A new energy vehicle energy management method, system, device and storage medium

Abstract

The application relates to a new energy automobile energy management method, system and storage medium. The PI-PMP method is used to solve the energy management problem of a fuel cell automobile. First, a simulation model of a fuel cell automobile power system is established, including a fuel cell system, a power battery and a motor system. Then, an optimization problem with a minimum hydrogen consumption as an optimization target is constructed based on a PMP algorithm, and the influence of different covariants on the calculation result is discussed. Finally, the covariants in the PMP algorithm are adjusted through a PI controller, so that the battery SOC state is controlled and the fuel consumption of the fuel cell automobile is reduced. The battery management method has good energy saving performance and high calculation efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automobiles, and in particular to a new energy automobile energy management method, system, device and storage medium. BACKGROUND

[0002] The system architecture using fuel cells as power sources has the problem of slow dynamic response, so fuel cell vehicles generally use a hybrid power mode of "fuel cell + power battery" or "fuel cell + power battery + super capacitor". For a hybrid power system vehicle, a reasonable energy management strategy can improve fuel economy while ensuring vehicle power.

[0003] The equivalent fuel consumption minimization strategy (ECMS) based on the PMP algorithm can quickly solve the optimal control problem of the hybrid power system. Many scholars have studied the application of ECMS in hybrid electric vehicles and proposed different control strategies to reduce fuel consumption. However, this method faces the problem of difficulty in determining the equivalent factor.

[0004] For a parallel hybrid electric vehicle, the prior art proposes to obtain a dynamic equivalent factor through global working conditions, realizes real-time control of the equivalent factor, and verifies the effectiveness and feasibility of the method through simulation. In addition, some technicians also consider a control strategy that combines the equivalent factor with the driving range to improve the energy efficiency of plug-in fuel cell vehicles. However, these management processes are complex, the energy management efficiency of the vehicle battery is poor, and they cannot meet the battery management requirements of today's new energy vehicles. SUMMARY

[0005] Therefore, it is necessary to provide a new energy automobile energy management method, system, device and storage medium to solve the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides a new energy automobile energy management method, which comprises:

[0007] According to the configuration of the fuel cell vehicle power system in the new energy vehicle, the motor system, fuel cell system and lithium battery system of the fuel cell vehicle are modeled;

[0008] For the modeling results, an energy control strategy with hydrogen consumption variable as the optimization target is constructed, and the energy control strategy is solved using the minimum value principle;

[0009] According to the influence of different covariants of the fuel cell on the calculation results of the energy control strategy, the covariants in the PMP algorithm are adjusted through a PI controller to control the battery SOC state and reduce the fuel consumption of the new energy vehicle.

[0010] Furthermore, based on the configuration of the fuel cell vehicle power system in new energy vehicles, the modeling of the motor system, fuel cell system, and lithium battery system of the fuel cell vehicle includes:

[0011] Based on the principles of vehicle longitudinal dynamics, the required power P during vehicle operation is obtained. dem ;

[0012] Based on the working efficiency η of the vehicle motor mot Based on the vehicle's driving state, the relationship between the vehicle's required power and the electrical and mechanical energy of the motor is obtained. The vehicle motor is powered by both a fuel cell and a lithium battery, and the electrical energy P is converted into electrical energy. mot Converted into mechanical energy P em ;

[0013] Based on the principle of converting the chemical energy of the electrochemical reaction between hydrogen and oxygen in the fuel cell into electrical energy, the hydrogen consumption is obtained. With system output power P fc Relationship;

[0014] The dynamic characteristics of the lithium battery are captured by an equivalent circuit model composed of the resistance and open-circuit voltage in the lithium battery, and the state of charge (SOC) and current I of the lithium battery are obtained. b , capacity Q b Open circuit voltage V oc Internal resistance R b Power P b The relationship.

[0015] Furthermore, based on the modeling results, an energy control strategy is constructed with hydrogen consumption as the optimization objective, and the energy control strategy is solved using the minimum principle, including:

[0016] Based on driving power demand and fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption throughout the driving process as the objective function.

[0017] The objective function is expressed as:

[0018]

[0019] Among them, P b,max and P b,min These are the battery's maximum and minimum power, respectively, P fc,max and P fc,min These represent the maximum and minimum output power of the fuel cell system, respectively, and the State of Charge (SOC). high and SOC low These are the upper and lower limits of the battery's state of charge;

[0020] Solve the optimal control problem of the objective function by PMP method, wherein the battery SOC is taken as a state variable, and the fuel cell output power P fc As a control variable, define a Hamilton function H, and obtain the solution of the objective function according to the condition that the Hamilton function is minimum.

[0021] Further, the influence of different covariants of the fuel cell on the calculation result of the energy control strategy is used to adjust the covariant in the PMP algorithm through a PI controller, so as to control the battery SOC state and reduce the fuel consumption of the new energy vehicle, including:

[0022] The value of the covariant is adjusted through a proportional integral controller to obtain a PI-PMP control strategy;

[0023] According to the covariant in the Hamilton function, the relationship between the covariant and the battery state of charge SOC is obtained, and the value of the covariant is obtained through the following formula:

[0024]

[0025] Wherein, λ0 is the initial value of the covariant, SOC ref is a reference SOC trajectory.

[0026] On the other hand, the embodiment of the present application also provides a new energy vehicle energy management system, including:

[0027] A model construction module is configured to model the motor system, fuel cell system and lithium battery system of the fuel cell vehicle according to the configuration of the fuel cell vehicle power system in the new energy vehicle;

[0028] An energy control module is configured to construct an energy control strategy taking hydrogen consumption variable as an optimization target according to the modeling result, and solve the energy control strategy by using the minimum value principle;

[0029] An energy coordination module is configured to adjust the covariant in the PMP algorithm through a PI controller according to the influence of different covariants of the fuel cell on the calculation result of the energy control strategy, so as to control the battery SOC state and reduce the fuel consumption of the new energy vehicle.

[0030] Further, the model construction module includes a relationship acquisition unit, which is configured to:

[0031] According to the vehicle longitudinal dynamics principle, the required power P dem of the vehicle during driving is obtained.

[0032] According to the working efficiency η motThe relationship between the required power of the vehicle and the electric energy and mechanical energy of the motor is obtained according to the state of the vehicle driving, the motor of the vehicle is provided with electric energy by the fuel cell and the lithium battery, and the electric energy P mot is converted into mechanical energy P em .

[0033] According to the principle that the chemical energy of the hydrogen and oxygen electrochemical reaction in the fuel cell is converted into electric energy, the hydrogen consumption is obtained and the relationship with the system output power P fc .

[0034] The dynamic characteristics of the lithium battery are captured through an equivalent circuit model composed of a resistor and an open-circuit voltage in the lithium battery, and the relationship between the state of charge SOC of the lithium battery and the current I b , the capacity Q b , the open-circuit voltage V oc , the internal resistance R b , and the power P b is obtained.

[0035] Further, the energy control module includes a minimum value solving unit, and the minimum value solving unit is used to:

[0036] According to the driving power demand and the fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption in the entire driving process as a target function;

[0037] The target function is expressed as:

[0038]

[0039] Wherein, P b,max and P b,min are the maximum power and the minimum power of the battery, P fc,max and P fc,min are the maximum output power and the minimum output power of the fuel cell system, SOC high and SOC low are the upper and lower limits of the state of charge of the battery.

[0040] The optimal control problem of the target function is solved by the PMP method, wherein the battery SOC is used as a state variable, the fuel cell output power P fc is used as a control variable, a Hamilton function H is defined, and the solution of the target function is obtained according to the condition that the Hamilton function is minimum.

[0041] The energy coordination module includes a proportional coordination unit, and the proportional coordination unit is used to:

[0042] The value of the covariant is adjusted by a proportional-integral controller to obtain a PI-PMP control strategy.

[0043] According to the covariant in the Hamilton function, the relationship between the covariant and the battery state of charge (SOC) is obtained, and the value of the covariant is obtained by the following formula:

[0044]

[0045] Wherein, λ0 is the initial value of the covariant, SOC ref is a reference SOC trajectory.

[0046] The embodiment of the application further provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes the following steps when executing the computer program:

[0047] According to the configuration of a fuel cell vehicle power system in a new energy vehicle, a motor system, a fuel cell system and a lithium battery system of the fuel cell vehicle are modeled;

[0048] According to the modeling result, an energy control strategy with a hydrogen consumption variable as an optimization target is constructed, and the energy control strategy is solved by using a minimum value principle;

[0049] According to the influence of different covariants of the fuel cell on the calculation result of the energy control strategy, the covariant in the PMP algorithm is adjusted by a PI controller, the battery SOC state is controlled, and the fuel consumption of the new energy vehicle is reduced.

[0050] The embodiment of the application further provides a computer readable storage medium, and a computer program is stored in the computer readable storage medium, and the following steps are realized when the computer program is executed by a processor:

[0051] According to the configuration of a fuel cell vehicle power system in a new energy vehicle, a motor system, a fuel cell system and a lithium battery system of the fuel cell vehicle are modeled;

[0052] According to the modeling result, an energy control strategy with a hydrogen consumption variable as an optimization target is constructed, and the energy control strategy is solved by using a minimum value principle;

[0053] According to the influence of different covariants of the fuel cell on the calculation result of the energy control strategy, the covariant in the PMP algorithm is adjusted by a PI controller, the battery SOC state is controlled, and the fuel consumption of the new energy vehicle is reduced.

[0054] The new energy vehicle energy management method, system, device and storage medium, the PI-PMP method is adopted to solve the energy management problem of the fuel cell vehicle. First, a simulation model of the power system of the fuel cell vehicle is established, including the fuel cell system, the power battery and the motor system. Then, an optimization problem with the minimum hydrogen consumption as the optimization objective is constructed based on the PMP algorithm, and the influence of different covariates on the calculation result is discussed. Finally, the covariates in the PMP algorithm are adjusted through the PI controller, so as to control the SOC state of the battery and reduce the fuel consumption of the fuel cell vehicle. Compared with the DP strategy, the energy saving performance of the algorithm is better, and the calculation efficiency is higher. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 It is a flowchart of the new energy vehicle energy management method in an embodiment;

[0056] Figure 2 It is a flowchart of modeling the fuel cell of the vehicle in an embodiment;

[0057] Figure 3 It is a structural diagram of the power system structure diagram in an embodiment;

[0058] Figure 4 It is a structural diagram of the new energy vehicle energy management system in an embodiment;

[0059] Figure 5 It is an internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0061] In an embodiment, as shown in Figure 1 a new energy vehicle energy management method is provided, the method comprising:

[0062] Step 101, according to the configuration of the fuel cell vehicle power system in the new energy vehicle, modeling the motor system, fuel cell system and lithium battery system of the fuel cell vehicle;

[0063] Step 102, for the modeling result, constructing an energy control strategy with hydrogen consumption variable as the optimization objective, and solving the energy control strategy by using the minimum value principle;

[0064] Step 103: Based on the influence of different covariates of the fuel cell on the calculation results of the energy control strategy, adjust the covariates in the PMP algorithm through the PI controller to control the battery SOC state and reduce the fuel consumption of new energy vehicles.

[0065] Specifically, this embodiment of the new energy vehicle energy management method mainly employs the PI-PMP method to solve the energy management problem of fuel cell vehicles. First, a simulation model of the fuel cell vehicle's power system is established, including the fuel cell system, power battery, and motor system. Then, based on the PMP algorithm, an optimization problem is constructed with the goal of minimizing hydrogen consumption, and the impact of different covariates on the calculation results is discussed. Finally, the covariates in the PMP algorithm are adjusted using a PI controller to control the battery's state of charge (SOC) while reducing the fuel consumption of the fuel cell vehicle. Furthermore, this battery management method exhibits good energy efficiency and high computational efficiency.

[0066] In one embodiment, such as Figure 2 As shown, the fuel cell modeling process for a vehicle includes the following steps:

[0067] Step 201: Based on the principle of vehicle longitudinal dynamics, obtain the power demand P during vehicle operation. dem ;

[0068] Step 202, based on the vehicle motor's operating efficiency η mot Based on the vehicle's driving state, the relationship between the vehicle's required power and the electrical and mechanical energy of the motor is obtained. The vehicle motor is powered by both a fuel cell and a lithium battery, and the electrical energy P is converted into electrical energy. mot Converted into mechanical energy P em ;

[0069] Step 203: Based on the principle of converting the chemical energy of the electrochemical reaction between hydrogen and oxygen in the fuel cell into electrical energy, obtain the hydrogen consumption. With system output power P fc Relationship;

[0070] Step 204: Capture the dynamic characteristics of the lithium battery using an equivalent circuit model composed of the resistance and open-circuit voltage in the lithium battery, and obtain the state of charge (SOC) and current I of the lithium battery. b , capacity Q b Open circuit voltage V oc Internal resistance R b Power P b The relationship.

[0071] Specifically, this embodiment requires modeling the longitudinal dynamics of the vehicle, based on... Figure 3 A diagram illustrating the vehicle's powertrain, showing the power demand P during vehicle operation. dem This can be expressed as:

[0072]

[0073] wherein: m, g, f, v, C D , A, p are the mass of the fuel cell vehicle, the gravitational acceleration, the tire rolling resistance coefficient, the vehicle speed, the air resistance coefficient, the air density constant, respectively.

[0074] In addition, the motor is powered by both the fuel cell and the lithium battery, and converts the electric energy P mot into mechanical energy P em , which is output to the wheels through the transmission to provide the required power of the vehicle. Considering the working efficiency η mot of the motor and the state of the vehicle, the required power of the vehicle and the electric energy and mechanical energy of the motor have the following relationship:

[0075]

[0076] The relationship between the rotational speed ω axle and the torque T axle of the vehicle drive shaft and the rotational speed ω em and the torque T em of the motor is:

[0077]

[0078] wherein: R wheel , g f are the tire radius and the reduction ratio, respectively.

[0079] The fuel cell system is a power generation device that can convert the chemical energy of hydrogen and oxygen into electric energy through electrochemical reaction under the action of a catalyst. The internal structure is complex, but in order to facilitate research, the relationship between hydrogen consumption and system output power P fc is simplified as follows:

[0080]

[0081] wherein: E H2 is the low heat value of hydrogen (120 MJ / kg), and η fc is the efficiency of the fuel cell system.

[0082] The lithium battery, as an auxiliary energy source of the vehicle, can provide peak power and recover braking energy. In energy management, an equivalent circuit model composed of a resistor and an open circuit voltage is usually used to capture the dynamic characteristics of the lithium battery.

[0083] Therefore, the state of charge SOC of the battery is related to the current I b , the capacity Q b , and the open circuit voltage Voc , internal resistance R b , power P b The relationship can be expressed as follows:

[0084]

[0085] In one embodiment, the flow performed on data transmission includes:

[0086] According to the driving power demand and fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption in the entire driving process as the objective function;

[0087] The objective function is expressed as:

[0088]

[0089] s.t.P b,min ≤P b ≤P b,max

[0090] P fc,min ≤P fc ≤P fc,max

[0091] SOC low ≤SOC≤SOC high ;

[0092] Wherein, P b,max and P b,min are the maximum and minimum power of the battery, P fc,max and P fc,mi n are the maximum and minimum output power of the fuel cell system, SOC high and SOC low are the upper and lower limits of the battery state of charge;

[0093] The optimal control problem of the objective function is solved by PMP method, wherein the battery SOC is taken as the state variable, the fuel cell output power P fc is taken as the control variable, the Hamilton function H is defined, and the solution of the objective function is obtained according to the condition of minimum Hamilton function.

[0094] The main task of the energy management strategy is to meet the driving power demand while improving fuel economy. Existing research shows that PMP method not only has fast calculation speed, but also can guarantee global optimality. In solving the optimal control problem, the objective function is expressed as the cumulative hydrogen consumption in the entire driving process.

[0095] In this embodiment, PMP method is used to solve the optimal control problem, wherein the battery SOC is taken as the state variable, and the fuel cell output power P fcAs a control variable, the Hamilton function H is defined as:

[0096]

[0097] where λ is a covariant variable. To obtain the optimal solution, the following condition must be met:

[0098]

[0099] The condition for minimizing the Hamilton function is:

[0100]

[0101] where U is the fuel cell output power P fc of the feasible region.

[0102] In one embodiment, the influence of different covariant variables of the fuel cell on the calculation result of the energy control strategy is adjusted by a PI controller to adjust the covariant variable in the PMP algorithm, control the battery SOC state, and reduce the fuel consumption of the new energy vehicle, including:

[0103] The value of the covariant variable is adjusted by a proportional integral controller to obtain a PI-PMP control strategy;

[0104] According to the covariant variable in the Hamilton function, the relationship between the covariant variable and the battery state of charge SOC is obtained, and the value of the covariant variable is obtained by the following formula:

[0105]

[0106] where λ0 is the initial value of the covariant variable, SOC ref is the reference SOC trajectory.

[0107] It should be understood that although each step in the above flowchart is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the above flowchart can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0108] In one embodiment, as shown in Figure 4 a new energy vehicle energy management system is provided, including:

[0109] The model building module 401 is used to model the motor system, fuel cell system and lithium battery system of the fuel cell vehicle according to the configuration of the power system of the fuel cell vehicle in the new energy vehicle.

[0110] The energy control module 402 is used to construct an energy control strategy with hydrogen consumption variable as the optimization objective based on the modeling results, and to solve the energy control strategy using the minimum principle.

[0111] The energy coordination module 403 is used to adjust the covariates in the PMP algorithm through a PI controller based on the influence of different covariates of the fuel cell on the calculation results of the energy control strategy, thereby controlling the battery SOC state and reducing the fuel consumption of new energy vehicles.

[0112] In one embodiment, the model building module 401 includes a relationship acquisition unit, which is used for:

[0113] Based on the principles of vehicle longitudinal dynamics, the required power P during vehicle operation is obtained. dem ;

[0114] Based on the working efficiency η of the vehicle motor mot Based on the vehicle's driving state, the relationship between the vehicle's required power and the electrical and mechanical energy of the motor is obtained. The vehicle motor is powered by both a fuel cell and a lithium battery, and the electrical energy P is converted into electrical energy. mot Converted into mechanical energy P em ;

[0115] Based on the principle of converting the chemical energy of the electrochemical reaction between hydrogen and oxygen in the fuel cell into electrical energy, the hydrogen consumption is obtained. With system output power P fc Relationship;

[0116] The dynamic characteristics of the lithium battery are captured by an equivalent circuit model composed of the resistance and open-circuit voltage in the lithium battery, and the state of charge (SOC) and current I of the lithium battery are obtained. b , capacity Q b Open circuit voltage V oc Internal resistance R b Power P b The relationship.

[0117] In one embodiment, the energy control module 402 includes a minimum value solving unit, which is used for:

[0118] Based on driving power demand and fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption throughout the driving process as the objective function.

[0119] The objective function is expressed as:

[0120]

[0121] where P b,max and P b,min are the maximum and minimum power of the battery, respectively, P fc,max and P fc,min are the maximum and minimum output power of the fuel cell system, respectively, SOC hig h and SOC low are the upper and lower limits of the battery state of charge, respectively;

[0122] solving the optimal control problem of the objective function by the PMP method, wherein the battery SOC is taken as a state variable, the fuel cell output power P fc is taken as a control variable, a Hamilton function H is defined, and the solution of the objective function is obtained according to the condition that the Hamilton function is minimum.

[0123] In one embodiment, the energy coordination module 403 comprises a proportional coordination unit, which is configured to:

[0124] adjusting the value of the covariant by a proportional-integral controller to obtain a PI-PMP control strategy;

[0125] obtaining the relationship between the covariant and the battery state of charge SOC according to the covariant in the Hamilton function, the value of the covariant being obtained by the following formula:

[0126]

[0127] where λ0 is the initial value of the covariant, SOC ref is the reference SOC trajectory.

[0128] Specifically, the minimum value of the Hamilton function is obtained to obtain the optimal trajectory of the battery SOC during vehicle driving, the PMP method is used to simulate the standard NEDC condition according to the specific parameters of the given fuel cell vehicle model, and different SOC trajectories are obtained according to the simulation results by using different assignments of the covariant λ. When λ = -285, the battery power can be kept unchanged during driving. When λ < -285, the proportion of the energy consumption of the fuel cell system in the equivalent fuel consumption is small, and the strategy tends to let the fuel cell system provide power for the vehicle, so the position of the SOC at the terminal time is high. Conversely, when λ > -285, the strategy tends to let the battery release energy to reduce hydrogen consumption, and the position of the SOC at the terminal time is low.

[0129] The specific definition of the new energy vehicle energy management system can refer to the definition of the new energy vehicle energy management method in the foregoing, and will not be described here. Each module in the new energy vehicle energy management system can be realized by software, hardware, and a combination thereof, in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each of the above-mentioned modules.

[0130] Figure 5 An internal structure diagram of a computer device in an embodiment is shown. As shown in Figure 5 The computer device includes a processor, a memory, a network interface, an input device, and a display screen connected by a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and can also store a computer program, which, when executed by the processor, can enable the processor to implement the new energy vehicle energy management method. The internal memory can also store a computer program, which, when executed by the processor, can enable the processor to execute the new energy vehicle energy management method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball, or touchpad provided on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0131] Those skilled in the art can understand, Figure 5 The structure shown in the foregoing is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the diagram, or combine certain components, or have a different arrangement of components.

[0132] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0133] According to the configuration of the fuel cell vehicle power system in the new energy vehicle, the motor system, the fuel cell system, and the lithium battery system of the fuel cell vehicle are modeled;

[0134] For the modeling result, an energy control strategy with a hydrogen consumption variable as an optimization objective is constructed, and the energy control strategy is solved by using the minimum value principle;

[0135] Based on the influence of different covariates of the fuel cell on the calculation results of the energy control strategy, the covariates in the PMP algorithm are adjusted by the PI controller to control the battery SOC state and reduce the fuel consumption of new energy vehicles.

[0136] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0137] Based on the principles of vehicle longitudinal dynamics, the required power P during vehicle operation is obtained. dem ;

[0138] Based on the working efficiency η of the vehicle motor mot Based on the vehicle's driving state, the relationship between the vehicle's required power and the electrical and mechanical energy of the motor is obtained. The vehicle motor is powered by both a fuel cell and a lithium battery, and the electrical energy P is converted into electrical energy. mot Converted into mechanical energy P em ;

[0139] Based on the principle of converting the chemical energy of the electrochemical reaction between hydrogen and oxygen in the fuel cell into electrical energy, the hydrogen consumption is obtained. With system output power P fc Relationship;

[0140] The dynamic characteristics of the lithium battery are captured by an equivalent circuit model composed of the resistance and open-circuit voltage in the lithium battery, and the state of charge (SOC) and current I of the lithium battery are obtained. b , capacity Q b Open circuit voltage V oc Internal resistance R b Power P b The relationship.

[0141] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0142] Based on driving power demand and fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption throughout the driving process as the objective function.

[0143] The objective function is expressed as:

[0144]

[0145] Among them, P b,max and P b,min These are the battery's maximum and minimum power, respectively, P fc,max and P fc,min These represent the maximum and minimum output power of the fuel cell system, and the State of Charge (SOC). high and SOC low These are the upper and lower limits of the battery's state of charge;

[0146] The optimal control problem of the objective function is solved by a PMP method, wherein the battery SOC is taken as a state variable, and the fuel cell output power P fc As a control variable, a Hamilton function H is defined, and the solution of the objective function is obtained according to the condition that the Hamilton function is minimum.

[0147] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0148] The value of the covariant is adjusted by a proportional-integral controller to obtain a PI-PMP control strategy.

[0149] According to the covariant in the Hamilton function, the relationship between the covariant and the battery state of charge SOC is obtained, and the value of the covariant is obtained by the following formula:

[0150]

[0151] Wherein, λ0 is the initial value of the covariant, SOC ref is a reference SOC trajectory.

[0152] In one embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium. The computer program is executed by a processor to implement the following steps:

[0153] According to the configuration of the fuel cell vehicle power system in the new energy vehicle, the motor system, the fuel cell system and the lithium battery system of the fuel cell vehicle are modeled.

[0154] For the modeling results, an energy control strategy is constructed with the hydrogen consumption variable as the optimization objective, and the energy control strategy is solved by using the minimum value principle.

[0155] According to the influence of different covariants of the fuel cell on the calculation results of the energy control strategy, the covariant in the PMP algorithm is adjusted by a PI controller to control the battery SOC state and reduce the fuel consumption of the new energy vehicle.

[0156] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0157] According to the vehicle longitudinal dynamics principle, the required power P dem of the vehicle during driving is obtained.

[0158] According to the working efficiency η mot of the vehicle motor and the state of the vehicle driving, the relationship between the required power of the vehicle and the motor electric energy and mechanical energy is obtained, the vehicle motor is provided with electric energy by the fuel cell and the lithium battery, and the electric energy P mot is converted into mechanical energy P em .

[0159] According to the principle that the chemical energy of hydrogen and oxygen electrochemical reaction in the fuel cell is converted into electric energy, the hydrogen consumption is obtained and the relationship between the system output power P fc ;

[0160] The dynamic characteristics of the lithium battery are captured by an equivalent circuit model composed of resistance and open circuit voltage in the lithium battery, and the relationship between the state of charge SOC of the lithium battery and the current I b , capacity Q b , open circuit voltage V oc , internal resistance R b , power P b is obtained.

[0161] In one embodiment, the processor also implements the following steps when executing the computer program:

[0162] According to the driving power demand and fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption in the entire driving process as the objective function;

[0163] The objective function is expressed as:

[0164]

[0165] Where P b,max and P b,min are the maximum and minimum power of the battery, P fc,max and P fc,min are the maximum and minimum output power of the fuel cell system, SOC high and SOC low are the upper and lower limits of the state of charge;

[0166] The optimal control problem of the objective function is solved by the PMP method, wherein the battery SOC is the state variable, the fuel cell output power P fc is the control variable, the Hamilton function H is defined, and the solution of the objective function is obtained according to the condition that the Hamilton function is minimum.

[0167] In one embodiment, the processor also implements the following steps when executing the computer program:

[0168] The value of the covariant is adjusted by a proportional integral controller to obtain a PI-PMP control strategy;

[0169] According to the covariant in the Hamilton function, the relationship between the covariant and the state of charge SOC of the battery is obtained, and the value of the covariant is obtained by the following formula:

[0170]

[0171] wherein λ0 is an initial value of the covariate, SOC ref is a reference SOC trajectory. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiment methods.

[0172] The technical features of the above embodiments can be combined in any manner. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present disclosure.

[0173] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent of the present application should be subject to the appended claims.

Claims

1. A new energy vehicle energy management method, characterized in that, The method comprises: According to the configuration of the fuel cell vehicle power system in the new energy vehicle, the motor system, fuel cell system and lithium battery system of the fuel cell vehicle are modeled; According to the modeling results, an energy control strategy is constructed with hydrogen consumption variable as the optimization target, and the energy control strategy is solved by using the minimum value principle; According to the influence of different covariants of the fuel cell on the calculation results of the energy control strategy, the covariants in the PMP algorithm are adjusted by the PI controller to control the battery SOC state and reduce the fuel consumption of the new energy vehicle; According to the modeling results, an energy control strategy is constructed with hydrogen consumption variable as the optimization target, and the energy control strategy is solved by using the minimum value principle, comprising: According to the driving power demand and fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption in the entire driving process as the objective function; The objective function is expressed as: where P b,max and P b,min are the maximum and minimum power of the battery, respectively, P fc,max and P fc,min are the maximum and minimum output power of the fuel cell system, respectively, SOC high and SOC low are the upper and lower limits of the battery state of charge, respectively, P b is the battery power, is the hydrogen consumption, E H2 is the lower heating value of hydrogen, η fc is the fuel cell system efficiency, and t is time. The optimal control problem of the objective function is solved by a PMP method, wherein a battery SOC is taken as a state variable and a fuel cell output power P fc A Hamilton function H is defined as a control variable, and a solution of the objective function is obtained according to a condition of minimum Hamilton function. The Hamilton function is expressed as: where λ is a covariate, is the SOC change amount per unit time; According to the influence of different covariants of the fuel cell on the calculation results of the energy control strategy, the covariants in the PMP algorithm are adjusted by the PI controller to control the battery SOC state and reduce the fuel consumption of the new energy vehicle, comprising: The value of the covariant is adjusted by the proportional integral controller to obtain the PI-PMP control strategy; According to the covariant in the Hamilton function, the relationship between the covariant and the battery state of charge SOC is obtained, and the value of the covariant is obtained by the following formula: where λ0is the initial value of the covariate, SOC ref is the reference SOC trajectory, k p is the proportional coefficient, k i is the integral coefficient.

2. The new energy vehicle energy management method according to claim 1, characterized in that, According to the configuration of the fuel cell vehicle power system in the new energy vehicle, the motor system, fuel cell system and lithium battery system of the fuel cell vehicle are modeled, comprising: According to the principle of vehicle longitudinal dynamics, the required power P of the vehicle during driving is obtained dem ; According to the working efficiency η of the vehicle motor mot With the state of vehicle travel, the relationship between the required power of the vehicle and the motor electric energy and mechanical energy is obtained, the vehicle motor is jointly provided with electric energy by the fuel cell and the lithium battery, and the electric energy P mot is converted into mechanical energy P em ; According to the principle of the chemical energy conversion into electric energy of the hydrogen and oxygen electrochemical reactions in the fuel cell, the hydrogen consumption is obtained in relation to the system output power P fc . The dynamic characteristics of the lithium battery are captured by an equivalent circuit model composed of a resistance and an open circuit voltage in the lithium battery, to obtain the relationship between the lithium battery state of charge SOC and current I b , capacity Q b , open circuit voltage V oc , internal resistance R b , and power P b .

3. A new energy vehicle energy management system, characterized in that, Comprising: A model construction module is configured to model the motor system, fuel cell system and lithium battery system of the fuel cell vehicle according to the configuration of the fuel cell vehicle power system in the new energy vehicle; An energy control module is configured to construct an energy control strategy with hydrogen consumption variable as the optimization target according to the modeling results, and solve the energy control strategy by using the minimum value principle; An energy coordination module is configured to adjust the covariants in the PMP algorithm by the PI controller according to the influence of different covariants of the fuel cell on the calculation results of the energy control strategy, control the battery SOC state and reduce the fuel consumption of the new energy vehicle; The energy control module comprises a minimum value solving unit, which is configured to: According to the driving power demand and fuel economy, an energy management strategy is constructed with the cumulative hydrogen consumption in the entire driving process as the objective function; The objective function is expressed as: where P b,max and P b,min are the maximum and minimum power of the battery, respectively, P fc,max and P fc,min are the maximum and minimum output power of the fuel cell system, respectively, SOC high and SOC low are the upper and lower limits of the battery state of charge, respectively, P b is the battery power, is the hydrogen consumption, E H2 is the lower heating value of hydrogen, η fc is the fuel cell system efficiency, and t is time. The optimal control problem of the objective function is solved by a PMP method, wherein a battery SOC is taken as a state variable and a fuel cell output power P fc A Hamilton function H is defined as a control variable, and a solution of the objective function is obtained according to a condition of minimum Hamilton function. The Hamilton function is expressed as: where λ is a covariate, is the SOC change amount per unit time; According to the influence of different covariants of the fuel cell on the calculation results of the energy control strategy, the covariants in the PMP algorithm are adjusted by the PI controller to control the battery SOC state and reduce the fuel consumption of the new energy vehicle, comprising: The value of the covariant is adjusted by the proportional integral controller to obtain the PI-PMP control strategy; According to the covariant in the Hamilton function, the relationship between the covariant and the battery state of charge SOC is obtained, and the value of the covariant is obtained by the following formula: where λ0is the initial value of the covariate, SOC ref is the reference SOC trajectory, k p is the proportional coefficient, k i is the integral coefficient.

4. The new energy vehicle energy management system according to claim 3, characterized in that, The model construction module comprises a relationship acquisition unit, which is configured to: According to the principle of vehicle longitudinal dynamics, the required power P of the vehicle during driving is obtained dem ; According to the working efficiency η of the vehicle motor mot With the state of vehicle travel, the relationship between the required power of the vehicle and the electrical energy and mechanical energy of the motor is obtained, the vehicle motor is jointly provided with electrical energy by the fuel cell and the lithium battery, and the electrical energy P mot is converted into mechanical energy P em ; According to the principle of the chemical energy conversion into electric energy of the hydrogen and oxygen electrochemical reactions in the fuel cell, the hydrogen consumption is obtained in relation to the system output power P fc . The dynamic characteristics of the lithium battery are captured by an equivalent circuit model composed of a resistance and an open circuit voltage in the lithium battery, to obtain the relationship of the lithium battery state of charge SOC and current I b , capacity Q b , open circuit voltage V oc , internal resistance R b , power P b .

5. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 2.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 2.

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