CN102831768B

CN102831768B - Hybrid power bus driving condition forecasting method based on internet of vehicles

Info

Publication number: CN102831768B
Application number: CN201210291137.8A
Authority: CN
Inventors: 周雅夫; 连静; 吕仁志; 李琳辉; 李海波; 贾朴; 庞博
Original assignee: Dalian University of Technology
Current assignee: Dalian University of Technology
Priority date: 2012-08-15
Filing date: 2012-08-15
Publication date: 2014-10-15
Anticipated expiration: 2032-08-15
Also published as: CN102831768A

Abstract

The invention discloses a method for predicting the driving condition of a hybrid electric passenger car based on the Internet of Vehicles, which belongs to the field of modern transportation technology. Its feature is to match and store the real-time vehicle location information with the running data; send the location information of the vehicle in real time, and receive the location information of the surrounding vehicles, and filter out the vehicle in front that is in the same direction as the vehicle and is located at a certain distance in front; The car sends the historical data of a certain distance from the workshop; according to the distance between the vehicle in front and the workshop, the weight of the driving parameters of the vehicle in front to the driving conditions of the vehicle is determined, and the characteristic parameters of the vehicle in front are used to calculate the predicted driving of the vehicle Working condition characteristic parameters; according to the predicted driving condition characteristic parameters of the vehicle, the driving conditions within a certain distance in front of the vehicle are identified and predicted according to the fuzzy recognition model; according to the prediction results, the HCU adjusts the vehicle control parameters. The effects and benefits of the invention are that the hysteresis of the traditional method is eliminated; the prediction accuracy is improved, thereby improving the fuel economy and emission performance of the vehicle.

Description

A method for predicting driving conditions of hybrid electric buses based on Internet of Vehicles

技术领域technical field

本发明属于现代交通技术领域，涉及到一种混合动力汽车行驶工况预测方法，特别涉及到一种基于车联网的混合动力客车行驶工况预测方法。The invention belongs to the field of modern traffic technology, and relates to a method for predicting the driving condition of a hybrid electric vehicle, in particular to a method for predicting the driving condition of a hybrid electric vehicle based on the Internet of Vehicles.

背景技术Background technique

行驶工况对车辆的动力匹配、排放水平以及燃油消耗有很大的影响，所以其对混合动力汽车的动力匹配、控制策略制定等有至关重要的作用。近几年我国经研究构建了北京、上海等大城市的行驶工况，但是汽车的实际运行工况随着时间、地点、环境、气候等因素变化，是一个随机的、不确定的过程，现有行驶工况预测方法是根据一定周期的行驶工况数据做出混合动力汽车控制策略的自适应调整，但是该方法是基于车辆运行一定周期的数据积累之后进行识别并调节控制策略，所以具有滞后性，并且准确率低，对车辆未来运行状态的控制参考意义不大。所以，目前需要一种混合动力客车行驶工况预测的方法，来对车辆未来行驶工况进行识别预测，以解决车辆行驶工况识别滞后和识别准确率低的问题。Driving conditions have a great influence on the power matching, emission level and fuel consumption of the vehicle, so it plays a vital role in the power matching and control strategy formulation of hybrid electric vehicles. In recent years, my country has studied and constructed the driving conditions of big cities such as Beijing and Shanghai. However, the actual operating conditions of automobiles change with time, place, environment, climate and other factors, which is a random and uncertain process. The driving condition prediction method is to make an adaptive adjustment of the control strategy of the hybrid vehicle based on the driving condition data of a certain period, but this method is based on the data accumulation of the vehicle running for a certain period to identify and adjust the control strategy, so it has a lag and the accuracy rate is low, so it has little reference significance for the control of the future operating state of the vehicle. Therefore, there is currently a need for a method for predicting driving conditions of hybrid electric buses to identify and predict the future driving conditions of the vehicle, so as to solve the problems of lagging and low recognition accuracy of vehicle driving conditions.

发明内容Contents of the invention

本发明要解决的技术问题是：针对混合动力客车行驶工况识别存在滞后和准确率低的缺点，提出一种基于车联网的混合动力客车行驶工况预测方法，通过将前方车辆的行驶工况历史数据传输给本车，本车根据前车数据对未来的运行工况进行识别预测，并根据工况识别预测结果对车辆未来运行控制参数进行调整，实时保证车辆的最优控制，达到最好的燃油经济性和排放性。The technical problem to be solved in the present invention is: Aiming at the shortcomings of hysteresis and low accuracy in the identification of hybrid electric bus driving conditions, a method for predicting the driving conditions of hybrid electric passenger cars based on the Internet of Vehicles is proposed. The historical data is transmitted to the vehicle, and the vehicle identifies and predicts the future operating conditions based on the data of the previous vehicle, and adjusts the future operating control parameters of the vehicle according to the identification and prediction results of the operating conditions, so as to ensure the optimal control of the vehicle in real time and achieve the best performance. fuel economy and emissions.

本发明的技术方案是：本发明由数据采集模块、射频识别模块、短距离通讯模块、全球定位系统(Global Positioning System，GPS)模块、中央处理模块构成。其中数据采集模块与车辆控制器局域网络(Controller Area Network，CAN)总线相连，负责采集车辆运行数据；射频识别模块负责车辆间的身份信息验证；短距离通讯模块负责前方车辆与本车间的数据传输通讯；GPS模块负责本车定位；中央处理模块负责总体协调该系统的数据采集、车辆定位、数据通讯、计算、工况识别预测工作，并且与混合动力整车控制器(Hybrid Control Unit，HCU)通信，给HCU提供行驶工况识别预测信息，以使HCU实时调整车辆的控制参数。The technical scheme of the present invention is: the present invention is made of data acquisition module, radio frequency identification module, short distance communication module, global positioning system (Global Positioning System, GPS) module, central processing module. Among them, the data acquisition module is connected with the vehicle controller area network (Controller Area Network, CAN) bus and is responsible for collecting vehicle operation data; the radio frequency identification module is responsible for the identity information verification between vehicles; the short-distance communication module is responsible for the data transmission between the vehicle in front and the workshop Communication; the GPS module is responsible for the positioning of the vehicle; the central processing module is responsible for the overall coordination of the system's data collection, vehicle positioning, data communication, calculation, and working condition identification and prediction, and is in charge of the hybrid vehicle controller (Hybrid Control Unit, HCU) Communication, to provide HCU with driving condition identification and prediction information, so that HCU can adjust the control parameters of the vehicle in real time.

基于车联网的混合动力客车行驶工况预测方法包括准备阶段、通信阶段、识别预测阶段，三者交叉同步进行。The method for predicting driving conditions of hybrid electric buses based on the Internet of Vehicles includes a preparation stage, a communication stage, and a recognition and prediction stage, and the three are carried out simultaneously.

准备阶段：Preparation Phase:

GPS模块实时对本车定位并与电子地图匹配，将实时位置信息(x₀,y₀,z₀)传给短距离通讯模块和中央处理模块；短距离通讯模块实时向周围发送本车位置信息(x₀,y₀,z₀)，同时接收周围N辆其他车的位置信息(x_p,y_p,z_p)传入中央处理模块；数据采集模块通过CAN总线实时采集本车运行参数，将实时参数与GPS模块传来的实时位置信息(x₀,y₀,z₀)匹配，存入中央处理模块；中央处理模块通过周围N辆他车位置信息(x_p,y_p,z_p)与本车位置信息(x₀,y₀,z₀)，分析周围N辆他车的道路信息、方向信息并计算与本车间距离s_p，若两车不同向或不同路或距离s_p大于L，其中，L表示对前方车辆的筛选距离，由于距离太远道路状况变化过大，参考价值小，根据短距离通讯模块的通讯距离和通行道路的交通水平进行调节，则放弃通讯；若两车同向且同路且距离s_p小于L，则进行通讯阶段，其中，p＝1,2,…,N。The GPS module locates the vehicle in real time and matches it with the electronic map, and transmits the real-time location information (x ₀ , y ₀ , z ₀ ) to the short-distance communication module and the central processing module; the short-distance communication module sends the vehicle location information to the surroundings in real time ( x ₀ , y ₀ , z ₀ ), and at the same time receive the position information (x _p , y _p , z _p ) of other N cars around and transmit it to the central processing module; the data acquisition module collects the running parameters of the vehicle in real time through the CAN bus, and The real-time parameters are matched with the real-time position information (x ₀ , y ₀ , z ₀ ) from the GPS module and stored in the central processing module; the central processing module passes the position information (x _p , y _p , z _p ) of the surrounding N vehicles Analyze the road information and direction information of the surrounding N other cars and calculate the distance s _p from the car’s position information (x ₀ , y ₀ , z ₀ ), if the two cars are in different directions or different roads or the distance s _p is greater than L, where, L represents the screening distance to the vehicle in front, because the distance is too far and the road conditions change too much, the reference value is small, adjust according to the communication distance of the short-distance communication module and the traffic level of the passing road, then give up the communication; if two If the vehicles are in the same direction and on the same road and the distance s _p is less than L, then the communication stage will be performed, where p=1,2,...,N.

通信阶段：Communication phase:

经准备阶段的确定后，从周围的N辆他车中筛选出与本车同向且同路且位于本车前方距离小于L的车辆，确定为前车1、前车2、……、前车M，共M辆，其中0≤M≤N；若M>0，本车的射频识别模块向前车1、前车2、……、前车M发送通信请求；前方车辆射频识别模块对接收的通信请求识别通过后，通过短距离通讯模块以固定通讯协议向本车发送本车目前位置(x₀,y₀,z₀)至前方预定位置(x₀+Δs,y₀+Δs,z₀+Δs)之间的历史运行参数和位置信息，其中Δs为距离变化量，然后进行识别预测阶段；若M＝0，本车提取自身数据采集模块采集的一定周期的历史行驶数据。After being determined in the preparatory stage, from the surrounding N other cars, select the vehicles that are in the same direction and on the same road as the own car and are located at a distance of less than L in front of the own car, and are determined as the front car 1, the front car 2, ..., the front car Vehicle M, a total of M vehicles, where 0≤M≤N; if M>0, the radio frequency identification module of this vehicle sends a communication request to the front vehicle 1, the front vehicle 2, ..., the front vehicle M; the front vehicle radio frequency identification module After the received communication request is identified and passed, send the current position of the vehicle (x ₀ , y ₀ , z ₀ ) to the predetermined position ahead (x ₀ +Δs,y ₀ +Δs, z ₀ +Δs), where Δs is the distance change, and then proceed to the identification and prediction stage; if M=0, the vehicle extracts a certain period of historical driving data collected by its own data acquisition module.

识别预测阶段：Identify the prediction stage:

当M>0，本车接收到数据后，对其进行解析，并且中央处理模块根据前方不同车辆前车1、前车2、……、前车M与本车的距离s_q来确定前车q的行驶参数对本车行驶工况的预测权重ω_q，采用公式(1)确定权重ω_q，其中，q＝1,2,......,M，M≤N：When M>0, the vehicle receives the data and analyzes it, and the central processing module determines the vehicle in front according to the distance s _q between the vehicle in front 1, vehicle 2, ..., the vehicle in front M and the vehicle in front The driving parameter of q is the prediction weight ω _q of the driving condition of the vehicle, and the weight ω _q is determined by formula (1), where q=1,2,...,M, M≤N:

${ω ω}_{q q} = = \frac{{s the s}_{M m - - q q + + 11}}{{s the s}_{11} + + {s the s}_{22} + + \cdot \cdot \cdot \cdot \cdot \cdot + + {s the s}_{q q} + + \cdot &Center Dot; \cdot \cdot \cdot &Center Dot; + + {s the s}_{M m}} (({s the s}_{q q} \leq \leq 3030,, q q = = 1,2 1,2,, \cdot \cdot \cdot \cdot \cdot \cdot,, M m)) - - - - - - ((11))$

采用公式(2)计算预测的本车行驶工况特征参数：Calculate and predict the characteristic parameters of the driving condition of the vehicle using the formula (2):

$T T = = (\begin{matrix} {a a}_{1111} & \cdot \cdot \cdot \cdot \cdot \cdot & {a a}_{11 q q} & \cdot \cdot \cdot \cdot \cdot \cdot & {a a}_{11 M m} \\ \cdot \cdot & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot \cdot \\ \cdot \cdot & \cdot \cdot & \cdot \cdot & \cdot \cdot & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot \cdot \\ {a a}_{k k 11} & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & {a a}_{kq kq} & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & {a a}_{kM kM} \\ \cdot &Center Dot; & \cdot \cdot & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot \cdot & \cdot \cdot & \cdot \cdot \\ \cdot \cdot & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ {a a}_{K K 11} & \cdot \cdot \cdot &Center Dot; \cdot \cdot & {a a}_{Kq Q} & \cdot \cdot \cdot \cdot \cdot \cdot & {a a}_{KM KM} \end{matrix}) (\begin{matrix} {ω ω}_{11} \\ \cdot \cdot \\ \cdot \cdot \\ \cdot \cdot \\ {ω ω}_{q q} \\ \cdot \cdot \\ \cdot \cdot \\ \cdot \cdot \\ {ω ω}_{M m} \end{matrix}) = = (({t t}_{11},, \cdot \cdot \cdot \cdot \cdot \cdot,, {t t}_{k k},, \cdot \cdot \cdot \cdot \cdot \cdot {t t}_{K K})) - - - - - - ((22))$

式中，T表示预测的本车行驶工况特征参数向量；In the formula, T represents the characteristic parameter vector of the predicted driving condition of the vehicle;

a_kq表示前车q的第k个特征参数，其中，q＝1,2,......,M，k＝1,2,......,K，K为行驶工况的特征参数个数；a _kq represents the kth characteristic parameter of the front vehicle q, where q=1,2,...,M, k=1,2,...,K, K is the driving condition The number of characteristic parameters of ;

ω_M表示前车M的行驶工况的特征参数对本车行驶工况预测的权重；ω _M represents the weight of the characteristic parameters of the driving conditions of the preceding vehicle M to the prediction of the vehicle's driving conditions;

t_k表示预测的本车行驶工况的第k个特征参数其中，q＝1,2,......,M，k＝1,2,......,K，K为行驶工况的特征参数个数；t _k represents the kth characteristic parameter of the predicted driving condition of the vehicle, among which, q=1,2,...,M, k=1,2,...,K, K is The number of characteristic parameters of driving conditions;

中央处理模块中存储H类行驶工况作为标准工况，HCU中存储与各个工况相应的控制参数；本车的中央处理模块根据预测的行驶工况特征参数向量T，采用模糊识别模型(公式(3))对本车目前位置(x₀,y₀,z₀)至前方预定位置(x₀+Δs,y₀+Δs,z₀+Δs)之间的行驶工况进行识别预测其所属的行驶工况类别。若M＝0，提取通讯阶段的历史数据的行驶工况特征参数，采用公式(3)进行行驶工况的识别。The central processing module stores class H driving conditions as the standard working conditions, and the HCU stores control parameters corresponding to each working condition; the central processing module of the vehicle adopts the fuzzy recognition model (formula (3)) Identify and predict the driving conditions between the current position of the vehicle (x ₀ , y ₀ , z ₀ ) and the predetermined position ahead (x ₀ +Δs, y ₀ +Δs, z ₀ +Δs) Driving cycle category. If M=0, extract the driving condition characteristic parameters of the historical data in the communication phase, and use the formula (3) to identify the driving condition.

${u u}_{hT h} = = \frac{11}{{Σ Σ}_{{h h}^{' '} = = 11}^{H h} \frac{{Σ Σ}_{k k = = 11}^{K K} {[[{μ μ}_{k k} (({r r}_{kT kT} - - {x x}_{kh kh}))]]}^{22}}{{Σ Σ}_{k k = = 11}^{K K} {[[{μ μ}_{k k} (({r r}_{kT kT} - - {x x}_{{kh kh}^{' '}}))]]}^{22}}} - - - - - - ((33))$

其中，H为中央处理模块中存储的标准工况个数；Wherein, H is the number of standard working conditions stored in the central processing module;

h为中央处理模块中存储的H类标准工况的第h类标准工况，1≤h≤H；h is the hth standard working condition of the H standard working condition stored in the central processing module, 1≤h≤H;

h'为中央处理模块中存储的H类标准工况的第h'类标准工况，1≤h'≤H；h' is the h'th standard working condition of the H-class standard working condition stored in the central processing module, 1≤h'≤H;

u_hT为预测的本车行驶工况特征参数向量T属于第h类标准工况的相对隶属度；u _hT is the relative degree of membership of the predicted characteristic parameter vector T of the driving condition of the vehicle belonging to the h-th standard working condition;

r_kT为预测的本车行驶工况特征参数向量T的第k个特征参数t_k的规格化值；r _kT is the normalized value of the kth characteristic parameter t _k of the predicted driving condition characteristic parameter vector T of the vehicle;

x_kh为第h类标准工况的第k个特征参数的规格化值；x _kh is the normalized value of the kth characteristic parameter of the h-th standard working condition;

μ_k为行驶工况的第k个特征参数的权重；μ _k is the weight of the kth characteristic parameter of the driving condition;

K为行驶工况特征参数的个数；K is the number of characteristic parameters of driving conditions;

经过对行驶工况的识别之后，中央处理模块将识别结果传输给HCU，HCU根据识别结果调出与之相适应的控制参数，使车辆实时达到最优控制。After identifying the driving conditions, the central processing module transmits the identification results to the HCU, and the HCU calls out the corresponding control parameters according to the identification results, so that the vehicle can achieve optimal control in real time.

本发明的效果和益处是：本发明减小道路交通状况变化对车辆运行状况的影响；消除了传统行驶工况预测方法的滞后性；提高了行驶工况预测的准确性，从而使混合动力客车的控制参数更加适合实时变化的行驶工况，提高了车辆的燃油经济性和排放性。The effects and benefits of the present invention are: the present invention reduces the impact of road traffic condition changes on vehicle operating conditions; eliminates the hysteresis of traditional driving conditions prediction methods; improves the accuracy of driving conditions prediction, thereby making hybrid passenger cars The control parameters are more suitable for real-time changing driving conditions, which improves the fuel economy and emissions of the vehicle.

附图说明Description of drawings

图1是本发明混合动力客车行驶工况预测方法的流程图。Fig. 1 is a flow chart of the method for predicting driving conditions of a hybrid electric passenger car in the present invention.

图2是本发明混合动力客车行驶工况预测方法工作原理图。Fig. 2 is a working principle diagram of the method for predicting driving conditions of a hybrid electric passenger car in the present invention.

图中：1本车；2前车1；3前车2；4前车3；5卫星；In the figure: 1 main car; 2 front car 1; 3 front car 2; 4 front car 3; 5 satellite;

s₁、s₂、s₃本车1与前车1、2、3的距离。s ₁ , s ₂ , s ₃ are the distances between the host vehicle 1 and the preceding vehicles 1, 2, and 3.

具体实施方式Detailed ways

以下结合技术方案和附图详细叙述本发明的具体实施方式。The specific embodiments of the present invention will be described in detail below in conjunction with the technical solutions and accompanying drawings.

实施例Example

本发明的技术方案如图1所示。The technical scheme of the present invention is shown in Figure 1.

基于车联网的混合动力客车行驶工况预测方法，图2是本发明的工作原理图，其具体过程如下：Based on the method for predicting driving conditions of a hybrid electric passenger car in the Internet of Vehicles, Fig. 2 is a working principle diagram of the present invention, and its specific process is as follows:

准备阶段：Preparation Phase:

GPS模块实时对本车(1)定位并与电子地图匹配，将实时位置信息(x₀,y₀,z₀)传给短距离通讯模块和中央处理模块；短距离通讯模块实时向周围发送本车(1)位置信息(x₀,y₀,z₀)，同时接收周围N辆其他车的位置信息(x_p,y_p,z_p)传入中央处理模块；数据采集模块通过CAN总线实时采集本车(1)运行参数车速v等，将实时参数与GPS模块传来的实时位置信息(x₀,y₀,z₀)匹配，存入中央处理模块；中央处理模块通过N辆他车位置信息(x_p,y_p,z_p)与本车(1)位置信息(x₀,y₀,z₀)，分析N辆他车的道路信息、方向信息并计算与本车(1)间距离s_p，若两车不同向或不同路或距离s_p大于L，其中，L可根据短距离通讯模块的通讯距离和通行道路的交通水平进行调节，，则放弃通讯；若两车同向且同路且距离s_p小于L，则进行通讯阶段，其中，p＝1,2,…,N。The GPS module locates the vehicle (1) in real time and matches it with the electronic map, and transmits the real-time position information (x ₀ , y ₀ , z ₀ ) to the short-distance communication module and the central processing module; the short-distance communication module sends the vehicle around in real time (1) The location information (x ₀ , y ₀ , z ₀ ), while receiving the location information (x _p , y _p , z _p ) of other cars around the surrounding N vehicles and sending them to the central processing module; the data acquisition module collects them in real time through the CAN bus (1) The running parameters of the vehicle, such as speed v, etc., match the real-time parameters with the real-time position information (x ₀ , y ₀ , z ₀ ) from the GPS module, and store them in the central processing module; information (x _p , y _p , z _p ) and the position information (x ₀ , y ₀ , z ₀ ) of the vehicle (1), analyze the road information and direction information of N other vehicles and calculate the distance between the vehicle (1) and If the two vehicles are in different directions or on different roads or the distance s _p is greater _than L, where L can be adjusted according to the communication distance of the short-distance communication module and the traffic level of the passing road, the communication will be abandoned; if the two vehicles are in the same direction And on the same road and the distance s _p is less than L, then proceed to the communication stage, where p=1,2,...,N.

通信阶段：Communication phase:

经准备阶段的确定后，从周围的N辆他车中筛选出M辆与本车(1)同向且同路且位于本车(1)前方距离小于L的车辆，确定为前车1(2)、前车2(3)、……前车M，共M辆，其中0≤M≤N；若M>0，本车(1)的射频识别模块向前车1(2)、前车2(3)、……前车M发送通信请求；前方车辆射频识别模块对接收的通信请求识别通过后，通过短距离通讯模块以固定通讯协议向本车(1)发送本车(1)目前位置(x₀,y₀,z₀)至前方预定位置(x₀+Δs,y₀+Δs,z₀+Δs)之间的历史运行参数和位置信息，其中Δs为距离变化量，然后进行识别预测阶段；若M＝0，本车(1)提取自身数据采集模块采集的一定周期的历史行驶数据。After being determined in the preparatory stage, M vehicles that are in the same direction and on the same road as the vehicle (1) and located in front of the vehicle (1) with a distance less than L are selected from the surrounding N other vehicles, and are determined as the preceding vehicle 1 ( 2), the front vehicle 2 (3), ... the front vehicle M, a total of M vehicles, where 0≤M≤N; if M>0, the radio frequency identification module of the vehicle (1) is the front vehicle 1 (2), Vehicle 2(3), ... the vehicle M in front sends a communication request; after the radio frequency identification module of the vehicle in front recognizes the received communication request, it sends the vehicle (1) to the vehicle (1) through the short-distance communication module with a fixed communication protocol Historical operating parameters and position information between the current position (x ₀ , y ₀ , z ₀ ) and the predetermined position ahead (x ₀ +Δs, y ₀ +Δs, z ₀ +Δs), where Δs is the distance change, and then Carry out the stage of identification and prediction; if M=0, the vehicle (1) extracts historical driving data of a certain period collected by its own data acquisition module.

识别预测阶段：Identify the prediction stage:

当M>0，本车(1)接收到数据后，对其进行解析，并且中央处理模块根据前方不同车辆前车1(2)、前车2(3)、……前车M与本车(1)的距离s_q来确定前车q的行驶参数对本车(1)行驶工况的预测权重ω_q，采用公式(1)确定权重ω_q，其中，q＝1,2,......,M，M≤N：When M>0, the vehicle (1) analyzes the data after receiving the data, and the central processing module according to the different vehicles in front of the front vehicle 1 (2), the front vehicle 2 (3), ... the front vehicle M and the own vehicle The distance s _q of (1) is used to determine the prediction weight ω _q of the driving parameters of the preceding vehicle q to the driving condition of the vehicle (1), and the weight ω _q is determined by formula (1), where q=1,2,... ..., M, M≤N:

${ω ω}_{q q} = = \frac{{s the s}_{M m - - q q + + 11}}{{s the s}_{11} + + {s the s}_{22} + + \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; + + {s the s}_{M m}} (({s the s}_{q q} \leq \leq 3030,, q q = = 1,2 1,2,, \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot;,, M m)) - - - - - - ((11))$

采用公式(2)计算本车(1)预测行驶工况特征参数：Formula (2) is used to calculate the characteristic parameters of the predicted driving conditions of the vehicle (1):

$T T = = (\begin{matrix} {a a}_{1111} & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & {a a}_{11 q q} & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & {a a}_{11 M m} \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot \cdot & \cdot \cdot & \cdot &Center Dot; & \cdot \cdot \\ {a a}_{k k 11} & \cdot \cdot \cdot \cdot \cdot \cdot & {a a}_{kq kq} & \cdot \cdot \cdot \cdot \cdot \cdot & {a a}_{kM kM} \\ \cdot \cdot & \cdot \cdot & \cdot &Center Dot; & \cdot \cdot & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot \cdot & \cdot &Center Dot; & \cdot \cdot & \cdot &Center Dot; \\ \cdot \cdot & \cdot &Center Dot; & \cdot \cdot & \cdot \cdot & \cdot &Center Dot; \\ {a a}_{K K 11} & \cdot \cdot \cdot &Center Dot; \cdot &Center Dot; & {a a}_{Kq Q} & \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; & {a a}_{KM KM} \end{matrix}) (\begin{matrix} {ω ω}_{11} \\ \cdot &Center Dot; \\ \cdot &Center Dot; \\ \cdot &Center Dot; \\ {ω ω}_{q q} \\ \cdot &Center Dot; \\ \cdot &Center Dot; \\ \cdot &Center Dot; \\ {ω ω}_{M m} \end{matrix}) = = (({t t}_{11},, \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot;,, {t t}_{k k},, \cdot &Center Dot; \cdot \cdot \cdot &Center Dot; {t t}_{K K})) - - - - - - ((22))$

中央处理模块中存储上海、北京、EDC、USADC、JDC等H类行驶工况作为标准工况，HCU中存储与各个工况相应的控制参数；本车(1)的中央处理模块根据预测的行驶工况特征参数向量T，采用模糊识别模型(公式(3))对本车(1)目前位置(x₀,y₀,z₀)至前方预定位置(x₀+Δs,y₀+Δs,z₀+Δs)之间的行驶工况进行识别预测其所属的行驶工况类别。若M＝0，提取通讯阶段的历史数据的行驶工况特征参数，采用公式(3)进行行驶工况的识别。The central processing module stores Shanghai, Beijing, EDC, USADC, JDC and other H-class driving conditions as standard working conditions, and the HCU stores control parameters corresponding to each working condition; the central processing module of the vehicle (1) according to the predicted driving conditions Working condition feature parameter vector T, use the fuzzy recognition model (formula (3)) to compare the vehicle (1) from the current position (x ₀ , y ₀ , z ₀ ) to the predetermined position ahead (x ₀ +Δs,y ₀ +Δs,z ₀ +Δs) to identify and predict the category of driving conditions it belongs to. If M=0, extract the driving condition characteristic parameters of the historical data in the communication phase, and use the formula (3) to identify the driving condition.

h为中央处理模块中存储的H类标准工况的第h类标准工况，1≤h≤H；h is the h-th standard working condition of the H-class standard working condition stored in the central processing module, 1≤h≤H;

经过对行驶工况的识别之后，中央处理模块将识别结果传输给HCU，HCU根据识别结果调出与之相适应的控制参数，使车辆实时达到最优的控制。After identifying the driving conditions, the central processing module transmits the identification results to the HCU, and the HCU calls out the corresponding control parameters according to the identification results, so that the vehicle can achieve optimal control in real time.

每一个车辆都是三个阶段不断循环进行，在此不再赘述。Each vehicle is carried out continuously in three stages, which will not be repeated here.

Claims

1. a hybrid power passenger car driving cycle Forecasting Methodology of networking based on car, is characterized in that comprising with the next stage:

Preparatory stage:

GPS module is located also and electronic map match this car in real time, by real-time position information (x ₀, y ₀, z ₀) pass to short distance communication module and central processing module; Short distance communication module sends this truck position information (x in real time towards periphery ₀, y ₀, z ₀), receive the positional information (x of N other cars around simultaneously _p, y _p, z _p) import central processing module into; Data acquisition module is by this car of CAN bus Real-time Collection operational factor, the real-time position information (x that real-time parameter and GPS module are transmitted ₀, y ₀, z ₀) coupling, deposit central processing module in; Central processing module is by N his truck position information (x around _p, y _p, z _p) and this truck position information (x ₀, y ₀, z ₀), analyze around road information, directional information the calculating and this vehicle headway s of N his car _pif two cars are not in the same way or do not go the same way or apart from s _pbe greater than L, wherein, L represents the screening distance to front vehicles, and because distance road conditions change too far away is excessive, reference value is little, according to the traffic level of the communication distance of short distance communication module and passing road, regulates, and abandons communication; If two cars in the same way and go the same way and apart from s _pbe less than L, carry out the communication stage, wherein, p=1,2 ..., N;

Stage of communication:

After the determining of preparatory stage, from N his car around, filter out with this car in the same way and go the same way and be positioned at the vehicle that this front side distance is less than L, be defined as front truck 1, front truck 2 ..., front truck M, altogether M, wherein 0≤M≤N; If M>0, the radio frequency identification module of this car to front truck 1, front truck 2 ..., front truck M sends communication request; Front vehicles radio frequency identification module sends this car current position (x with fixed telecommunication agreement to this car by short distance communication module after the communication request identification receiving is passed through ₀, y ₀, z ₀) to precalculated position, the place ahead (x ₀+ Δ s, y ₀+ Δ s, z ₀+ Δ s) history run parameter and positional information between, wherein Δ s is change of distance amount, then carries out the identification prediction stage; If M=0, this car extracts the historical running data of the some cycles of its data acquisition module collection;

The identification prediction stage:

Work as M>0, this car receives after data, it is resolved, and central processing module according to the place ahead different vehicle front truck 1, front truck 2 ..., front truck M and this car distance s _qdetermine the prediction weights omega of parameter to this car driving cycle of travelling of front truck q _q, adopt formula (1) to determine weights omega _q, wherein, q=1,2 ..., M, M≤N:

ω_{q} = \frac{s_{M - q + 1}}{s_{1} + s_{2} + \cdot \cdot \cdot + s_{q} + \cdot \cdot \cdot + s_{M}} (s_{q} \leq 30, q = 1,2, \cdot \cdot \cdot, M) - - - (1)

This car that adopts formula (2) to calculate prediction operating mode feature parameter of travelling:

T = (\begin{matrix} a_{11} & \cdot \cdot \cdot & a_{1 q} & \cdot \cdot \cdot & a_{1 M} \\ \cdot & \cdot & \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot & \cdot & \cdot \\ a_{k 1} & \cdot \cdot \cdot & a_{kq} & \cdot \cdot \cdot & a_{kM} \\ \cdot & \cdot & \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot & \cdot & \cdot \\ a_{K 1} & \cdot \cdot \cdot & a_{Kq} & \cdot \cdot \cdot & a_{KM} \end{matrix}) (\begin{matrix} ω_{1} \\ \cdot \\ \cdot \\ \cdot \\ ω_{q} \\ \cdot \\ \cdot \\ \cdot \\ ω_{M} \end{matrix}) = (t_{1}, \cdot \cdot \cdot, t_{k}, \cdot \cdot \cdot t_{K}) - - - (2)

In formula, T represents this car operating mode feature parameter vector that travels of prediction;

A _kqk the characteristic parameter that represents front truck q, wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;

ω _mthe weight of the characteristic parameter of the driving cycle of expression front truck M to the prediction of this car driving cycle;

T _krepresent prediction this car driving cycle k characteristic parameter wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;

In central processing module, store H class driving cycle as standard condition, in HCU, storage is controlled parameter accordingly with each operating mode; The central processing module of this car, according to the driving cycle characteristic parameter vector T of prediction, adopts Fuzzy Identification Model (formula (3)) to this car current position (x ₀, y ₀, z ₀) to precalculated position, the place ahead (x ₀+ Δ s, y ₀+ Δ s, z ₀+ Δ s) driving cycle between carries out its affiliated driving cycle classification of identification prediction; If M=0, the driving cycle characteristic parameter of the historical data in extraction communication stage, adopts formula (3) to carry out the identification of driving cycle;

u_{hT} = \frac{1}{Σ_{h^{'} = 1}^{H} \frac{Σ_{k = 1}^{K} {[μ_{k} (r_{kT} - x_{kh})]}^{2}}{Σ_{k = 1}^{K} {[μ_{k} (r_{kT} - x_{{kh}^{'}})]}^{2}}} - - - (3)

Wherein, H is the standard condition number of storing in central processing module;

H is the h class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h≤H;

H' is the h' class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h'≤H;

U _hTfor this car of prediction operating mode feature parameter vector T that travels belongs to the relative degree of membership of h class standard operating mode;

R _kTfor travel k the characteristic parameter t of operating mode feature parameter vector T of this car of prediction _knormalized value;

X _khit is the normalized value of k characteristic parameter of h class standard operating mode;

μ _kweight for k characteristic parameter of driving cycle;

K is the number of driving cycle characteristic parameter;

Process is to after the identification of driving cycle, and central processing module is transferred to HCU by recognition result, and HCU recalls the control parameter adapting with it according to recognition result, make vehicle reach in real time optimum control.