Vehicle energy management method, management system and vehicle

Abstract

The invention provides a vehicle energy management method, a management system and a vehicle. The method comprises the following steps: determining a state variable and a control variable in vehicle energy management; obtaining values of state variables of a starting point and an ending point of a vehicle driving journey; based on the values of the state variables of the starting point and the ending point, a forward enumeration method and a dynamic programming algorithm are adopted, and the minimum accumulated energy consumption of the driving stroke is taken as a target for parallel calculation; when the forward enumeration method and the dynamic planning algorithm finish calculation of a certain middle stage in the driving stroke at the same time, forward and reverse splicing is carried out; comprising the following steps of: respectively splicing a forward optimal control route and a forward optimal control variable sequence which are positively calculated by a forward enumeration method with a reverse optimal control route and a reverse optimal control variable sequence which are reversely calculated by a dynamic programming algorithm; and obtaining a global optimal control route and a global optimal control variable sequence of the whole driving stroke. The method can reduce the calculation amount of a dynamic planning algorithm, and improves the operation speed.

Description

Technical Field

[0001] This application relates to the technical field of energy management, and particularly to a vehicle energy management method, a management system, and a vehicle. Background Art

[0002] Energy management algorithms are important factors affecting the economy of the whole vehicle. Commonly used energy management algorithms include the forward enumeration method and the dynamic programming algorithm. The forward enumeration method lists all possible energy distribution schemes and selects the best scheme that meets the conditions. This method is intuitive and easy to understand, facilitating understanding and implementation. However, its disadvantage is that it requires a relatively large amount of computing power, occupies a large storage capacity, has a slow calculation speed, and poor economy. The dynamic programming algorithm can significantly improve the efficiency of the algorithm by avoiding repeated calculation of sub-problems. Compared with the forward enumeration method, it reduces the storage capacity and improves the calculation speed. However, the calculation speed of the dynamic programming algorithm is still difficult to meet the real-time power distribution request of the whole vehicle. Summary of the Invention

[0003] The purpose of the embodiments of this application is to provide a vehicle energy management method, a management system, and a vehicle, which can reduce the calculation amount of the dynamic programming algorithm and improve the operation speed of the whole vehicle energy management algorithm.

[0004] One aspect of the embodiments of this application provides a vehicle energy management method. The method includes: determining state variables and control variables in vehicle energy management; obtaining the values of the state variables at the start and end points of the vehicle driving itinerary; respectively using the forward enumeration method and the dynamic programming algorithm based on the values of the state variables at the start and end points and performing parallel calculations with the minimum cumulative energy consumption of the driving itinerary as the target; when the forward enumeration method and the dynamic programming algorithm simultaneously complete the calculation of a certain intermediate stage within the driving itinerary, perform forward and reverse splicing, including: splicing the forward optimal control route and the forward optimal control variable sequence calculated forward by the forward enumeration method with the reverse optimal control route and the reverse optimal control variable sequence calculated backward by the dynamic programming algorithm to obtain the global optimal control route and the global optimal control variable sequence of the entire driving itinerary.

[0005] Further, the determining state variables and control variables in vehicle energy management includes: when the vehicle is in a pure electric range extender configuration, using the state of charge (SOC) of the power battery as the state variable and the power of the range extender as the control variable; when the vehicle is in a hybrid configuration, using the SOC of the power battery as the state variable and the motor torque as the control variable.

[0006] Further, the method further includes: detecting the running phases of the dynamic programming algorithm and the forward enumeration method, wherein when it is detected that the dynamic programming algorithm has passed the k-th phase and is running towards before the (k - 1)-th phase, and at the same time it is detected that the forward enumeration method has passed the k-th phase and is running towards after the (k + 1)-th phase, the forward and backward splicing is performed, where 1 ≤ k ≤ N, and N is the number of phases into which the driving itinerary is divided.

[0007] Further, the performing of the forward and backward splicing includes: triggering the synchronous detection of the values of the state variables; enumerating the values of all state variables in the k-th phase; splicing the forward minimum cumulative energy consumption calculated by the forward enumeration method and the backward minimum cumulative energy consumption calculated by the dynamic programming algorithm corresponding to the values of each state variable in the k-th phase to obtain the global minimum cumulative energy consumption corresponding to the values of each state variable in the k-th phase; finding the value of the state variable corresponding to the minimum value among the global minimum cumulative energy consumptions corresponding to the values of each state variable in the k-th phase; respectively splicing the forward optimal control route and the forward optimal control variable sequence calculated by the forward enumeration method and the backward optimal control route and the backward optimal control variable sequence calculated by the dynamic programming algorithm corresponding to the value of this state variable to obtain the global optimal control route and the global optimal control variable sequence.

[0008] Further, the method further includes: when it is detected that the dynamic programming algorithm and the forward enumeration method simultaneously complete the calculation of the k-th phase, stopping the calculation of the dynamic programming algorithm and the forward enumeration method.

[0009] Further, the method further includes: running the dynamic programming algorithm and the forward enumeration method in a first controller and a second controller respectively for distributed synchronous calculation.

[0010] Further, the method further includes: monitoring the memory load rate of the second controller in real time; when the memory load rate of the second controller exceeds a predetermined limit value, stopping the calculation of the forward enumeration method of the second controller, continuing to maintain the calculation of the dynamic programming algorithm of the first controller, and recording the stopping phase of the forward enumeration method; when the dynamic programming algorithm runs to the stopping phase of the forward enumeration method, performing the forward and backward splicing.

[0011] Further, the method further includes: obtaining the planned vehicle speeds at N stages of the driving trip, where the parallel calculation of the values of the state variables based on the starting point and the ending point by using the forward enumeration method and the dynamic programming algorithm with the goal of the minimum cumulative energy consumption of the driving trip includes: according to the value of the state variable at the starting point and the planned vehicle speeds at each stage, using the forward enumeration method in each stage running from the first stage to the Nth stage, traversing all the values of the control variables corresponding to each value of the state variable, calculating the stage energy consumption corresponding to all the values of the control variables, so as to obtain the minimum cumulative energy consumption corresponding to each value of the state variable of the whole vehicle at each stage; according to the value of the state variable at the ending point and the planned vehicle speeds at each stage, using the dynamic programming algorithm in each stage running from the Nth stage to the first stage, traversing all the values of the control variables corresponding to each value of the state variable, calculating the stage energy consumption corresponding to all the values of the control variables, so as to obtain the minimum cumulative energy consumption corresponding to each value of the state variable of the whole vehicle at each stage.

[0012] Further, the obtaining the planned vehicle speeds at N stages of the driving trip includes: through the anticipatory cruise technology, predicting the road information in front of the vehicle; based on the road information in front, pre-planning the vehicle speed, so as to obtain the planned vehicle speeds at N stages of the driving trip of the vehicle.

[0013] Another aspect of the embodiments of the present application provides a vehicle energy management system. The vehicle energy management system includes a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the vehicle energy management method as described above.

[0014] Another aspect of the embodiments of the present application provides a vehicle. The vehicle includes the vehicle energy management system as described above.

[0015] The vehicle energy management method, management system, and vehicle of one or more embodiments of the present application can reduce the computational amount of the dynamic programming algorithm, improve the running speed of the vehicle energy management algorithm, and improve the vehicle economy by using the dynamic programming algorithm and the forward enumeration method for parallel calculation.

[0016] In addition, the vehicle energy management method, management system, and vehicle of one or more embodiments of the present application can realize the distributed calculation of the dynamic programming algorithm and the forward enumeration method by running the dynamic programming algorithm and the forward enumeration method in different controllers respectively, and can make full use of the controllers with excess computing power in the vehicle.

[0017] Furthermore, the vehicle energy management method, management system, and vehicle of one or more embodiments of the present application can realize the fast switching of the algorithm without affecting the vehicle functions by monitoring the load rate of the controller in real time. Description of the Drawings

[0018] Figure 1 It is a flowchart of a vehicle energy management method according to an embodiment of the present application.

[0019] Figure 2 It is a main schematic diagram of the vehicle powertrain architecture of a pure electric range extender configuration vehicle.

[0020] Figure 3 It is a schematic diagram of discretizing state variables and control variables according to an embodiment of the present application.

[0021] Figure 4 It is a schematic diagram of discretizing state variables and control variables according to another embodiment of the present application.

[0022] Figure 5 It is a schematic diagram of the transport capacity when the forward solution and the backward solution algorithms are run synchronously according to an embodiment of the present application.

[0023] Figure 6 It is a schematic diagram of the globally optimal control route obtained by forward and backward splicing according to an embodiment of the present application.

[0024] Figure 7 It is a schematic block diagram of a vehicle energy management system according to an embodiment of the present application. Detailed Description of the Embodiments

[0025] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices consistent with some aspects of the present application as detailed in the appended claims.

[0026] Next, with reference to the drawings, the vehicle energy management method, management system, and vehicle of each embodiment of the present application will be described in detail. Without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0027] The present application provides a vehicle energy management method. Figure 1 The flowchart of the vehicle energy management method according to an embodiment of the present application is disclosed. As Figure 1 shown, the vehicle energy management method according to an embodiment of the present application may include steps S11 to S14.

[0028] In step S11, the state variables and control variables in vehicle energy management are determined.

[0029] The vehicle can include a pure electric range extender configuration and a hybrid configuration. When the vehicle is in the pure electric range extender configuration, the state of charge (SOC) of the power battery is used as the state variable, and the power of the range extender is used as the control variable; when the vehicle is in the hybrid configuration, the SOC of the power battery is used as the state variable, and the motor torque is used as the control variable.

[0030] In step S12, the values of the state variables at the starting point and the ending point of the vehicle driving itinerary are obtained.

[0031] For both the pure electric range extender configuration vehicle and the hybrid configuration vehicle, the state variable is the SOC of the power battery. Therefore, in step S12, the SOC values at the starting point and the ending point of the vehicle driving itinerary can be obtained.

[0032] In step S13, based on the values of the state variables at the starting point and the ending point, the forward enumeration method and the dynamic programming algorithm are respectively used, and parallel calculations are performed with the minimum cumulative energy consumption of the driving itinerary as the goal.

[0033] In some embodiments, the vehicle energy management method of the present application further includes: obtaining the planned vehicle speeds at N stages of the driving itinerary. The vehicle energy management method of the present application performs vehicle energy management when the vehicle speed is known.

[0034] Optionally, through the predictive cruise technology, the road information in front of the vehicle can be predicted; based on the road information in front of the vehicle, the vehicle speed can be pre-planned, so that the planned vehicle speeds at N stages of the driving itinerary can be obtained.

[0035] Among them, the parallel calculation in step S13 based on the values of the state variables at the starting point and the ending point using the forward enumeration method and the dynamic programming algorithm with the minimum cumulative energy consumption of the driving itinerary as the goal can include step S131 and step S132.

[0036] In step S131, according to the value of the state variable at the starting point and the planned vehicle speeds at each stage, the forward enumeration method is used to traverse all the values of the control variables corresponding to each value of the state variable at each stage from the first stage to the Nth stage, and calculate the stage energy consumption corresponding to all the values of the control variables, so as to obtain the minimum cumulative energy consumption corresponding to each value of the state variable of the whole vehicle at each stage.

[0037] In step S132, according to the value of the state variable at the ending point and the planned vehicle speeds at each stage, the dynamic programming algorithm is used to traverse all the values of the control variables corresponding to each value of the state variable at each stage from the Nth stage to the first stage, and calculate the stage energy consumption corresponding to all the values of the control variables, so as to obtain the minimum cumulative energy consumption corresponding to each value of the state variable of the whole vehicle at each stage.

[0038] In step S14, when the forward enumeration method and the dynamic programming algorithm complete the calculation of a certain intermediate stage within the driving journey simultaneously, forward and reverse splicing is performed, including: splicing the forward optimal control route and the forward optimal control variable sequence calculated by the forward enumeration method in the forward direction respectively with the reverse optimal control route and the reverse optimal control variable sequence calculated by the dynamic programming algorithm in the reverse direction, so as to obtain the global optimal control route and the global optimal control variable sequence of the entire driving journey.

[0039] The vehicle energy management method of the present application adopts a forward and reverse collaborative recursive energy management algorithm that performs parallel calculations using the dynamic programming algorithm and the forward enumeration method. When the dynamic programming algorithm is performing reverse solution, the forward enumeration method is also simultaneously performing forward solution. The two algorithms run synchronously towards the intermediate stage of the driving journey, and when a certain intermediate stage is completed simultaneously, reverse and forward splicing is performed, thereby obtaining the global optimal control route and the global optimal control variable sequence of the entire driving journey. Therefore, compared with the traditional independent operation of the dynamic programming algorithm that requires completing the reverse solution of the entire driving journey, the dynamic programming algorithm of the vehicle energy management method of the present application only needs to complete the reverse solution of a part of the driving journey. Thus, the computational amount of the dynamic programming algorithm is reduced, the computational cycle of the dynamic programming algorithm is reduced, and the computational speed is improved.

[0040] Figure 2 Reveals a main schematic diagram of the vehicle powertrain architecture of a pure electric range extender configuration vehicle. As Figure 2 shown, for a pure electric range extender configuration vehicle, the energy sources are the power battery and the range extender. The range extender can be an engine range extender or a fuel cell, and the motor is the only power source.

[0041] Hereinafter, a pure electric range extender configuration vehicle will be used as an example to elaborate in detail on the forward and reverse collaborative recursive energy management algorithm in the vehicle energy management method of the present application.

[0042] I. Dynamic programming algorithm (i.e., reverse solution method)

[0043] (1) Define the state variable x(k) and the control variable u(k)

[0044] Select the range extender power P ext as the control variable for vehicle energy management, and the battery SOC as the state variable for vehicle energy management. Its specific form is:

[0045] x(k) = SOC(k) (1)

[0046] u(k) = P ext (k) (2)

[0047] Among them, x(k) represents the state variable at the k-th stage, and u(k) represents the control variable at the k-th stage.

[0048] (2) Define the state transition equation

[0049] x(k + 1) = f(x(k), u(k)) (3)

[0050] Among them, f is the state transition function of the state transition equation.

[0051] (3) Construction of the objective function

[0052]

[0053] Among them, J is the total energy consumption of the driving trip; N is the total number of discrete steps after discretizing the driving trip, which depends on the discrete interval, that is, the driving trip is divided into N discrete stages; L is the transient energy consumption of a single step.

[0054] (4) Parameter constraints

[0055] P extmin ≤ P ext ≤ P extmax (5)

[0056] P batmin ≤ P bat ≤ P batmax (6)

[0057] SOC min ≤ SOC ≤ SOC max (7)

[0058] Among them, P extmax 、P extmin are the upper and lower limits of the range extender power; P bat is the power of the power battery, P batmax 、P batmin are the upper and lower limits of the power of the power battery respectively, SOC max 、SOC min are the upper and lower limits of the SOC of the power battery respectively.

[0059] It can be understood that the above parameter constraint conditions are only listed as an example of this application. However, this application is not limited thereto. In other embodiments, other or more parameter constraint conditions can be set according to the actual application situation of the vehicle. This application does not limit this.

[0060] (5) Variable discretization

[0061] Discretize the state variable and the control variable. As Figure 3 shown, the discrete state variable sequence and the discrete control variable sequence are respectively:

[0062] SOC = [SOC min , SOC min + ζ, SOC min + 2ζ, …, SOC max (8)

[0063] P ext = [P extmin , P extmin + λ, P extmin + 2λ, …, P extmax (9)

[0064] where ζ and λ represent the discrete intervals of the state variables and the control variables, respectively.

[0065] In Figure 3 , the abscissa represents each stage of the driving journey division. The numbers in the parentheses of SOC correspond to each stage of the abscissa. The subscript of SOC represents each discrete SOC value, and x is the number of discrete SOC values.

[0066] (6) Reverse calculation

[0067] The dynamic programming algorithm runs from the Nth stage to the 1st stage.

[0068] When the vehicle is in the Nth stage (i.e., the end of the driving journey), the corresponding stage energy consumption L i,N = 0. Therefore, the minimum energy consumption of the state transition accumulated by the whole vehicle in the Nth stage

[0069] When the vehicle is in the (N - 1)th stage, there are x values of state variables in the power system, and the values of each state variable are denoted as SOC i,N-1 , where i = 1, 2, …, x. First, traverse the values of each state variable SOC i,N-1 corresponding to all control variables P ext in the (N - 1)th stage, calculate the stage energy consumption L ext corresponding to all values of the control variable P i,N-1 = [SOC(N - 1), P ext (N - 1)], add L i,N-1 = [SOC(N - 1), P ext (N - 1)] to and compare them to obtain the minimum cumulative energy consumption of the state transition accumulated by the whole vehicle at this stage Then, calculate the value of the state variable SOC i (N - 1) in the (N - 1)th stage through the state transition equation, and finally save as well as obtain Stage-optimal control variables

[0070] When the vehicle is in the k-th (1 ≤ k ≤ N - 2) stage, reverse calculation is performed in the same way as in the (N - 1)-th stage until the calculation of the 1st stage is completed. At this time, the optimal control variables for all N stages of the entire driving journey can be obtained This provides a basis for subsequent forward optimization to obtain the global optimal control variable sequence

[0071] (7) Forward optimization

[0072] Forward optimization is to perform iterative optimization on the state variables and control variables of the power system based on the initial state of the power system and the data saved by reverse calculation. The optimization steps are as follows

[0073] S21: Set the initial state of the power system as SOC(1), and let k = 1

[0074] S22: Based on the data saved by reverse calculation, obtain the minimum cumulative energy consumption of the k-th stage through iterative optimization and the optimal control variables

[0075] S23: Calculate the state SOC(k + 1) of the power system at the (k + 1)-th stage from the state transition equation

[0076] S24: Perform optimization based on the data saved by reverse calculation to obtain the minimum cumulative energy consumption of the (k + 1)-th stage and the optimal control variables

[0077] S25: Repeat steps S23 - S24 until the optimization of the N-th stage is completed, so as to obtain the global optimal control route and the global optimal control variable sequence of the minimum cumulative energy consumption of the entire driving journey

[0078] II. Forward enumeration method (forward solution method)

[0079] The discretization of the forward solution method is the same as that of the reverse solution method, as Figure 4 shown

[0080] Different from the reverse solution method, the forward solution method enumerates from the starting point of the driving journey. Specifically

[0081] When the vehicle is in the 1st stage (i.e., the starting point of the driving journey), the corresponding stage energy consumption L i,1 = 0. Therefore, the minimum energy consumption for state transition accumulated by the whole vehicle in the 1st stage

[0082] When the vehicle is in the second stage, there are x state variables in the power system, and the values of each state variable are denoted as SOC i,2 , where i = 1, 2,..., x. First, traverse the values SOC i,2 of all control variables P ext corresponding to each state variable value in the second stage, calculate the stage energy consumption L ext corresponding to the values of all control variables P i,2 = [SOC(2), P ext (2)], add L i,2 = [SOC(2), P ext (2)] to and compare them, then the minimum cumulative state transition energy consumption of the whole vehicle in this stage can be obtained Then, calculate the value SOC i (2) of the state variable in the second stage through the state transition equation, and finally save and obtain the optimal control variable of the stage

[0083] When in the k-th (3 ≤ k ≤ N) stage, the forward calculation is also carried out in the same way as in the second stage until the calculation of the N-th stage is completed. At this time, the optimal control variables of all N stages of the entire driving journey can be obtained

[0084] The above forward solution method and reverse solution method can both calculate the optimal control route and the optimal control variable sequence of energy management when running alone. However, the forward solution has a slower calculation speed and occupies more space, while the reverse solution has a faster calculation speed. But when deployed on the controller, the calculation speed is still difficult to meet the vehicle computing speed requirements

[0085] Therefore, the vehicle energy management method of this application uses the forward enumeration method and the dynamic programming algorithm to perform parallel calculations, and performs collaborative recursion between the forward solution and the reverse solution, so as to make full use of the parallel computing power and the remaining computing power of the controller, and run the forward solution and the reverse solution algorithms synchronously Figure 5 reveals the transport capacity schematic diagram when the forward solution and the reverse solution algorithms are synchronously run in an embodiment of this application

[0086] Since the reverse solution speed is higher than the forward solution, therefore, the energy management calculation in the vehicle energy management method of this application is mainly solved by the reverse solution algorithm. Because it is parallel computing, while the reverse solution is being calculated, the forward solution is also being calculated synchronously, and the two run synchronously towards the middle stage. Thus, the calculation amount of the dynamic programming algorithm can be reduced, and the operation speed of the vehicle energy management algorithm can be improved

[0087] In some embodiments, the vehicle energy management method of the present application may further include: detecting the running stages of the dynamic programming algorithm and the forward enumeration method.

[0088] When it is detected that the dynamic programming algorithm has run past stage k towards before stage k - 1, and at the same time it is detected that the forward enumeration method has run past stage k towards after stage k + 1, that is, when the dynamic programming algorithm and the forward enumeration method have both completed the calculation of stage k, forward and reverse splicing is performed, where 1 ≤ k ≤ n, and n is the number of stages into which the driving journey is divided.

[0089] Figure 6 Disclosed is a schematic diagram of the global optimal control route obtained by forward and reverse splicing according to an embodiment of the present application. The following will be combined with Figure 6 to elaborate in detail how forward and reverse splicing is performed in the present application.

[0090] As Figure 6 shown, when it is detected that the dynamic programming algorithm and the forward enumeration method have both completed the calculation of stage k, the synchronous detection of the value of the state variable SOC i (k) is triggered.

[0091] Enumerate the values of all state variables SOC i (k) in stage k, where i = 1, 2,..., x.

[0092] Splice the forward minimum cumulative energy consumption calculated by the forward enumeration method corresponding to the values of each state variable in stage k with the reverse minimum cumulative energy consumption calculated by the dynamic programming algorithm Thereby, the global minimum cumulative energy consumption corresponding to the values of each state variable SOC i (k) in stage k can be obtained

[0093] Search for the value of the state variable SOC i corresponding to the minimum value among the global minimum cumulative energy consumptions corresponding to the values of each state variable SOC in stage k i (k).

[0094] Respectively splice the forward optimal control route and the forward optimal control variable sequence calculated by the forward enumeration method corresponding to the value of this state variable with the reverse optimal control route and the reverse optimal control variable sequence calculated by the dynamic programming algorithm Thereby, the global optimal control route and the global optimal control variable sequence can be obtained

[0095] In some embodiments, the vehicle energy management method of the present application may further include: when it is detected that the dynamic programming algorithm and the forward enumeration method simultaneously complete the calculation of the k-th stage, stop the calculations of the dynamic programming algorithm and the forward enumeration method.

[0096] In some embodiments, the vehicle energy management method of the present application may further include: running the dynamic programming algorithm and the forward enumeration method in a first controller and a second controller respectively for distributed synchronous calculation. Thus, distributed calculation of the dynamic programming algorithm and the forward enumeration method can be achieved. The vehicle energy management method of the present application can make full use of the controllers with excess computing power in the vehicle.

[0097] Since the enumeration quantity of the complete forward solution algorithm is x 2 pieces, and the storage space occupied increases rapidly as the calculation progresses. Therefore, in some embodiments, the vehicle energy management method of the present application may further include: real-time monitoring of the memory load rate of the second controller; when the memory load rate of the second controller exceeds a predetermined limit value, stop the calculation of the forward enumeration method of the second controller, continue to maintain the calculation of the dynamic programming algorithm of the first controller, and record the stop stage of the forward enumeration method; when the dynamic programming algorithm runs to the stop stage of the forward enumeration method, perform forward and reverse splicing.

[0098] By adopting the forward and reverse collaborative recursive energy management algorithm, the vehicle energy management method of the present application can reduce the calculation period of the dynamic programming algorithm compared with the traditional dynamic programming algorithm, and combined with the parallel forward enumeration algorithm, improve the calculation speed; in addition, by monitoring the load rate of the controller, rapid switching of the algorithm can be achieved without affecting the functions of the whole vehicle.

[0099] The present application also provides a vehicle energy management system. Figure 7 A schematic block diagram of a vehicle energy management system 700 according to an embodiment of the present application is disclosed. As Figure 7 shown, a vehicle energy management system 700 according to an embodiment of the present application includes a processor 701, an internal bus 702, a network interface 703, a memory 704, and a non-volatile memory 705. Of course, there may also be other hardware required for other services. The processor 701 can read the corresponding computer program from the non-volatile memory 705 into the memory 704 and then run it to implement the steps of the vehicle energy management method as described above. Of course, in addition to the software implementation method, the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, and can also be hardware or a logic component.

[0100] The vehicle energy management system 700 of the present application can have beneficial technical effects similar to those of the vehicle energy management method described above, so it will not be elaborated here.

[0101] The present application also provides a vehicle. The vehicle includes the vehicle energy management system as described above.

[0102] The vehicle energy management method, management system and vehicle provided by the embodiments of the present application have been introduced in detail above. Specific examples are used herein to elaborate on the vehicle energy management method, management system and vehicle of the embodiments of the present application. The description of the above embodiments is only used to help understand the core idea of the present application, and is not intended to limit the present application. It should be noted that for those of ordinary skill in the art, without departing from the spirit and principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications should also fall within the protection scope of the appended claims of the present application.

Claims

1. A vehicle energy management method, characterized in that: include: Determine state variables and control variables in vehicle energy management; Obtain the values ​​of the state variables of the starting point and the end point of the vehicle driving trip; The values ​​of the state variables based on the starting point and the end point are respectively calculated in parallel using a forward enumeration method and a dynamic programming algorithm and taking the minimum cumulative energy consumption of the driving trip as a target; When the forward enumeration method and the dynamic programming algorithm simultaneously complete the calculation of a certain intermediate stage within the driving journey, forward and reverse splicing is performed, including: splicing the forward optimal control route and the forward optimal control variable sequence forward calculated by the forward enumeration method with the reverse optimal control route and the reverse optimal control variable sequence reversely calculated by the dynamic programming algorithm, respectively, to obtain the global optimal control route and the global optimal control variable sequence of the entire driving journey.

2. The method according to claim 1, characterized in that: Determining the state variables and control variables in vehicle energy management includes: When the vehicle is in a pure electric range-extended configuration, the power battery SOC is used as the state variable, and the range-extender power is used as the control variable; When the vehicle is of hybrid configuration, the power battery SOC is used as the state variable, and the motor torque is used as the control variable.

3. The method according to claim 1, characterized in that: The method further comprises: Detecting the running phases of the dynamic programming algorithm and the forward enumeration method, When it is detected that the dynamic programming algorithm has passed the kth stage and is running before the k-1th stage, and when it is detected that the forward enumeration method has passed the kth stage and is running after the k+1th stage, the forward and reverse splicing is performed, wherein 1≤k≤N, and N is the number of stages into which the driving journey is divided.

4. The method according to claim 3, characterized in that: The forward and reverse splicing comprises: Trigger synchronous detection of the value of the state variable; Enumerate the values ​​of all state variables in stage k; The forward minimum cumulative energy consumption calculated by the forward enumeration method and the reverse minimum cumulative energy consumption calculated by the dynamic programming algorithm corresponding to the values ​​of each state variable in the kth stage are spliced ​​to obtain the global minimum cumulative energy consumption corresponding to the values ​​of each state variable in the kth stage; Find the value of the state variable corresponding to the minimum value of the global minimum cumulative energy consumption corresponding to the values ​​of each state variable in the kth stage; The forward optimal control route and the forward optimal control variable sequence calculated by the forward enumeration method corresponding to the value of the state variable are respectively spliced ​​with the reverse optimal control route and the reverse optimal control variable sequence calculated by the dynamic programming algorithm to obtain the global optimal control route and the global optimal control variable sequence.

5. The method according to claim 3, characterized in that: The method further comprises: When it is detected that the dynamic programming algorithm and the forward enumeration method have simultaneously completed the calculation of the kth stage, the calculation of the dynamic programming algorithm and the forward enumeration method is stopped.

6. The method according to claim 1, characterized in that: The method further comprises: The dynamic programming algorithm and the forward enumeration method are respectively run in the first controller and the second controller to perform distributed synchronous computing.

7. The method according to claim 6, characterized in that: The method further comprises: monitoring the memory load rate of the second controller in real time; When the memory load rate of the second controller exceeds a predetermined limit, the forward enumeration method of the second controller is stopped, the dynamic programming algorithm of the first controller is continued to be calculated, and the stop stage of the forward enumeration method is recorded; When the dynamic programming algorithm runs to the stop phase of the forward enumeration method, the forward and reverse splicing is performed.

8. The method according to any one of claims 1 to 7, characterized in that: Also includes: Obtaining the planned vehicle speeds at N stages of the driving journey; The values ​​of the state variables based on the starting point and the end point are respectively calculated by forward enumeration method and dynamic programming algorithm and are calculated in parallel with the minimum cumulative energy consumption of the driving trip as the goal, including: According to the value of the state variable at the starting point and the planned vehicle speed at each stage, the forward enumeration method is used to traverse the values ​​of all control variables corresponding to the value of each state variable in each stage from the first stage to the Nth stage, and the stage energy consumption corresponding to the value of all control variables is calculated to obtain the minimum cumulative energy consumption corresponding to the value of each state variable of the whole vehicle at each stage; According to the value of the state variable at the end point and the planned vehicle speed at each stage, the dynamic programming algorithm is used to traverse the values ​​of all control variables corresponding to the value of each state variable in each stage running from the Nth stage to the first stage, and the stage energy consumption corresponding to the value of all control variables is calculated to obtain the minimum cumulative energy consumption corresponding to the value of each state variable of the whole vehicle at each stage.

9. The method according to claim 8, characterized in that: The obtaining of the planned vehicle speeds in the N stages of the driving journey includes: Predict the road ahead of the vehicle through predictive cruise technology; Based on the road ahead information, the vehicle speed is pre-planned to obtain the planned vehicle speeds at the N stages of the driving journey.

10. A vehicle energy management system, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the vehicle energy management method as claimed in any one of claims 1 to 9.

11. A vehicle, characterized in that: Comprising the vehicle energy management system as claimed in claim 10.

Images