Hydrogen fuel cell passenger car energy management method and device and storage medium

Abstract

The invention discloses a hydrogen fuel cell passenger car energy management method and device and a storage medium, and the method comprises the steps: obtaining a plurality of optimal equivalent factors corresponding to a plurality of different driving condition types, according to a hybrid power system model of the hydrogen fuel cell passenger car and a plurality of typical working condition databases corresponding to the hydrogen fuel cell passenger car under various different driving working condition types, an improved sparrow search algorithm is used for optimizing equivalent factors in an equivalent consumption minimum strategy; obtaining current working condition data of the hydrogen fuel cell passenger car, performing dimensionality reduction on the current working condition data, inputting the current working condition data into the working condition recognition model to obtain a current driving working condition type, and screening out a matched optimal equivalent factor to update an equivalent consumption minimum strategy; and performing energy management on the hydrogen fuel cell passenger car by utilizing the updated equivalent consumption minimum strategy according to the hybrid power system model and the current working condition data. According to the method, the control performance of the hydrogen fuel cell passenger car by the equivalent consumption minimum strategy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen fuel cell bus application, and in particular to a hydrogen fuel cell bus energy management method, equipment and storage medium. Background Art

[0002] Energy management strategy plays a key role in improving the overall performance and economy of hydrogen fuel cell buses and extending the life of fuel cells. Formulating an effective energy management strategy can reasonably coordinate the power distribution among various energy sources in hydrogen fuel cell buses and ensure that the hybrid power system of hydrogen fuel cell buses provides stable power output under load conditions.

[0003] At present, the most mainstream instantaneous optimization strategy is the Equivalent Consumption Minimization Strategy (ECMS), which can achieve approximately global optimal power allocation in real-time applications. As the core parameter of the Equivalent Consumption Minimization Strategy, the equivalent factor can be understood as the conversion factor between the power battery power and the fuel cell hydrogen consumption. Its value is closely related to the final performance achieved when the Equivalent Consumption Minimization Strategy is applied. Hydrogen fuel cell buses may experience changes in operating conditions during actual driving, but the value of the equivalent factor has not been adjusted accordingly with the changes in the driving conditions. This will affect the control effect of the Equivalent Consumption Minimization Strategy on hydrogen fuel cell buses, and further affect the power maintenance status and vehicle economy of hydrogen fuel cell buses. Summary of the invention

[0004] The present invention provides a hydrogen fuel cell bus energy management method, device and storage medium to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0005] In a first aspect, a hydrogen fuel cell bus energy management method is provided, the method comprising:

[0006] Acquire multiple optimal equivalent factors corresponding to multiple different driving conditions, wherein the multiple optimal equivalent factors are obtained by optimizing the equivalent factors included in the equivalent consumption minimum strategy using an improved sparrow search algorithm based on a pre-constructed hybrid power system model of a hydrogen fuel cell bus and multiple typical operating condition databases corresponding to the hydrogen fuel cell bus under the multiple different driving conditions;

[0007] Acquire current operating condition data of the hydrogen fuel cell bus, and perform dimension reduction on the current operating condition data to obtain key operating condition data;

[0008] Inputting the key operating condition data into a trained operating condition identification model to obtain the current driving condition type of the hydrogen fuel cell bus;

[0009] Acquire an optimal equivalent factor matching the current driving condition type from the multiple optimal equivalent factors to perform parameter update on the equivalent consumption minimum strategy;

[0010] According to the hybrid power system model and the current operating condition data, the energy management of the hydrogen fuel cell bus is performed using an equivalent consumption minimum strategy after parameter update.

[0011] Furthermore, in the improved sparrow search algorithm, the Tent chaotic map and the Sobol sequence are used to initialize the position of the sparrow population, and in each iterative update process of the sparrow population, a nonlinear inertia weight is introduced to update the position of the sparrow individual acting as the discoverer, and the Levy flight strategy is used to update the position of the sparrow individual acting as the freeloader, and after the position of each sparrow is updated, a dynamic probability factor is generated according to the current number of iterations and the final number of iterations, and when the dynamic probability factor is less than the random number, the adaptive t-distribution mutation operator is used to perturb the position of each sparrow.

[0012] Furthermore, each typical operating condition database includes multiple groups of values, each group of values ​​includes historical values ​​of several driving characteristic parameters; and the current operating condition data includes current values ​​of the several driving characteristic parameters.

[0013] Furthermore, the multiple optimal equivalent factors corresponding to the multiple different driving conditions are obtained in the following manner:

[0014] Taking the equivalent hydrogen consumption of the hybrid power system of the hydrogen fuel cell bus as a first fitness function, taking the equivalent factor included in the equivalent consumption minimum strategy as a first target variable, and the first fitness function including the first target variable;

[0015] For any driving condition type, multiple sets of values ​​contained in a typical condition database corresponding to the driving condition type are input into the hybrid power system model for simulation calculation to obtain corresponding multiple sets of system parameter values, each set of system parameter values ​​including relevant parameter values ​​involved in the first fitness function;

[0016] In combination with the multiple groups of system parameter values ​​and the first fitness function, the first objective variable is optimized using an improved sparrow search algorithm to obtain an optimal equivalent factor corresponding to the driving condition type.

[0017] Furthermore, the working condition recognition model is constructed based on a support vector machine model, which is trained in the following manner:

[0018] Using the recognition accuracy of the support vector machine model for the driving condition type as a second fitness function, and using the penalty factor and kernel function density included in the support vector machine model as a second target variable;

[0019] Performing dimension reduction on the multiple typical operating condition databases to obtain multiple corresponding operating condition key databases;

[0020] The support vector machine model is trained using the multiple operating condition key databases, and the second target variable is optimized using an improved sparrow search algorithm to obtain a trained operating condition recognition model.

[0021] Furthermore, the dimensionality reduction of the multiple typical operating condition databases to obtain the corresponding multiple operating condition key databases includes:

[0022] Analyzing the plurality of typical operating condition databases by using a correlation coefficient method and a principal component analysis method, so as to extract a plurality of key driving characteristic parameters from the plurality of driving characteristic parameters;

[0023] For any typical operating condition database, parameters of the typical operating condition database are screened, and only historical values ​​of the multiple driving characteristic parameters are retained to obtain a key operating condition database.

[0024] Furthermore, the step of reducing the dimension of the current operating condition data to obtain key operating condition data includes:

[0025] Parameter screening is performed on the current operating condition data, and only current values ​​of the multiple driving characteristic parameters are retained to obtain key operating condition data.

[0026] Furthermore, the hybrid power system of the hydrogen fuel cell bus includes a fuel cell and a power battery; the energy management of the hydrogen fuel cell bus using the equivalent consumption minimum strategy after parameter update according to the hybrid power system model and the current operating condition data includes:

[0027] Inputting the current operating condition data into the hybrid power system model for simulation calculation to obtain the current system demand power and the current power battery SOC value;

[0028] Analyzing the current system demand power and the current power battery SOC value by using the equivalent consumption minimum strategy after parameter update to obtain the first optimal output power of the fuel cell and the second optimal output power of the power battery;

[0029] The fuel cell is controlled to operate at the first optimal output power, and the power battery is controlled to operate at the second optimal output power.

[0030] In a second aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the hydrogen fuel cell bus energy management method as described in the first aspect.

[0031] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the hydrogen fuel cell bus energy management method as described in the first aspect is implemented.

[0032] The present invention has at least the following beneficial effects: by setting a more matching optimal equivalent factor for each driving condition type in advance, a more targeted equivalent consumption minimum strategy can be quickly formulated when the driving condition type of the hydrogen fuel cell bus is currently determined, thereby realizing the energy management of the hydrogen fuel cell bus more efficiently and reliably, so as to improve the vehicle economy of the hydrogen fuel cell bus, and enable the hydrogen fuel cell bus to maintain the power maintenance mode as much as possible. By proposing improvements to the population initialization strategy, the discoverer position update strategy and the freeloader position update strategy on the basis of the original sparrow search algorithm, and introducing a position disturbance strategy during the population iterative update process, the improved sparrow search algorithm can have a more reliable parameter optimization capability, and when the improved sparrow search algorithm is applied to solve the optimization problem of the equivalent factor included in the equivalent consumption minimum strategy, the selection of the equivalent factor can be avoided to be completely dependent on the experience of the developer, and when the improved sparrow search algorithm is applied to solve the optimization problem of the kernel function density and the penalty factor included in the support vector machine model, the recognition accuracy of the working condition recognition model can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0034] Figure 1 is a structural composition diagram of a hybrid power system of a hydrogen fuel cell bus in an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the vehicle speed following effect achieved when the hydrogen fuel cell bus in the embodiment of the present invention is controlled by adopting the equivalent consumption minimum strategy constrained by the same equivalent factor under different driving conditions;

[0036] Figure 3 It is a schematic diagram of the relevant effects achieved when the hydrogen fuel cell bus in the embodiment of the present invention adopts the equivalent consumption minimum strategy for control under low-speed conditions;

[0037] Figure 4It is a schematic diagram of the relevant effects achieved when the hydrogen fuel cell bus in the embodiment of the present invention is controlled by adopting the equivalent consumption minimum strategy under medium speed conditions;

[0038] Figure 5 It is a schematic diagram of the relevant effects achieved when the hydrogen fuel cell bus in the embodiment of the present invention adopts the equivalent consumption minimum strategy for control under high-speed conditions;

[0039] Figure 6 is a flow chart of a hydrogen fuel cell bus energy management method in an embodiment of the present invention;

[0040] Figure 7 It is a schematic diagram of the hardware structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order from the module division in the system or the order in the flow chart. The terms "first", "second", "third", "fourth", etc. in the specification of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, such as processes, methods, systems, products or devices that include a series of steps or units, which are not necessarily limited to those steps or units clearly listed, but may include other steps or units inherent to these processes, methods, products or devices that are not clearly listed.

[0043] First, the equivalent consumption minimum strategy used in the present invention is described as follows:

[0044] The equivalent consumption minimum strategy is a real-time energy management control strategy proposed based on the Pontryagin minimum principle. It can solve the instantaneous optimal power allocation problem of the hybrid power system of hydrogen fuel cell buses under constrained conditions.

[0045] The hybrid power system of hydrogen fuel cell buses includes at least a fuel cell module, a power battery module and a motor module. Figure 1As shown, the fuel cell module includes a fuel cell and a Boost type DC-DC converter, the power battery module includes a power battery and a Buck-Boost type DC-DC converter, the motor module includes a motor controller and a drive motor, the fuel cell is connected to the motor controller through the Boost type DC-DC converter, the power battery is connected to the motor controller through the Buck-Boost type DC-DC converter, the motor controller is connected to the drive motor, and the power battery is preferably a lithium-ion battery.

[0046] The equivalent consumption minimization strategy is applied to the hybrid power system of hydrogen fuel cell buses, with the system equivalent hydrogen consumption (i.e. the sum of the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the power battery) as the performance indicator. The implementation goal is to minimize the system instantaneous equivalent hydrogen consumption.

[0047] When using the equivalent consumption minimum strategy to solve the instantaneous optimal power allocation problem of the hybrid power system of hydrogen fuel cell buses, the balance relationship between the system demand power, the output power of the fuel cell and the output power of the power battery is clarified as follows:

[0048] P fc (t)η DC1 +P bat (t)η DC2 =P req (t) (1)

[0049] Where P fc (t) is the output power of the fuel cell, Pbat(t) is the output power of the power battery, P req (t) is the system required power, η DC1 is the efficiency of the Boost DC-DC converter, η DC2 is the efficiency of the Buck-Boost DC-DC converter;

[0050] The output power of the fuel cell P fc (t) is the control variable, and the equivalent hydrogen consumption of the hybrid power system of the hydrogen fuel cell bus is taken as the performance index. The relevant expression used to characterize the instantaneous power distribution problem of the hybrid power system of the hydrogen fuel cell bus is established as follows:

[0051]

[0052] Where J is the performance index, t0 is the start time of the hydrogen fuel cell bus operation, t f The end time of hydrogen fuel cell bus operation. is the equivalent hydrogen consumption of the hybrid power system of the hydrogen fuel cell bus at the current moment, which can also be understood as the instantaneous equivalent hydrogen consumption;

[0053] By introducing the equivalent factor and Hamiltonian function, the relevant expression for characterizing the instantaneous equivalent hydrogen consumption of the hybrid power system of hydrogen fuel cell buses is established as follows:

[0054]

[0055] Where H represents the Hamiltonian function, is the instantaneous power battery SOC value, t is the time, is the instantaneous hydrogen consumption of the fuel cell, is the instantaneous equivalent hydrogen consumption of the power battery, η fc is the efficiency of the fuel cell, Q LHV is the lower calorific value of hydrogen, preferably set to 120 MJ / kg, and s(t) is the equivalent factor;

[0056] In the present invention, the hydrogen fuel cell bus is set to the power maintenance mode. Without considering the mid-trip power replenishment of the power battery, the penalty function is used to correct the above formula (3) to obtain the following:

[0057]

[0058] In the formula, ω(SOC) is the penalty function, SOC target To enable the hydrogen fuel cell bus to maintain the target power battery SOC value in the power maintenance mode, SOC min is the lower limit of the power battery SOC value, SOC max is the upper limit of the power battery SOC value, and α is the exponential coefficient;

[0059] By solving the above formula (4), when the minimum instantaneous equivalent hydrogen consumption of the hybrid power system of the hydrogen fuel cell bus is solved, the output power P of the fuel cell can be obtained: fc (t) and the output power P of the power battery bat The optimal allocation result of (t).

[0060] The control effect of the hydrogen fuel cell bus energy management when the equivalent consumption minimum strategy is selected to use the same equivalent factor when the hydrogen fuel cell bus is in different driving conditions is analyzed and compared as follows:

[0061] The simulation test was conducted using the non-city bus operating conditions (i.e., CHTC-C) included in the CHTC (China Heavy-duty Commercial Vehicle Test Cycle). CHTC-C includes low-speed operating conditions, medium-speed operating conditions, and high-speed operating conditions. When the initial power battery SOC value was 0.4, the simulation cycle time was set to 3000 seconds after combining various operating conditions according to multiple cycles.

[0062] The equivalent factor used in various working conditions is set to 1.52, and then the equivalent consumption minimum strategy with the set equivalent factor is used to simulate the control of the hydrogen fuel cell bus. The relevant simulation results are explained:

[0063] See also Figure 2 As shown, it can be seen that the hydrogen fuel cell bus has good speed following performance under different driving conditions.

[0064] See also Figure 3 As shown, it can be seen that the system power demand of hydrogen fuel cell buses under low-speed conditions is not high, and the equivalent consumption minimum strategy tends to allow the power battery and fuel cell to be turned on together to meet the system power demand, and the power battery SOC value is maintained at around 0.4, that is, the equivalent consumption minimum strategy allows the hydrogen fuel cell bus to maintain the power maintenance mode under low-speed conditions, avoiding the power battery SOC value from falling into an unsuitable working range after further decline;

[0065] See also Figure 4 As shown in the figure, the system power demand of hydrogen fuel cell buses under medium speed conditions increases, and the equivalent consumption minimum strategy tends to only turn on the fuel cell in the early stage to meet the system power demand, until the power battery SOC value drops to about 0.32 and then the power position mode is turned on. That is, the equivalent consumption minimum strategy fails to keep the hydrogen fuel cell bus in the power maintenance mode and the power battery SOC value decreases.

[0066] See also Figure 5 As shown in the figure, when the power demand of the power battery is large, the equivalent consumption minimum strategy should control the power battery and the fuel cell to be turned on together to meet the power demand of the system, so that the output power of the fuel cell is slightly increased compared with that under low-speed and medium-speed conditions. However, at this time, the equivalent consumption minimum strategy is still more inclined to only turn on the power battery to meet the power demand of the system, so that the power battery SOC value is reduced from 0.4 to the range of [0.296, 0.34]. That is, the equivalent consumption minimum strategy cannot keep the hydrogen fuel cell bus in the power maintenance mode.

[0067] It can be seen that the equivalent consumption minimum strategy using the same equivalent factor cannot achieve the same good control effect on hydrogen fuel cell buses in different driving conditions, and cannot make the hydrogen fuel cell buses always maintain the power maintenance mode. To this end, the present invention takes into account the situation that the working condition type of hydrogen fuel cell buses will change during actual driving, and proposes to make adaptive adjustments to the equivalent factors required to be applied in the equivalent consumption minimum strategy according to the current driving condition of the hydrogen fuel cell bus, so as to improve the control performance of the equivalent consumption minimum strategy on the hydrogen fuel cell bus.

[0068] Please refer to Figure 6 , Figure 6 1 is a flow chart of a hydrogen fuel cell bus energy management method provided by an embodiment of the present invention, the method comprising the following steps:

[0069] Step S110, obtaining a plurality of optimal equivalent factors corresponding to a plurality of different driving conditions;

[0070] Among them, multiple optimal equivalent factors are obtained by optimizing the equivalent factors included in the equivalent consumption minimum strategy using an improved sparrow search algorithm based on a pre-built hybrid power system model of a hydrogen fuel cell bus and multiple typical operating condition databases corresponding to hydrogen fuel cell buses under various driving conditions.

[0071] Step S120, obtaining current operating condition data of the hydrogen fuel cell bus, and performing dimension reduction on the current operating condition data to obtain key operating condition data;

[0072] Step S130, inputting the key operating condition data into the trained operating condition recognition model to obtain the current driving condition type of the hydrogen fuel cell bus;

[0073] Step S140, obtaining an optimal equivalent factor matching the current driving condition type from a plurality of optimal equivalent factors, so as to update parameters of the equivalent consumption minimum strategy;

[0074] Step S150: Based on the hybrid power system model and current operating condition data, the equivalent consumption minimum strategy with updated parameters is used to perform energy management on the hydrogen fuel cell bus.

[0075] In some embodiments, the improved sparrow search algorithm mentioned in the above step S110 is obtained by improving the original sparrow search algorithm (SSA), which refers to the foraging behavior of sparrows in nature to solve the optimization problem, and mainly proposes the following three improvements:

[0076] First, the original sparrow search algorithm uses a random method to generate the initial position of the sparrow population, resulting in uneven distribution of the sparrow population in the entire solution space, which may greatly limit the global optimization ability of the algorithm. In order to enhance the search ability of the algorithm in the early iteration stage and the probability of ultimately finding a higher quality solution, the present invention chooses to use the Sobol sequence and the Tent chaotic map to initialize the position of the sparrow population. The corresponding mathematical expression is as follows:

[0077]

[0078] In the formula, w i is the chaotic sequence associated with the initialization of the position of the i-th sparrow, w n is a chaotic domain and satisfies w n ∈(0,1), α0 is the Tent parameter and is preferably set to 0.5, x i is the initial position of the i-th sparrow, x lb is the upper bound of the search space where the sparrow population is located, x ub is the lower bound of the search space where the sparrow population is located.

[0079] Tent chaotic mapping is often used as a randomization tool in the framework of optimization algorithms to generate the initial population in order to overcome the limitations of conventional random methods. The initial solution distribution generated by Tent chaotic mapping has a stronger global coverage characteristic, allowing the population to explore the solution space more comprehensively. If the Tent chaotic mapping is used alone to initialize the sparrow population, the problem that the algorithm is prone to fall into the local optimum can be improved to a certain extent, but there are still problems with randomness and correlation between adjacent points. Therefore, the Sobol sequence is used to reasonably select the sampling direction so that the sampling points can fill the search space more evenly and efficiently. Here, the Tent chaotic mapping is applied to the search space to replace the random numbers generated in the Sobol sequence.

[0080] Second, individuals in a sparrow population are usually divided into three roles: discoverers, freeloaders, and guardians. Here we mainly improve the location update strategy of discoverers and freeloaders, while keeping the location update strategy of guardians unchanged. The specific instructions are as follows:

[0081] (1) The discoverer is mainly responsible for leading other sparrows in the population to actively explore new areas and obtaining more food by guiding the population to move in a suitable direction. In the position update strategy of the discoverer proposed in the improved sparrow search algorithm, considering that the choice of the search step size has a great impact on the performance of the algorithm. If the step size is too large, the algorithm may jump too much in the solution space and miss high-quality solutions. If the step size is too small, the algorithm may fall into a local optimal solution. The present invention proposes that in each iterative update process of the sparrow population, a non-linear inertia weight is used to control the search range when updating the position of the discoverer. The following mathematical expression is used to update the position of the sparrow individual acting as the discoverer:

[0082]

[0083] In the formula, is the position of the i-th sparrow after the (k + 1)-th iterative update, is the position of the i-th sparrow after the k-th iterative update, φ is the non-linear inertia weight, Q is a random number under the normal distribution, L is a matrix with all elements being 1, R2 is a preset safety warning value and its value range is preferably set to [0, 1], ST is the search step size (i.e., the amplitude of the discoverer moving in the search space) and its value range is preferably set to [0.5, 1]. When R2 < ST, it means that no danger is found around the sparrow population, and the discoverer can expand the scope of searching for food. When R2 ≥ ST, it means that the guardians in the sparrow population issue a warning, and all sparrow individuals need to immediately move closer to the safe area. k max is the maximum number of iterations set by the algorithm, and k is the current number of iterations.

[0084] (2) The freeloader is mainly responsible for observing and following the sparrow individuals that find food sources among the discoverers to go to the same place to forage, and improving its own optimal solution by performing local search near the known high-quality solutions. In the position update strategy of the freeloader proposed in the improved sparrow search algorithm, considering that the Lévy flight strategy, as a commonly used random walk strategy in search algorithms, has the step size distribution characteristics of coexistence of a large number of small step sizes and a small number of large step sizes and the long-distance search characteristics. The present invention proposes that in each iterative update process of the sparrow population, the position of the sparrow individual acting as the freeloader is updated by the Lévy flight strategy, and the corresponding mathematical expression is as follows:

[0085]

[0086] Where: σ v = 1,

[0087] In the formula, is the position of the individual sparrow with the worst fitness value in the sparrow population after the kth iteration update, is the position of the sparrow individual with the best fitness value among all the sparrow individuals that act as discoverers after the k+1th iteration update, Levy(d) is the path that obeys the Levy distribution, n is the number of individuals in the sparrow population, when i>n / 2, it means that the freeloader has not found food and needs to fly elsewhere to find food, when i≤n / 2, it means that the freeloader can randomly choose a place around the current best position to forage, is the dot product symbol, γ is the shape parameter of the jump probability distribution, and θ is the shape parameter of the jump probability distribution. u A normally distributed random number in the range, ω is a random number that conforms to σ v A normally distributed random number in the range, Γ is the gamma function.

[0088] By introducing the Levy flight strategy into the freeloader position update strategy, it can better enable most freeloaders to continuously search around the discoverer while a small number of freeloaders search in a farther search space, effectively preventing the algorithm from falling into the local optimum, and helping to improve the algorithm's operating efficiency and accelerate the algorithm's convergence speed.

[0089] (3) Guardians are mainly responsible for monitoring the foraging environment. Once danger is detected, they will chirp to achieve an early warning function. When the chirping sound exceeds the safety threshold, they will immediately guide the population to other safe locations to find food again. The present invention proposes that in each iterative update process of the sparrow population, the position of the individual sparrow acting as a guardian is updated by the following mathematical expression:

[0090]

[0091] In the formula, x' best is the global optimal position in the sparrow population after the kth iteration update, x' worst is the global worst position in the sparrow population after the kth iteration update, β is the control step size and it obeys the normal distribution, K is a random number generated in the interval [0,1], f i is the fitness value calculated after the i-th sparrow completes the k-th iteration update, f w is the optimal fitness value calculated after the sparrow population completes the kth iterative update, f g is the worst fitness value calculated after the sparrow population completes the kth iterative update, ε is a parameter used to control the denominator to be non-zero and its value is small.

[0092] Thirdly, the present invention proposes that in each iterative update process of the sparrow population, when each sparrow completes the position update, in order to reduce the amount of calculation and improve the algorithm running speed, a dynamic probability factor is introduced to determine whether it is necessary to use an adaptive t-distribution mutation operator to indiscriminately disturb the position of each sparrow to achieve a dynamic balance between global search and local search. The dynamic probability factor is generated according to the current number of iterations and the maximum number of iterations set by the algorithm. The mathematical expression used in this implementation process is as follows:

[0093]

[0094] In the formula, is the position of the i-th sparrow after the disturbance after the k+1th iterative update, t(M) is the t distribution with the current number of iterations as the degree of freedom parameter, p is the dynamic probability factor, rand is a random number generated in the interval [0,1], w1 is the upper limit of the dynamic probability factor p and is preferably set to 0.6, w2 is the variation range of the dynamic probability factor p and is preferably set to 0.1.

[0095] In some embodiments, the various driving conditions mentioned in step S110 include congested conditions, unobstructed conditions, and high-speed conditions, which are specifically described below:

[0096] (1) Congestion conditions refer to the situation where hydrogen fuel cell buses encounter heavy traffic and many pedestrians on urban roads. At this time, hydrogen fuel cell buses frequently start, stop, slow down or accelerate over short distances, and the average speed is low, generally less than 45 km / h;

[0097] (2) Smooth running conditions refer to the situation where hydrogen fuel cell buses encounter light traffic and few pedestrians on urban roads. At this time, hydrogen fuel cell buses can travel relatively continuously, smoothly and at a higher speed, with significantly fewer starts, stops, accelerations and decelerations, and a higher average speed, generally greater than 45 km / h but less than 70 km / h;

[0098] (3) High-speed operating conditions refer to the situation where the hydrogen fuel cell bus is running continuously on the highway. At this time, the hydrogen fuel cell bus maintains continuous driving at a speed close to or reaching the maximum speed limit, the number of starts, stops and accelerations and decelerations is minimized, and the average vehicle speed is the highest, generally greater than 70km / h.

[0099] In some embodiments, the typical operating condition database corresponding to each driving condition type of the hydrogen fuel cell bus contains multiple groups of values, each group of values ​​contains historical values ​​of several driving characteristic parameters, and similarly, the current operating condition data of the hydrogen fuel cell bus contains current values ​​of several driving characteristic parameters; wherein the several driving characteristic parameters include average vehicle speed, maximum vehicle speed, speed standard deviation, maximum positive acceleration, average positive acceleration, positive acceleration standard deviation, the proportion of driving time corresponding to positive acceleration in the interval [0,1] to the total driving time, and the proportion of positive acceleration in the interval (1,3] to the The corresponding driving time is the proportion of the total driving time, the proportion of the driving time corresponding to the positive acceleration in the interval (3, +∞) in the total driving time, the maximum negative acceleration, the average negative acceleration, the standard deviation of negative acceleration, the proportion of the driving time corresponding to the negative acceleration in the interval (-1,0] in the total driving time, the proportion of the driving time corresponding to the negative acceleration in the interval (-1.5, -1] in the total driving time, the proportion of the driving time corresponding to the negative acceleration in the interval (-∞, -1.5] in the total driving time, driving distance, total number of stops, average number of stops per kilometer and the proportion of idling time.

[0100] In some embodiments, the process of obtaining multiple optimal equivalent factors corresponding to the multiple different driving conditions mentioned in step S110 specifically includes the following steps:

[0101] Step S111: taking the equivalent hydrogen consumption of the hybrid power system of the hydrogen fuel cell bus as the first fitness function to be applied to the improved sparrow search algorithm, that is, the above formula (4), and taking the equivalent factor involved in the equivalent consumption minimum strategy as the first target variable to be optimized by the improved sparrow search algorithm;

[0102] Step S112: for any type of driving condition, multiple groups of values ​​contained in a typical condition database corresponding to the hydrogen fuel cell bus under the driving condition type are used as input parameters, and a hybrid power system model of the hydrogen fuel cell bus built in the Matlab / Simulink environment is controlled to perform simulation operations to output multiple groups of corresponding system parameter values, wherein each group of system parameter values ​​includes relevant parameter values ​​involved in the first fitness function, such as the output power of the fuel cell, the output power of the power battery, and the SOC value of the power battery;

[0103] Step S113: combining the first fitness function and the multiple groups of system parameter values ​​corresponding to the driving condition type, iteratively optimizing the first objective variable by using an improved sparrow search algorithm to obtain the optimal equivalent factor corresponding to the driving condition type.

[0104] More specifically, the implementation process of the above step S113 includes but is not limited to the following:

[0105] Step S113.1, setting the basic parameters required for applying the improved sparrow search algorithm, including the maximum number of iterations, the number of sparrow populations, the proportion of discoverers, the proportion of freeloaders, and the proportion of guardians;

[0106] Step S113.2, initializing the sparrow population, that is, initializing the position of each sparrow by using the above formula (5) and the above formula (6);

[0107] Step S113.3, combining the first fitness function and the multiple groups of system parameter values ​​corresponding to the driving condition type, calculating the initial fitness value corresponding to each sparrow contained in the sparrow population and sorting them, obtaining the initial optimal sparrow individual and recording it as the optimal individual of the population, and obtaining the initial fitness value corresponding to the initial optimal sparrow individual and recording it as the optimal fitness value of the population;

[0108] Step S113.4, start the kth iteration update, first divide the sparrow population into a discoverer group, a freeloader group and a guardian group according to the current fitness value corresponding to each sparrow contained in the sparrow population determined after the k-1th iteration update, then update the position of the discoverer group by the above formula (7) and the above formula (8), update the position of the freeloader group by the above formula (9) and the above formula (10), and update the position of the guardian group by the above formula (11), and after completing the position update of the sparrow population, disturb the position of the sparrow population by the above formula (12) and the above formula (13);

[0109] Step S113.5, combining the first fitness function and the multiple groups of system parameter values ​​corresponding to the driving condition type, calculating the current fitness value corresponding to each sparrow contained in the sparrow population and sorting them, obtaining the current best sparrow individual and recording it as the first best sparrow individual, and obtaining the current fitness value corresponding to the current best sparrow individual and recording it as the first current fitness value;

[0110] Step S113.6, obtaining the optimal fitness value of the population determined after the k-1th iterative update and comparing it with the first current fitness value: when the optimal fitness value of the population is less than or equal to the first current fitness value, the optimal individual of the population determined after the k-1th iterative update is used as the optimal individual of the population determined after the kth iterative update; when the optimal fitness value of the population is greater than the first current fitness value, the first optimal sparrow individual is used as the optimal individual of the population determined after the kth iterative update;

[0111] Step S113.7, determine whether the current number of iterations k reaches the maximum number of iterations; if so, output the position of the optimal individual in the population determined after the kth iteration update as the optimal solution of the first objective variable; if not, assign k+1 to k, and then return to execute the above step S113.4;

[0112] It should be noted that the above step S113.4 is executed starting from k=1.

[0113] In some embodiments, the operating condition identification model mentioned in the above step S130 is constructed based on the existing support vector machine model (SVM, Support Vector Machines). The support vector machine model is based on statistical theory to solve the classification problem of nonlinear feature data. Whether it can achieve a good classification effect is mainly affected by two key parameters: kernel function density and penalty factor. When the kernel function density is smaller, there are more support vectors and the model is prone to overfitting problems. When the kernel function density is larger, different types of nonlinear feature data are difficult to distinguish. When the penalty factor is too large or too small, it is easy to make the model generalization ability worse. For this reason, considering the need to optimize the kernel function density and the penalty factor, the operating condition identification model is trained, and the corresponding implementation process is as follows:

[0114] Step A1, using the recognition accuracy of the support vector machine model for the driving condition type as the second fitness function required to be applied to the improved sparrow search algorithm, and using the kernel function density and penalty factor involved in the support vector machine model as the second target variable required to be optimized for the improved sparrow search algorithm;

[0115] Step A2, reducing the dimensions of multiple typical operating condition databases corresponding to hydrogen fuel cell buses under multiple different driving operating condition types to obtain multiple corresponding operating condition key databases;

[0116] Step A3: training the support vector machine model through multiple key operating condition databases, and iteratively optimizing the second target variable through an improved sparrow search algorithm to obtain a trained operating condition recognition model.

[0117] More specifically, the typical operating condition database corresponding to each driving condition type of the hydrogen fuel cell bus contains multiple sets of values, each set of values ​​contains several historical values ​​of driving characteristic parameters, and the implementation process of the above step A2 includes but is not limited to the following:

[0118] Step A2.1, analyzing multiple typical operating condition databases by combining principal component analysis and correlation coefficient method to select multiple more critical driving characteristic parameters from a plurality of driving characteristic parameters;

[0119] Furthermore, the existing correlation coefficient method is used to perform a preliminary analysis on multiple typical operating condition databases to screen out M driving characteristic parameters with stronger correlation from a number of driving characteristic parameters; multiple typical operating condition databases are preliminarily screened so that each typical operating condition database only contains historical values ​​of M driving characteristic parameters; the existing principal component analysis method is used to re-analyze the multiple typical operating condition databases after the preliminary screening to screen out K more critical driving characteristic parameters from the M driving characteristic parameters; wherein K<M<N, K, M and N are all positive integers, N is the number of a number of driving characteristic parameters, and M is preferably set to 7, and K is preferably set to 5.

[0120] Step A2.2: For any typical operating condition database, only historical values ​​of multiple more critical driving characteristic parameters are retained as a constraint condition, and the parameters of the typical operating condition database are screened to obtain a corresponding operating condition key database.

[0121] Similarly, the current operating condition data of the hydrogen fuel cell bus includes the current values ​​of several driving characteristic parameters. The dimensionality reduction method for the current operating condition data mentioned in the above step S120 is: only retaining the current values ​​of more critical multiple driving characteristic parameters as constraints, performing parameter screening on the current operating condition data, and obtaining corresponding key operating condition data.

[0122] It should be noted that in the above step A3, emphasis is placed on using multiple key operating condition databases to train the support vector machine model. This can be understood as the trained operating condition identification model must use the specific values ​​of multiple more critical driving characteristic parameters as input parameter values ​​when it is officially put into use. That is, it can be understood as reducing the dimension of the current operating condition data of the hydrogen fuel cell bus according to all parameter types allowed to be input by the trained operating condition identification model, so that the trained operating condition identification model can normally identify the current driving condition type of the hydrogen fuel cell bus.

[0123] More specifically, the key operating condition database corresponding to each driving condition type of the hydrogen fuel cell bus contains multiple sets of values ​​after dimension reduction, and each set of values ​​after dimension reduction contains historical values ​​of multiple more critical driving characteristic parameters. The implementation process of the above step A3 includes but is not limited to the following:

[0124] Step A3.1, assigning a corresponding classification label to each group of values ​​after dimension reduction contained in the key operating condition database corresponding to each driving condition type of the hydrogen fuel cell bus, and then merging all the key operating condition databases assigned with classification labels to obtain a data set for training the support vector machine model;

[0125] Step A3.2, dividing the data set into a training data set and a test data set according to a preset ratio, wherein the preset ratio is preferably set to 7:3;

[0126] Step A3.3, set the basic parameters required for the improved sparrow search algorithm, including the maximum number of iterations, the number of sparrow populations, the proportion of discoverers, the proportion of freeloaders, and the proportion of guardians;

[0127] Step A3.4, initializing the sparrow population, that is, initializing the position of each sparrow by using the above formula (5) and the above formula (6);

[0128] Step A3.5, combining the training data set, the support vector machine model and the second fitness function, calculating the initial fitness value corresponding to each sparrow contained in the sparrow population and sorting them, obtaining the initial optimal sparrow individual and recording it as the optimal individual of the population, and obtaining the initial fitness value corresponding to the initial optimal sparrow individual and recording it as the optimal fitness value of the population;

[0129] Step A3.6, start the kth iteration update, first divide the sparrow population into a discoverer group, a freeloader group and a guardian group according to the current fitness value corresponding to each sparrow contained in the sparrow population determined after the k-1th iteration update, then update the position of the discoverer group by the above formula (7) and the above formula (8), update the position of the freeloader group by the above formula (9) and the above formula (10), and update the position of the guardian group by the above formula (11), and after completing the position update of the sparrow population, disturb the position of the sparrow population by the above formula (12) and the above formula (13);

[0130] Step A3.7, combining the training data set, the support vector machine model and the second fitness function, calculating the current fitness value corresponding to each sparrow contained in the sparrow population and sorting them, obtaining the current best sparrow individual and recording it as the second best sparrow individual, and obtaining the current fitness value corresponding to the current best sparrow individual and recording it as the second current fitness value;

[0131] Step A3.8, obtaining the optimal fitness value of the population determined after the k-1th iterative update and comparing it with the second current fitness value: when the optimal fitness value of the population is less than or equal to the second current fitness value, the optimal individual of the population determined after the k-1th iterative update is used as the optimal individual of the population determined after the kth iterative update; when the optimal fitness value of the population is greater than the second current fitness value, the second optimal sparrow individual is used as the optimal individual of the population determined after the kth iterative update;

[0132] Step A3.9: Determine whether the current number of iterations k reaches the maximum number of iterations; if so, output the position of the optimal individual in the population determined after the kth iteration update as the optimal solution of the second objective variable; if not, assign k+1 to k, and then return to execute the above step A3.6;

[0133] Step A3.10, inputting the optimal solution of the second objective variable into the support vector machine model to form a trained working condition recognition model, and then using the test data set to perform a performance test on the trained working condition recognition model;

[0134] It should be noted that the above step A3.6 is executed starting from k=1.

[0135] In some embodiments, the implementation process of the above step S150 includes but is not limited to the following:

[0136] Step S151: input the current operating condition data of the hydrogen fuel cell bus into the hybrid power system model of the hydrogen fuel cell bus built in the Matlab / Simulink environment for simulation calculation to output the current power battery SOC value and the current system required power;

[0137] Step S152: Analyze the current power battery SOC value and the current system required power through the equivalent consumption minimum strategy after parameter update, so as to further allocate the current system required power to obtain the first optimal output power of the fuel cell and the second optimal output power of the power battery;

[0138] Step S153, controlling the operation of the fuel cell according to the first optimal output power, and controlling the operation of the power battery according to the second optimal output power, so as to achieve energy management of the hydrogen fuel cell bus.

[0139] In an embodiment of the present invention, by setting a more matching optimal equivalent factor for each driving condition type in advance, a more targeted equivalent consumption minimum strategy can be quickly formulated when the driving condition type of the hydrogen fuel cell bus is currently determined, thereby realizing the energy management of the hydrogen fuel cell bus more efficiently and reliably, so as to improve the vehicle economy of the hydrogen fuel cell bus, and enable the hydrogen fuel cell bus to maintain the power maintenance mode as much as possible. By proposing improvements to the population initialization strategy, the discoverer position update strategy and the freeloader position update strategy on the basis of the original sparrow search algorithm, and introducing a position disturbance strategy in the population iterative update process, the improved sparrow search algorithm can have a more reliable parameter optimization capability, and when the improved sparrow search algorithm is applied to solve the optimization problem of the equivalent factor included in the equivalent consumption minimum strategy, the selection of the equivalent factor can be avoided to be completely dependent on the experience of the developer, and when the improved sparrow search algorithm is applied to solve the optimization problem of the kernel function density and the penalty factor included in the support vector machine model, the recognition accuracy of the working condition recognition model can be improved.

[0140] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, a hydrogen fuel cell bus energy management method in the above embodiment is implemented. Among them, the computer-readable storage medium includes but is not limited to any type of disk (including floppy disk, hard disk, optical disk, CD-ROM and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. In other words, the storage device includes any medium that stores or transmits information in a readable form by a device (such as a computer, a mobile phone, etc.), which can be a read-only memory, a disk or an optical disk, etc.

[0141] also, Figure 7 2 is a schematic diagram of the hardware structure of a computer device provided in an embodiment of the present invention. The computer device includes a processor 220, a memory 230, an input unit 240, a display unit 250 and other devices. Those skilled in the art can understand that Figure 7The device structure device shown does not constitute a limitation on all devices, and may include more or fewer components than shown, or combine certain components. The memory 230 can be used to store the computer program 210 and various functional modules, and the processor 220 runs the computer program 210 stored in the memory 230, thereby executing various functional applications and data processing of the device. The memory can be an internal memory or an external memory, or include an internal memory and an external memory. The internal memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, or a random access memory. The external memory may include a hard disk, a floppy disk, a U disk, a magnetic tape, etc. The memory 230 disclosed in the embodiment of the present invention includes but is not limited to the above-mentioned types of memory. The memory 230 disclosed in the embodiment of the present invention is only an example and not a limitation.

[0142] The input unit 240 is used to receive the input of the signal and the keyword input by the user. The input unit 240 may include a touch panel and other input devices. The touch panel can collect the user's touch operation on or near it (such as the user's operation on or near the touch panel using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a pre-set program; other input devices may include but are not limited to one or more of a physical keyboard, a function key (such as a playback control key, a switch key, etc.), a trackball, a mouse, a joystick, etc. The display unit 250 can be used to display the information input by the user or the information provided to the user and various menus of the terminal device. The display unit 250 can be in the form of a liquid crystal display, an organic light emitting diode, etc. The processor 220 is the control center of the terminal device, which uses various interfaces and lines to connect various parts of the entire device, and executes various functions and processes data by running or executing software programs and / or modules stored in the memory 230, and calling data stored in the memory 230.

[0143] As an embodiment, the computer device includes a processor 220, a memory 230 and a computer program 210, wherein the computer program 210 is stored in the memory 230 and is configured to be executed by the processor 220, and the computer program 210 is configured to execute a hydrogen fuel cell bus energy management method in the above-mentioned embodiment.

[0144] Although the present application has been described in considerable detail and specifically with respect to several of the above-mentioned embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be considered to be effectively covered by the intended scope of the present application by referring to the appended claims and taking into account the prior art to provide a broad possible interpretation for these claims. In addition, the present application is described above with the embodiments foreseeable by the inventors, the purpose of which is to provide a useful description, and those non-substantial changes to the present application that have not yet been foreseen may still represent equivalent changes to the present application.

Claims

1. A hydrogen fuel cell bus energy management method, characterized in that: The method comprises: Acquire multiple optimal equivalent factors corresponding to multiple different driving conditions, wherein the multiple optimal equivalent factors are obtained by optimizing the equivalent factors included in the equivalent consumption minimum strategy using an improved sparrow search algorithm based on a pre-constructed hybrid power system model of a hydrogen fuel cell bus and multiple typical operating condition databases corresponding to the hydrogen fuel cell bus under the multiple different driving conditions; Acquire current operating condition data of the hydrogen fuel cell bus, and perform dimension reduction on the current operating condition data to obtain key operating condition data; Inputting the key operating condition data into a trained operating condition identification model to obtain the current driving condition type of the hydrogen fuel cell bus; Acquire an optimal equivalent factor matching the current driving condition type from the multiple optimal equivalent factors to perform parameter update on the equivalent consumption minimum strategy; According to the hybrid power system model and the current operating condition data, the energy management of the hydrogen fuel cell bus is performed using an equivalent consumption minimization strategy after parameter update.

2. The hydrogen fuel cell bus energy management method according to claim 1, characterized in that: In the improved sparrow search algorithm, the Tent chaotic map and Sobol sequence are used to initialize the position of the sparrow population, and in each iterative update process of the sparrow population, the nonlinear inertia weight is introduced to update the position of the sparrow individual acting as the discoverer, and the Levy flight strategy is used to update the position of the sparrow individual acting as the freeloader. After the position of each sparrow is updated, a dynamic probability factor is generated according to the current number of iterations and the final number of iterations. When the dynamic probability factor is less than the random number, the adaptive t-distribution mutation operator is used to perturb the position of each sparrow.

3. The hydrogen fuel cell bus energy management method according to claim 1, characterized in that: Each typical operating condition database includes multiple groups of values, each group of values ​​includes historical values ​​of several driving characteristic parameters; the current operating condition data includes current values ​​of the several driving characteristic parameters.

4. The hydrogen fuel cell bus energy management method according to claim 3, characterized in that: The multiple optimal equivalent factors corresponding to the multiple different driving conditions are obtained in the following manner: Taking the equivalent hydrogen consumption of the hybrid power system of the hydrogen fuel cell bus as a first fitness function, taking the equivalent factor included in the equivalent consumption minimum strategy as a first target variable, and the first fitness function including the first target variable; For any driving condition type, multiple sets of values ​​contained in a typical condition database corresponding to the driving condition type are input into the hybrid power system model for simulation calculation to obtain corresponding multiple sets of system parameter values, each set of system parameter values ​​including relevant parameter values ​​involved in the first fitness function; In combination with the multiple groups of system parameter values ​​and the first fitness function, the first objective variable is optimized using an improved sparrow search algorithm to obtain an optimal equivalent factor corresponding to the driving condition type.

5. The hydrogen fuel cell bus energy management method according to claim 3, characterized in that: The working condition recognition model is constructed based on the support vector machine model, which is trained in the following way: Using the recognition accuracy of the support vector machine model for the driving condition type as a second fitness function, and using the penalty factor and kernel function density included in the support vector machine model as a second target variable; Performing dimension reduction on the multiple typical operating condition databases to obtain multiple corresponding operating condition key databases; The support vector machine model is trained using the multiple operating condition key databases, and the second target variable is optimized using an improved sparrow search algorithm to obtain a trained operating condition recognition model.

6. The hydrogen fuel cell bus energy management method according to claim 5, characterized in that: The dimensionality reduction of the multiple typical operating condition databases to obtain the corresponding multiple operating condition key databases includes: Analyzing the plurality of typical operating condition databases by using a correlation coefficient method and a principal component analysis method, so as to extract a plurality of key driving characteristic parameters from the plurality of driving characteristic parameters; For any typical operating condition database, parameters of the typical operating condition database are screened, and only historical values ​​of the multiple driving characteristic parameters are retained to obtain a key operating condition database.

7. The hydrogen fuel cell bus energy management method according to claim 6, characterized in that: The step of reducing the dimension of the current operating condition data to obtain the key operating condition data includes: Parameter screening is performed on the current operating condition data, and only current values ​​of the multiple driving characteristic parameters are retained to obtain key operating condition data.

8. The hydrogen fuel cell bus energy management method according to claim 1, characterized in that: The hybrid power system of the hydrogen fuel cell bus includes a fuel cell and a power battery; the energy management of the hydrogen fuel cell bus using the equivalent consumption minimum strategy after parameter update according to the hybrid power system model and the current operating condition data includes: Inputting the current operating condition data into the hybrid power system model for simulation calculation to obtain the current system demand power and the current power battery SOC value; Analyzing the current system demand power and the current power battery SOC value by using the equivalent consumption minimum strategy after parameter update to obtain the first optimal output power of the fuel cell and the second optimal output power of the power battery; The fuel cell is controlled to operate at the first optimal output power, and the power battery is controlled to operate at the second optimal output power.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: The processor executes the computer program to implement the hydrogen fuel cell bus energy management method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the hydrogen fuel cell bus energy management method according to any one of claims 1 to 8 is implemented.

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