Ecms hydrogen fuel electric vehicle energy management method based on sparrow search algorithm

Abstract

The application provides an ECMS hydrogen fuel electric vehicle energy management method based on a sparrow search algorithm, and builds a vehicle dynamics model, a fuel cell model, a lithium battery model and a super capacitor model through the structural characteristics of a hydrogen fuel hybrid electric vehicle; an equivalent factor of the comprehensive lithium battery and super capacitor is designed, a hydrogen fuel cell electric vehicle energy management model of a minimum equivalent hydrogen consumption strategy (ECMS) is built, the equivalent factor in the ECMS is adjusted in real time by using a sparrow search algorithm (ASS), and the sparrow search algorithm has good global optimality; and the technical scheme can realize reasonable energy distribution between the hydrogen fuel cell and the auxiliary battery (lithium battery and super capacitor), and maximum fuel economy is obtained.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicles, and in particular to an ECMS energy management method for hydrogen fuel cell electric vehicles based on a sparrow search algorithm. Background Technology

[0002] Fuel cell vehicles are gaining popularity due to their advantages such as zero pollution, long driving range, and fast hydrogen refueling. There are three main types of hybrid power systems for fuel cell vehicles: fuel cell + battery hybrid system, fuel cell + supercapacitor hybrid system, and fuel cell + battery + supercapacitor hybrid system. The energy management strategies employed differ for each configuration, with the control objective being to improve fuel economy and extend the lifespan of the power source. In the third configuration, the supercapacitor acts as a "peak shaving and valley filling" mechanism, preventing the battery from experiencing high-current charging and discharging, thus effectively extending battery life. The energy management strategies employed for different hybrid system configurations also vary; currently, the commonly used control strategy is the Equivalent Consumption Minimum Strategy (ECMS), based on vehicle economy.

[0003] The strategy of minimizing equivalent hydrogen consumption is an instantaneous optimization method that can be implemented as a real-time controller. However, the equivalent factor in ECMS has a significant impact on the fuel economy of the vehicle. How to design the equivalent factor and how to optimize and adjust the equivalent factor in real time are the key issues of the strategy of minimizing equivalent hydrogen consumption of hydrogen fuel cell electric vehicles.

[0004] The Sparrow Search Algorithm (SSA) is a novel swarm intelligence algorithm designed by Xue Jiankai and Shen Bo in 2020. SSA is primarily inspired by the foraging and anti-predation behaviors of sparrows. This algorithm features a simple structure, fast convergence speed, and stable iterative process, attracting considerable attention from scholars and having been successfully applied to numerous practical problems. Summary of the Invention

[0005] Purpose of the invention: The technical problem to be solved by this invention is to address the shortcomings of existing technologies by providing an ECMS energy management method for hydrogen fuel cell electric vehicles based on a sparrow search algorithm, including:

[0006] Step 1: Based on the power system structure of hydrogen fuel cell hybrid electric vehicles, establish a power system model for hydrogen fuel cell electric vehicles;

[0007] Step 2: Taking the total hydrogen consumption as the target, design the equivalent factor of the integrated lithium battery and supercapacitor, and establish the energy management model of hydrogen fuel cell electric vehicle based on the ECMS strategy of minimizing equivalent hydrogen consumption.

[0008] Step 3: For the optimization problem of the equivalent factor in the ECMS strategy of minimizing equivalent hydrogen consumption, the Sparrow Search Algorithm (SSA) is used to adjust the equivalent factor in the ECMS strategy of minimizing equivalent hydrogen consumption.

[0009] In step 1, the hydrogen fuel cell electric vehicle power system model is as follows:

[0010]

[0011] Among them, P req Let v be the required power, a be the vehicle speed, a be the vehicle acceleration, m be the total vehicle mass, ρ be the air density, A be the vehicle's frontal area, and C be the vehicle's frontal area. D η is the air resistance coefficient, g is the acceleration due to gravity, μ is the rolling resistance coefficient, θ is the road slope, and η is the angular velocity. m For motor efficiency;

[0012] The power relationships between the batteries in the car are as follows:

[0013] P req =P fc +P bat +P sc

[0014] Among them, P fc For the power of the fuel cell stack, P bat For lithium battery power, P sc This refers to the power of the supercapacitor.

[0015] Step 2 includes:

[0016] Step 2-1: Establish the equivalent relationship between the hydrogen consumption of the hydrogen fuel cell and the electrical energy consumed by the auxiliary batteries (lithium batteries and supercapacitors):

[0017]

[0018] in, For the total equivalent hydrogen consumption, For hydrogen consumption in hydrogen fuel cells, This is the equivalent hydrogen consumption of a lithium battery. This is the equivalent hydrogen consumption of the supercapacitor. denoted as the calorific value of hydrogen, and s is the design equivalence factor.

[0019] Step 2-2: Establish the following objective function J to complete the establishment of the ECMS hydrogen fuel cell electric vehicle energy management model with the strategy of minimizing equivalent hydrogen consumption:

[0020]

[0021] In step 2-1, the equivalent factor s of the design is as follows:

[0022] s=η bat K bat +η sc K sc

[0023] Where, η bat η represents the charge / discharge efficiency of a lithium battery. batsc K represents the charge / discharge efficiency of a supercapacitor. bat K sc These are the penalty coefficients for lithium batteries and supercapacitors, respectively, used to limit the range of SOC (state of charge) variation of the auxiliary battery. If the auxiliary battery SOC is higher than the upper limit, the penalty coefficient will decrease the equivalent factor to promote battery discharge; if the auxiliary battery SOC is lower than the lower limit, the penalty coefficient will increase the equivalent factor to promote battery charging. The penalty function is as follows:

[0024]

[0025] Where, μ bat and μ sc These are the SOC balance coefficients for lithium batteries and supercapacitors, respectively. batmax and SOC batmin These are the maximum and minimum SOC values ​​for lithium batteries. bat This represents the current SOC value of the lithium battery. scmax and SOC scmin These represent the maximum and minimum SOC values ​​of the supercapacitor, respectively. sc This represents the current SOC value of the supercapacitor. scinit This is the initial value of the supercapacitor's state of charge (SOC).

[0026] Step 3 includes:

[0027] Step 3-1: Sparrow flock initialization. The state input constitutes a sparrow individual. Set the range of the state input. According to the upper and lower limits of each state input, the state input within the upper and lower limits constitute the sparrow population. Determine the ratio of discoverers and joiners. The discoverers account for 20% of the total population.

[0028] Step 3-2: Design the fitness function f(t), calculate the fitness of the initial population and sort them in ascending order, and determine the current optimal value (highest fitness), worst value (lowest fitness), and the positions of the optimal and worst values;

[0029] Step 3-3: Update the location of the discoverer, the location of the joiner, and the location of the sparrow that sensed the danger (the early warning sparrow);

[0030] Steps 3-4: Calculate fitness values ​​and update sparrow positions;

[0031] Step 3-5: Determine if the stopping condition is met. If it is, exit and output the final result to obtain the global optimal value. Otherwise, repeat steps 3-2 to 3-4.

[0032] In step 3-1, the state input quantity [P] fc SOC bat ,P bat ,I bat SOC sc ,P sc ,I sc The scope includes:

[0033]

[0034] Among them, P fcmin and P fcmax P represents the minimum and maximum power of the hydrogen fuel cell, respectively. batmin and P batmax These are the minimum and maximum power values ​​of the lithium battery, respectively. bat For lithium battery current, I batcharge_lim and I batdischarge_lim These represent the lower and upper limits of the charging and discharging current for lithium batteries, respectively, P. sctmin and P scmax These are the minimum and maximum values ​​of the supercapacitor power, I. sc For the supercapacitor current, I sccharge_lim and I scdischarge_lim These are the lower and upper limits of the charging and discharging current for supercapacitors, respectively.

[0035] In step 3-2, the fitness function f(t) is:

[0036]

[0037] Where g1 represents a value of 1 when both the hydrogen fuel cell and the auxiliary battery power are within their respective power ranges, and 0 otherwise; g2 represents a value of 1 when the auxiliary battery SOC is between its upper and lower charge limits, and 0 otherwise; SOC batinit This represents the initial SOC value of the lithium battery. This represents the cumulative equivalent hydrogen consumption of the entire vehicle.

[0038] The present invention also provides a storage medium storing a computer program or instructions, which, when executed, implements the ECMS energy management method for hydrogen fuel cell electric vehicles based on the sparrow search algorithm.

[0039] Beneficial effects: Compared with the prior art, the present invention uses a sparrow search algorithm to comprehensively consider the auxiliary battery SOC, equivalent hydrogen consumption, fuel cell power and auxiliary battery power to set the fitness function. It can quickly adjust and optimize the equivalent factor s in the strategy of minimizing equivalent hydrogen consumption, and realize the reasonable power distribution between the fuel cell and the auxiliary battery. Under the premise of meeting the vehicle's power performance, it can obtain the maximum economy and driving smoothness. Attached Figure Description

[0040] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0041] Figure 1 This is a schematic diagram of the structural model of a hydrogen fuel cell electric vehicle system.

[0042] Figure 2 This is a flowchart illustrating the overall optimization process of the ECMS energy management and control strategy for hydrogen fuel cell electric vehicles based on the sparrow search algorithm.

[0043] Figure 3 Flowchart for solving the Sparrow Search algorithm. Detailed Implementation

[0044] Figure 1 The system structure model for a hydrogen fuel cell electric vehicle uses a hydrogen fuel cell, a lithium battery, and a supercapacitor as energy sources. A unidirectional DC / DC converter converts the fuel cell output voltage to the bus voltage, a bidirectional DC / DC converter converts the supercapacitor output voltage to the bus voltage, and an energy management system allocates power appropriately.

[0045] Figure 2 This is a flowchart illustrating the overall optimization process of the ECMS energy management and control strategy for hydrogen fuel cell electric vehicles based on the sparrow search algorithm. Figure 1 The vehicle system receives vehicle speed, acceleration, and SOC values ​​of lithium battery and supercapacitor status signals, which are input into the sparrow search optimization module. The equivalent factor s is optimized by combining the objective function and constraints using the sparrow search algorithm.

[0046] Figure 3 The flowchart for the sparrow search algorithm is shown, with seven state input quantities [P]. fc SOC bat ,P bat ,I bat SOCsc ,P sc ,I sc As an individual sparrow, a fitness function is designed and optimized using the sparrow algorithm to obtain the optimal equivalent factor.

[0047] This invention provides an ECMS energy management method for hydrogen fuel cell electric vehicles based on a sparrow search algorithm, comprising the following steps:

[0048] (1) The power system model of a hydrogen fuel cell electric vehicle is as follows:

[0049]

[0050] Among them, P req Let v be the required power, a be the vehicle speed, a be the vehicle acceleration, m be the total vehicle mass, ρ be the air density, A be the vehicle's frontal area, and C be the vehicle's frontal area. D η is the air resistance coefficient, g is the acceleration due to gravity, μ is the rolling resistance coefficient, θ is the road slope, and η is the angular velocity. m This refers to the motor efficiency.

[0051] The power relationships between the various batteries in the car are as follows:

[0052] P req =P fc +P bat +P sc

[0053] Among them, P fc For the power of the fuel cell stack, P bat For lithium battery power, P sc This refers to the power of the supercapacitor.

[0054] (2) Based on the strategy of minimizing equivalent hydrogen consumption, the energy consumption of the whole vehicle is converted into hydrogen consumption:

[0055]

[0056] in, For the total equivalent hydrogen consumption, For hydrogen consumption in hydrogen fuel cells, This is the equivalent hydrogen consumption of a lithium battery. This is the equivalent hydrogen consumption of the supercapacitor. Let be the calorific value of hydrogen, and s be the designed equivalence factor, whose formula is as follows:

[0057] s=η bat K bat +η sc K sc

[0058] Where, η bat η represents the charge / discharge efficiency of a lithium battery.sc K represents the charge / discharge efficiency of a supercapacitor. bat K sc These are penalty coefficients for the lithium battery and the supercapacitor, respectively. They are used to limit the range of SOC variation of the auxiliary battery. If the auxiliary battery SOC exceeds the upper limit, the penalty coefficient will decrease the equivalent factor to promote battery discharge. If the auxiliary battery SOC is below the lower limit, the penalty coefficient will increase the equivalent factor to promote battery charging. The penalty function is as follows:

[0059]

[0060] Where, μ bat and μ sc The balance factor of SOC for lithium batteries and supercapacitors, respectively. batmax and SOC batmin These are the maximum and minimum SOC values ​​for lithium batteries. bat This represents the current SOC value of the lithium battery. scmax and SOC scmin These represent the maximum and minimum SOC values ​​of the supercapacitor, respectively. sc This represents the current SOC value of the supercapacitor. scinit This is the initial value of the supercapacitor's state of charge (SOC).

[0061] Based on the strategy of minimizing equivalent hydrogen consumption, with the goal of minimizing total equivalent hydrogen consumption, the objective function is established as follows:

[0062]

[0063] (3) Based on the above economic objectives, the equivalent factor in the strategy of minimizing equivalent hydrogen consumption is optimized using the sparrow search algorithm (SSA). The specific steps are as follows:

[0064] ① Sparrow flock initialization, seven state inputs [P] fc SOC bat ,P bat ,I bat SOC sc ,P sc ,I sc To form a sparrow individual, set the range of state variables. Based on the upper and lower limits of each state variable, construct a sparrow population from the states within those limits. Determine the ratio of discoverers to joiners, taking discoverers to account for 20% of the total population. The range of state variables is as follows:

[0065]

[0066] Among them, P fcmin and Pfcmax P represents the minimum and maximum power of the hydrogen fuel cell, respectively. batmin and P batmax These are the minimum and maximum power values ​​of the lithium battery, respectively. bat For lithium battery current, I batcharge_lim and I batdischarge_lim These represent the lower and upper limits of the charging and discharging current for lithium batteries, respectively, P. sctmin and P scmax These are the minimum and maximum values ​​of the supercapacitor power, I. sc For the supercapacitor current, I sccharge_lim and I scdischarge_lim These are the lower and upper limits of the charging and discharging current for supercapacitors, respectively.

[0067] ② Calculate the fitness of the initial population and sort them in ascending order. Then select the current best value (minimum fitness value) and the worst value (maximum fitness value). The fitness function is designed as follows:

[0068]

[0069] Where g1 represents a value of 1 when both the hydrogen fuel cell and the auxiliary battery (lithium battery and supercapacitor) are within their respective power ranges, and 0 otherwise; g2 represents a value of 1 when the auxiliary battery's SOC is between its upper and lower charge limits, and 0 otherwise; SOC batinit This represents the initial SOC value of the lithium battery. This represents the cumulative equivalent hydrogen consumption of the entire vehicle.

[0070] ③ Update the location of the discoverer, the location of the joiner, and the location of the sparrow that is aware of the danger (the early warning sparrow).

[0071] ④ Calculate fitness values ​​and update sparrow positions.

[0072] ⑤ Determine if the stopping condition is met (take the number of iterations as 80, reaching 80 means the stopping condition is met). If it is met, exit, output the final result, and obtain the global optimal value. Otherwise, repeat ② to ④ above.

[0073] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the inventive content of the method provided by this invention and some or all of the steps in the various embodiments. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0074] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0075] This invention provides an ECMS energy management method for hydrogen fuel cell electric vehicles based on a sparrow search algorithm. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. An ECMS energy management method for hydrogen fuel cell electric vehicles based on the sparrow search algorithm, characterized in that, Includes the following steps: Step 1: Based on the power system structure of hydrogen fuel cell hybrid electric vehicles, establish a power system model for hydrogen fuel cell electric vehicles; Step 2: Taking the total hydrogen consumption as the target, design the equivalent factor of the integrated lithium battery and supercapacitor, and establish the energy management model of hydrogen fuel cell electric vehicle based on the ECMS strategy of minimizing equivalent hydrogen consumption. Step 3: For the optimization problem of the equivalent factor in the ECMS strategy of minimizing equivalent hydrogen consumption, the sparrow search algorithm is used to adjust the equivalent factor in the ECMS strategy of minimizing equivalent hydrogen consumption. Step 2 includes: Step 2-1: Establish the equivalent relationship between hydrogen consumption in the hydrogen fuel cell and electrical energy consumption in the auxiliary battery: in, For the total equivalent hydrogen consumption, For hydrogen consumption in hydrogen fuel cells, This is the equivalent hydrogen consumption of a lithium battery. This is the equivalent hydrogen consumption of the supercapacitor. denoted as the calorific value of hydrogen, and s is the design equivalence factor; Step 2-2: Establish the following objective function J to complete the establishment of the ECMS hydrogen fuel cell electric vehicle energy management model with the strategy of minimizing equivalent hydrogen consumption: In step 2-1, the equivalent factor s of the design is as follows: s=n bat K bat +n sc K sc Where, η bat η represents the charge / discharge efficiency of a lithium battery. sc K represents the charge / discharge efficiency of a supercapacitor. bat K sc These are the penalty coefficients for lithium batteries and supercapacitors, respectively. Step 3 includes: Step 3-1: Sparrow flock initialization. The state input constitutes a sparrow individual. Set the range of the state input. According to the upper and lower limits of each state input, the state input within the upper and lower limits constitute the sparrow population. Determine the ratio of discoverers and joiners. The discoverers account for 20% of the total population. Step 3-2: Design the fitness function f(t), calculate the fitness of the initial population and sort them in ascending order, and determine the positions of the current best value, worst value, best value and worst value; Step 3-3: Update the location of the discoverer, the location of the joiner, and the location of the sparrow that realized the danger; Steps 3-4: Calculate fitness values ​​and update sparrow positions; Step 3-5: Determine if the stopping condition is met. If it is, exit and output the final result to obtain the global optimal value. Otherwise, repeat steps 3-2 to 3-4.

2. The method according to claim 1, characterized in that, In step 1, the hydrogen fuel cell electric vehicle power system model is as follows: Among them, P req Let v be the required power, a be the vehicle speed, a be the vehicle acceleration, m be the total vehicle mass, ρ be the air density, A be the vehicle's frontal area, and C be the vehicle's frontal area. D η is the air resistance coefficient, g is the acceleration due to gravity, μ is the rolling resistance coefficient, θ is the road slope, and η is the angular velocity. m For motor efficiency; The power relationships between the batteries in the car are as follows: P req =P fc +P bat +P sc Among them, P fc For the power of the fuel cell stack, P bat For lithium battery power, P sc This refers to the power of the supercapacitor.

3. The method according to claim 2, characterized in that, In step 2-1, the penalty function is as follows: Where, μ bat and μ sc These are the SOC balance coefficients for lithium batteries and supercapacitors, respectively. batmax and SOC batmin These are the maximum and minimum SOC values ​​for lithium batteries. bat This represents the current SOC value of the lithium battery. scmax and SOC scmin These represent the maximum and minimum SOC values ​​of the supercapacitor, respectively. sc This represents the current SOC value of the supercapacitor. scinit This is the initial value of the supercapacitor's state of charge (SOC).

4. The method according to claim 3, characterized in that, In step 3-1, the state input quantity [P] fc SOC bat ,P bat ,I bat SOC sc ,P sc ,I sc The scope includes: Among them, P fcmin and P fcmax P represents the minimum and maximum power of the hydrogen fuel cell, respectively. batmin and P batmax These are the minimum and maximum power values ​​of the lithium battery, respectively. bat For lithium battery current, I batcharge_lim and I batdischarge_lim These represent the lower and upper limits of the charging and discharging current for lithium batteries, respectively, P. sctmin and P scmax These are the minimum and maximum values ​​of the supercapacitor power, I. sc For the supercapacitor current, I sccharge_lim and I scdischarge_lim These are the lower and upper limits of the charging and discharging current for supercapacitors, respectively.

5. The method according to claim 4, characterized in that, In step 3-2, the fitness function f(t) is: Where g1 represents a value of 1 when both the hydrogen fuel cell and the auxiliary battery power are within their respective power ranges, and 0 otherwise; g2 represents a value of 1 when the auxiliary battery SOC is between its upper and lower charge limits, and 0 otherwise; SOC batinit This represents the initial SOC value of the lithium battery. This represents the cumulative equivalent hydrogen consumption of the entire vehicle.

6. A storage medium, characterized in that, It stores a computer program or instructions that, when executed, implement the method as described in any one of claims 1 to 5.

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