Vehicle energy control method, device, equipment, medium and product

Abstract

The invention provides an energy control method, device and equipment of a vehicle, a medium and a product, and relates to the field of energy management of trains. Comprising the steps of obtaining train information of a to-be-controlled vehicle; determining a traction power demand value of the to-be-controlled vehicle according to the train information and the simple substance point dynamics model; determining an optimal power distribution sequence of the to-be-controlled vehicle according to the train information, the traction power demand value, a preset constraint strategy and a dynamic planning algorithm; acquiring a real-time train running distance of the to-be-controlled vehicle; and controlling the train power of the to-be-controlled vehicle in real time according to the optimal power distribution sequence and the real-time train running distance. The technical problem that the energy control efficiency of the vehicle is low due to the fact that the running cost is increased due to the fact that the prior art depends on a static strategy and is poor in flexibility is solved.

Description

Technical Field

[0001] This application relates to the field of train energy management, and more particularly to a method, device, equipment, medium, and product for energy control of vehicles. Background Technology

[0002] With the green and efficient characteristics of new energy sources and the transformation of energy layout, the transportation industry is actively developing multi-source hybrid trains to reduce pollution emissions. The power system of multi-source hybrid trains is more complex than that of traditional vehicles, containing at least two power sources. Energy management is the foundation for the safe and stable operation of multi-source hybrid systems, and optimizing energy management strategies aims to achieve the best power distribution among power sources.

[0003] Existing methods for power distribution and control among power sources in multi-source hybrid trains mainly rely on power consumption data from actual train operation or power consumption data obtained from traction calculations based on the train's actual speed-time curve to configure the basic parameters of each power source that meet the train's power performance.

[0004] However, existing technologies rely on static strategies, which are inflexible and lead to increased operating costs, resulting in lower energy control efficiency for vehicles. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for energy control of vehicles, in order to solve the problem that the prior art relies on static strategies, has poor flexibility, leads to increased operating costs, and thus results in low energy control efficiency of vehicles.

[0006] In a first aspect, this application provides a method for controlling the energy of a vehicle, comprising:

[0007] Obtain train information for the vehicles to be controlled;

[0008] Based on train information and single-mass dynamics model, determine the traction power requirement of the vehicle to be controlled;

[0009] Based on train information, traction power demand, preset constraint strategies, and dynamic programming algorithms, the optimal power allocation sequence for the vehicles to be controlled is determined.

[0010] Obtain the real-time train running distance of the vehicle to be controlled;

[0011] The train power of the vehicles to be controlled is controlled in real time based on the optimal power allocation sequence and the real-time train running distance.

[0012] In one possible design, train information includes train power source information, train power source power information, and train status information;

[0013] Based on train information, traction power demand, preset constraint strategies, and dynamic programming algorithms, the optimal power allocation sequence for the vehicles to be controlled is determined, including:

[0014] A power source model of the vehicle to be controlled is constructed based on the train power source information, train power source power information and traction power demand value;

[0015] Based on the power source power model, train state information, preset constraint strategies, and dynamic programming algorithms, the optimal power allocation sequence for the vehicles to be controlled is determined.

[0016] In one possible design, the train power source information includes: battery information, internal combustion engine information, traction network information, and pantograph information;

[0017] Based on the train power source information, train power source power information, and traction power demand value, a power source dynamic model of the vehicle to be controlled is constructed, including:

[0018] Based on the power information of the train's power source and the traction power demand, a power flow balance model of the vehicle to be controlled is constructed.

[0019] Based on the battery information, construct a zero-order equivalent circuit model of the vehicle to be controlled;

[0020] Based on the internal combustion engine information, construct an intake manifold absolute pressure pulse spectrum model of the vehicle to be controlled;

[0021] Based on the traction network information and pantograph information, a constant efficiency model for the vehicle to be controlled is constructed.

[0022] In one possible design, based on the power source power model, train state information, preset constraint strategies, and dynamic programming algorithms, the optimal power allocation sequence for the vehicle to be controlled is determined, including:

[0023] Discretization is performed based on the preset constraint strategy and train status information to determine the discrete stage set and discrete array;

[0024] Input the discrete stage set and the discrete array into the dynamic programming algorithm;

[0025] Based on the power flow balance model, the preset constraint strategy, and the dynamic programming algorithm, the discrete stage set and the discrete array are iterated in reverse to determine the optimal cumulative cost set.

[0026] Based on the power flow balance model, the preset constraint strategy, the dynamic programming algorithm, and the optimal cumulative cost set, forward backtracking is performed to determine the optimal path and obtain the optimal power allocation sequence corresponding to the optimal path.

[0027] In one possible design, the pre-defined constraint strategy includes constraint conditions;

[0028] The constraints include: power constraints and battery state-of-charge constraints.

[0029] Based on the preset constraint strategy and train state information, discretization is performed to determine the set of discrete stages and the discrete array, including:

[0030] The operating cycle is obtained based on train status information;

[0031] The operating cycle is discretized to determine a set of discrete stages; the set of discrete stages includes multiple discrete stages; each discrete stage corresponds to the same time step.

[0032] The battery state of charge constraints are discretized according to the time step and defined as state variables, and a discrete array of state variables is obtained.

[0033] The power constraint is discretized according to the time step and defined as a control quantity, and the discrete array of control quantities is obtained.

[0034] Based on the discrete arrays of state variables and control variables, a discrete array is obtained.

[0035] In one possible design, based on the power flow balance model, a pre-defined constraint strategy, and a dynamic programming algorithm, the discrete stage set and the discrete array are iterated in reverse to determine the optimal cumulative cost set, including:

[0036] Multiple paths are generated based on the discrete stage set, the discrete array of state variables, and the discrete array of control variables; each path has the same time step.

[0037] The node cost and penalty cost of each path are calculated based on the power flow balance model and the preset constraint strategy to determine the arc cost matrix;

[0038] Based on the dynamic programming algorithm and the arc cost matrix, the cumulative cost value of each path is minimized to determine the optimal set of cumulative cost values;

[0039] Determine the optimal set of control quantity pointers based on the optimal set of cumulative cost values;

[0040] The optimal cumulative cost set is determined based on the optimal cumulative cost value set and the optimal control quantity pointer set.

[0041] In one possible design, the pre-defined constraint strategy also includes a penalty strategy;

[0042] The constraints also include: variable power constraints and battery state of charge final value constraints.

[0043] Secondly, this application provides a vehicle energy control device, comprising:

[0044] The first acquisition module is used to acquire train information of the vehicle to be controlled;

[0045] The first determining module is used to determine the traction power requirement of the vehicle to be controlled based on the train information and the single-mass dynamics model.

[0046] The second determining module is used to determine the optimal power allocation sequence of the vehicles to be controlled based on train information, traction power demand value, preset constraint strategy and dynamic programming algorithm.

[0047] The second acquisition module is used to acquire the real-time train running distance of the vehicle to be controlled;

[0048] The control module is used to control the train power of the vehicles under control in real time based on the optimal power allocation sequence and the real-time train running distance.

[0049] Thirdly, this application provides an energy control device for a vehicle, including: a memory and a processor;

[0050] The memory stores the instructions that the computer executes;

[0051] The processor executes computer execution instructions stored in memory, causing the processor to perform the energy control method for a vehicle as described in the first aspect of the invention.

[0052] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the energy control method for a vehicle as described in the first aspect of the invention.

[0053] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the energy control method for a vehicle according to the first aspect of the invention.

[0054] This application provides a vehicle energy control method, device, equipment, medium, and product, including: acquiring train information of the vehicle to be controlled; determining the traction power demand value of the vehicle to be controlled based on the train information and a single-mass dynamics model; determining the optimal power allocation sequence of the vehicle to be controlled based on the train information, the traction power demand value, a preset constraint strategy, and a dynamic programming algorithm; acquiring the real-time train running distance of the vehicle to be controlled; and controlling the train power of the vehicle to be controlled in real time based on the optimal power allocation sequence and the real-time train running distance. Compared with existing technologies that rely on static strategies, which have poor flexibility and lead to increased operating costs, resulting in lower vehicle energy control efficiency, this application generates a globally optimal power allocation sequence through an offline algorithm, combines it with an online controller to adjust the power output in real time, and achieves multi-power source collaborative optimization based on the power balance equation and constraints (battery state of charge (SOC) boundary, power upper and lower limits, and rate of change), thereby improving the vehicle's energy control efficiency. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A schematic diagram of the system architecture of a vehicle energy control method provided in this application embodiment;

[0057] Figure 2 A schematic flowchart of a vehicle energy control method provided in this application embodiment. Figure 1 ;

[0058] Figure 3 This is a schematic diagram of the force situation of a single-mass dynamic model of a multi-source hybrid train provided in an embodiment of this application;

[0059] Figure 4 This is a schematic diagram of the structure of a multi-source hybrid train optimized energy management strategy system provided in an embodiment of this application;

[0060] Figure 5 A schematic diagram of the power system structure of a multi-source hybrid train provided in an embodiment of this application;

[0061] Figure 6 This is a schematic diagram of the actual vehicle communication structure of a multi-source hybrid train provided in an embodiment of this application;

[0062] Figure 7A schematic flowchart of a vehicle energy control method provided in this application embodiment. Figure 2 ;

[0063] Figure 8 A schematic flowchart of a vehicle energy control method provided in this application embodiment. Figure 3 ;

[0064] Figure 9 A schematic diagram of the dynamic programming algorithm provided in the embodiments of this application;

[0065] Figure 10 This is a schematic diagram of the dynamic programming algorithm provided in the embodiments of this application;

[0066] Figure 11 This is a schematic diagram of the online control structure for a multi-source hybrid train provided in an embodiment of this application;

[0067] Figure 12 This is a schematic diagram of the structure of the energy control device for a vehicle provided in an embodiment of this application;

[0068] Figure 13 This is a schematic diagram of the structure of a vehicle energy control device provided in an embodiment of this application. Detailed Implementation

[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0070] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, nor do they necessarily imply difference. It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0071] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the vehicle energy control method provided in the embodiments of this application is merely an example, and vehicle energy control methods may include more or fewer elements.

[0072] With the green and efficient characteristics of new energy sources and the transformation of energy layout, the transportation industry is actively developing multi-source hybrid trains to reduce pollution emissions. The power system of multi-source hybrid trains is more complex than that of traditional vehicles, containing at least two power sources. Energy management is the foundation for the safe and stable operation of multi-source hybrid systems, and optimizing energy management strategies aims to achieve the best power distribution among power sources.

[0073] Existing methods for power distribution and control among power sources in multi-source hybrid trains mainly rely on power consumption data from actual train operation or power consumption data obtained from traction calculations based on the train's actual speed-time curve to configure the basic parameters of each power source that meet the train's power performance.

[0074] While the existing method can obtain configuration parameters that meet the requirements of train operation, it does not consider the capacity configuration problem from the perspective of life cycle cost. Therefore, the obtained configuration parameters cannot guarantee good economic efficiency.

[0075] Existing energy control methods for network-sliced ​​vehicles have the following main shortcomings:

[0076] On the one hand, there is a lack of life-cycle cost optimization: only short-term operating costs (such as fuel consumption and electricity consumption) are considered, without comprehensively taking into account long-term economic factors such as power source lifespan loss, maintenance costs and energy price fluctuations.

[0077] On the one hand, the static allocation strategy lacks flexibility: the preset parameters cannot adapt to dynamic changes in actual operation (such as temporary line adjustments and passenger load fluctuations), causing energy allocation to deviate from the optimal state.

[0078] On the one hand, the real-time response capability is weak: the offline optimization results need to be executed through the offline controller, and the power distribution cannot be dynamically adjusted according to real-time operating conditions (such as battery SOC and power source status).

[0079] On the other hand, a closed-loop feedback mechanism was not established: the deviation between the actual output of the power source and the required power was not included in the optimization model, resulting in the accumulation of errors in energy allocation and actual operation.

[0080] To address the aforementioned problems, the inventors, during their research on the low energy control efficiency of vehicles, discovered that existing technologies rely on static strategies, resulting in poor flexibility and increased operating costs, thus leading to low energy control efficiency. Therefore, the inventors considered generating a globally optimal power allocation sequence using an offline algorithm, combined with real-time power output adjustment by an online controller, to achieve multi-power source collaborative optimization based on power balance equations and constraints (SOC boundaries, power upper and lower limits, and rate of change). Based on this, embodiments of this application provide a vehicle energy control method, device, equipment, medium, and product, applicable to the field of train energy management, aiming to solve the problem of low energy control efficiency in existing vehicle technologies.

[0081] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0082] Figure 1 This is a schematic diagram of the system architecture for a vehicle energy control method provided in an embodiment of this application. The vehicle's energy control system is a computer device. Figure 1 In the above architecture, at least one of data acquisition device 101, processing device 102 and display device 103 is included.

[0083] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the processing system architecture of the vehicle energy control method. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or divide some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0084] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface. The data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface to obtain the train information of the vehicle to be controlled and the real-time train running distance of the vehicle to be controlled.

[0085] The processing device 102 can control the train power of the vehicle to be controlled in real time based on the train information and the real-time train running distance of the vehicle to be controlled.

[0086] The display device 103 can also be a touch screen or the screen of a terminal device, used to receive user commands while displaying the above-mentioned content, so as to realize interaction with the user.

[0087] It should be understood that the aforementioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0088] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0089] The technical solution of this application will be described in detail below with reference to specific embodiments:

[0090] Figure 2 A schematic flowchart of a vehicle energy control method provided in this application embodiment. Figure 1 ,like Figure 2 As shown, the method includes:

[0091] S201. Obtain train information for the vehicle to be controlled.

[0092] The train information for the vehicles to be controlled includes: the multi-source hybrid train line operation plan, train and line parameters, and traction / braking characteristics.

[0093] The train timetable includes: train running time information and speed information.

[0094] The train and track parameters include: vehicle weight, basic resistance, curve resistance, gradient resistance, and tunnel resistance.

[0095] Among them, traction characteristics include: starting traction force, constant torque zone and constant power zone, turning speed, turning point torque, etc.

[0096] The braking characteristics include: braking force, inflection point speed and torque, and linear inflection point speed and torque.

[0097] S202. Based on the train information and the single-mass dynamics model, determine the traction power requirement of the vehicle to be controlled.

[0098] In this embodiment, the power demand of the traction system is calculated using a dynamic model based on the multi-source hybrid train line operation diagram, train and line parameters, and traction / braking characteristics.

[0099] The multi-source hybrid power train route operation plan includes train running time information and speed information.

[0100] The train and track parameters include vehicle weight, basic resistance, curve resistance, gradient resistance, and tunnel resistance.

[0101] Among them, traction characteristics include starting traction force, constant torque region and constant power region and turning speed, turning point torque, etc.; braking characteristics include braking force, turning point speed and torque, and linear region turning point speed and torque.

[0102] The dynamic model includes a single-mass model of acceleration force, braking force, basic resistance, slope resistance, tunnel resistance, and curve resistance.

[0103] Specifically, in order to obtain the traction force and traction power of the tram, a longitudinal dynamic model was established. Based on the basic parameters of the tram and the powertrain system configuration, considering the efficiency of each level of components, the traction characteristics of the whole vehicle were analyzed. Combined with the given power performance indicators, the power and energy requirements under different indicators can be obtained, providing a basis for the selection of the power system.

[0104] In one possible embodiment, Figure 3 This is a schematic diagram of the force situation of a single-mass dynamic model of a multi-source hybrid train provided in an embodiment of this application, as shown below. Figure 3 As shown, the locomotive is subjected to forces including driving force. Braking force Basic resistance Ramp resistance Curving resistance and acceleration .

[0105] Among them, driving force Adhesion needs to be met For all train-related force units, kN is used as a limit.

[0106]

[0107] Specifically, the force distribution is as follows: Acceleration force = Driving force - Braking force - (Basic resistance + Gradient resistance + Curving resistance). The driving force is determined by the motor's traction characteristic curve, with the maximum driving force corresponding to the motor's external traction characteristic curve.

[0108] Specifically, driving force The maximum driving force is determined by the motor traction characteristic curve, and the maximum driving force corresponds to the motor traction external characteristic curve.

[0109] Specifically, braking force It's necessary to distinguish between different braking methods. In trolleybuses, braking energy is first fed back to the bus by the electric motor. When the motor cannot handle the fed-back energy, it is consumed by the braking resistor. In emergencies, air braking is used in conjunction. When the trolleybus uses pure electric braking, the braking force is determined by the motor's braking curve, with the maximum braking force corresponding to the motor's external braking characteristic curve.

[0110] More specifically, the basic resistance is expressed as:

[0111]

[0112] in, The basic resistance coefficient is obtained from an empirical formula and is expressed as a quadratic equation for the train speed v.

[0113] More specifically, ramp resistance Expressed as:

[0114]

[0115] in, The gradient resistance coefficient (N / kN) needs to be calculated in conjunction with the segmented gradients of the line, and the unit is per thousand.

[0116] More specifically, cornering resistance Expressed as:

[0117]

[0118] in, This is the curve resistance coefficient (N / kN).

[0119] More specifically, acceleration Expressed as:

[0120]

[0121] in, This is the rotational mass conversion factor. Let be the acceleration. According to Newton's second law, the acceleration of an object is directly proportional to the net force acting on it; their relationship is:

[0122]

[0123] Where F is the net force acting on the object, in N; M is the mass of the object (kg); and a is the acceleration of the object. .

[0124] Finally, the power requirement can be obtained from the following formula:

[0125]

[0126] S203. Based on train information, traction power demand, preset constraint strategies, and dynamic programming algorithms, determine the optimal power allocation sequence for the vehicles to be controlled.

[0127] S204. Obtain the real-time train running distance of the vehicle to be controlled.

[0128] S205. Based on the optimal power allocation sequence and the real-time train running distance, control the train power of the vehicle to be controlled in real time.

[0129] In one possible embodiment, Figure 4 This is a schematic diagram of the multi-source hybrid train optimized energy management strategy system provided in the embodiments of this application, as shown below. Figure 4 As shown:

[0130] Among them, the optimized energy management method of the optimized energy management system of multi-source hybrid trains includes: based on the train operation schedule, train parameters, and traction characteristics, obtaining its operating traction power through a dynamic model, combining it with the power of auxiliary power supply equipment to form the total demand power, completing the optimal energy allocation through a dynamic programming algorithm, and injecting it into the online controller.

[0131] Specifically, the optimized energy management method includes: first, obtaining the multi-source hybrid train line operation diagram; second, calculating the traction system power demand based on the train's basic parameters, traction characteristics, and line operation diagram; third, allocating energy in the power system model using a dynamic programming algorithm to obtain the optimal power allocation; and finally, importing the optimal control commands into the energy management control unit developed based on a microcontroller for online control.

[0132] More specifically, the energy management strategy system loads the route diagram and train parameters through a general-purpose PC in the input phase, and transforms them into required power through a dynamic model; in the model building phase, dynamic programming algorithms and power system models are used to calculate the optimal strategy; in the control phase, the strategy is deployed to the train control system through the Flash Bootloader, and communication between the vehicle control unit and each power source control unit is realized through the CAN network; the collaborative logic realizes the adaptation of operating conditions through the hierarchical activation of power sources and dynamic power allocation, such as multi-source parallel output during acceleration and single-source efficient operation during cruising; the real-time control mechanism realizes rapid response to sudden operating conditions through state monitoring and strategy recalculation, forming a closed loop of intelligent energy management covering all operating conditions.

[0133] In this embodiment, dynamic programming algorithms and collaborative control strategies improve energy allocation accuracy; power source load balancing design extends equipment life and reduces maintenance costs; multi-source redundancy design and real-time control mechanisms ensure that fault switching is minimized and operational stability is improved; the system can automatically adapt to complex routes such as mountainous areas and high altitudes, and reduce emissions through intelligent power adjustment, perfectly meeting the needs of green transportation development.

[0134] In one possible embodiment, Figure 5 This is a schematic diagram of the power system structure of a multi-source hybrid train provided in an embodiment of this application, as shown below. Figure 5As shown, the power system structure of a multi-source hybrid train includes: a power source consisting of at least two of the following: an internal combustion generator set, a traction network-pantograph, and a power battery; a traction system consisting of an inverter-traction motor; a common DC bus; and auxiliary power supply equipment.

[0135] In one possible embodiment, Figure 6 This is a schematic diagram of the actual vehicle communication structure of the multi-source hybrid train provided in the embodiments of this application, as shown below. Figure 6 As shown, the actual vehicle communication structure includes: vehicle control unit, energy management control unit, traction control unit and multiple power source control units.

[0136] Optionally, the vehicle control unit consists of a CAN communication network, which sends information including running time and driving status; and receives information including running speed and status of each power source.

[0137] Optionally, the energy management control unit sends information including: output power commands for each power source; and receives information including: train speed, running time, and status of each power source.

[0138] Optionally, the traction control unit sends information including: operating speed, motor speed, and motor torque; and receives information including: driving status.

[0139] Optionally, the information sent by multiple power source control units includes: power source status; the information received includes: power source output power command.

[0140] This embodiment provides a vehicle energy control method, including: acquiring train information of the vehicle to be controlled; determining the traction power demand value of the vehicle to be controlled based on the train information and a single-mass dynamics model; determining the optimal power allocation sequence of the vehicle to be controlled based on the train information, the traction power demand value, a preset constraint strategy, and a dynamic programming algorithm; acquiring the real-time train running distance of the vehicle to be controlled; and controlling the train power of the vehicle to be controlled in real time based on the optimal power allocation sequence and the real-time train running distance. Compared with existing technologies that rely on static strategies, which have poor flexibility and lead to increased operating costs, resulting in lower vehicle energy control efficiency, this application generates a globally optimal power allocation sequence through an offline algorithm, combines it with an online controller to adjust the power output in real time, and achieves multi-power source collaborative optimization based on the power balance equation and constraints (battery state of charge (SOC) boundary, power upper and lower limits, and rate of change), thereby improving the vehicle's energy control efficiency.

[0141] Figure 7 A schematic flowchart of a vehicle energy control method provided in this application embodiment. Figure 2 ,like Figure 7As shown, the train information includes train power source information, train power source power information, and train status information; therefore, step S203 specifically includes:

[0142] S701. Construct a power source model for the vehicle to be controlled based on the train power source information, train power source power information and traction power demand value.

[0143] The train power source information includes: battery information, internal combustion engine information, traction network information, and pantograph information.

[0144] Specifically, step S701 includes:

[0145] Optionally, a power flow balance model of the vehicle to be controlled can be constructed based on the power information of the train's power source and the traction power demand value.

[0146] In this embodiment, the power flow model is used for the multi-source hybrid train power system model.

[0147] Where the total output power of the power source equals the total demand power, the power flow balance model of the vehicle to be controlled is:

[0148]

[0149]

[0150] Optionally, a zero-order equivalent circuit model of the vehicle to be controlled can be constructed based on the battery information.

[0151] The power battery model of the vehicle to be controlled adopts a zero-order equivalent circuit model.

[0152] Specifically, the SOC update is as follows:

[0153]

[0154] The current is calculated as follows:

[0155]

[0156] The battery efficiency is as follows:

[0157]

[0158] in, P is the battery discharge current, and P is the battery power. It is the discharge efficiency. It refers to charging efficiency.

[0159] Optionally, based on the internal combustion engine information, an intake manifold absolute pressure pulse spectrum model of the vehicle to be controlled can be constructed.

[0160] In this embodiment, the internal combustion engine uses an intake manifold absolute pressure pulse spectrum model to obtain its fuel consumption-speed curve, fuel consumption-voltage curve, and speed-power curve.

[0161] Optionally, a constant efficiency model of the vehicle to be controlled can be constructed based on the traction network information and the pantograph information.

[0162] The traction net-pantograph system uses a power source with constant efficiency.

[0163] S702. Based on the power source power model, train state information, preset constraint strategies and dynamic programming algorithms, determine the optimal power allocation sequence for the vehicles to be controlled.

[0164] In this embodiment, a power source power model is constructed collaboratively through a multi-source power model (power flow balance, zero-order circuit, intake pipe pulse spectrum, constant efficiency). Combined with discretization processing, the operating cycle is transformed into a multi-stage state-control discrete array. Based on dynamic programming algorithm, the cumulative cost is optimized in reverse iteration and the optimal path is backtracked forward. This realizes the automatic generation of the optimal power allocation sequence under multiple constraints such as power constraints and battery state of charge limitations. This not only ensures the economy of efficient collaborative operation of the power source, but also improves the dynamic response capability and operational stability of the train traction system.

[0165] Figure 8 A schematic flowchart of a vehicle energy control method provided in this application embodiment. Figure 3 ,like Figure 8 As shown, the specific implementation steps of S702 above include:

[0166] S801. Discretize the train status information according to the preset constraint strategy to determine the discrete stage set and discrete array.

[0167] Specifically, step S401 includes:

[0168] First, the operating cycle is obtained based on the train status information;

[0169] Secondly, the operating cycle is discretized to determine the set of discrete stages.

[0170] The discrete stage set includes multiple discrete stages.

[0171] Each discrete stage corresponds to the same time step.

[0172] Next, the battery state of charge constraints are discretized according to the time step and defined as state variables, and a discrete array of state variables is obtained.

[0173] Then, the power constraint is discretized according to the time step and defined as the control quantity, and the discrete array of the control quantity is obtained.

[0174] Finally, based on the discrete arrays of state variables and control variables, a discrete array is obtained.

[0175] For example, Figure 9 This is a schematic diagram of the dynamic programming algorithm provided in the embodiments of this application, such as... Figure 9 As shown, the entire running time is divided into steps. The system is discretized into T stages, which are equally spaced along the length of the driving cycle. The vertical axis is quantized into S distinct states.

[0176] Wherein, the state vector u is defined by a range from the upper limit of the power battery's state of charge. Up to the upper limit of the state of charge of the power battery The state of charge of the battery is discretized with equal step sizes.

[0177] It should be noted that dynamic programming is a numerical method used to solve multi-level decision problems. It can provide optimal solutions for problems of varying complexity. In this embodiment, the goal of dynamic programming is to minimize the cost function while satisfying constraints.

[0178] Furthermore, since the power of the entire bus is conserved, the power of one or more power sources can be considered as a control vector. The control vector x is composed of a range from the lower limit of the power of a certain power source to the upper limit of the power of a certain power source, discretized with equal step sizes.

[0179] Furthermore, the battery meets the remaining capacity requirements within the SOC constraint.

[0180] Specifically, the last one can be derived from the power balance equation through the bus power relationship, so this power fixed method helps to solve the problem more easily and is easy to program.

[0181] Furthermore, we define node cost and transition cost. Node cost primarily considers price cost and is calculated based on the efficiency paths of generating electricity through different power sources.

[0182] Specifically, transition costs are related to the feasibility of moving from one node to another. If hopping from one node to the next is not feasible, then very high costs are associated with transition costs. Conversely, if the link between nodes is feasible, then zero cost defines the transition cost.

[0183] The preset constraint strategy includes constraint conditions.

[0184] The constraints include: power constraints, battery state of charge constraints, variable power constraints, and battery state of charge final value constraints.

[0185] Optionally, the power constraint is: the power of each power source must be within its lower power limit. and upper limit between.

[0186] Optionally, the battery state of charge (SOC) constraint is as follows: The power battery also has limitations on its SOC; the SOC range should be within the lower limit of the power battery's SOC. Up to the upper limit of the state of charge of the power battery Between the states of charge of the battery.

[0187] Optionally, the variable power constraint is: the rate of change of power of each power source. It must not exceed the range allowed by the battery system.

[0188] Optionally, the final state of charge (SOC) constraint is that the SOC of the power battery is the same at the start and end times. This is to avoid additional charging processes, which is important for controlling the SOC of the power battery during the operating cycle.

[0189] Specifically, the constraint formula is as follows:

[0190]

[0191] The preset constraint strategies also include penalty strategies.

[0192] Specifically, the penalty strategy is a method for implementing constraints, employing two approaches: one is to impose a significant transition cost on nodes that do not meet the conditions, and the other is to implement the constraint by restricting the decision space. These two methods have different advantages when implementing different constraints.

[0193] Optionally, the constraint on the final value is achieved by imposing a large transition cost on the path deviating from the set value at the final time T. This transition cost increases with the degree of deviation, and its expression is:

[0194]

[0195] Optionally, the constraints on the SOC boundary of the power battery and the power change rate of the power source are achieved by imposing a huge transition cost on all nodes exceeding the boundary at each time step, forcing the algorithm to find other paths, as expressed by:

[0196]

[0197]

[0198]

[0199] Optionally, the battery SOC needs to be updated at each time step. When the battery power is positive, the battery is considered to be discharging and therefore feeding power to the load. When the power is negative, the battery is considered to be charging and is absorbing energy from the load.

[0200]

[0201] Optionally, the power relationship needs to be strictly followed in each state at a certain discrete time.

[0202]

[0203] S802, Input the discrete stage set and discrete array into the dynamic programming algorithm.

[0204] S803. Based on the power flow balance model, the preset constraint strategy, and the dynamic programming algorithm, perform reverse iteration on the discrete stage set and the discrete array to determine the optimal cumulative cost set.

[0205] Specifically, step S403 includes:

[0206] First, multiple paths are generated based on the discrete stage set, the discrete array of state variables, and the discrete array of control variables.

[0207] Each path has the same time step.

[0208] Secondly, based on the power flow balance model and the preset constraint strategy, the node cost and penalty cost of each path are calculated to determine the arc cost matrix.

[0209] Next, based on the dynamic programming algorithm and the arc cost matrix, the cumulative cost value of each path is minimized to determine the optimal set of cumulative cost values.

[0210] Then, the optimal control quantity pointer set is determined based on the optimal cumulative cost value set.

[0211] Finally, the optimal cumulative cost set is determined based on the optimal cumulative cost value set and the optimal control quantity pointer set.

[0212] It should be noted that the energy management algorithm based on dynamic programming obtains the allocation sequence that optimizes operating costs, with the optimization objective being to minimize operating costs.

[0213]

[0214] S804. Based on the power flow balance model, the preset constraint strategy, the dynamic programming algorithm and the optimal cumulative cost set, perform forward backtracking to determine the optimal path and obtain the optimal power allocation sequence corresponding to the optimal path.

[0215] For example, Figure 10This is a schematic diagram of the dynamic programming algorithm provided in the embodiments of this application, such as... Figure 10 As shown:

[0216] Specifically, the state vector is defined as Take it from the minimum value To the maximum value Discretize, with a walk length of , where vector The length is The same operation is performed on the control vector, which is then defined as follows: Include it from minimum to maximum value 1 element.

[0217] Specifically, when The cumulative cost over time is relative to the terminal cost LN, which is for It is calculated from each allowed state value. If the final state is restricted to a specific value or range, then in the last time step... There is only one allowed subset, which means that when the relevant cost reaches a disallowed state value, it will be set to infinity.

[0218] Specifically, as time progresses, the arc cost is calculated from the combination of all state values ​​and control variables. Stored in a matrix middle.

[0219] Here, pointers m and n correspond to the dimensions of the state and control variables, respectively. Matrix Includes each allowed node from time k ( The cost of moving each element to all reachable nodes at time k+1.

[0220] Furthermore, calculate the candidate values ​​for cumulative cost. He indicated the state starting from time k. and selected as the first control operation The cost of reaching the end point in the time domain. Optimal cumulative cost. Minimize by filtering control quantity The obtained state pointer m is used to generate a control pointer at time k by minimizing the cumulative cost function, and then stored in a matrix. middle.

[0221] Furthermore, repeat this operation until the initial time ( At this point, the optimal control sequence is found.

[0222] It should be noted that the total number of nodes is This depends on the selected state and the number of time samples. Each of these nodes can be indexed based on its current stage position and corresponding state. For example, a node... Corresponding to stage i and state The node at that location.

[0223] In the first phase, each node is represented by a cost function of its node cost. This is a discrete function that defines a specific objective. Node cost represents the cost of being in an associated state.

[0224] From the second phase until Each node has two associated costs: node cost and transfer cost. The transfer cost R is the cost of moving from the previous state at time N. The current state at point i has been migrated. The total cost associated with each node at a certain stage. It is the sum of its node cost and the minimum of all transfer costs from the previous stage to that node.

[0225]

[0226] If a transition to a node or a state of the current node violates any constraints, then that node is infeasible and cannot be considered in the optimal path. Removing it from the optimal path would require either an extremely high cost for the transition or a significantly higher cost.

[0227] The idea behind associating high-cost links with the path rather than removing transition nodes is to ensure faster algorithmic execution in Matlab. It assumes at least one possible transition is feasible, therefore removing links from the path is pointless.

[0228] Finally, in stage T, the minimum cost F is selected and traced back to stage one along the minimum cost path.

[0229] In this embodiment, by systematically acquiring the list of network system nodes to be allocated and accurately determining the deployment location of traffic probes, full-coverage collection and real-time monitoring of network node metrics are achieved, thereby improving network performance optimization, rapid fault location, and security situation awareness capabilities. This, in turn, enhances the energy control efficiency of the vehicle.

[0230] This application also provides a possible embodiment. Figure 11 This is a schematic diagram of the online control structure of a multi-source hybrid train provided in an embodiment of this application, such as... Figure 11 As shown, the steps The specific implementation method is as follows:

[0231] Specifically, the optimal control commands are imported into the energy management control unit developed based on a microcontroller for online control.

[0232] Furthermore, the cost-optimal power allocation sequence obtained through offline dynamic programming algorithm is imported into the online controller, where power control is performed using a lookup table indexed by the running distance.

[0233] Figure 12 This is a schematic diagram of the structure of the energy control device for a vehicle provided in an embodiment of this application, as shown below. Figure 12 As shown, the device includes: an acquisition module 121, a splitting processing module 122, an aggregation processing module 123, a calculation module 124, and an allocation module 125.

[0234] The first acquisition module 121 is used to acquire train information of the vehicle to be controlled;

[0235] The first determining module 122 is used to determine the traction power demand value of the vehicle to be controlled based on the train information and the single-mass dynamics model.

[0236] The second determining module 123 is used to determine the optimal power allocation sequence of the vehicles to be controlled based on train information, traction power demand value, preset constraint strategy and dynamic programming algorithm.

[0237] The second acquisition module 124 is used to acquire the real-time train running distance of the vehicle to be controlled;

[0238] The control module 125 is used to control the train power of the vehicle to be controlled in real time based on the optimal power allocation sequence and the real-time train running distance.

[0239] In one possible design, train information includes train power source information, train power source power information, and train status information;

[0240] Based on train information, traction power demand, preset constraint strategies, and dynamic programming algorithms, the optimal power allocation sequence for the vehicles to be controlled is determined, including:

[0241] The second determining module 123 is also used to construct a power source power model of the vehicle to be controlled based on the train power source information, the train power source power information and the traction power demand value.

[0242] Based on the power source power model, train state information, preset constraint strategies, and dynamic programming algorithms, the optimal power allocation sequence for the vehicles to be controlled is determined.

[0243] In one possible design, the train power source information includes: battery information, internal combustion engine information, traction network information, and pantograph information;

[0244] Based on the train power source information, train power source power information, and traction power demand value, a power source dynamic model of the vehicle to be controlled is constructed, including:

[0245] The second determining module 123 is also used to construct a power flow balance model of the vehicle to be controlled based on the power information of the train power source and the traction power demand value.

[0246] Based on the battery information, construct a zero-order equivalent circuit model of the vehicle to be controlled;

[0247] Based on the internal combustion engine information, construct an intake manifold absolute pressure pulse spectrum model of the vehicle to be controlled;

[0248] Based on the traction network information and pantograph information, a constant efficiency model for the vehicle to be controlled is constructed.

[0249] In one possible design, based on the power source power model, train state information, preset constraint strategies, and dynamic programming algorithms, the optimal power allocation sequence for the vehicle to be controlled is determined, including:

[0250] The second determining module 123 is also used to perform discretization processing based on the preset constraint strategy and train status information to determine the discrete stage set and discrete array.

[0251] Input the discrete stage set and the discrete array into the dynamic programming algorithm;

[0252] Based on the power flow balance model, the preset constraint strategy, and the dynamic programming algorithm, the discrete stage set and the discrete array are iterated in reverse to determine the optimal cumulative cost set.

[0253] Based on the power flow balance model, the preset constraint strategy, the dynamic programming algorithm, and the optimal cumulative cost set, forward backtracking is performed to determine the optimal path and obtain the optimal power allocation sequence corresponding to the optimal path.

[0254] In one possible design, the pre-defined constraint strategy includes constraint conditions;

[0255] The constraints include: power constraints and battery state-of-charge constraints.

[0256] Based on the preset constraint strategy and train state information, discretization is performed to determine the set of discrete stages and the discrete array, including:

[0257] The second determining module 123 is also used to obtain the operating cycle based on the train status information;

[0258] The operating cycle is discretized to determine a set of discrete stages; the set of discrete stages includes multiple discrete stages; each discrete stage corresponds to the same time step.

[0259] The battery state of charge constraints are discretized according to the time step and defined as state variables, and a discrete array of state variables is obtained.

[0260] The power constraint is discretized according to the time step and defined as a control quantity, and the discrete array of control quantities is obtained.

[0261] Based on the discrete arrays of state variables and control variables, a discrete array is obtained.

[0262] In one possible design, based on the power flow balance model, a pre-defined constraint strategy, and a dynamic programming algorithm, the discrete stage set and the discrete array are iterated in reverse to determine the optimal cumulative cost set, including:

[0263] The second determining module 123 is also used to generate multiple paths based on the discrete stage set, the discrete array of state variables, and the discrete array of control variables; wherein each path has the same time step.

[0264] The node cost and penalty cost of each path are calculated based on the power flow balance model and the preset constraint strategy to determine the arc cost matrix;

[0265] Based on the dynamic programming algorithm and the arc cost matrix, the cumulative cost value of each path is minimized to determine the optimal set of cumulative cost values;

[0266] Determine the optimal set of control quantity pointers based on the optimal set of cumulative cost values;

[0267] The optimal cumulative cost set is determined based on the optimal cumulative cost value set and the optimal control quantity pointer set.

[0268] In one possible design, the pre-defined constraint strategy also includes a penalty strategy;

[0269] The constraints also include: variable power constraints and battery state of charge final value constraints.

[0270] The vehicle energy control device provided in this embodiment can execute the vehicle energy control method of the above embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0271] In a specific implementation of the aforementioned energy control method for a vehicle, each module can be implemented as a processor. The processor can execute computer execution instructions stored in the memory, thereby enabling the processor to execute the aforementioned energy control method for a vehicle.

[0272] Figure 13 This is a schematic diagram of the structure of a vehicle energy control device provided in an embodiment of this application. Figure 13As shown, the vehicle's energy control device 130 includes at least one processor 131 and a memory 132. The vehicle's energy control device 130 also includes a communication component 133. The processor 131, memory 132, and communication component 133 are connected via a bus 134.

[0273] In the specific implementation process, at least one processor 131 executes computer execution instructions stored in memory 132, causing at least one processor 131 to execute a method in the field of train energy management as executed by the energy control device side of the above-mentioned vehicle.

[0274] The specific implementation process of processor 131 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0275] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0276] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage.

[0277] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0278] The above description of the functions implemented by the vehicle's energy control device and main control device illustrates the solutions provided by the embodiments of the present invention. It is understood that, in order to achieve the above functions, the vehicle's energy control device or main control device includes hardware structures and / or software modules corresponding to the execution of each function. By combining the units and algorithm steps of the various examples described in the embodiments of the present invention, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present invention.

[0279] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described method in the field of train energy management.

[0280] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0281] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the vehicle's energy control device or main control device.

[0282] This application also provides a computer program product, which includes: a computer program stored in a readable storage medium, at least one processor of the vehicle's energy control device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the vehicle's energy control device to perform the scheme provided in any of the above embodiments.

[0283] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0284] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling the energy of a vehicle, characterized in that, include: Obtain train information for the vehicles to be controlled; Based on the train information and the single-mass dynamics model, the traction power requirement of the vehicle to be controlled is determined. Based on the train information, the traction power demand value, the preset constraint strategy, and the dynamic programming algorithm, the optimal power allocation sequence of the vehicle to be controlled is determined. Obtain the real-time train running distance of the vehicle to be controlled; The train power of the vehicle to be controlled is controlled in real time based on the optimal power allocation sequence and the real-time train running distance.

2. The method according to claim 1, characterized in that, The train information includes train power source information, train power source power information, and train status information; The step of determining the optimal power allocation sequence for the vehicles to be controlled based on the train information, the traction power demand value, a preset constraint strategy, and a dynamic programming algorithm includes: The power source power model of the vehicle to be controlled is constructed based on the train power source information, the train power source power information and the traction power demand value. Based on the power source power model, the train state information, the preset constraint strategy, and the dynamic programming algorithm, the optimal power allocation sequence for the vehicle to be controlled is determined.

3. The method according to claim 2, characterized in that, The train power source information includes: battery information, internal combustion engine information, traction network information, and pantograph information; The step of constructing the power source dynamic model of the vehicle to be controlled based on the train power source information, the train power source power information, and the traction power demand value includes: Based on the power information of the train power source and the traction power demand value, a power flow balance model of the vehicle to be controlled is constructed. Based on the battery information, construct a zero-order equivalent circuit model of the vehicle to be controlled; Based on the internal combustion engine information, construct an intake manifold absolute pressure pulse spectrum model of the vehicle to be controlled; Based on the traction network information and the pantograph information, a constant efficiency model for the vehicle to be controlled is constructed.

4. The method according to claim 3, characterized in that, The step of determining the optimal power allocation sequence for the vehicle to be controlled based on the power source power model, the train state information, the preset constraint strategy, and the dynamic programming algorithm includes: Based on the preset constraint strategy and the train state information, the discrete stage set and discrete array are determined. Input the discrete stage set and the discrete array into the dynamic programming algorithm; Based on the power flow balance model, the preset constraint strategy, and the dynamic programming algorithm, the discrete stage set and the discrete array are iterated in reverse to determine the optimal cumulative cost set; Based on the power flow balance model, the preset constraint strategy, the dynamic programming algorithm, and the optimal cumulative cost set, forward backtracking is performed to determine the optimal path and obtain the optimal power allocation sequence corresponding to the optimal path.

5. The method according to claim 4, characterized in that, The preset constraint strategy includes constraint conditions; The constraints include: power constraints and battery state of charge constraints. The step of discretizing the train state information according to the preset constraint strategy to determine the discrete stage set and discrete array includes: The operating cycle is obtained based on the train status information; The operation cycle is discretized to determine the set of discrete stages; wherein the set of discrete stages includes multiple discrete stages; wherein each discrete stage corresponds to the same time step. The battery state of charge constraint is discretized according to the time step and defined as a state variable, and a discrete array of state variables is obtained. The power constraint condition is discretized according to the time step and defined as a control quantity, and a discrete array of control quantities is obtained. The discrete array is obtained by combining the discrete array of state variables and the discrete array of control variables.

6. The method according to claim 5, characterized in that, The step of determining the optimal cumulative cost set by performing reverse iteration on the discrete stage set and the discrete array based on the power flow balance model, the preset constraint strategy, and the dynamic programming algorithm includes: Multiple paths are generated based on the discrete stage set, the discrete array of state variables, and the discrete array of control variables; wherein each path has the same time step. The node cost and penalty cost of each path are calculated based on the power flow balance model and the preset constraint strategy to determine the arc cost matrix; Based on the dynamic programming algorithm and the arc cost matrix, minimize the cumulative cost value of each path to determine the optimal set of cumulative cost values; Determine the optimal set of control quantity pointers based on the optimal set of cumulative cost values; The optimal cumulative cost set is determined based on the optimal cumulative cost value set and the optimal control quantity pointer set.

7. The method according to any one of claims 1 to 6, characterized in that, The preset constraint strategy also includes a penalty strategy; The constraints also include: variable power constraints and battery state of charge final value constraints.

8. An energy control device for a vehicle, characterized in that, include: The first acquisition module is used to acquire train information of the vehicle to be controlled; The first determining module is used to determine the traction power requirement value of the vehicle to be controlled based on the train information and the single-mass dynamics model. The second determining module is used to determine the optimal power allocation sequence of the vehicle to be controlled based on the train information, the traction power demand value, the preset constraint strategy and dynamic programming algorithm. The second acquisition module is used to acquire the real-time train running distance of the vehicle to be controlled; The control module is used to control the train power of the vehicle to be controlled in real time based on the optimal power allocation sequence and the real-time train running distance.

9. An energy control device for a vehicle, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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