Vehicle energy management method and vehicle

Abstract

The present invention discloses a vehicle energy management method, which includes: obtaining a preset vehicle operating condition; constructing a state variable equation and a control variable equation under the preset vehicle operating 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 operating load and the number of vehicle starts and stops; constructing a power battery ampere-hour flow equation; using 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 optimal state variables and optimal control variables; and performing energy management on a fuel cell hybrid vehicle based on the optimal state variables and optimal control variables.

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] To address energy and environmental crises and achieve sustainable social development, the world is committed to developing new energy vehicles. Fuel cell hybrid electric vehicles (FCHEVs) overcome the shortcomings of pure electric vehicles, such as the slow charging speed, and the low engine thermal efficiency and fuel substitution rate of traditional hybrid vehicles. They are considered one of the most promising energy sources. Energy management is a key R&D area to reduce energy consumption, alleviate range concerns, and lower operating costs of new energy vehicles. Proper charging and discharging can also 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 to achieve a balance between vehicle performance and fuel cell life, thereby minimizing the attenuation of fuel cells and power batteries while meeting vehicle operation requirements and 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. 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 for replenishing instantaneous power. The method includes:

[0006] Obtain 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 a target quantity and minimizing the fuel cell attenuation in the preset vehicle operating condition as a goal, solving the algebraic constraint equation based on a nonlinear programming method to obtain optimal state variables and optimal control variables;

[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 operating load, and 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.

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

[0016]

[0017] Where, Indicates the fuel cell attenuation, K p 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.

[0018] Optionally, the power battery ampere-hour flow equation is expressed as follows:

[0019] Aheff(t)=∫0 tf σ(t)|i b |dt

[0020]

[0021] Where Ageff(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 depth of discharge, 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) = i b (t)V b (t)

[0024] p fc (t) = i fc (t)Vfc_out (t)

[0025] Where 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] Where, 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 fuel cell open circuit voltage, R fc represents the internal resistance of the fuel cell, and f1() represents the preset function.

[0031] Optionally, the vehicle demand power is 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:

[0032] Charge

[0033] discharge

[0034] Where 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 rerepresents the kinetic energy recovery efficiency, η m Indicates motor efficiency.

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

[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 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] Where, 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.

[0043] Optionally, the nonlinear programming method adopts Radau pseudospectral 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, which is configured to execute any one of the vehicle energy management methods described 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, 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 and the ampere-hour flow of the power battery as target quantities, 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 the fuel cell attenuation and the ampere-hour flow of the power battery are taken as target quantities to solve the state variable and the control variable, which can better balance the performance of the vehicle and the life of the fuel cell and the power battery, and help to meet the vehicle operation requirements. At the same time, it minimizes the attenuation of fuel cells and power batteries and extends 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, 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. 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 a flow chart of another vehicle energy management method in an embodiment;

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

[0050] Figure 4 NEDC operating condition diagram in the embodiment;

[0051] Figure 5 Schematic diagram of the vehicle power requirement in the embodiment;

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

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

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

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

[0058] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0059] Example 1

[0060] Figure 1 This 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, which can be:

[0064] New European Driving Cycle (NEDC): This is a common test cycle that includes urban and suburban driving conditions. It is characterized by relatively low speeds and gentle acceleration and deceleration.

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

[0066] U.S. Federal Test Procedure (FTP): This includes urban and highway driving conditions, evaluating vehicle performance 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 to be adopted. One of the above vehicle test conditions can be adopted according to actual needs, or other customized vehicle test conditions can also be adopted.

[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 solution, 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 with 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 variables 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, state variables can be battery SOC, power battery power, etc.; 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] For example, 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 as follows:

[0079] W fc (t)=∫0tf 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 Taking the first-order differential of the equation (t) with respect to time, 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] For example, in this solution, it can be 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, a control variable equation may 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 by performing polynomial interpolation on the test data using an interpolation function to obtain corresponding function expressions.

[0087] Exemplarily, in this solution, the algebraic constraint equations may represent 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 to obtain 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] For example, in this solution, the fuel cell attenuation equation can be as follows:

[0092]

[0093] Where, represents the fuel cell attenuation, n represents the number of vehicle starts, and t1 to t3 represent the vehicle operating load parameters.

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

[0095] Specifically, in this solution, fuel cell power is used to characterize vehicle operating load, and 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] For example, 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] Illustratively, in this solution, based on the battery characteristics of the fuel cell, the fuel cell attenuation equation can be determined through 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] For example, in this solution, the power battery power and the fuel cell power are set to constitute the vehicle required power under the preset vehicle operating conditions. At the same time, the constraints are set as follows:

[0100]

[0101] Where p b_out_min Indicates the minimum power of the power battery, p b_out_maxIndicates the maximum power of the power battery, p fc_out_min Indicates the minimum power of the fuel cell, p fc_out_max Indicates the maximum fuel cell power.

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

[0103] For example, in this solution, there is no limitation on the 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. Therefore, 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] For example, in this solution, energy management of a vehicle may be specifically as follows:

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

[0109] This embodiment provides 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; using the fuel cell attenuation as a target quantity, solving the state variable equation and the control variable equation based on a nonlinear programming method to obtain optimal state variables and optimal control variables; and performing vehicle energy management based on the optimal state variables and optimal control variables. Solving the state variables and control variables using the fuel cell attenuation as the target quantity can better balance vehicle performance and fuel cell lifespan, helping to minimize fuel cell attenuation and extend its service life while meeting vehicle operating requirements. Furthermore, extending the fuel cell lifespan and reducing the frequency of fuel cell replacement can reduce vehicle maintenance and operating costs. Slowing fuel cell attenuation can enhance the reliability and stability of the entire power system and reduce the likelihood of failures. This prevents unexpected failures during vehicle operation due to rapid degradation of fuel cell performance. Solving the control variables while considering attenuation can promote more efficient energy utilization and improve energy efficiency. Furthermore, the state variables and control variables can adapt to different vehicle usage scenarios and operating conditions, providing optimal control strategies for various operating 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] Where, Indicates the fuel cell attenuation, K p 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.

[0113] For example, in this solution, setting idle operation to represent a situation where the fuel cell output power is less than 10% of the rated power, setting large variable load operation to represent a situation where the absolute value of the load change rate per second is greater than 10% of the rated power, setting high load operation to represent a situation where the fuel cell output power is greater than 90% of the peak power, and setting a complete start and stop to represent one start and stop number.

[0114] For example, 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 correspond to 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 This is another flow chart of the vehicle energy management method 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] For example, 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 the 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 rate 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] For example, in this solution, Figure 1Based on the constraints of the scheme shown, when the power battery ampere-hour flow rate is used as the target quantity, the power battery current is set to be less than the charge and discharge current threshold. The charge and discharge current threshold can be determined based on experience, simulation tests, or calibration tests;

[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 solution, 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 Based on the beneficial effects of the illustrated scheme, in this scheme, the fuel cell attenuation and the power battery ampere-hour flow rate are used as target variables to solve the state variables and control variables. Compared with using the fuel cell attenuation as the target variable, the state variable equation and the control variable equation are solved based on the nonlinear programming method, and the vehicle energy management is performed based on the optimal state variables and 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. This 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, which can reduce the maintenance cost and use cost of the vehicle. Slowing 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 experience unexpected failures during operation due to the rapid decline of the fuel cell performance. Solving the control variable while considering the attenuation can promote more efficient energy utilization and improve energy utilization efficiency. In addition, the state variables and control variables can adapt to different vehicle usage scenarios and operating conditions, providing the optimal control strategy for various operating conditions.

[0134] exist Figure 1Building on the benefits of the previously mentioned scheme, this solution employs multiple objectives (i.e., battery ampere-hour flow and fuel cell attenuation) to optimize the target variable. Compared to considering fuel cell attenuation alone, the multi-objective optimization solution balances these objectives, achieving a balance between fuel cell and battery attenuation. Simultaneously considering both fuel cell attenuation and battery ampere-hour flow allows for a comprehensive balance between the attenuation of both power sources, extending the life of the entire power system. This avoids the problem of neglecting the impact of battery state changes on overall vehicle performance when considering fuel cell attenuation alone. Simultaneously considering both objectives allows for more precise energy allocation. Based on varying driving requirements and power source status, the output power of the fuel cell and battery is rationally allocated to achieve optimal energy efficiency. More efficient energy management can reduce vehicle operating costs. Reducing fuel cell attenuation prolongs its service life and reduces replacement costs. Controlling battery ampere-hour flow reduces the number of charge and discharge cycles, extending battery life and reducing replacement and maintenance costs.

[0135] Based on the aforementioned solution of using the power battery ampere-hour flow rate as the target quantity, in one possible implementation scheme, the power battery ampere-hour flow rate equation is expressed as follows:

[0136] Aheff(t)=∫0 tf σ(t)|i b |dt

[0137]

[0138] Where 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 depth of discharge, 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] For example, in this solution, 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, 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] Where 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] Where, 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 fuel cell open circuit voltage, R fc represents the internal resistance of the fuel cell, and f1() represents the preset function.

[0150] For example, in this solution, the specific form of the preset function f1 is not limited and can be determined through simulation experiments.

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

[0152] The basic idea of ​​the pseudospectral 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, interpolation functions are used to approximate the state variables and control variables. For example, for the Gaussian pseudospectral method, Lagrange interpolation polynomials can be used to approximate the state variables and control variables.

[0155] The system's dynamic equations are approximated at the discretized nodes to obtain a set of algebraic equations (i.e., algebraic constraint equations). For example, in the Gaussian pseudospectral method, the derivatives of the interpolating 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 further used as a constraint condition for solving the optimal state variables and the optimal control variables. The vehicle demand power, the power of the power battery, and the power of the fuel cell satisfy the following equation:

[0157] Charge

[0158] discharge

[0159] Where 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, using the vehicle's required power as a constraint ensures that the resulting control strategy can provide sufficient power to meet the vehicle's driving needs and ensure normal operation. Solving the optimal control problem using pseudospectral methods can identify a control strategy that minimizes energy consumption or maximizes energy efficiency while ensuring the vehicle can deliver the required power. Using the required power as a constraint helps optimize the control strategy by fully accounting for energy limitations and improving the vehicle's range. This approach makes the model more realistic. The resulting control strategy is therefore more practical and adaptable to diverse driving conditions and requirements.

[0161] Based on any of the above solutions, in one possible implementation, the power battery SOC is further 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 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, using the power battery SOC as a constraint ensures that the power battery will not over-discharge to an extremely low power level during vehicle operation, thereby preventing the vehicle from being unable to continue driving due to power exhaustion. It also ensures that the battery can always provide a stable power supply, avoiding 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 battery SOC as a constraint allows the battery's energy state to be considered during the optimization process, leading to a more energy-efficient control strategy. Using the battery SOC as a constraint also allows the battery's power output capability to be considered during the optimization process, leading to a control strategy that better suits the vehicle's driving needs.

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

[0169]

[0170] Where, 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.

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

[0172] Exemplarily, in this scheme, the nonlinear programming method adopts the Radau pseudospectral method, and the Radau pseudospectral 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 pseudospectral method can well handle the constraints of state variables, control variables, etc., 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] For example, in this solution, the optimization calculation of selected state variables and control variables under NEDC operating 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 working 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 This is another flow chart of the vehicle energy management method 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 This is a schematic diagram of the NEDC operating conditions 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 solution, the vehicle parameters are set as shown in Table 2.

[0180] Table 2

[0181]

[0182] Figure 5 This is a schematic diagram of the vehicle power requirement in the embodiment, refer to Figure 5 In this solution, under NEDC conditions, the vehicle required power under NEDC conditions can be calculated based on the vehicle required 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 optimal control variables. The general steps of the Radau pseudo-spectral method include: selecting a suitable Radau node distribution, constructing interpolation polynomials for 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 The graph of the relationship between the fuel cell power, fuel cell current and fuel cell voltage in the embodiment is shown in 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 the black line.

[0193] Combine Figure 6 According to Table 3, in this scheme, the fuel cell power (average) can be expressed as follows:

[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 relationship between the power battery cell voltage and the power battery SOC.

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

[0199]

[0200] Where 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 pseudospectral method discretizes the state variables and control variables, and then uses Lagrange difference polynomials to approximate the state variables and control variables. The differential operation of the state equation and the integral operation in the objective function are converted into algebraic operations. Finally, the optimal control problem is transformed 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's driving under NEDC conditions is divided into Q stages, and its initial time points, i.e., Q-1 segment points, are recorded as T0 to TQ respectively;

[0204] In the time domain, each time period [Tq-1, Tq] from T0 to 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 (τ0, τ0 = -1), and its node output is N+1. For the VIP 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 of the power battery and the fuel cell 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 at 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 Sub-Lagrange difference basis function, specifically:

[0214]

[0215] The power battery current and fuel cell current are used 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 at 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 assignment 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 below:

[0222]

[0223] Where, τ qk is the collocation point of the qth stage (where k = 1, 2, ... N), N q ·(N q +1) differential matrix, 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 dynamic equation constraints of the optimal control problem of fuel cell hybrid vehicle energy management in the first stage q can be transformed into the constraints of the dynamic equations at the distribution 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 solution, 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, the algebraic constraint equations are solved based on the nonlinear programming method to obtain the optimal state variables and the optimal control variables.

[0235] In this solution, 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 using the high-dimensional sparse NLP solver SNOPT to obtain 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 showing the optimized fuel cell power curve in the embodiment, Figure 9 This is the optimized power battery power curve in the embodiment, refer to Figure 4 、 Figure 8 and Figure 9 , NEDC operating curve is a speed-time interval. 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 Figure 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 fuel cell current drops slowly at the beginning of each deceleration stage of the vehicle 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] Figure 10 This is the optimized power battery SOC curve diagram in the embodiment, refer to Figure 10 , the initial SOC of the power battery is set to 70%, through Figure 8 and Figure 9It can be seen 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 during 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 recharge energy is sufficient 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 power battery life. At the same time, maintaining the SOC in a higher range is beneficial to improving the cruising range.

[0242] Example 2

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

[0244] Obtain 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 rate as target quantities, with the goal of improving the fuel cell life and power battery life under the preset vehicle operating 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] Illustratively, in this embodiment, the vehicle energy management unit can be configured to implement any one of the vehicle energy management methods in Example 1. The implementation process and beneficial effects of the method are the same as the corresponding content recorded in Example 1, and the specific content will not be repeated here.

[0250] Example 3

[0251] Figure 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 claimed herein.

[0252] like Figure 11 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. 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 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0253] Multiple 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 magnetic 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 via a computer network such as the Internet and / or various telecommunication networks.

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

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

[0256] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

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

[0259] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0260] The systems and techniques described herein can be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or 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 back-end components, middleware components, or front-end components. The components of the system can 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 clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0262] Example 4

[0263] This embodiment proposes a vehicle, which is configured to execute any one of the vehicle energy management methods recorded in Example 1. The implementation process and beneficial effects of the method are the same as the corresponding contents recorded in Example 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 employed. 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 detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. 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 replenishing instantaneous power, characterized in that: include: Obtain 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 rate as target quantities, and aiming to improve the fuel cell life and the power battery life in the preset vehicle operating condition, the algebraic constraint equation is solved 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 operating load, and 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.

2. The vehicle energy management method according to claim 1, characterized in that: The fuel cell attenuation is expressed by the following formula: Where, Indicates the fuel cell attenuation, K p 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, wherein: The power battery ampere-hour flow equation is expressed as follows: Where 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 depth of discharge, 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, wherein: The state variable equation includes: p b (t)=i b (t)V b (t) p fc (t)=i fc (t)V fc_out (t) Where 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)) Where, 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 fuel cell open circuit voltage, 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: The method further includes using the vehicle demand power as a constraint condition for solving the optimal state variables and the optimal control variables, wherein the vehicle demand power, the power of the power battery, and the power of the fuel cell satisfy the following equation: Where 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.

6. The vehicle energy management method according to claim 1, wherein: 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: Where, 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, wherein: 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 according to any one of claims 1 to 9.

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