestion. Congestion is thus indirectly incorporated in these models. It is recommended, that, in order to get more accurate (local) emission predictions and to achieve correct application in particular situations, it is important to improve current average speed models by including a congestion algorithm, or alternatively, at least provide information on the level of congestion in the driving patterns on which these models are based and recommendations on what applications the models are suitable for.     

Combined quasi-static backward modeling and look-ahead fuzzy control of vehicles

Vehicle modeling can play an important role in vehicle power train design, control and energy management investigation. This paper presents a method for vehicle power train modeling. The key feature of the method is its presentation of the dynamic of vehicle based on the road information. This ability makes the method suitable for look-ahead energy management and fuel economy optimal control problems. With the aid of a road slope database, road geometry ahead of the vehicle is extracted. A fuzzy controller is developed that receives this information and controls the velocity of the vehicle with respect to its fuel consumption. In order to maintain the operation of the combustion engine near its efficient region, the fuzzy controller commands a continuously variable transmission. Simulations are carried out using real road data. The results are presented and discussed.     

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.     

基于NARX神经网络的热负荷预测中关键影响因素分析

在区域供热（DH）网络中,精确预测热负荷已被认为是提高效率和节省成本的重要环节。为了提高预测精度,研究不同影响因素对热负荷预测的影响极为重要。使用引入不同影响因素的数据集训练得到带外部输入的非线性自回归（NARX）神经网络模型,并比较其预测性能,以讨论直接太阳辐射和风速对热负荷预测的影响程度。实验结果表明,直接太阳辐射和风速都是热负荷预测中的关键影响因素。只引入风速时,预测模型的平均绝对百分比误差（MAPE）和均方根误差（RMSE）均低于只引入直接太阳辐射,同时引入风速和直接太阳辐射能够得到最佳的模型预测性能,但是对于MAPE和RMSE降低的贡献不大。     

车队速度滚动时域动态规划及非线性控制

考虑自主车辆队列的节能安全问题，本文提出一种车辆队列协同控制方法，该方法可保证车队低能耗安全行驶.首先，充分考虑道路坡度以及车队异质性建立车队非线性模型，利用基于油耗模型的优化指标构建车队速度优化问题，提出一种滚动时域动态规划算法（Receding horizon dynamic programming，RHDP），获得车队的参考速度.然后，基于非线性车辆模型，运用反步法设计车辆跟踪控制器，并进行车队队列稳定性分析.这种协同控制方法的有效性已通过数值仿真和智能交通实验平台的验证.