CN111152780A

CN111152780A - Vehicle global energy management method based on 'information layer-substance layer-energy layer' framework

Info

Publication number: CN111152780A
Application number: CN202010017847.6A
Authority: CN
Inventors: 许楠; 孔岩; 赵迪; 初亮; 杨志华; 睢岩
Original assignee: Jilin University
Current assignee: Jilin University
Priority date: 2020-01-08
Filing date: 2020-01-08
Publication date: 2020-05-15
Anticipated expiration: 2040-01-08
Also published as: CN111152780B

Abstract

The invention discloses a vehicle global energy management method based on an 'information layer-material layer-energy layer' framework, which comprises three main layers: information layer, substance layer, energy layer, and two interfacing layers: a layer of interface between information layer and material layer, a layer of interface between material layer and energy layer, and finally an application layer related to a real vehicle application. The method comprises the steps that an information layer acquires working condition information such as vehicle speed, slip rate and road gradient, a junction layer between the information layer and a material layer completes dispersion of SOC feasible regions, working modes corresponding to triggering conditions are determined in the material layer based on a dynamic/potential energy-vehicle energy conservation framework, a junction layer between the material layer and the energy layer completes determination of a fuel matrix, output of an SOC optimal track region is completed in the energy layer based on a global domain searching algorithm, and a map for real-time application of a real vehicle is formed in an application layer. The framework of 'information layer-material layer-energy layer' provided by the invention can standardize the overall energy management control flow.

Description

Vehicle global energy management method based on 'information layer-substance layer-energy layer' framework

Technical Field

The invention belongs to the technical field of vehicle energy management, and particularly relates to a vehicle global energy management method based on an information layer-material layer-energy layer framework.

Background

Aiming at vehicles, particularly multi-energy source vehicles (including oil-electricity hybrid, electric hybrid, electricity-hydrogen hybrid and the like), the energy management control strategy based on the global optimization algorithm can obtain theoretically global optimal control, and the fuel economy of the whole vehicle is improved to the maximum extent. However, if the control strategy is to realize real-time performance, the following aspects need to be overcome:

①, it is necessary to know the whole operation condition information in advance, i.e. the geographical position distribution information or the time distribution information of the vehicle speed, the gradient, the slip ratio, the wind speed and the wind direction, etc. from the departure place to the destination.

②, the calculation load is reduced and the calculation time is shortened in the aspects of reducing the SOC feasible domain range, realizing the fast optimization of the algorithm and the like.

③ specifies the flow of global energy management control policies to implement engineering.

Therefore, how to realize the normalization of the global optimization control strategy control flow is one of the key problems in solving the global energy management of the vehicle.

The implementation of the global energy management control strategy requires that the whole working condition information is acquired in advance, so how to acquire more comprehensive and accurate working condition information in advance is the second key problem to be solved.

In addition, the implementation of the global energy management control strategy is limited by the calculation load and the calculation time, how to output the optimal control result in a more efficient manner, and meanwhile, ensuring the global optimal fuel economy is the third key problem to be solved.

Disclosure of Invention

The invention designs and develops a vehicle global energy management method based on an 'information layer-material layer-energy layer' framework, which comprises three main layers: information layer, substance layer, energy layer, and two interfacing layers: a layer of interface between information layer and material layer, a layer of interface between material layer and energy layer, and finally an application layer related to a real vehicle application. The method comprises the following steps that an information layer acquires working condition information such as vehicle speed, slip rate and road gradient, a junction layer between the information layer and a substance layer realizes dispersion of SOC (system on chip) feasible regions, the substance layer determines working modes corresponding to various trigger conditions based on a dynamic/potential energy-vehicle energy conservation framework, the junction layer between the substance layer and the energy layer completes determination of a fuel matrix, an SOC optimal track region is output in the energy layer based on a global domain searching algorithm, and an application layer forms a map with the vehicle speed, the battery SOC, the vehicle required torque or required power and a distribution ratio as a coordinate system based on an optimal control result so as to realize real-time application of a real vehicle; the invention aims to standardize an energy management control flow based on a global optimization algorithm, and simultaneously can obtain theoretical global optimal control, thereby improving the fuel economy of the whole vehicle to the maximum extent.

The invention has the beneficial effects that: the vehicle global energy management method based on the 'information layer-material layer-energy layer' framework provided by the invention standardizes the flow of vehicle global energy management control from three levels of information acquisition, material determination and energy distribution; from the perspective of energy, a 'dynamic/potential energy-vehicle energy conservation' framework is provided, decoupling of each trigger condition and a working mode is realized, and the unique working mode of the corresponding vehicle power system controllable component under each trigger condition is determined; a global domain-searching algorithm is provided, so that the optimal distribution of global energy is ensured, and the fuel economy of the whole vehicle is improved; meanwhile, the optimal result is output in the form of the SOC optimal track domain, and the calculation efficiency of the algorithm is improved.

Drawings

Fig. 1 is a structural association diagram of an "information layer-material layer-energy layer" framework according to the present invention.

Fig. 2 is a normal distribution diagram of the vehicle speed distribution of the mixed flow and the single flow.

FIG. 3 is a state transition probability matrix diagram for urban operating conditions in vehicle speed prediction.

FIG. 4 is a state transition probability matrix diagram for a medium and high speed condition of vehicle speed prediction.

FIG. 5 is a state transition probability matrix for a hybrid operating condition in vehicle speed prediction.

FIG. 6 is a diagram of the results of vehicle speed prediction simulation when the predicted CSUDC operating condition duration is 5 s.

FIG. 7 is a graph showing the results of vehicle speed prediction simulation for a predicted duration of HWFET operation for 5 s.

FIG. 8 is a diagram of vehicle speed prediction simulation results when the predicted duration of the NEDC operating conditions is 5 s.

Fig. 9 is a graph showing a relationship between road surface adhesion coefficient and slip ratio at a constant vehicle speed.

FIG. 10 is a graph of the effect of different vehicle speeds on slip rate under wet asphalt pavement.

FIG. 11 is a graph of a data matrix obtained from UDDS condition training under wet asphalt pavement.

Fig. 12 is a flowchart of the road surface maximum adhesion coefficient identification model.

Fig. 13 is a graph showing the result of identifying the adhesion coefficient of a single road surface (wet asphalt, maximum adhesion coefficient 0.8).

Fig. 14 is a graph of the results of adhesion coefficient recognition for a two-way surface combination (dry road and snow road, maximum adhesion coefficients of 0.6 and 0.2, respectively).

FIG. 15 is a graph showing the results of the recognition of the adhesion coefficients of the multi-road surface mix 1 (step road surface, maximum adhesion coefficients of 0.2/0.4/0.6/0.8/1.0, respectively).

FIG. 16 is a graph showing the results of the recognition of the adhesion coefficients of the multi-road surface mix 2 (a full-mix road surface, maximum adhesion coefficients of 0.2/0.4/0.6/0.8/1.0, respectively).

Fig. 17 is a diagram of a CRJ network structure.

FIG. 18 shows the UDDS operating condition prediction results.

FIG. 19 shows the predicted results of HWFET operating conditions.

FIG. 20 is a schematic illustration of selected regions in road slope prediction.

FIG. 21 is a data map of road grade values in a selected area.

FIG. 22 is a diagram of a state transition probability matrix for constructing a slope prediction.

Fig. 23 is a flowchart of gradient prediction.

FIG. 24 is a graph of simulation results for road grade prediction for route 3 in the selected area.

Fig. 25 is a schematic view of vehicle speed when only the position of the indicator light and the speed limit information in the road can be known in advance.

FIG. 26 is a schematic diagram of the time or geographic location corresponding to each turning point in the SOC feasible region.

Fig. 27 is a schematic view of a fuel matrix storage manner.

FIG. 28 is a flow chart of a global domain finding algorithm.

Fig. 29 is a diagram illustrating the renumbering of all SOC discrete points.

FIG. 30 is a schematic diagram of sequential solution in the global domain-finding algorithm.

FIG. 31 is a diagram illustrating reverse-order searching in the global domain-finding algorithm.

Fig. 32 is a schematic diagram of the SOC optimum trajectory domain.

FIG. 33 is a graphical representation of engine displacement (transmission gear ratio) power, economy.

Fig. 34 is a fuel economy versus acceleration time graph.

FIG. 35 is a graphical representation of transmission gear-power and economy.

Detailed Description

The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.

As shown in fig. 1, the present invention provides a vehicle global energy management method based on an "information layer-material layer-energy layer" framework, which is implemented as follows:

because the implementation of the vehicle global energy management control strategy needs to depend on a specific vehicle type, and needs to know working condition information in advance, and the optimal distribution of the global energy is realized on the basis of the vehicle type and the working condition information, the invention provides an information layer-material layer-energy layer framework, which is based on three main layers: information layer, material layer, energy layer, two handing-over layers: the information layer-material layer interface layer, the material layer-energy layer interface layer and the application layer standardize the vehicle global energy management control flow to realize the optimal distribution of global energy.

It should be noted that any two of the three main layers are known to solve for the third one, namely:

(1) if the working condition information included in the information layer and the vehicle working mode and the power system parameters included in the material layer are known, the minimum oil consumption (or the minimum energy consumption) of the vehicle under the working condition information can be obtained under the global energy management framework.

(2) If the working condition information included in the information layer and the related energy consumption constraint included in the energy layer are known, the optimal vehicle power system matching and working mode design result under the working condition information and the energy consumption constraint can be solved under the global energy management framework.

(3) If the vehicle dynamic system parameters and the working mode included in the material layer and the related energy consumption constraint included in the energy layer are known, the most economic driving method of the driver on the road section under the vehicle and energy consumption constraint and the optimal traffic flow control of a traffic signal facility of a traffic control department can be obtained under the global energy management framework.

The relevant definition and implementation method of each layer are as follows:

an information layer

On the information level, the acquisition and prediction of information such as vehicle speed, road gradient, slip rate and the like are realized, and more comprehensive and accurate working condition information is provided for a vehicle global optimization energy management control strategy. The operating condition information is: time distribution information or geographical position distribution information of a vehicle speed, a gradient, a slip ratio, a wind speed, a wind direction, and the like from a departure place to a destination. According to the uncertainty of the information which can be known in advance, the acquisition of the working condition information is realized from three levels:

(one), the specific working condition information under the whole working condition can be known in advance

At the moment, the working condition information such as the running speed, the road gradient, the slip ratio and the like under the whole working condition is completely known and can be directly used for a global energy management control strategy to solve global optimal energy distribution.

For the road gradient, the altitude corresponding to each time or geographic position may be collected by means of the GPS system, and the road gradient α (k) is calculated, that is:

h (k + Δ t), h (k) are altitude of the next time or the geographic position, the current time or the geographic position respectively, v (k + Δ t), v (k) are vehicle speed of the next time or the geographic position, the current time or the geographic position respectively, and Δ t is a time interval.

(II) only know the law followed by the working condition information under the whole working condition in advance

According to whether a certain exact probability distribution function or probability density function can be used for expressing the distribution rule of each working condition information of the vehicle on a certain road section, the acquisition of the second-level working condition information can be analyzed from the following two layers:

1. there is a definite probability distribution function or probability density function to express the distribution rule of the working condition information

At this time, the distribution situation of each working condition information of the vehicle on a certain road section can be expressed by using a certain probability distribution function f (X) or a probability density function f (X), so as to obtain the probability of the current state X in a certain interval (a, b), that is:

wherein, a is the current time or the current position, and b is the next time or the next geographic position. The probability value corresponds to the state transition probability of the vehicle speed, the slip rate or the road gradient at the current time or the current position, and the vehicle speed/the slip rate/the road gradient at the next time or the next geographic position are predicted based on the maximum state transition probability.

Taking the acquisition of the vehicle speed information as an example, it is known that the vehicle speed distributions of the mixed traffic flow and the single traffic flow both conform to the normal distribution, as shown in fig. 2. The probability that the vehicle speed v takes a value within a certain range satisfies the probability density function f (v):

or the probability distribution function f (v):

where μ is the mean and σ is the variance, both constants.

Therefore, the maximum probability when the vehicle speed at the current moment or the current geographic position is transferred to another vehicle speed is selected, and the vehicle speed prediction at the next moment or the next geographic position can be realized.

2. There is no definite probability distribution or probability density function to express the distribution rule of the working condition information

At this time, there is no certain exact probability distribution function or probability density function to express the distribution rule of each working condition information of the vehicle on a certain road segment, but a certain amount of historical driving data can be acquired. When the predicted duration is within a certain range, the automobile running speed and the path selection at the multiple intersections conform to Markov property, so that under the support of certain historical running data, a state transition probability matrix of each piece of working condition information can be obtained based on training historical data, the distribution condition of each piece of working condition information is reflected, and the prediction of each piece of working condition information is further realized. The specific implementation process is as follows:

1) acquisition of vehicle speed information

In consideration of the diversity of the driving conditions, the vehicle speed prediction using the unique state transition probability matrix will undoubtedly generate a large deviation, and thus different driving conditions need to be classified. And training different types of working conditions into respective state transition probability matrixes respectively, and expecting to use the state transition matrixes of the same type to predict the driving working conditions of the same type so as to improve the prediction precision. The specific process is as follows:

① Classification of operating conditions based on selected characteristic variables

Comparing some characteristic parameters of different working conditions, as shown in the table 1:

TABLE 1 some characteristic parameters corresponding to different types of working conditions

From the above table, it can be seen that: for some characteristic parameters, the difference between the working conditions is large, and the driving working conditions can be roughly divided into three categories of city, high speed and mixing.

In order to distinguish the type of the driving condition, part of characteristic parameters are required to be selected as the basis for identifying the working condition. In physical terms, the more accepted characteristic parameters of the running condition of the automobile are 23. The running time and the corresponding time (0-10 km/h, 10-20 km/h, 20-30 km/h, 30-40 km/h and 40-50 km/h) and the proportion (acceleration, deceleration, constant speed and idle speed) of the running time and each vehicle speed state, the maximum speed, the acceleration/deceleration, the average speed value, the standard deviation and the like are included. Due to the fact that the number of the characteristic parameters of the driving conditions is large, most of the characteristic parameters have small influence on judgment when the types are judged, even redundant characteristic parameters exist, and rapid classification is not facilitated. Therefore, the 23 feature parameters are used as basic feature parameters, the 23 feature parameters are screened by using a feature selection method in feature engineering, and finally, a small number of 7 feature parameters which have the largest influence on the classification result are obtained, specifically: average deceleration, average acceleration, constant speed time proportion, deceleration time, acceleration and deceleration standard deviation, constant speed time and running distance. And taking the screened 7 driving condition characteristic parameters as the input of the classifier, wherein the output result is 0/1/2, and the parameters respectively correspond to cities, high speeds and mixed working conditions.

② generate corresponding state transition probability matrices

The vehicle speed distribution is described by a state transition probability matrix. When the predicted vehicle speed duration is within a certain range, the vehicle speed V is determined_nThe method comprises the following steps:

P{V(t_n)≤v_n|V(t₁)＝v₁，V(t₂)＝v₂，…，V(t_n-1)＝v_n-1}

＝P{V(t_n)≤v_n|V(t_n-1)＝v_n-1}，v_n∈R

the system is in state i, warp at time n or at geographical position nAfter the step h, the conditional probability that the state transition of the system at the time of the n + h or the n + h geographic position becomes j is P { X (n + h) ═ j | X (n) ═ i }, and is recorded as P { X (n + h) } j | X (n) ═ i }

If the state space is S ═ {1, 2, …, m }, the Markov chain h step transition matrix p^(h)Comprises the following steps:

if h is 1, the markov chain one-step transfer matrix P is:

taking the speed and the acceleration as system state variables, and dividing the system state variables into sections, wherein the number of the sections is pp and qq, the speed state i belongs to {1, 2, …, pp }, and the acceleration state j belongs to {1, 2, …, qq }. Taking speed and acceleration as coordinate systems, and enabling state transition probability matrix T of automobile running speed to belong to R^pp×qqIs defined as:

wherein, V_k+m-lRepresenting the speed state at the kth time or at the kth geographical location, a_k+mAcceleration states representing the k + L time or k + L geographic location, L being the span of the state transition probability matrix, m e {1, 2, …, L_p}，L_pIndicating the predicted duration.

And training the state transition probability matrixes into respective state transition probability matrixes according to the types of the working conditions aiming at various standard working conditions. And collecting historical driving data of the corresponding road section, judging the class of the working condition, and using the historical data as training data to update the state transition probability matrix of the corresponding working condition type. And finally, acquiring state transition probability matrixes of three working condition types including city, high speed and mixed state, as shown in figures 3-5.

③ vehicle speed prediction based on state transition probability matrix

And knowing the vehicle speed at the current moment or the current geographic position, and selecting the acceleration corresponding to the maximum probability as the acceleration of the current moment or the current geographic position based on the selected state transition probability matrix to realize vehicle speed prediction. Taking the working conditions of CSUDC, HWFET and NEDC as examples, the prediction is carried out once per second, the prediction time length is 5s, and the simulation results are shown in FIGS. 6-8.

2) Slip rate information acquisition

Based on the segmentation of the sliding rate interval, fuzzy recognition is adopted for smaller sliding rate, and accurate recognition is carried out for larger sliding rate. And constructing a recognition model of the maximum adhesion coefficient of the road surface, and further constructing a prediction model according to a formula to analyze and predict the slip rate. The specific process is as follows:

① construction of recognition model of maximum road adhesion coefficient

Because the maximum adhesion coefficient of the road surface cannot be identified according to the slope of the mu-lambda curve, the identification and prediction of the maximum adhesion coefficients of different road surfaces are realized by establishing an off-line database. It is known that: when the vehicle speed is constant, the relationship between the road adhesion coefficients and the slip rates is as shown in FIG. 9; when the road surface type is constant (wet asphalt), the influence of different vehicle speeds on the slip ratio is shown in fig. 10 (i.e., when the road surface type is determined, the slip ratio changes when the vehicle accelerates or decelerates), so the vehicle running speed and acceleration (deceleration) are considered when constructing the off-line database. And performing off-line training on 5 kinds of common road surface dry asphalt, wet asphalt, dry soil road, wet soil road and snow road, and constructing a slip ratio database corresponding to different speed intervals and acceleration intervals of different road surfaces. Wherein, the maximum adhesion coefficient of the dry asphalt pavement is set to be 1, and the maximum adhesion coefficients of the wet asphalt pavement, the dry soil pavement, the wet soil pavement and the snow pavement are set to be 0.8, 0.6, 0.4 and 0.2 in sequence. In order to reduce the database space and improve the data accuracy as much as possible, the interval of the speed interval is set to be 5km/h, and the interval of the acceleration interval is set to be 0.2m/s²And training corresponding matrixes according to different road surface types. Taking a wet asphalt pavement as an example, a data matrix obtained by the UDDS working condition training is shown in fig. 11.

For a large slip rate section, the adhesion coefficients of all the road surfaces are obviously distinguished, so that the input slip rate is only required to be compared with all the road surfaces, the type of the road surface at the moment is determined by adopting a minimum difference value method, and the maximum adhesion coefficient at the moment is further obtained. For a small slip rate interval, because the adhesion coefficients of various road surface types are relatively close and difficult to distinguish when the slip rate is relatively small, the difference value between the input slip rate and the slip rate corresponding to each road surface type is calculated based on an off-line database of different road surface slip rates, if the difference value calculated with the road surface type at the previous moment or the previous geographic position is smaller than the mean value of all the difference values, the road surface type is considered to be unchanged, otherwise, if the difference value is larger than the mean value of all the difference values, the road surface type corresponding to the minimum difference value is taken as the identified road surface type, and then the corresponding maximum adhesion coefficient is given. The specific flow is shown in fig. 12.

And (3) building a maximum adhesion coefficient recognition model of the road surface according to the flow chart, and recognizing the road surfaces of different combinations after setting corresponding parameters, wherein the recognition condition is shown in figures 13-16. The accuracy of the different road surface model identifications is shown in table 2:

TABLE 2 different pavement model identification accuracy table

Pavement combination	Number of road surface types	Frequency of change of road surface type	Accuracy of model identification
				Single road surface	1	0	96.28％
Double-pavement	2	1	94.82％
				Multi-pavement combination 1	5	4	90.51％
Multi-path surface combination 2	4	5	81.75％

As can be seen from table 2, the less the road surface types, the higher the recognition accuracy. For the single type and the two-way surface combined type, although errors are generated in individual sections, the overall recognition rate is high, and for the multi-way surface type combined road surface, the more the road surface type changes, the larger the generated errors are. However, in general, the frequency of the road surface change of the automobile actually running in a working condition is not very high, so that the model can well identify the maximum adhesion coefficient of the road surface, and main parameter values are provided for constructing a slip rate prediction model.

② realizing slip rate prediction based on road adhesion coefficient and vehicle speed

Slip rate prediction based on deterministic skip cycle state networks

As a novel circulating Neural Network (RNN) and a deterministic Jump circulation State Network (CRJ), the Network has a great improvement in nonlinear system identification compared with the traditional Neural Network, and particularly has high processing capability in one-dimensional time sequence. The basic structure is shown in fig. 17, and is composed of an input layer, an output layer and a larger reserve pool. Wherein, in the reserve pool, connected neuron nodes pass through the singleThe backward loop edge is connected with the bidirectional skip edge. All weights are not random at CRJ initialization, and the input weight, the cyclic weight, and the skip weight are represented by r_i、r_c、r_jAnd (4) determining.

For a CRJ state network with M input nodes, N reserve pool neurons and D output nodes, a hyperbolic tangent function is used as an activation function, and a state equation is as follows:

x(k+1)＝tanh(W_mid·x(k)+W_in·u(k))

in the formula, W_inTo input the weight, from r_iDetermination of W_midWeights for the output of the pool pair, including round robin weight and skip weight, W_outIs the output weight, which is determined by the trained data. When r is_iWhen determined, the weight W is input_inComprises the following steps:

wherein, | W_ij|＝r_i，W_ijThe sign of (a) may be generated randomly or empirically. While W in the equation of state_midRelatively complex, cyclic weight matrix W_reThe initialization is as follows:

in the formula, W _ij1 means that the ith neuron is connected to the jth neuron, and that W is a unidirectional connection _ji0. When r is_cWhen the determination is made, then:

if r_jAfter the jump step length L is determined, the jump connection is bidirectional, so that:

wherein i ═ is (1, 2, …, K), and K ═ is (K)₁，k₂，…，k_K+1)＝(1，1+L，1+2L，1+3L，…，1+KL)，

W is to be_reAfter the corresponding weight in the above formula is replaced, the new weight matrix is the weight matrix output by the reserve pool pair.

Initializing a CRJ network, inputting slip rate, vehicle speed and road surface maximum adhesion coefficient of the current time (or current geographic position), outputting the slip rate predicted by the next time (or next geographic position), setting a reserve pool neuron to be 1000, setting sparsity to be 4%, setting input unit scale to be 1, and setting spectrum radius r_i0.5, and r_c＝0.8，r_j0.7. And (4) training the CRJ network by using various standard running conditions, and predicting the actual running condition by using the trained CRJ network. The results of the slip rate prediction of the vehicle driving are shown in fig. 18-19, taking UDDS and HWFET operating conditions as examples.

3) Acquisition of road grade information

Since the original route is changed at the intersection due to road congestion, traffic lights, and the like during the actual running of the vehicle, the route from the starting point to the destination is not unique, i.e., the road gradient prediction at the intersection is required. In order to realize the prediction of the road gradient, the method mainly comprises the following three steps:

① obtaining road gradient data of each road in the area

All possible driving roads and absolute altitudes thereof in a certain regional map are acquired through the Google map. The area diagram is shown in fig. 20, where point a is the starting point and point B is the destination. And taking the first point as a reference point, and obtaining the relative altitude and slope value of each road in the area after calculation. The collected road gradient values are shown in fig. 21.

② determining a state transition probability matrix

Selecting relative altitude and gradient value as state variables of Markov, similarly to speed and acceleration, dividing the relative altitude and gradient value according to intervals, wherein the number of the intervals is tt and ss, the relative altitude state p belongs to {1, 2, …, tt }, the gradient value state q belongs to {1, 2, …, ss }, and the state transition matrix T belongs to R of road gradient^tt×ssIs defined as:

wherein H_kRepresenting the relative altitude state, θ, at time k or at the kth geographic location_k+lRepresents the slope value state at time k + l or at geographic position k + l, as shown in FIG. 22. And constructing a state transition matrix by using the relative altitude and slope values in a certain range at all intersections meeting the constraint conditions in the advancing process, wherein the length of the taken range is determined by the speed and the predicted time length. In the selected area, 6 paths with the shortest distance and almost consistent size exist from the point A to the point B, and the paths are intersected with each other to generate 4 effective intersections. Therefore, the vehicle speed at each intersection is predicted, the slope prediction length is determined by combining the prediction duration, the relative altitude data of all the exits at the intersection under the prediction length is obtained and converted into the state variable corresponding to the markov, and the state transition probability matrix of the slope prediction is constructed, wherein the specific prediction process is shown in fig. 23.

③ enabling prediction of road slope at an intersection

Taking route 3 as an example, road slope prediction at 2 intersections is achieved based on the state transition probability matrix and the slope prediction length (the next time or the next geographical position is determined according to the prediction duration and the vehicle speed). The road gradient at other positions in the whole working condition does not need to be predicted, and the road gradient is the collected original data. The simulation results are shown in fig. 24. It should be noted that, if the vehicle travels to the destination point C and the travel distance of the vehicle cannot meet the travel distance required by the operating condition, the vehicle will travel from point B to point a and then continue to travel from point a to point B, and so on until the travel distance required by the operating condition is met.

Determining an intersection position interval corresponding to the next time or the next geographic position according to the predicted length and the vehicle speed at the current time or the current geographic position, acquiring the road gradient of the intersection position interval based on the state transition probability matrix of the road gradient, and selecting the slope value corresponding to the maximum probability as the slope value at the next time or the next geographic position.

(III) only the constraint conditions applied to the working condition information under the whole working condition can be known in advance

At this time, the working condition information profile from the departure point to the destination with time or geographic position as coordinates can be obtained only by obtaining the maximum value of the working condition information of the vehicle at each time or each geographic position on a certain road section. Taking the acquisition of the vehicle speed information as an example, only the position of the indicator light in the road, the speed limit information, and the like can be known in advance. By combining the extreme acceleration and braking capabilities of the vehicle, a vehicle speed profile with coordinates of geographic positions or time from the departure point to the destination can be drawn, and the profile represents the maximum vehicle speed of the vehicle at each geographic position or each time on the route, as shown in fig. 25.

The information of each working condition at each time or each geographical position can be 0-X_maxAnd (4) taking values, wherein the probability distribution of each working condition information is difficult to measure. According to the constraint condition of each working condition information, the following 2 cases are discussed:

1. only the maximum value of the running speed at each time or geographical position on each road section is restricted

The running speed at each time or geographic position is taken as a random variable X, and possible m values are X ═ X₁，x₂，…，x_i，…x_m]And X is less than or equal to X_maxThe probability for each value is p₁，p₂，...，p_i，…，p_mIn this case, the uncertainty of the random variable X is represented by the information entropy h (X), that is:

wherein, the constraint condition is that the sum of the probabilities of the values is 1, namely:

to accurately estimate the state of a random variable, it is common practice to maximize entropy, the principle of which is to admit a known thing (knowledge) and make no assumptions about the unknown thing, without any prejudice. Thus, to maximize h (x), lagrangian function L (p, λ) is constructed:

the above formula is p_iAnd (5) calculating a partial derivative to obtain:

obtaining by solution: p is a radical of_i＝e^λ-1，i＝1，2，...，m。

According to the constraint condition, obtaining

Namely: p is a radical of₁＝p₂＝…p_i…＝p_m＝1/m。

From the above, for a system containing m events, when the entropy is the maximum, the probability of the event inevitably satisfies the equal probability. Therefore, the traveling speed at each unknown time or geographic position is processed according to the equal probability that the traveling speed at each time or geographic position is 0 to X_maxAnd obtaining the running speed corresponding to the next moment or geographic position according to the uniformly distributed values.

And then according to the running speed, acquiring the road gradient of the corresponding road section according to the acquisition method of the road gradient without the distribution rule of the certain exact probability distribution or probability density function expression working condition information.

2. The mean value and the variance of the running vehicle speed of each time or geographic position on each road section are restrained

If the mean value mu and the variance sigma of the running vehicle speed X distribution of each time or geographic position in the road section are known, the constraint condition is met

Namely, it is

Constructing the Lagrangian function L (p, lambda)₁，λ₂，λ₃)：

The above formula is p_iAnd (5) calculating a partial derivative to obtain:

obtaining by solution:

according to the constraint conditions

Obtaining:

substituting the above formula into the constraint condition

Obtaining:

λ₁＝1-log(2πσ²)，λ₃＝-1/2σ²

based on the working condition information outer contour line, according to a 3 sigma criterion, the vehicle speed close to the mean value is taken as a prediction basis, and the vehicle speed information is predicted; and then according to the running speed, acquiring the road gradient of the corresponding road section according to the acquisition method of the road gradient without the distribution rule of the certain exact probability distribution or probability density function expression working condition information.

As can be seen from the above, according to the maximum entropy principle, there is always the maximum entropy (such as gaussian distribution, average distribution, etc.) of a certain distribution, and the prediction of each operating condition information is further realized based on the probability distribution.

Aiming at the acquisition of the working condition information of the third layer, if the accuracy of the acquired information is improved, a certain amount of historical driving data needs to be acquired in advance, the acquisition of the information of the third layer is converted into the acquisition of the information of the second layer, and then the prediction of the information of each working condition is realized on the basis of the second layer.

Second, the interface layer between the information layer and the material layer

There is a layer of interface between the information layer and the material layer for determining the SOC matrix. The SOC matrix is a two-dimensional matrix formed by sequentially numbering from top to bottom according to the number of discrete points at each time or geographic position and storing the corresponding SOC value in the corresponding position. The number of discrete points is the number of all discrete points at each time or geographical position calculated from the SOC maximum value. The SOC maximum value means the maximum SOC and the minimum SOC at each time or geographical position, that is, the SOC value on each boundary line of the SOC possible region.

When the vehicle model and the road condition information are determined, parameters such as the maximum charging/discharging current limit of the battery, the SOC maximum value, the running time and the like can be determined. Since the solution of the global optimization energy management control strategy belongs to a numerical solution problem, state variables need to be discretized. The state variables comprise vehicle speed and battery SOC, so that SOC needs to be dispersed, and the dispersion process mainly comprises the steps of determining the quantity of dispersion points at each moment or each geographic position and the SOC value corresponding to each dispersion point. In order to enable equal SOC transfer to exist between adjacent time or adjacent geographic position state points and the number of the state points at any time or geographic position does not change violently, the SOC needs to be dispersed uniformly, so that the initial SOC and the termination SOC can be defined as two reference lines, and the discrete points are determined at certain SOC discrete intervals on the basis of the reference lines.

As shown in fig. 26, SOC possible field dispersion mainly includes the following four steps:

1. determining SOC feasible region shape

Taking the maximum SOC of the vehicle-mounted power battery as a horizontal line, taking an initial SOC point as a starting point, taking the maximum charging current of the vehicle-mounted power battery as a slope, obtaining an initial charging curve, and determining an intersection point of the initial charging curve and the maximum SOC horizontal line as an initial maximum SOC point;

taking the minimum SOC of the vehicle-mounted power battery as a horizontal line, taking an initial SOC point as a starting point, taking the maximum discharge current of the vehicle-mounted power battery as a slope, obtaining an initial discharge curve, and determining an intersection point of the initial discharge curve and the minimum SOC horizontal line as an initial minimum SOC point;

respectively obtaining parallel lines of an initial charging curve and an initial discharging curve by taking the termination SOC point as a termination point, and respectively determining an intersection point of the initial charging curve and the initial discharging curve and an intersection point of the maximum SOC horizontal line and the minimum SOC horizontal line as the termination maximum SOC point and the termination minimum SOC point;

and determining the shape of the SOC feasible region through the sequential straight line connection of the initial SOC point, the initial maximum SOC point, the termination SOC, the termination minimum SOC point and the initial minimum SOC point.

2. Partitioning of SOC feasible regions

Making a horizontal line for the initial SOC, determining the intersection point of the initial SOC and the parallel line of the initial discharge curve as a secondary initial SOC point, making a horizontal line for the termination SOC, and determining the intersection point of the termination SOC and the parallel line of the initial charge curve as a secondary termination SOC point;

respectively making vertical lines for the initial SOC point, the initial maximum SOC point, the secondary termination SOC point, the initial minimum SOC point, the termination maximum SOC point, the secondary initial SOC point and the termination SOC point, and obtaining corresponding time or geographic position: 0, t₁，T_A，t₂，t₃，T_B，t₄，t₅So as to perform zone division on the SOC feasible region.

3. Determining the number of SOC discrete points at each time or geographic location

According to the maximum charging and discharging current limit of the batteryLimiting the maximum motor power and the required power at each time (or geographical location) determines the maximum discrete interval of the SOC δ SOC_maxFurther determining the actual SOC discrete interval delta SOC (the delta SOC is not more than delta SOC)_max)；

And respectively making horizontal lines parallel to the datum line upwards and downwards at intervals of delta SOC, and intersecting the vertical lines corresponding to the moments or the geographic positions to obtain SOC discrete points at the moments or the geographic positions.

And acquiring the SOC maximum value at each moment or geographic position according to the initial discharge curve and the parallel line thereof, the initial charge curve and the parallel line thereof, the parallel line of the maximum SOC and the parallel line of the minimum SOC.

And obtaining the number of SOC discrete points corresponding to each time or geographical position in each area according to the SOC maximum value, the initial SOC and the final SOC of each time or geographical position.

4. Determining SOC matrix

And numbering the SOC discrete points at each moment or geographic position, wherein the 1 st discrete point corresponds to the number 1 and is numbered sequentially from top to bottom. If the maximum value of the number of the SOC discrete points at each time or geographic position is n, the dimension of the SOC matrix is n multiplied by n. And calculating the SOC value corresponding to each time or geographical position in each area according to the initial SOC, the termination SOC, the maximum SOC, the minimum SOC, the delta SOC, the maximum value and the minimum value of the SOC at each time or geographical position and the number of the SOC discrete points, and storing the SOC value at the position corresponding to the SOC matrix according to the number of the SOC values.

Third, the material layer

On the matter level, from the viewpoint of energy, the conversion between the external energy and the energy of the vehicle is actually realized during the driving process of the vehicle. Therefore, a 'dynamic/potential energy-vehicle energy' conservation framework is provided, and external factors and internal factors are reasonably and feasibly combined to finally form the triggering condition of the controllable component of the vehicle power system. Under each triggering condition, an additional condition is added, so that the unique working mode of the corresponding controllable component of the vehicle power system under each triggering condition can be determined, the decoupling of the triggering condition and the working mode is further realized, and a foundation is laid for the determination of a subsequent fuel matrix.

The method specifically comprises the following 3 aspects:

1. determining vehicle coasting conditions

The invention can effectively reduce the fuel consumption by the modes of braking energy recovery, sliding, intermittent acceleration and deceleration and the like. In order to make full use of the vehicle sliding mode to reduce fuel consumption, various road vehicle sliding conditions are determined, which mainly include, but are not limited to, the following three conditions:

① horizontal road surface, wherein the vehicle speed at the current time or the current geographical position is not 0 and the acceleration is less than 0

At this time, the vehicle can coast by inertia, and the coasting mode is a coasting deceleration mode.

② climbing slope, wherein the speed of the vehicle at the current time or the current geographical position and the speed of the vehicle at the next time or the next geographical position are not 0, the acceleration of the current time or the previous geographical position is less than 0, and the requirements are met

③ going downhill, if the speed of the vehicle at the current time or the current geographical position is not 0, the acceleration of the vehicle at the current time or the previous geographical position is greater than 0 and satisfies the requirement

When the vehicle slides by using the component force of gravity, the vehicle is in a downhill free sliding mode; if the vehicle speed at the current moment or the current geographic position is not 0 as that at the next moment or the next geographic position, the acceleration of the current moment or the previous geographic position is less than 0 and meets the requirement that

Wherein m is the total vehicle mass, g is the gravity acceleration, f is the rolling resistance coefficient of the total vehicle, α is the road gradientAngle, A is the windward area of the whole vehicle, C_DIs the coefficient of air resistance, v_cFor the vehicle speed, δ is the rotating mass conversion factor and du/dt is the longitudinal acceleration.

2. Construction of dynamic/potential energy-vehicle energy conservation frame

In order to realize the decoupling of each triggering condition and a working mode, the invention provides a dynamic/potential energy-vehicle-mounted energy conservation framework so as to determine the unique working mode of the corresponding controllable component of the vehicle power system under each triggering condition. The conservation framework of the kinetic/potential energy-vehicle-mounted energy specifically refers to the following steps: the external factors and the internal factors are combined reasonably feasible to ultimately form the triggering condition for the controllable component of the vehicle powertrain.

Among these, the so-called external factors include, but are not limited to, the following: (1) the difference between the next time (or the next geographic position) corresponding to the vehicle speed and the kinetic energy of the vehicle at that time (or that geographic position), i.e., the amount of change Δ E in the kinetic energy of the vehicle_k(ii) a (2) The potential energy difference between the next time (or the next geographical position) corresponding to the altitude and the vehicle at the time (or the geographical position), namely the change amount delta E of the potential energy_p；(3)ΔE_kAnd Delta E_pAnd the corresponding next moment (or next geographical position) and the change Δ E of the total mechanical energy at this moment (or geographical position); (4) and (6) vehicle speed. The internal factor includes, but is not limited to, the difference between the state of charge (state of charge) of the vehicle power battery at the next time (or the next geographical location) and the time (or the geographical location), i.e., the change Δ SOC of the battery SOC.

It is to be noted that: for a multi-power source vehicle, the vehicle-mounted energy relates to oil-electricity hybrid, electric-hydrogen hybrid and the like, and a conservation framework is the conversion between kinetic/potential energy and vehicle-mounted energy (including electric energy); for a single energy source (such as a pure electric vehicle), the vehicle-mounted energy is electric energy, and the conservation framework is the conversion between the kinetic/potential energy and the electric energy.

3. Determining vehicle operating modes under various triggering conditions

The working modes of the vehicle related by the invention are not limited to the traditional series, parallel and series-parallel connection working modes or the separately calibrated working modes such as economy and movement, but are the working state combinations of all power execution components which are mechanically or electrically connected and feasible. Such as: for a single-motor vehicle, the control components comprise an engine, a motor and a clutch, wherein the engine state comprises two modes of work (V) and non-work (X), the motor state comprises three modes of non-work (X), power generation and electric drive, and the clutch state comprises two modes of engagement (0) and disengagement (1), so that the work modes are 2 × 2 × 3-12.

For a hybrid vehicle with a parallel structure (the motor is positioned behind the clutch and in front of the transmission), the working mode determined by the system under each triggering condition is analyzed based on a dynamic/potential energy-vehicle energy conservation framework.

The following points are noted:

① an additional condition for a vehicle to be able to operate in an electric-only mode is that the electric machine power is greater than the vehicle demand power.

② the conditions for the vehicle to slide need to satisfy the 3 mentioned in the first aspect, that is, whether the vehicle can slide can be judged according to the potential energy change, the vehicle speed and the stress analysis.

③ for Δ SOC, if Δ SOC < 0 (i.e., SOC ↓), the motor operating state is only for electric driving, if Δ SOC > 0 (i.e., SOC ↓), the motor operating state is only for electric power generation, and if Δ SOC ═ 0 (i.e., SOC-), the motor operating state is only for non-operation.

The operating mode of the corresponding vehicle powertrain controllable component is determined based on each of the triggering conditions, specifically each of the triggering conditions as follows, as shown in table 3.

TABLE 3 trigger conditions

Under each triggering condition, the unique working mode of the controllable component of the vehicle power system corresponding to each triggering condition can be determined by adding additional conditions that the vehicle slides, the power demand of the motor is greater than the power demand of the vehicle and the like, and a foundation is laid for subsequently determining a fuel matrix to realize the optimal distribution of the global energy.

For a hybrid vehicle with a series structure or a series-parallel structure, if the working mode of the vehicle under each trigger condition is to be determined, the working mode limited by the configuration can be increased or decreased on the basis of the parallel configuration vehicle working mode analysis (table analysis) based on a dynamic/potential energy-vehicle-mounted energy conservation framework, so that the determination of the working mode under each condition can be realized. Such as:

① if the electric machine is located before the clutch, the vehicle configuration is in series, limited by the vehicle configuration, there is no electric drive mode.

② if the motor is located at the output of the gearbox and is coaxial with the engine, the motor must be connected to the axle and the motor cannot be used to start the engine, so there is no motor back-drive engine mode.

③ if the motor is placed in the drive axle to drive the wheels directly, the system cannot be switched between pure electric drive and pure engine drive at will.

It is noted that the proposed method based on the "kinetic/potential energy-on-board energy" conservation framework to determine the operating mode at each triggering condition is still applicable to two-motor vehicles and hybrid vehicles with planetary rows.

For a two-motor vehicle, the control components include an engine, a motor M1, a motor M2, and a clutch, and if the engine state includes two states of active (v) and inactive (x), the motor M1 state includes three states of inactive (x), motoring, and generating, the motor M2 state includes three states of inactive (x), motoring, and generating, and the clutch state includes two states of engaged (0) and disengaged (1), the operating mode has a total of 2 × 3 × 3 × 2 — 36.

If so:

P₀＝min{P_{M1_max}，P_{M2_max}}＝P_{M1_max}

P₁＝max{P_{M1_max}，P_{M2_max}}＝P_{M2_max}P₂＝P_{M1_max}+P_{M2_max}

P₃＝P_{e_max}+P₀＝P_{M1_max}+P_{e_max}

P₄＝P_{e_max}+P₁＝P_{M2_max}+P_{e_max}

P₅＝P_{e_max}+P_{M1_max}+P_{M2_max}

wherein, P_{M1_max}，P_{M2_max}，P_{e_max}The maximum power of the motor M1, the maximum power of the motor M2 and the maximum power of the engine. Therefore, the following steps are carried out: p₀＜P₁＜P₂＜P_{e_max}＜P₃＜P₄＜P₅。

In this case, the additional conditions include ① whether free-wheeling or freewheeling at the downhill slope can occur, ② P_req＜P₀Or P₀＜P_req＜P₁Or P₁＜P_req＜P₂Or P₂＜P_req＜P_{e_max}Or P_{e_max}＜P_req＜P₃Or P₃＜P_req＜P₄Or P₄＜P_req＜P₅。

For a hybrid vehicle with a planetary row, control components comprise an engine, a motor M1 and a motor M2, if the engine state comprises two states of working (V) and non-working (X), and the states of the motor M1 and the motor M2 comprise three states of non-working (X), electric driving and power generation, the working modes have 2 x 3 to 18 in total.

Fourthly, a junction layer between the material layer and the energy layer

A cross-connection layer exists between the material layer and the energy layer, the gear is considered based on a dynamic/potential energy-vehicle energy conservation framework, the lowest fuel consumption corresponding to the determined working mode under each trigger condition is determined, and the value of the lowest fuel consumption is stored into a three-dimensional fuel matrix fuel (k, i, j) in a (k, i, j) form, wherein i represents the ith discrete point of the jth time or the jth geographic position, k represents the kth discrete point of the (j + l) time or the (j + l) geographic position, j represents the working condition time or the number of the geographic position points, and l represents the storage precision of the fuel matrix. The specific storage process is as follows: the fuel consumption from the first discrete point at the first time or the first geographical position to the first discrete point at the second time or the first geographical position exists at the (1, 1, 1) position in the three-dimensional matrix, the fuel consumption from the first discrete point at the first time or the first geographical position to the second discrete point at the second time or the second geographical position exists at the (2, 1, 1) position in the three-dimensional matrix, and so on, the fuel consumption from the i-th discrete point at the j-th time or the j-th geographical position to the k-th discrete point at the (j + l) th time or the (j + l) th geographical position exists at the (k, i, j) position in the three-dimensional matrix. Finally, a fuel matrix fuel (k, i, j) is generated, which represents the fuel consumption from the i-th discrete point at the j-th time or the j-th geographic position to the k-th discrete point at the (j + l) th time or the (j + l) th geographic position.

Respectively storing corresponding engine power, motor power, engine torque, motor torque, engine speed, motor speed, clutch state and gear state into a three-dimensional control matrix P while calculating the fuel consumption under the determined working mode under each trigger condition_e(k，i，j)、P_m(k，i，j)、T_e(k，i，j)、T_m(k，i，j)、n_e(k，i，j)、n_mAnd (k, i, j), clutch (k, i, j) and gear (k, i, j) lay a foundation for searching the optimal control sequence according to the optimal state point.

The solving process of the fuel consumption and each control variable is as follows:

aiming at the determined working mode, according to the vehicle speed obtained by the information layer, the slip ratio information and the vehicle configuration determined by the material layer, the engine speed n is solved by utilizing a kinetic equation_eAnd motor speed n_m. Namely: for the P0-P2 configuration, the engine speed and the motor speed satisfy: n is_e＝n_m＝i_gi₀·n_w(ii) a For the P3 configuration, the engine speed and the motor speed satisfy: n is_e＝i_gi₀·n_w，n_m＝i₀·n_w(ii) a For the P4 configuration, the engine speed and the motor speed satisfy: n is_e＝i_qi₀·n_w，n_m＝n_w. Wherein i₀Is a main reduction ratio i_gTo the transmission ratio of the variator, n_wIs the wheel speed of the vehicle

r is the wheel radius, v_ωIs the wheel speed.

Determining the required torque F of each gear of the vehicle at each moment or each geographic position based on the running equation of the vehicle according to the information of the vehicle speed, the acceleration, the road gradient, the transmission ratio of each gear and the like_reqAnd the required power P_reqNamely:

wherein m is the mass of the whole automobile, g is the gravity acceleration, f is the rolling resistance coefficient of the whole automobile, α is the road slope angle, A is the windward area of the whole automobile, C_DIs the coefficient of air resistance, v_cDelta is a rotating mass conversion coefficient which is the running speed of the vehicle,

for longitudinal acceleration, η_TFor the total mechanical transmission efficiency, I_wIs the moment of inertia of the wheel, I_fIs the moment of inertia of the flywheel.

Based on the demanded power P_reqAnd the SOC interval delta SOC between two discrete points, and solving the engine torque T in the working mode_eAnd motor torque T_mNamely:

P_req＝P_e+P_bat；

P_e＝T_en_e/9550；

wherein, U_ocIs the open circuit voltage of the battery, R_intIs the internal resistance of the battery, C is the battery capacity, P_batTo battery power, η_mFor motor efficiency, the motor speed n_mAnd motor torque T_mLooking up the motor map to obtain η_m＝f(T_m，n_m)。

Based on the determined engine speed n_eAnd engine torque T_eSearching the map of the engine to obtain the corresponding fuel consumption Q_eNamely: q_e＝F(T_e，n_e)。

Fifth, energy layer

In the energy level and the stage oil consumption calculation process, as a plurality of SOC shifts with the same variation exist in the same stage and the solving process based on multi-stage optimization can be regarded as the accumulated calculation of the cost in different stages, more than one SOC optimal track is finally obtained when the lowest oil consumption is searched based on the global optimization algorithm. Therefore, a global domain searching algorithm is provided, and the SOC optimal track domain is output, so that the calculation efficiency of the algorithm is effectively improved.

As shown in fig. 27, the global domain-finding algorithm flow includes the following steps:

step one, sequentially solving and storing the optimal value from each state point to the starting point

In the energy level, each discrete point in the SOC feasible region is regarded as a state point, the fuel matrix is converted into the weight between the state points in the graph theory, and the global energy optimal distribution problem is converted into the shortest path problem from the starting point to the end point.

As shown in fig. 28, according to the original numbers of the discrete points of the SOC, the optimal values (shortest distances) from the respective state points to the starting point are sequentially solved from the starting point and stored in the distance matrix D (i, j) in a two-dimensional form, and the process of solving the optimal values includes the steps of:

step 1, starting from the starting point s₀Starting from the remaining vertices s_kThe initial values of the shortest path length that may be achieved are: d (k) ═ min { W (i, k) | s_kE.g. V- { i } }; wherein fuel (k, i, j) represents fuel consumption from the ith state point at the jth time or the jth geographic position to the kth state point at the (j + l) th time or the (j + l) th geographic position, the weight of an edge corresponding to the ith point in the jth column to the kth point in the (j + l) th column in the two-dimensional graph is W (i, k), and V is a set formed by discrete points;

step 2, selecting s_jSo that: d (j) min { d (k) s |_k∈V-M}，s_jFor the end point of the shortest path from the starting point obtained at present, let M be M ∪ { j }, M be the already obtained starting point s known₀A set of end points of the shortest path from (the first time or the SOC discrete point at the first position), the initial state of which is an empty set;

step 3, modifying the starting point s₀Starting from any vertex s of the set V-M_kThe shortest path length that can be reached; if D (j) + W (j, k) < D (k), updating D (k) to: d (k) ═ d (j) + W (j, k);

and 4, repeating the step 2 and the step 3 to obtain and record the shortest distance from the starting point to each discrete point.

Step two, sequentially solving and storing the optimal state point of the previous time or the geographical position of each discrete point

The number N of state points at all times or all positions_i(j ═ 1, 2.. times, n) are added to yield the total number of state points

As shown in fig. 29, all the state points are renumbered, and the state points at each time or each geographic position are numbered sequentially, that is, the state point number at the first time or the first geographic position is 1, and the state at the last time or the last geographic positionThe point number is Num, and the state point numbers at the mth moment or the mth geographical position are sequentially:

storing each number in a two-dimensional matrix SOC_numberIn (i, j), the position is consistent with the number of the original SOC discrete point; in the present embodiment, SOC_numberThe (1, 1) location stores the number 1 of the state point at the first time or the first geographical location, (1, 2) location stores the number 2 of the first state point at the second time or the second geographical location, and so on.

As shown in fig. 30, based on the new state point numbers, while solving the shortest path from a certain state point at a certain time or a certain geographical position to the starting point, all the shortest paths are searched, and the numbers of the state points passing through the previous time or the previous geographical position on all the shortest paths are recorded and stored in the corresponding column of the two-dimensional matrix prev; the number of columns of prev is determined by the total number of status points, i.e. the number of columns is Num (the number of columns of the prev matrix is the number of all discrete points, i.e. Num columns).

Step three, searching and storing the optimal state point at each moment or geographic position in a reverse order

And searching a number Nun stored in the last column in the prev matrix from the end point, wherein the number is the optimal state point at the (n-l) th time or the (n-l) th geographic position, and storing the optimal state point in the (n-l) th column of the point matrix. And according to the found serial number Nun, continuously searching the serial number stored in the Nun th column in the prev matrix, wherein the obtained serial number is the optimal state point at the (n-2l) th time or the (n-2l) th geographic position, and storing the optimal state point in the (n-2l) th column of the point matrix. If a plurality of numbers Nun are found at this time₁，Nun₂,., then sequentially searching for the Nun th bit in the prev matrix₁，Nun₂,.. storing the serial numbers in the column, storing the serial numbers in the (n-2l) th column of the point matrix, namely, a plurality of optimal state points exist in the (n-2l) th time or the (n-2l) th geographic position, and so on until the starting point (the serial number 1 appears) is searched in the reverse order, and the obtained point matrix is as shown in fig. 31, wherein the stored point matrix is storedThe stored new numbers 1-Num of all the optimal state points at each time or each geographic position (the first time and the last time are both provided with only one discrete point, namely the only optimal state point, and the corresponding numbers are 1 and Num).

Step four, forming an SOC optimal track domain

As shown in fig. 32, to obtain the SOC optimal trajectory domain, the state point numbers stored in the point matrix need to be restored to the original numbers, that is, the positions of the optimal state points in the SOC feasible domain are found; the number corresponding to the optimal state point at the current moment or the current geographical position is subtracted by the number of all state points ending to the previous moment or the previous geographical position, so that the positions of all state points on all SOC optimal tracks can be obtained, an SOC optimal track domain is formed, the optimal solution is output in the form, and the calculation efficiency can be effectively improved.

Sixth, application layer

According to the corresponding position (k, i, j) of each optimal state point, P generated by the cross-connection layer between the material layer and the information layer is searched_e(k，i，j)、P_m(k，i，j)、T_e(k，i，j)、T_m(k，i，j)、n_e(k，i，j)、n_mThe values of the corresponding positions in the (k, i, j), clutch (k, i, j) and gear (k, i, j) three-dimensional control matrix can obtain the optimal control sequence of each time or each geographic position, which is as follows: engine power, motor power, engine torque, motor torque, engine speed, motor speed, clutch operating state, and gear state.

Obtaining optimal results under various standard working conditions based on a global optimization energy management control strategy respectively in three types of city, high speed and mixing, wherein the optimal results comprise control sequences of engine power, motor power, engine torque, motor torque, engine rotating speed, motor rotating speed, clutch working state, gear state and the like; wherein the engine torque T is adjusted_eWith vehicle demand torque T_reqThe ratio is defined as the torque distribution ratio TSR ═ T_e/T_reqOr to engine power P_eWith the power P demanded of the vehicle_reqThe ratio is defined as the power distribution ratio PSR ═ P_e/P_reqCollectively referred to as distribution ratio R_e-mAnd the method is used for representing the corresponding vehicle working mode in the optimal control decision.

Based on vehicle speed v, battery SOC and vehicle demand torque T_reqOr the power required by the vehicle P_reqDistribution ratio R_e-mAnd establishing a map for real-time application of the global optimization control strategy for a coordinate system, and obtaining the corresponding rotating speed and torque of the engine and the motor according to the map in the actual running process of the vehicle so as to realize real-time application of vehicle energy management.

In another embodiment, if the condition information included in the information layer and the related energy consumption constraint included in the energy layer are known, the optimal vehicle power system matching and working mode design result under the condition information and energy consumption constraint can be obtained under the global energy management framework

Known energy consumption constraints include a power consumption constraint Q_mAnd fuel consumption constraint Q_eThe obtained power system matching comprises an engine, a motor and a speed changer (gear number and speed ratio i)_g) Equal control components, power battery parameters and final reduction ratio i₀The design of (3).

Knowing the vehicle parameters m, g, f, C_DA and delta, selecting a certain vehicle configuration (P0-P4), and determining the required power P of each time or each geographic position of the vehicle according to the working condition information such as the vehicle speed, the slip ratio, the road gradient and the like acquired by the information layer_reqRequired torque F_req：

Wherein m is the mass of the whole automobile, g is the gravity acceleration, f is the rolling resistance coefficient of the whole automobile, a is the road slope angle, A is the windward area of the whole automobile, C_DIs the coefficient of air resistance, v_cDelta is a rotating mass conversion coefficient which is the running speed of the vehicle,

is the longitudinal acceleration.

(1) Power battery parameter matching

Determining a maximum battery current based on power consumption constraints at each time or each geographic location

According to

A battery SOC maximum discrete interval is determined. And determining the battery capacity C according to the electric quantity consumption sum based on the electric quantity consumption constraint of each time or each geographic position. Determining open-circuit voltage U of the power battery based on the internal resistance model of the battery according to the required power in the pure electric mode_ocAnd internal resistance R_intNamely:

and (3) taking a ternary lithium ion battery (the voltage U of the single battery is 3.7V, and the resistance R) as a single battery, and calculating the number n of the required serial or parallel single batteries. When in series connection, the number of the monomers is n ═ U_ocN ═ R/R in parallel_int。

(2) Motor parameter matching

Based on the electric quantity consumption constraint and the vehicle required power at each moment or each geographic position, corresponding to the motor individual driving mode, and determining delta SOC (state of charge) P corresponding to each moment or each geographic position according to the required power in the pure electric mode_req/(3.6·U_ocC); determining delta SOC corresponding to each time or each geographic position based on the optimal SOC track domain, and preliminarily determining a lower limit value P 'of rated power of the motor according to dSOC ═ max { delta SOC, delta SOC }, wherein'_mNamely: p'_m＝3.6·U_ocdSOC.C. Preliminary determination of peak power P of motor_mmax＝λP′_mWherein λ is the overload coefficient, which is generally 2-2.5.

Since β is n_mmax/n′_mWherein n is_mmaxIs the highest rotation speed of the motor, n'_mβ is the rated rotating speed of the motor, generally takes 2-3 as the constant power coefficient of the motor, in order to fully utilize the working characteristics of low-speed constant torque and high-speed constant power of the motor, a medium-high speed driving motor is generally adopted, the constant power range is wide, the motor can run at high efficiency, meanwhile, the mass, the size and the like of the motor are relatively small, the mass of the whole vehicle is reduced, the space in the vehicle is saved, the driving motor with the highest rotating speed of 6000r/min or below is generally selected, and based on the analysis, the rated rotating speed n 'of the motor is preliminarily determined'_m(i.e. the dividing point of constant torque and constant power), the maximum speed n of the motor_mmaxFurther preliminarily determining rated torque T 'of the motor'_m＝P_req·9550/n′_mMotor peak torque T_mmax＝P_req·9550/n_mmax。

Based on the obtained motor parameters, searching the existing motor model to meet the motor parameters and take the motor model with the most similar external characteristics as the motor with final design, and determining the final motor parameters: including motor speed range (n)_mmin，n_mmax) Motor peak torque T_mmaxRated rotation speed n 'of motor'_mRated torque T 'of motor'_mRated power P 'of motor'_mThe motor efficiency map comprises a motor external characteristic curve and a motor efficiency map.

(3) Engine parameter matching

Determining the engine power required by each moment or each geographic position corresponding to the engine individual driving mode based on the fuel consumption constraint and the vehicle required power of each moment or each geographic position, and taking the maximum value as the lower limit value P 'of the rated power of the engine'_e(ii) a Similarly, a lower limit value T 'of rated torque of the engine is determined'_e. Attention is paid to: for trucks, the specific vehicle power is also analyzed to determine the engine rating, i.e.:

preliminary determination of peak engine power

And peak engine torque

Wherein

The meaning of the engine torque reserve coefficient is consistent with the overload coefficient lambda of the motor. According to n_emax＝9550P_emax/T_emaxPreliminarily determining the peak engine speed n_emax. If the engine is coaxial with the motor, n_emaxCan be reacted with n_mmaxAnd (5) the consistency is achieved.

Based on the fuel consumption constraints at various moments or various geographic positions, the engine displacement V is preliminarily determined according to the relation curve (shown in FIG. 33) of the engine displacement and the dynamic and economic performances, and the dynamic and economic performances are considered.

Based on fuel consumption constraint Q at each time or each geographic position_eAnd corresponding engine torque and engine speed, and preliminarily determining an engine map, namely: q_e＝F(T_e，n_e). According to the preliminarily matched engine parameters and the map, searching the existing engine model, taking the engine model with the most similar external characteristics, the engine displacement and the map as a finally designed engine, and determining the final motor parameters: comprising engine displacement V, engine speed range (n)_emin，n_emax) And rated power P 'of engine'_eRated torque T 'of engine'_ePeak power P of the engine_emaxPeak engine torque T_emaxAn engine external characteristic curve and an engine map.

(4) Determination of the transmission ratio of the final drive

Based on the energy consumption constraint at each time or each geographic position, according to a fuel economy-acceleration time curve (as shown in fig. 34), if both the power performance and the fuel economy are considered, the final speed reduction ratio i is determined₀2.6; if dynamic is the main objective, then larger i can be selected₀A value; if the economic efficiency is the main target, a smaller i can be selected₀The value is obtained.

(5) Transmission gear and ratio determination

Based on the determined final reduction ratio i₀Maximum grade α obtained from information layer_maxInformation, maximum vehicle speed v_maxInformation and the maximum required torque of the vehicle, determining the maximum transmission ratio:

① is determined according to the maximum climbing gradient:

wherein, T_max＝min{T_emax，T_mmax}。

② is determined according to the attachment conditions:

note: the above formula corresponds to the case where the engine and the motor are coaxial.

Neglecting the acceleration resistance (gradient i is 0), then:

③ is determined according to the maximum output torque of the engine and the motor:

based on the determined final reduction ratio i₀And determining the minimum transmission ratio according to the maximum rotating speed of the engine and the corresponding peak torque, the maximum rotating speed of the motor and the corresponding peak torque:

① is determined according to the maximum rotating speed of the engine, the maximum rotating speed of the motor and the maximum vehicle speed:

② is based on the maximum output torque corresponding to the maximum engine speed, the maximum output torque corresponding to the maximum motor speed and the running resistance F corresponding to the maximum vehicle speed_maxDetermining:

based on the results, the maximum transmission ratio and the minimum transmission ratio range are determined, and the maximum transmission ratio and the minimum transmission ratio are determined according to the condition that the ratio of the maximum transmission ratio to the minimum transmission ratio is not larger than 1.7-1.8 and respectively correspond to the lowest gear and the highest gear of the transmission.

Based on energy consumption constraints at various times or various geographic positions, the transmission gear is determined according to the relation curves of the transmission gear-dynamic property and economy (as shown in figure 35) and considering both the dynamic property and the economy. It should be noted that for light and medium duty trucks, a 5-speed transmission is typically employed; for cars, a 5-6 gear transmission is generally adopted.

The transmission ratios of the gears are distributed according to geometric progression based on the transmission gear, the lowest gear transmission ratio and the highest gear transmission ratio. Such as: in the case of a 5-speed transmission, the relationship between the gear ratios of the respective gears and the gear ratio of the 1-speed gear is as follows:

(6) determining vehicle operating modes

Based on the selected vehicle configuration, a vehicle operating mode is determined.

And determining the engine speed and the motor speed at each moment or each geographic position. For the P0-P2 configuration, the engine speed and the motor speed satisfy: n is_e＝n_m＝i_g·i₀·n_w(ii) a For the P3 configuration, the engine speed and the motor speed satisfy: n is_e＝i_gi₀·n_w，n_m＝i₀·n_w(ii) a For the P4 configuration, launchThe motor rotating speed and the motor rotating speed meet the following conditions: n is_e＝i_gi₀·n_w，n_m＝n_w。

And determining the vehicle working modes at all times or all the geographic positions based on the energy consumption constraints at all the times or all the geographic positions and the vehicle speed and road gradient information obtained by the information layer, wherein the vehicle working modes at all the times or all the geographic positions mainly comprise a pure electric driving mode, an engine single driving mode, a mixed mode, a sliding mode and a driving charging mode. Taking a vehicle with the P2 configuration as an example, the operating states of the control components in the operating modes are shown in table 4:

TABLE 4 working states of the respective control units and design basis of the respective working modes

Mode of operation	Engine	Electric machine	Clutch device	According to
					Pure electric drive	×	Electric drive	Separation of	0＜P_req≤P_mmax
With separate driving of the engines	√	×	Joining	P_mmax≤P_req≤P_emax
					Hybrid drive	√	Electric drive	Joining	P_req≥P_mmax+P_emax
Driving charging	√	Power generation	Joining	-P_mmax≤P_req≤P_mmax
					Braking energy recovery	×	Power generation	Separation of	-P_mmax≤P_req≤0
Coast mode	×	×	Separation of	When the sliding condition is satisfied

Note: the basis of each working mode also comprises a wheel speed n_wThe rotating speed ranges of the engine and the motor need to be met.

In addition, the following points are to be noted when determining the operation mode:

①, the change of the engine torque required in each working mode at the adjacent time or geographical position cannot be too large, namely delta T is less than or equal to b (b is a set constant);

② the engine start cannot be too frequent;

③ it should be noted that gear changes are not too frequent when determining gear.

Determining the engine torque T required at each moment or each geographic position based on the required power of the vehicle, the delta SOC, the determined vehicle working mode, the determined engine map and the motor efficiency map_eMotor torque T_mNamely:

P_req＝P_e+P_bat

P_e＝T_en_e/9550

based on the above results, it is verified whether the matched powertrain parameters satisfy the vehicle required power and required torque (determined by the operating condition information obtained from the information layer).

In another embodiment, if the vehicle dynamic system parameters and working mode included in the material layer and the related energy consumption constraint included in the energy layer are known, the most economical driving method of the driver of the road section under the vehicle and energy consumption constraint and the optimal traffic flow control of the traffic signal facility of the traffic control department and the like can be obtained under the global energy management framework

The energy consumption constraint related to each time or each geographic position is known, and the electric quantity Q required by the vehicle at each time or each geographic position is determined_mAnd fuel consumption Q_eAnd further determining engine power, motor power, engine torque, motor torque, engine speed and motor speed distributed to the vehicle at each moment or each geographic position based on the vehicle configuration, the motor map, the engine map and the working mode determined by the material layer, namely:

η_m＝G(T_m，n_m)

Q_e＝F(T_e，n_e)

P_e＝T_en_e/9550

P_m＝T_mn_m/9550

and determining the vehicle speed corresponding to each moment or each geographic position according to the gear state, the vehicle configuration and the main reduction ratio, namely determining the most economical driving method corresponding to the driver on the road section under the constraint of the vehicle and the energy consumption. Taking a P2 configuration vehicle as an example (when slip ratio is not considered), the solving process is as follows:

total power demand P of vehicle determined based on energy consumption constraint related to each time or each geographic position_req＝P_e+P_batAccording to the automobile power balance equation:

the grade angle α is solved, i.e. the road grade information in the corresponding information layer.

According to the solved vehicle speed v_cAnd determining the position of the traffic signal lamp on the road section according to the geographic position corresponding to the vehicle speed of 0.

When a plurality of vehicles run on the road section, the optimal traffic flow control of a traffic control department to traffic signal facilities can be determined according to the most economical driving method (namely, vehicle speed information) of the drivers planned by the vehicles and the overlapped part of the corresponding geographic positions when the vehicle speed is 0.

While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims

1. A vehicle global energy management method based on an information layer-material layer-energy layer framework is characterized by comprising three main layers:

on the information layer, acquiring the running condition information of the vehicle;

determining parameters and working modes of a vehicle power system on the material layer;

determining, at an energy horizon, an energy consumption constraint of a vehicle;

two handover levels:

determining an SOC matrix at an interface layer between the information layer and the material layer;

determining a fuel matrix and each control matrix at a junction layer between the material layer and the energy layer;

and on an application layer, obtaining a map of the vehicle speed, the battery SOC, the required torque or the required power, and the torque or power distribution ratio according to the running condition information of the vehicle and the corresponding vehicle working mode and energy consumption constraint, and further performing global energy optimal distribution.

2. The vehicle global energy management method based on the 'information layer-material layer-energy layer' framework according to claim 1,

according to the running condition information of the vehicle on the information layer and the parameters and the working mode of the vehicle power system on the material layer, the corresponding minimum energy consumption or minimum oil consumption of the vehicle can be obtained;

according to the running condition information of the vehicle on the information layer and the related energy consumption constraint of the vehicle on the energy layer, the parameters and the working mode of the corresponding optimal vehicle power system can be obtained; and

according to the parameters and the working mode of the vehicle power system of the material layer and the energy consumption constraint of the vehicle of the energy layer, the most economic driving method of the corresponding driver and the optimal traffic flow control of the traffic signal facility by the traffic control department can be obtained.

3. The vehicle global energy management method based on the information layer-material layer-energy layer framework is characterized in that the process of obtaining the minimum energy consumption or the minimum oil consumption of the corresponding vehicle according to the running condition information of the vehicle on the information layer and the parameters and the working modes of the vehicle power system on the material layer comprises the following steps:

step one, acquiring a vehicle running speed, a road gradient and a slip rate on an information layer;

step two, at a connection layer between the information layer and the material layer, dispersing the SOC feasible region of the vehicle-mounted power battery, determining delta SOC and the quantity of discrete points, and obtaining an SOC matrix;

step three, determining the unique working mode of the corresponding controllable component of the vehicle power system under each triggering condition on the substance layer based on a dynamic/potential energy-vehicle energy conservation framework;

step four, determining a fuel matrix and each control matrix at a junction layer of the material layer and the energy layer;

and fifthly, outputting an SOC optimal track domain and a corresponding optimal control point on the energy layer according to the fuel matrix and based on a global domain searching algorithm.

4. The vehicle global energy management method based on the information layer-material layer-energy layer framework as claimed in claim 3, wherein in the step one, the acquisition of the vehicle speed, the slip rate and the road gradient information is realized from three levels according to the uncertainty of the information which can be known in advance:

when the vehicle can obtain the running speed and the road altitude under the whole working condition, obtaining the road gradient and the slip rate of the corresponding acquisition time or the geographic position according to the vehicle speed and the road altitude;

when the rule followed by the working condition information of the vehicle running under the whole working condition can be obtained, the running speed, the road gradient and the slip rate of the vehicle are obtained according to the followed rule;

when the constraint condition applied to the running condition information of the vehicle under the whole working condition can be obtained, the rule followed by the running condition information of the vehicle under the whole working condition is obtained according to the constraint condition, and the running speed, the road gradient and the slip rate of the vehicle are obtained.

5. The vehicle global energy management method based on the information layer-material layer-energy layer framework as claimed in claim 4, characterized in that in the third step, the dynamic/potential-vehicle energy conservation framework is a reasonable feasible combination of external factors and internal factors, and the only working mode of the controllable components of the vehicle power system is determined on the basis of the accessory conditions;

wherein the external factors include, but are not limited to, the following: the vehicle speed; the vehicle kinetic energy variation delta E of the next moment or the next geographic position corresponding to the vehicle speed and the current moment or the geographic position_k(ii) a The potential energy variation delta E of the vehicle at the next moment or the next geographic position corresponding to the altitude and the current moment or the geographic position_p；ΔE_kAnd Delta E_pThe variation delta E of the total mechanical energy of the corresponding next time or the next geographic position and the current time or the geographic position;

the internal factors include, but are not limited to, the state of charge SOC variation Δ SOC of the vehicle-mounted battery at the next time or the next geographical location and the current time or the geographical location;

the accessory conditions include, but are not limited to, the following factors:

whether the vehicle can perform inertia deceleration sliding or downhill free sliding; and

and comparing the required power of the vehicle with the maximum required power of the motor and the engine.

6. The vehicle global energy management method based on the information layer-material layer-energy layer framework as claimed in claim 5, wherein in the fourth step, the determining the fuel matrix process comprises:

based on a dynamic/potential energy-vehicle-mounted energy conservation framework, determining the engine power P under the working mode determined between two state points at two adjacent moments or geographic positions according to a kinetic equation_eMotor power P_mEngine torque T_eMotor torque T_mEngine speed n_eMotor speed n_mThe method comprises the following steps of obtaining fuel consumption fuel under each working mode based on an engine map, a clutch state clutch and a gear state gear, and storing the obtained values in a corresponding three-dimensional matrix, wherein the fuel consumption fuel comprises the following steps: fuel matrix fuel (k, i, j) and control matrix P_e(k，i，j)、P_m(k，i，j)、T_e(k，i，j)、T_m(k，i，j)、n_e(k，i，j)、n_m(k，i，j)、clutch(k，i，j)、gear(k，i，j)；

Wherein i represents the ith state point of the jth time or the jth geographic position, k represents the kth state point of the (j + l) th time or the (j + l) th geographic position, j represents the number of the working condition time or the geographic position points, and l represents the accuracy of the constructed fuel matrix and the constructed control matrix.

7. The vehicle global energy management method based on the information layer-material layer-energy layer framework as claimed in claim 3 or 6, wherein in the step five, based on the global domain searching algorithm, the process of outputting the SOC optimal trajectory domain comprises the following steps:

step a, renumbering all state points in an SOC feasible domain, and converting the fuel matrix into weights among the state points;

step b, sequentially solving the shortest distance from each state point to the starting point, and determining the optimal state point of each state point at the previous moment or the previous geographical position;

c, determining the optimal state point of each time or each geographic position in a reverse order, and restoring the optimal state points to the original number;

and d, determining the position of each optimal state point in the SOC feasible domain after the serial number is restored, and determining the SOC optimal track domain.

8. The vehicle global energy management method based on the information layer-material layer-energy layer framework is characterized in that the process of obtaining the corresponding optimal vehicle power system parameters and working modes according to the driving condition information of the vehicle on the information layer and the related energy consumption constraint of the vehicle on the energy layer comprises the following steps:

step 1, determining the required power P of each time or each geographic position of a vehicle according to the working condition information such as the vehicle speed, the slip rate, the road gradient and the like acquired by the information layer_reqAnd the required torque F_req；

Step 2, according to the electric quantity consumption constraint Q of each time and each geographic position_mFuel consumption constraint Q_eRequired power P_reqAnd the required torque F_reqDetermining power battery parameters and engine parameters;

step 3, determining motor power parameters according to the power battery parameters; determining the transmission ratio of a main speed reducer according to a fuel economy-acceleration time curve;

step 4, determining the gear and the speed ratio of the transmission according to the transmission ratio of the main speed reducer;

and 5, determining the vehicle working mode according to the selected vehicle configuration.

9. The vehicle global energy management method based on 'information layer-material layer-energy layer' frame as claimed in claim 8, wherein in the step 4, the process of determining the transmission gear and speed ratio according to the final drive transmission ratio comprises the following steps:

step I, according to the maximum climbing gradient, determining a first value range of the maximum transmission ratio as follows:

wherein, T_max＝min{T_emax，T_mmax}；

According to the adhesion condition, determining a second value range of the maximum transmission ratio as follows:

wherein n is_min＝min{n_emin，n_mmin}；

According to the maximum output torque of the engine and the motor, determining a third value range of the maximum transmission ratio as follows:

according to the maximum rotating speed of the engine, the maximum rotating speed of the motor and the maximum vehicle speed, determining a first value range of the minimum transmission ratio as follows:

wherein n is_max＝min{n_emax，n_mmax}；

According to the maximum output torque corresponding to the maximum rotating speed of the engine, the maximum output torque corresponding to the maximum rotating speed of the motor and the running resistance F corresponding to the maximum vehicle speed_maxAnd determining a second value range of the minimum transmission ratio as follows:

in the formula, α_maxAt the maximum climbing gradient, v_maxAt maximum vehicle speed, r is wheel radius, η_tFor mechanical efficiency, G ═ mg; t is_mmaxIs the motor peak torque, n_mmaxThe peak rotating speed of the motor;

step II, determining the maximum transmission ratio i according to the following conditions_maxAnd a minimum transmission ratio i_min：

Wherein the maximum gear ratio corresponds to a highest gear of the engine and the minimum gear ratio corresponds to a lowest gear of the engine;

and step III, distributing the transmission ratio of each gear according to the geometric progression based on the gear of the transmission, the lowest gear transmission ratio and the highest gear transmission ratio.

10. The vehicle global energy management method based on the information layer-material layer-energy layer framework as claimed in claim 2, is characterized in that the corresponding driver most economical driving method is obtained according to the parameters and working modes of the vehicle dynamic system of the material layer and the energy consumption constraint of the vehicle of the energy layer, and the process of the optimal traffic flow control of the traffic signal facility by the traffic control department comprises the following steps:

step A, determining electric quantity and fuel consumption required by vehicles at various moments or various geographic positions according to related energy consumption constraints, and determining corresponding control variables;

b, determining the vehicle speed corresponding to each moment or each geographic position according to the gear state, the vehicle configuration and the main reduction ratio;

step C, determining total required power of the vehicle based on relevant energy consumption constraints, and obtaining road gradient information in an information layer according to an automobile power balance equation;

d, determining the position of a traffic signal lamp on the road section according to the calculated speed;

and E, determining the optimal traffic flow control of the traffic signal facilities by the traffic control department according to the overlapped part of the corresponding geographic positions when the speed of the vehicles is 0 in the planned speed track when the vehicles run on the road section.