Vehicle energy management method and vehicle

Abstract

The invention discloses a vehicle energy management method. The method comprises the following steps: acquiring a preset vehicle operation condition; constructing a state variable equation and a control variable equation under a preset vehicle operation condition, and determining an algebraic constraint equation based on the state variable equation and the control variable equation; constructing a fuel cell attenuation equation based on the vehicle operation load and the vehicle start-stop times; constructing an ampere-hour circulation equation of the power battery; solving the algebraic constraint equation on the basis of a nonlinear programming method by taking the fuel cell attenuation amount and the power cell ampere-hour circulation amount as target quantities and aiming at prolonging the service life of the fuel cell and the service life of the power cell in a preset vehicle working condition to obtain an optimal state variable and an optimal control variable; and performing energy management on the fuel cell hybrid vehicle based on the optimal state variable and the optimal control variable.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of vehicle engineering technology, and in particular to a vehicle energy management method and a vehicle. Background Art

[0002] In order to cope with the energy and environmental crisis and achieve sustainable social development, all parts of the world are committed to the development of new energy vehicles. Fuel Cell Hybrid Electric Vehicle (FCHEV) overcomes the shortcomings of pure electric vehicles that are not fast enough to charge and traditional hybrid vehicles that have low engine thermal efficiency and low fuel substitution rate, and is considered to be one of the most promising energy forms at present. Energy management is one of the important research and development contents to reduce the energy consumption of new energy vehicles, alleviate the anxiety of driving range, and reduce the cost of use. At the same time, reasonable charging and discharging can extend the service life of energy storage batteries.

[0003] Selecting performance indicators is one of the important links in energy management. Appropriate performance indicators can ensure the effectiveness, practicality and solution efficiency of energy management. Summary of the invention

[0004] The present invention provides a vehicle energy management method and a vehicle, so as to achieve a balance between vehicle performance and fuel cell life, and minimize the attenuation of fuel cells and power batteries while meeting vehicle operation requirements, thereby extending their service life.

[0005] In a first aspect, an embodiment of the present invention provides a vehicle energy management method for energy management of a fuel cell hybrid vehicle, wherein the fuel cell hybrid vehicle includes a fuel cell and a power battery, wherein the fuel cell is used for vehicle endurance, and the power battery is used for supplementing instantaneous power, and the method includes:

[0006] Obtaining preset vehicle operating conditions;

[0007] Under the preset vehicle operating condition, constructing a state variable equation and a control variable equation, and determining an algebraic constraint equation based on the state variable equation and the control variable equation;

[0008] Construct a fuel cell attenuation equation based on vehicle operating load and vehicle start and stop times;

[0009] Construct the power battery ampere-hour flow equation based on the power battery current;

[0010] Taking the fuel cell attenuation as the target quantity, taking the minimum fuel cell attenuation in the preset vehicle operating condition as the target, solving the algebraic constraint equation based on the nonlinear programming method to obtain the optimal state variable and the optimal control variable;

[0011] Performing energy management on the fuel cell hybrid vehicle based on the optimal state variable and the optimal control variable;

[0012] Among them, the state variables include power battery power and fuel cell power; the control variables include power battery current and fuel cell current;

[0013] The power battery power and the fuel cell power constitute the vehicle required power under the preset vehicle operating condition;

[0014] The fuel cell power is used to characterize the vehicle operation load, and the vehicle operation load includes one or more of idling operation, large variable load operation, and high load operation. Different vehicle operation loads correspond to different fuel cell power ranges.

[0015] Optionally, the fuel cell attenuation is expressed by the following formula:

[0016]

[0017] In the formula, Indicates the fuel cell attenuation, K p Represents the attenuation rate correction coefficient, k 1 Indicates the idle speed correction factor, k 2 Indicates the large variable load operation attenuation coefficient, k 3 Indicates the high load operation attenuation coefficient, k 4 represents the start-stop attenuation coefficient, t 1 Indicates the idling time, t 2 Indicates the large load change operation time, t 3 Indicates the high-load operation time, and n indicates the number of starts and stops.

[0018] Optionally, the power battery ampere-hour flow equation is expressed by the following formula:

[0019] Aheff(t)=∫ 0 tf σ(t)|i b |dt

[0020]

[0021] Where Ageff(t) represents the ampere-hour flow rate of the power battery, σ is the penalty factor used to balance the charge and discharge rate, temperature and discharge depth, and k b is the battery charge and discharge rate, i b is the power battery current, and Q represents the power battery capacity.

[0022] Optionally, the state variable equation includes:

[0023] p b (t) = ib (t)V b (t)

[0024] p fc (t) = i fc (t)V fc_out (t)

[0025] In the formula, p b (t) represents the power of the battery at time t, i b (t) represents the power battery current at time t, V b (t) represents the power battery voltage at time t, p fc (t) represents the fuel cell power at time t, i fc (t) represents the fuel cell current at time t, V fc_out (t) represents the fuel cell voltage at time t;

[0026] when i b (t)>0, the power battery is in the discharge state. b When (t)<0, the power battery is in charging state;

[0027] The control variable equation includes:

[0028] V b (t) = u b (SOC b (t)-i b (t)R b SOC b (t))

[0029]

[0030] In the formula, SOC b (t) represents the power battery SOC at time t, R b Indicates the internal resistance of the power battery, u b Indicates the open circuit voltage of the power battery, u fc represents the open circuit voltage of the fuel cell, R fc represents the internal resistance of the fuel cell, f 1 () indicates a preset function.

[0031] Optionally, the vehicle demand power is used as a constraint condition for solving the optimal state variable and the optimal control variable, and the vehicle demand power satisfies the following formula with the power battery power and the fuel cell power:

[0032] Charge

[0033] Discharge

[0034] In the formula, pd represents the vehicle demand power, p b Indicates the power of the power battery, p fc represents the fuel cell power, η dcdc represents the DC / DC converter efficiency, η re represents the kinetic energy recovery efficiency, η m Indicates motor efficiency.

[0035] Optionally, the power battery SOC is also used as a constraint condition for solving the optimal state variable and the optimal control variable;

[0036] The power battery SOC constraint satisfies the following formula:

[0037] SOC b_min ≤SOC b ≤SOC b_max

[0038] SOC b Indicates power battery SOC, SOC b_min Indicates the minimum SOC value of the power battery, SOC b_max Indicates the maximum SOC value of the power battery;

[0039] The power battery SOC is determined based on the power battery current.

[0040] Optionally, the power battery SOC is expressed by the following formula:

[0041]

[0042] In the formula, SOC b (t) represents the SOC of the power battery at time t, SOC b (t 0 ) represents the initial SOC of the power battery, i b (t) represents the power battery current at time t, and Q represents the battery capacity.

[0043] Optionally, the nonlinear programming method adopts Radau pseudo-spectral method.

[0044] Optionally, the preset vehicle operating condition is a NEDC condition.

[0045] In a second aspect, an embodiment of the present invention further provides a vehicle, wherein the vehicle is configured to execute any one of the vehicle energy management methods recorded in the embodiments of the present invention.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a vehicle energy management method, the method comprising constructing a fuel cell attenuation equation based on the vehicle operating load and the number of vehicle starts and stops; taking the fuel cell attenuation and the ampere-hour flow of the power battery as target quantities, solving the state variable equation and the control variable equation based on a nonlinear programming method to obtain the optimal state variable and the optimal control variable; performing energy management on the vehicle based on the optimal state variable and the optimal control variable, wherein taking the fuel cell attenuation and the ampere-hour flow of the power battery as target quantities to solve the state variable and the control variable can better balance the vehicle performance and the life of the fuel cell and the power battery, and help to meet the vehicle operation requirements. At the same time, it can minimize the attenuation of fuel cells and power batteries and extend their service life; at the same time, extending the life of fuel cells and power batteries can reduce the frequency of replacing fuel cells and power batteries, and can reduce the maintenance cost and use cost of the vehicle. Slowing down the attenuation of fuel cells can enhance the reliability and stability of the entire power system and reduce the possibility of failures. The vehicle will not experience unexpected failures during operation due to the rapid decline in fuel cell performance; solving the control quantity while considering the attenuation can enable the system to use energy more efficiently and improve energy utilization efficiency; in addition, state variables and control variables can adapt to different vehicle usage scenarios and operating conditions, and provide the optimal control strategy to meet various working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of a vehicle energy management method in an embodiment;

[0048] Figure 2 is another vehicle energy management method flow chart in the embodiment;

[0049] Figure 3 is another vehicle energy management method flow chart in the embodiment;

[0050] Figure 4 is a schematic diagram of NEDC operating conditions in the embodiment;

[0051] Figure 5 is a schematic diagram of the vehicle required power in the embodiment;

[0052] Figure 6 is a graph showing the relationship between the fuel cell power, the fuel cell current and the fuel cell voltage in the embodiment;

[0053] Figure 7 is a curve diagram showing the corresponding relationship between the power battery cell voltage and SOC in the embodiment;

[0054] Figure 8 is a fuel cell power curve diagram after optimization in the embodiment;

[0055] Fig. 9 is a power curve diagram of the optimized power battery in the embodiment;

[0056] Fig.10 is an optimized power battery SOC curve diagram in the embodiment;

[0057] Fig.11 Schematic diagram of the structure of an electronic device in an embodiment. DETAILED DESCRIPTION

[0058] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0059] Embodiment 1

[0060] Figure 1 is a flow chart of the vehicle energy management method in the embodiment, refer to Figure 1 , methods include:

[0061] S101. Obtain preset vehicle operating conditions.

[0062] In this solution, a vehicle energy management method is set for energy management of a fuel cell hybrid vehicle. The fuel cell hybrid vehicle includes a fuel cell and a power battery. The fuel cell is used for vehicle endurance, and the power battery is used to supplement instantaneous power.

[0063] In this solution, the preset vehicle operating condition is a specified vehicle test condition, and the vehicle test condition can be:

[0064] New European Driving Cycle (NEDC): This is a common test cycle, including urban and suburban conditions, characterized by relatively low vehicle speeds and gentle acceleration and deceleration;

[0065] Worldwide Harmonized Light Vehicle Test Cycle (WLTC): more complex and closer to real driving conditions than NEDC, covering higher speeds and more frequent acceleration and deceleration;

[0066] US Federal Test Procedure (FTP): includes urban and highway conditions, and evaluates the performance of vehicles under different driving conditions;

[0067] China Light Vehicle Cycle (CLTC): It combines China's road characteristics and traffic conditions and is more in line with the actual use of domestic vehicles.

[0068] In this solution, there is no limitation on the vehicle test conditions used. One of the above vehicle test conditions can be used according to actual needs, or other customized vehicle test conditions can also be used.

[0069] S102. Under a preset vehicle operating condition, construct a state variable equation and a control variable equation, and determine an algebraic constraint equation based on the state variable equation and the control variable equation.

[0070] In this scheme, the state variables and control variables are determined, and the corresponding state variable equations and control variable equations are constructed according to the selected vehicle operating conditions.

[0071] In this solution, the state variable equation is used to describe the variables of the system state that change over time. The state variable equation can be a differential equation, a difference equation, or other forms of mathematical expressions.

[0072] In this scheme, the control variable equation is used to specify the value range, constraint conditions or relationship between the control variable and other variables.

[0073] In this solution, for the energy management of (fuel cell hybrid) vehicles, state variables and control variables can be determined according to demand. For example, the state variables can be battery SOC, power battery power, etc.; the control variables can be power battery power, power battery current, etc.

[0074] In this solution, a vehicle energy management method is set for energy management of a fuel cell hybrid vehicle. Preferably, the set state variables include power battery power and fuel cell power; and the control variables include power battery current and fuel cell current.

[0075] Exemplarily, in this solution, the output energy of the power battery can be expressed by the following formula:

[0076] W b (t) = ∫ 0 tf p b (t)dt=∫ 0 tf i b (t)V b (t)dt

[0077] Where W b (t) represents the output energy of the power battery, p b (t) represents the power of the power battery, i b (t) represents the power battery current, V b (t) represents the power battery voltage;

[0078] The output energy of a fuel cell is expressed by the following formula:

[0079] Wfc (t) = ∫ 0 tf p fc (t)dt=∫ 0 tf i fc (t)V fc_out (t)dt

[0080] Where W fc (t) represents the output energy of the fuel cell, p fc (t) represents the fuel cell power, i fc (t) represents the fuel cell current, V fc_out (t) represents the fuel cell voltage;

[0081] For the above W b (t), W fc (t) Taking the first-order differential of the time corresponding equation, we can obtain:

[0082] W b ′ (t) = p b (t) = i b (t)V b (t)

[0083] W f ′ c (t) = p fc (t) = i fc (t)V fc_out (t)

[0084] Exemplarily, in this solution, based on W b (t), W fc (t) The state variable equation is constructed by the first-order differential of the corresponding equation in time.

[0085] Exemplarily, in this solution, the control variable equation can be constructed based on the relationship between the fuel cell voltage and the fuel cell current and the relationship between the power battery voltage and the power battery current;

[0086] The relationship between the fuel cell voltage and the fuel cell current and the relationship between the power battery voltage and the power battery current can be determined through simulation tests, or the corresponding function expressions can be obtained by performing polynomial interpolation on the test data through an interpolation function.

[0087] Exemplarily, in this embodiment, the algebraic constraint equation may represent the physical limitations, boundary conditions or specific performance requirements of the fuel cell hybrid system;

[0088] In this scheme, the specific derivation process of determining the algebraic constraint equation based on the state variable equation and the control variable equation is not limited;

[0089] For example, the algebraic constraint equation can be obtained by combining and simplifying the state variable equation and the control variable equation, or the state variable and the control variable can be discretized using a specified method, and the state variable and the control variable can be approximated using interpolation polynomials, and the differential operation of the state variable equation can be converted into algebraic operation, thereby obtaining the corresponding algebraic constraint equation.

[0090] S103. Construct a fuel cell attenuation equation based on the vehicle operating load and the number of vehicle starts and stops.

[0091] Exemplarily, in this embodiment, the fuel cell attenuation equation can be as follows:

[0092]

[0093] In the formula, represents the fuel cell attenuation, n represents the number of vehicle starts, t 1 ~t 3 Indicates vehicle operating load parameters.

[0094] Exemplarily, in this solution, the vehicle operation load may include multiple situations, each of which corresponds to a vehicle operation load parameter. For example, the vehicle operation load may include the fuel cell output power being less than the rated power X%, the fuel cell output power being greater than the peak power X%, etc.;

[0095] Specifically, in this solution, the fuel cell power is used to characterize the vehicle operating load. The vehicle operating load includes one or more of idling operation, large variable load operation, and high load operation. Different vehicle operating loads correspond to different fuel cell power ranges.

[0096] Exemplarily, in this solution, the vehicle operating load parameter can be determined based on the fuel cell power, and the specific correlation between the two can be determined through simulation experiments.

[0097] Exemplarily, in this solution, based on the battery characteristics of the fuel cell, the fuel cell attenuation equation can be determined by simulation experiments.

[0098] S104. Taking the fuel cell attenuation as the target quantity and minimizing the fuel cell attenuation in the preset vehicle operating condition as the goal, the algebraic constraint equation is solved based on the nonlinear programming method to obtain the optimal state variables and the optimal control variables.

[0099] Exemplarily, in this solution, the power battery power and the fuel cell power are set to constitute the vehicle demand power under the preset vehicle operating conditions, and at the same time, the constraints are set as follows:

[0100]

[0101] In the formula, p b_out_min Indicates the minimum power of the power battery, p b_out_max Indicates the maximum power of the power battery, p fc_out_min represents the minimum power of the fuel cell, p fc_out_max Indicates the maximum power of the fuel cell.

[0102] Based on the objective function, algebraic constraint equations and constraint conditions, the algebraic constraint equations are solved by nonlinear programming methods to obtain the optimized power battery power, fuel cell power, power battery current and fuel cell current.

[0103] Exemplarily, in this solution, there is no limitation on the use of nonlinear programming method. For example, the nonlinear programming method may be a quadratic sequence programming method.

[0104] In this solution, the process of solving the above equations using the nonlinear programming method is the same as that in the prior art, and the specific process will not be described in detail.

[0105] Electric vehicles convert direct current into alternating current through a power conversion module and precisely control the frequency and amplitude of the current, thereby controlling the speed and torque of the drive motor to achieve the purpose of adjusting the motor power, so current is selected as the control variable.

[0106] S105. Perform energy management on the fuel cell hybrid vehicle based on the optimal state variables and the optimal control variables.

[0107] Exemplarily, in this solution, the energy management of the vehicle may be specifically as follows:

[0108] Based on the above solution results, under the current (driving) operating conditions, the power battery current and the fuel cell current are controlled, and then the power battery power and the fuel cell (average) power are adjusted to minimize the fuel cell attenuation.

[0109] This embodiment proposes a vehicle energy management method, which includes constructing a fuel cell attenuation equation based on the vehicle operating load and the number of vehicle starts and stops; taking the fuel cell attenuation as the target amount, solving the state variable equation and the control variable equation based on the nonlinear programming method to obtain the optimal state variable and the optimal control variable; performing energy management on the vehicle based on the optimal state variable and the optimal control variable, wherein taking the fuel cell attenuation as the target amount to solve the state variable and the control variable can better balance the performance of the vehicle and the life of the fuel cell, which helps to minimize the attenuation of the fuel cell and extend its service life while meeting the vehicle operation requirements; at the same time, extending the life of the fuel cell and reducing the frequency of replacing the fuel cell can reduce the maintenance cost and use cost of the vehicle, slowing down the attenuation of the fuel cell can enhance the reliability and stability of the entire power system, reduce the possibility of failure, and the vehicle will not have unexpected failures during operation due to the rapid decline in the performance of the fuel cell; solving the control variable under the condition of considering the attenuation can prompt the system to use energy more efficiently and improve energy utilization efficiency; in addition, the state variable and the control variable can adapt to different vehicle usage scenarios and operating conditions, and provide the optimal control strategy to meet various working conditions.

[0110] exist Figure 1 Based on the scheme shown, in one possible implementation scheme, the fuel cell attenuation is expressed by the following formula:

[0111]

[0112] In the formula, Indicates the fuel cell attenuation, K p Represents the attenuation rate correction coefficient, k 1 Indicates the idle speed correction factor, k 2 Indicates the large variable load operation attenuation coefficient, k 3 Indicates the high load operation attenuation coefficient, k 4 represents the start-stop attenuation coefficient, t 1 Indicates the idling time, t 2 Indicates the large load change operation time, t 3 Indicates the high-load operation time, and n indicates the number of starts and stops.

[0113] Exemplarily, in this scheme, setting idle operation means that the fuel cell output power is less than 10% of the rated power, large variable load operation means that the absolute value of the load change rate per second is greater than 10% of the rated power, high load operation means that the fuel cell output power is greater than 90% of the peak power, and setting a complete start and stop means one start and stop cycle.

[0114] Exemplarily, in this solution, the fuel cell attenuation equation is determined through simulation experiments, and the coefficients in the fuel cell attenuation equation can be specifically shown in Table 1.

[0115] Table 1

[0116]

[0117]

[0118] Referring to Table 1, in this solution, t1 to t3 are in corresponding relationship with the fuel cell power. Based on the discretized fuel cell power solution, the fuel cell attenuation equation can be used to determine whether the fuel cell attenuation reaches the expected value.

[0119] Figure 2 is another vehicle energy management method flow chart in the embodiment, refer to Figure 2 Based on any of the above solutions, in one possible implementation scheme, the vehicle energy management method includes:

[0120] S201. Obtain preset vehicle operating conditions.

[0121] S202. Under a preset vehicle operating condition, construct a state variable equation and a control variable equation, and determine an algebraic constraint equation based on the state variable equation and the control variable equation.

[0122] S203. Construct a fuel cell attenuation equation based on the vehicle operating load and the number of vehicle starts and stops.

[0123] In this solution, the implementation method of S201 to S203 is the same as the corresponding content recorded in S101 to S103.

[0124] S204. Construct the ampere-hour flow equation of the power battery.

[0125] Exemplarily, in this solution, the power battery ampere-hour flow equation is constructed based on the ampere-hour flow of the power battery current. The power battery ampere-hour flow equation can be as follows:

[0126] Aheff(t)=g(σ,i b ,Q)

[0127] In this scheme, the power battery ampere-hour flow rate represents the ampere-hour flow of the power battery current, where σ is a penalty factor used to balance the charge and discharge rate, temperature and discharge depth, i b is the power battery current, and Q represents the power battery capacity.

[0128] S205. Taking the fuel cell attenuation and the power battery ampere-hour flow as target quantities, with the goal of improving the fuel cell life and the power battery life in the preset vehicle operating conditions, based on the nonlinear programming method, the algebraic constraint equations are solved to obtain the optimal state variables and the optimal control variables.

[0129] Exemplarily, in this solution, Figure 1 On the basis of the constraints of the scheme shown, when the power battery ampere-hour flow rate is taken as the target amount, the power battery current is set to be less than the charge and discharge current threshold, and the charge and discharge current threshold can be determined based on experience, simulation test or calibration test;

[0130] Setting the power battery current to be less than the charge and discharge current threshold as a constraint condition can avoid large current discharge of the power battery.

[0131] Exemplarily, in this scheme, the fuel cell attenuation and the power battery ampere-hour circulation should be as small as possible. The fuel cell attenuation is used to equivalently represent the fuel cell life. The minimum fuel cell attenuation corresponds to the longest fuel cell life. The power battery ampere-hour circulation reflects the power battery attenuation. The power battery attenuation is used to equivalently represent the power battery life. The minimum power battery attenuation corresponds to the longest power battery life.

[0132] S206. Perform energy management on the vehicle based on the optimal state variables and the optimal control variables.

[0133] exist Figure 1 On the basis of the beneficial effects of the scheme shown, in this scheme, the fuel cell attenuation and the power battery ampere-hour flow are used as target quantities to solve the state variables and control variables. Compared with the fuel cell attenuation as the target quantity, the state variable equation and the control variable equation are solved based on the nonlinear programming method, and the vehicle is energy managed based on the optimal state variables and the optimal control variables. Under the premise of minimizing the attenuation of the fuel cell and the power battery and extending their service life, the vehicle performance and the life of the fuel cell and the power battery can be better balanced, which helps to minimize the attenuation of the fuel cell and the power battery and extend their service life while meeting the vehicle operation requirements; at the same time, extending the life of the fuel cell and the power battery can reduce the frequency of replacing the fuel cell and the power battery, and can reduce the maintenance cost and use cost of the vehicle. Slowing down the attenuation of the fuel cell can enhance the reliability and stability of the entire power system and reduce the possibility of failure. The vehicle will not have unexpected failures during operation due to the rapid decline in the performance of the fuel cell; solving the control quantity under the condition of considering the attenuation can prompt the system to use energy more efficiently and improve energy utilization efficiency; in addition, the state variables and control variables can adapt to different vehicle usage scenarios and operating conditions, and provide the optimal control strategy to meet various working conditions.

[0134] exist Figure 1 On the basis of the beneficial effects of the scheme shown, in this scheme, multiple objectives (i.e., the ampere-hour flow of the power battery and the attenuation of the fuel cell) are used to optimize the quantity to be optimized. Compared with considering the attenuation of the fuel cell alone, in the multi-objective optimization solution, the optimization objectives are mutually balanced, and the attenuation of the fuel cell and the battery can be balanced. Considering the attenuation of the fuel cell and the ampere-hour flow of the power battery at the same time can comprehensively balance the attenuation of the two power sources, so that the life of the entire power system can be more comprehensively extended, avoiding the problem that when considering the attenuation of the fuel cell alone, the impact of the state change of the power battery on the overall performance of the vehicle may be ignored. Considering the two target quantities at the same time can allocate energy more accurately. According to different driving needs and power source states, the output power of the fuel cell and the power battery is reasonably allocated to achieve the best energy utilization efficiency. Through more reasonable energy management, the operating cost of the vehicle can be reduced. On the one hand, reducing the attenuation of the fuel cell can extend its service life and reduce the replacement cost; on the other hand, controlling the ampere-hour flow of the power battery can reduce the number of battery charge and discharge, extend the battery life, and reduce the battery replacement and maintenance costs.

[0135] Based on the above solution of taking the power battery ampere-hour flow rate as the target amount, in one possible implementation scheme, the power battery ampere-hour flow rate equation is expressed by the following formula:

[0136] Aheff(t)=∫ 0 tf σ(t)|i b |dt

[0137]

[0138] In the formula, Aheff(t) represents the ampere-hour flow of the power battery, σ is the penalty factor used to balance the charge and discharge rate, temperature and discharge depth, and k b is the battery charge and discharge rate, i b is the power battery current, and Q represents the power battery capacity.

[0139] In this scheme, the ampere-hour flow of the power battery is taken as the target quantity. In order to avoid large current discharge of the power battery, a penalty factor σ is added to its function. The value of σ is mainly related to the battery charge and discharge rate k. b Related.

[0140] Exemplarily, in this scheme, the Gauss-Radau integration method can be used to approximate the power battery ampere-hour flow equation into a discrete quantity equation (a discrete quantity equation refers to converting the originally continuous power battery ampere-hour flow equation into a series of discrete mathematical expressions through a specific numerical method), and then participate in the calculation of the optimal quantity.

[0141] Based on any of the foregoing solutions, in one possible implementation scheme, setting the state variable equation includes:

[0142] p b (t) = i b (t)V b (t)

[0143] p fc (t) = i fc (t)V fc_out (t)

[0144] In the formula, p b (t) represents the power of the battery at time t, i b (t) represents the power battery current at time t, V b (t) represents the power battery voltage at time t, p fc (t) represents the fuel cell power at time t, i fc (t) represents the fuel cell current at time t, V fc_out (t) represents the fuel cell voltage at time t;

[0145] when i b (t)>0, the power battery is in the discharge state. b When (t)<0, the power battery is in charging state;

[0146] The control variable equations include:

[0147] V b (t) = u b (SOC b (t)-i b (t)R b SOC b (t))

[0148]

[0149] In the formula, SOC b (t) represents the power battery SOC at time t, R b Indicates the internal resistance of the power battery, u b Indicates the open circuit voltage of the power battery, u fc represents the open circuit voltage of the fuel cell, R fc represents the internal resistance of the fuel cell, f 1 () indicates a preset function.

[0150] Exemplarily, in this solution, the preset function f 1 The specific form of is not limited and can be determined through simulation experiments.

[0151] Exemplarily, in this scheme, a pseudospectral method algebraic constraint equation can be used, wherein the pseudospectral method can use Gauss Pseudospectral Method (GPM), Legendre Pseudospectral Method (LPM), Radau Pseudospectral Method (RPM), etc.

[0152] The basic idea of ​​the pseudo-spectral method is to discretize the time interval into a finite number of nodes and approximate the state variables and control variables at these nodes, including:

[0153] The time interval is discretized into nodes, where and are the initial time and terminal time respectively. Usually, equidistant nodes or non-equidistant nodes can be used for discretization.

[0154] At the discretized nodes, the state variables and control variables are approximated by interpolation functions. For example, for the Gaussian pseudospectral method, Lagrange interpolation polynomials can be used to approximate the state variables and control variables;

[0155] The dynamic equations of the system are approximated at the discretized nodes to obtain a set of algebraic equations (i.e., algebraic constraint equations). For example, for the Gaussian pseudospectral method, the derivatives of the interpolation polynomials can be used to approximate the derivatives of the state variables, thereby converting the dynamic equations into algebraic constraints.

[0156] Based on any of the above solutions, in one possible implementation, the vehicle demand power is also used as a constraint condition for solving the optimal state variables and the optimal control variables, and the vehicle demand power, the power of the power battery and the power of the fuel cell satisfy the following formula:

[0157] Charge

[0158] Discharge

[0159] In the formula, p d represents the vehicle's required power, p b Indicates the power of the power battery, p fc represents the fuel cell power, η dcdc represents the DC / DC converter efficiency, η re represents the kinetic energy recovery efficiency, η m Indicates motor efficiency.

[0160] In this solution, the vehicle power demand is used as a constraint to ensure that the solved control strategy can provide enough power to meet the vehicle's driving needs and ensure that the vehicle can drive normally. By solving the optimal control problem by pseudo-spectral method, a control strategy that minimizes energy consumption or maximizes energy efficiency can be found, while ensuring that the vehicle can provide the required power. Using the required power as a constraint helps to fully consider energy limitations when optimizing the control strategy and improve the vehicle's endurance. Considering the vehicle power demand as a constraint can make the model closer to the actual situation. The control strategy solved in this way will have more practical application value and can better adapt to different driving conditions and needs.

[0161] Based on any of the above solutions, in one possible implementation scheme, the power battery SOC is also used as a constraint condition for solving the optimal state variable and the optimal control variable;

[0162] The constraints of the power battery SOC satisfy the following formula:

[0163] SOC b_min ≤SOC b ≤SOC b_max

[0164] SOC b Indicates power battery SOC, SOC b_min Indicates the minimum SOC value of the power battery, SOC b_max Indicates the maximum SOC value of the power battery.

[0165] Exemplarily, in this solution, the power battery SOC is determined based on the power battery current.

[0166] In this solution, the power battery SOC is used as a constraint condition to ensure that the power battery will not be over-discharged to an extremely low power level during vehicle driving, thereby preventing the vehicle from being unable to continue driving due to power exhaustion. It can also ensure that the battery can always provide a stable power supply to avoid vehicle performance degradation or system failure caused by power fluctuations;

[0167] When solving the optimal state variables and optimal control variables using the pseudo-spectral method, using the power battery SOC as a constraint can take into account the energy state of the power battery during the optimization process, thereby finding a more energy-saving control strategy. Using the power battery SOC as a constraint can also take into account the power output capacity of the power battery during the optimization process, thereby finding a control strategy that is more suitable for vehicle driving needs.

[0168] Based on any of the foregoing solutions, in one possible implementation scheme, the power battery SOC is expressed by the following formula:

[0169]

[0170] In the formula, SOC b (t) represents the SOC of the power battery at time t, SOC b (t 0 ) represents the initial SOC of the power battery, i b (t) represents the power battery current at time t, and Q represents the battery capacity.

[0171] Based on any of the foregoing solutions, in one possible implementation, the nonlinear programming method uses the Radau pseudospectral method.

[0172] Exemplarily, in this scheme, the nonlinear programming method adopts the Radau pseudo-spectral method, and the Radau pseudo-spectral method is used to solve the state variables and control variables. By selecting specific nodes in the time interval and using polynomials to approximate the state variables and control variables, a higher solution accuracy can be achieved. At the same time, the Radau pseudo-spectral method can handle the constraints of state variables, control variables, etc. well, and has high computational efficiency.

[0173] Based on any of the foregoing solutions, in one possible implementation scheme, the vehicle operating condition is preset to be a NEDC condition.

[0174] Exemplarily, in this solution, the optimization calculation of selected state variables and control variables under NEDC conditions can ensure a relatively uniform benchmark when comparing and evaluating different fuel cell hybrid vehicles;

[0175] In addition, the test conditions and procedures of this operating condition are relatively fixed, which is convenient for replication in different experiments and studies to verify and compare different energy management strategies.

[0176] Figure 3 is another vehicle energy management method flow chart in the embodiment, refer to Figure 3 Based on any of the above solutions, in one possible implementation scheme, the vehicle energy management method includes:

[0177] S301. Obtain NEDC operating conditions.

[0178] Figure 4 is a schematic diagram of the NEDC operating condition in the embodiment, refer to Figure 4 In this solution, the vehicle operating condition is set to the NEDC operating condition, and the applicable vehicle is set to a fuel cell hybrid passenger car. In the NEDC operating condition, it is limited that the (vehicle's) high-voltage electrical system has no over-temperature fault, and will not cause power limitation due to system overheating.

[0179] In this scheme, the vehicle parameters are set as shown in Table 2.

[0180] Table 2

[0181]

[0182] Figure 5 is a schematic diagram of the vehicle power requirement in the embodiment, refer to Figure 5 In this scheme, under NEDC conditions, the vehicle demand power under NEDC conditions can be calculated according to the vehicle demand power formula and the vehicle parameters shown in Table 2.

[0183] Combined with Table 2, the formula for the vehicle's required power is:

[0184]

[0185] Where u is the vehicle speed and a is the slope angle (on a flat road, a=0).

[0186] In order to reflect the relationship between the required power and the fuel cell power and the power battery power, the vehicle required power p d Rewrite it as follows:

[0187]

[0188] S302. Under the NEDC operating condition, construct a state variable equation and a control variable equation, and determine an algebraic constraint equation based on the state variable equation and the control variable equation.

[0189] In this scheme, the Radau pseudo-spectral method is used to obtain the optimal state variables and the optimal control variables. The general steps of the Radau pseudo-spectral method include: selecting a suitable Radau node distribution, constructing interpolation polynomials of state variables and control variables, converting the optimal control problem into a nonlinear programming problem, and solving it using a nonlinear programming solver.

[0190] Table 3

[0191]

[0192] Figure 6 is a graph showing the relationship between the fuel cell power, the fuel cell current and the fuel cell voltage in the embodiment, with reference to Table 3 and Figure 6 The corresponding relationship between the fuel cell voltage and the fuel cell current is shown in Table 3. By using the Matlab interpolation function interp1 to perform polynomial interpolation calculation, it can be obtained that Figure 6 The fuel cell voltage and fuel cell current corresponding relationship curve is shown in red line, and the fuel cell power curve is shown in Figure 6 Shown by black line.

[0193] Combination Figure 6 and Table 3, in this scheme, the fuel (average) battery power can be expressed by the following formula:

[0194]

[0195] Table 4

[0196]

[0197] The corresponding relationship between the power battery cell voltage and the power battery SOC is shown in Table 4. By using the Matlab interpolation function interp1 to perform polynomial interpolation calculation, it can be obtained that Figure 7 The graph shows the corresponding relationship between the power battery cell voltage and the power battery SOC.

[0198] Combination Figure 7 According to Table 4, in this solution, the power of the power battery can be expressed by the following formula:

[0199]

[0200] In the formula, u b Indicates the open circuit voltage of the power battery, R b Indicates the internal resistance of the power battery.

[0201] In this scheme, the optimal numerical solution steps include time domain transformation, approximation of state variables and control variables, conversion of differential equation constraints into algebraic constraints, and approximation of the objective function.

[0202] The Radau pseudo-spectral method discretizes the state variables and control variables respectively, and then uses Lagrange difference polynomials to approximate the state variables and control variables, converting the differential operation of the state equation and the integral operation in the objective function into algebraic operations, and finally converting the optimal control problem into a nonlinear programming problem with the state variables at the node and the control variables at the distribution point as the parameters to be optimized.

[0203] Specifically, the vehicle driving under NEDC conditions is divided into Q stages, and the initial time points, i.e., Q-1 segment points, are respectively recorded as T0 to TQ;

[0204] In the time domain, each time period [Tq-1, Tq] in T0~TQ is converted to the interval [-1, 1] so that it satisfies the definition interval of the Lagrange orthogonal polynomial, that is, it satisfies the following formula:

[0205]

[0206] The matching points of the pseudo-spectral method are LGR points, that is, the value interval is τ∈(-1,1] or τ∈[-1,1]. In this scheme, the value interval is set to τ∈(-1,1], which is the polynomial P N (τ)-P N-1 The root of (τ), where P N When (τ) is an N-order Lagrange polynomial, that is:

[0207]

[0208] The nodes of the pseudo-spectral method are N LGR points plus the initial time point (τ 0 , τ 0 =-1), its node output is N+1. For the Vipu splicing method with Q stages, the number of nodes in each stage can be different, denoted by N q +1, and the nodes in the qth stage are recorded as τ qi (i=0,1,2…N q );

[0209] The power battery power and fuel cell power are taken as state variables and discretized at the nodes. The state variables in each stage can be expressed as:

[0210]

[0211] The time history curve of the state variable in each stage can be obtained by N q +1 Lagrange difference polynomial to approximate, as follows:

[0212]

[0213] Where, L qi (τ) is the qth stage N q The sub-Lagrange difference basis function is:

[0214]

[0215] The power battery current and fuel cell current are taken as control variables and discretized at the distribution points. The control variables at each stage can be expressed as:

[0216]

[0217] The time history curve of the control variable in each stage can be obtained by N q It is approximated by a Lagrange difference polynomial, as follows:

[0218]

[0219] Where, L qi (τ) is the qth stage N q -1 Lagrange difference basis function, specifically:

[0220]

[0221] After point matching and discretization, the state variables are approximated by global difference polynomials, and the state equation is converted into algebraic constraints. Its differential is expressed by taking the derivative of the interpolation polynomial of equation (5), as shown in the following equation:

[0222]

[0223] In the formula, τ qk is the collocation point of the qth stage (where k = 1, 2, ... N), N q ·(N q +1), which represents the differential value of the Lagrange difference basis function at each matching point in the qth stage;

[0224] The power battery SOC can be expressed by the following formula:

[0225]

[0226] In this scheme, by combining equations (2) to (8), the optimal control problem of energy management of fuel cell hybrid vehicles can be transformed from the kinetic equation constraints in the qth stage to the constraints at the matching point τ qk The algebraic constraint equations at .

[0227] S303. Construct a fuel cell attenuation equation based on the vehicle operating load and the number of vehicle starts and stops.

[0228] In this scheme, the fuel cell attenuation equation is:

[0229]

[0230] S304. Construct the ampere-hour flow equation of the power battery.

[0231] In this scheme, the ampere-hour flow of power batteries is expressed by the following formula:

[0232] Aheff(t)=∫ 0 tf σ(t)|i b |dt

[0233]

[0234] S305. Taking the fuel cell attenuation and the power battery ampere-hour flow as target quantities, with the goal of improving the fuel cell life and the power battery life in the preset vehicle operating conditions, based on the nonlinear programming method, the algebraic constraint equations are solved to obtain the optimal state variables and the optimal control variables.

[0235] In this scheme, the constraints are set as follows:

[0236]

[0237] Based on the algebraic constraint equations, target quantities and constraint conditions obtained in steps S302 to S304, the algebraic constraint equations can be solved by a high-dimensional sparse NLP solver SNOPT, thereby obtaining optimized power battery power, fuel cell power, power battery current and fuel cell current;

[0238] The method for solving the algebraic constraint equation is the same as that in the prior art, and the specific derivation process of the solution will not be described in detail.

[0239] S306. Perform energy management on the vehicle based on the optimal state variables and the optimal control variables.

[0240] Figure 8 is a graph of the optimized fuel cell power in the embodiment, Fig. 9 is the optimized power battery power curve in the embodiment, refer to Figure 4 , Figure 8 and Fig. 9 , the NEDC operating curve is a speed-time interval. The NEDC operating curve includes four urban operating cycles of 0-200s, 200-400s, 400-600s, and 600-800s, and a suburban operating cycle of 800-1200s. Among them, the urban operating cycle includes several acceleration and deceleration stages, with a maximum speed of 50km / h and an average speed of 19km / h. The suburban operating cycle includes several acceleration and deceleration stages, with a maximum speed of 120km / h and an average speed of 62km / h. Taking the time on the time axis as the reference, through Figure 8 and Fig. 9 It can be seen that after the fuel cell hybrid vehicle energy management strategy of this scheme is used to control the vehicle, the power battery is in the charging stage during the deceleration stage. Due to the characteristics of the fuel cell, its current change rate is smaller than that of the power battery. The initial fuel cell current of each deceleration stage of the vehicle decreases slowly and is still discharging for a period of time. Therefore, the recharge energy of the power battery during the deceleration stage of the vehicle comes from braking force feedback and fuel cell discharge. It can be concluded that the fuel cell hybrid vehicle energy management strategy of this scheme can reduce the power output of the fuel cell while meeting the power demand of the entire vehicle, thereby extending the life of the fuel cell.

[0241] Fig.10 This is the optimized power battery SOC curve diagram in the embodiment, refer to Fig.10 The initial SOC of the power battery is set to 70%. Figure 8 and Fig. 9It can be obtained that when the fuel cell hybrid vehicle energy management strategy of this scheme is used for vehicle control, the recharge energy of the power battery in the vehicle deceleration stage comes from the braking force feedback and the fuel cell discharge. Due to the braking force feedback and the fuel cell discharge, the power battery has sufficient recharge energy and the power retention effect is good. At the end of the suburban operating cycle, the power battery power (relative to the initial SOC of the power battery) did not show a significant decrease (the power battery SOC is about 72%). The power battery is in a higher SOC working range for a long time, which is beneficial to reducing power attenuation and achieving the purpose of extending the life of the power battery. At the same time, maintaining the SOC in a higher range is beneficial to improving the cruising range.

[0242] Embodiment 2

[0243] This embodiment provides a vehicle energy management device, including a vehicle energy management unit, which is used to:

[0244] Obtaining preset vehicle operating conditions;

[0245] Under a preset vehicle operating condition, construct a state variable equation and a control variable equation, and determine an algebraic constraint equation based on the state variable equation and the control variable equation;

[0246] The fuel cell attenuation equation is constructed based on the vehicle operating load and the number of vehicle starts and stops, and the power battery ampere-hour flow equation is constructed based on the power battery current;

[0247] Taking the fuel cell attenuation and the power battery ampere-hour flow as target quantities, with the goal of improving the fuel cell life and power battery life in the preset vehicle working conditions, the algebraic constraint equations are solved based on the nonlinear programming method to obtain the optimal state variables and optimal control variables;

[0248] Energy management of fuel cell hybrid vehicles is performed based on optimal state variables and optimal control variables.

[0249] Exemplarily, in this embodiment, the vehicle energy management unit can be configured to implement any one of the vehicle energy management methods in Embodiment 1. The implementation process and beneficial effects of the method are the same as the corresponding contents recorded in Embodiment 1, and the specific contents will not be repeated here.

[0250] Embodiment 3

[0251] Fig.11A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0252] like Fig.11 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0253] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0254] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an electric vehicle energy management method.

[0255] In some embodiments, the electric vehicle energy management method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the electric vehicle energy management method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the electric vehicle energy management method in any other appropriate manner (e.g., by means of firmware).

[0256] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0257] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0258] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0259] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0260] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0261] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0262] Embodiment 4

[0263] This embodiment proposes a vehicle, which is configured to execute any one of the vehicle energy management methods recorded in Embodiment 1. The implementation process and beneficial effects of the method are the same as the corresponding contents recorded in Embodiment 1, and the specific contents will not be repeated here.

[0264] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A vehicle energy management method for energy management of a fuel cell hybrid vehicle, wherein the fuel cell hybrid vehicle comprises a fuel cell and a power battery, wherein the fuel cell is used for vehicle endurance and the power battery is used for supplementing instantaneous power, and wherein: include: Obtaining preset vehicle operating conditions; Under the preset vehicle operating condition, constructing a state variable equation and a control variable equation, and determining an algebraic constraint equation based on the state variable equation and the control variable equation; Construct a fuel cell attenuation equation based on vehicle operating load and vehicle start and stop times; Construct the power battery ampere-hour flow equation based on the power battery current; Taking the fuel cell attenuation and the power battery ampere-hour flow as target quantities, with the goal of improving the fuel cell life and the power battery life in the preset vehicle operating condition, solving the algebraic constraint equation based on a nonlinear programming method to obtain the optimal state variables and the optimal control variables; Performing energy management on the fuel cell hybrid vehicle based on the optimal state variable and the optimal control variable; Among them, the state variables include power battery power and fuel cell power; the control variables include power battery current and fuel cell current; The power battery power and the fuel cell power constitute the vehicle required power under the preset vehicle operating condition; The fuel cell power is used to characterize the vehicle operation load, and the vehicle operation load includes one or more of idling operation, large variable load operation, and high load operation. Different vehicle operation loads correspond to different fuel cell power ranges.

2. The vehicle energy management method according to claim 1, characterized in that: The fuel cell attenuation is expressed by the following formula: In the formula, Indicates the fuel cell attenuation, K p It represents the attenuation rate correction coefficient, k1 represents the idle operation correction coefficient, k2 represents the large variable load operation attenuation coefficient, k3 represents the high load operation attenuation coefficient, k4 represents the start-stop attenuation coefficient, t1 represents the idle operation time, t2 represents the large variable load operation time, t3 represents the high load operation time, and n represents the number of starts and stops.

3. The vehicle energy management method according to claim 1, characterized in that: The power battery ampere-hour flow equation is expressed by the following formula: In the formula, Aheff(t) represents the ampere-hour flow of the power battery, σ is the penalty factor used to balance the charge and discharge rate, temperature and discharge depth, and k b is the battery charge and discharge rate, i b is the power battery current, and Q represents the power battery capacity.

4. The vehicle energy management method according to claim 1, characterized in that: The state variable equations include: p b (t)=i b (t)V b (t) p fc (t)=i fc (t)V fc_out (t) In the formula, p b (t) represents the power of the battery at time t, i b (t) represents the power battery current at time t, V b (t) represents the power battery voltage at time t, p fc (t) represents the fuel cell power at time t, i fc (t) represents the fuel cell current at time t, V fc_out (t) represents the fuel cell voltage at time t; when i b (t)>0, the power battery is in the discharge state. b When (t)<0, the power battery is in charging state; The control variable equation includes: V b (t)=u b (SOC b (t)-i b (t)R b SOC b (t)) In the formula, SOC b (t) represents the power battery SOC at time t, R b Indicates the internal resistance of the power battery, u b Indicates the open circuit voltage of the power battery, u fc represents the open circuit voltage of the fuel cell, R fc represents the internal resistance of the fuel cell, and f1() represents the preset function.

5. The vehicle energy management method according to claim 4, characterized in that: It also includes taking the vehicle demand power as a constraint condition for solving the optimal state variable and the optimal control variable, and the vehicle demand power satisfies the following formula with the power battery power and the fuel cell power: In the formula, p d represents the vehicle demand power, p b Indicates the power of the power battery, p fc represents the fuel cell power, η dcdc represents the DC / DC converter efficiency, η re represents the kinetic energy recovery efficiency, η m Indicates motor efficiency.

6. The vehicle energy management method according to claim 1, characterized in that: It also includes taking the power battery SOC as a constraint condition for solving the optimal state variables and optimal control variables; The power battery SOC constraint satisfies the following formula: SOC b_min ≤SOC b ≤SOC b_max SOC b Indicates power battery SOC, SOC b_min Indicates the minimum SOC value of the power battery, SOC b_max Indicates the maximum SOC value of the power battery; The power battery SOC is determined based on the power battery current.

7. The vehicle energy management method according to claim 6, characterized in that: The power battery SOC is expressed by the following formula: In the formula, SOC b (t) represents the SOC of the power battery at time t, SOC b (t0) represents the initial SOC of the power battery, i b (t) represents the power battery current at time t, and Q represents the battery capacity.

8. The vehicle energy management method according to claim 1, characterized in that: The nonlinear programming method adopts the Radau pseudospectral method.

9. The vehicle energy management method according to claim 1, characterized in that: The preset vehicle operating condition is the NEDC condition.

10. A vehicle, characterized in that: The vehicle is configured to execute the vehicle energy management method described in any one of claims 1 to 9.

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