Solar charging method and device for vehicle, electronic equipment and storage medium

Abstract

The invention discloses a solar charging method and device for a vehicle, electronic equipment and a storage medium, and the method comprises the steps: constructing a mathematical model which represents the mapping relation between the output voltage and the output current of a photovoltaic cell for charging the vehicle through employing a first preset parameter and a second preset parameter; based on the mathematical model and a particle swarm algorithm, determining a first array voltage corresponding to each particle in a particle swarm for searching the maximum output power of the photovoltaic cell; determining the maximum output power of the photovoltaic cell based on the first array voltage, a preset self-learning factor and a preset social learning factor; and charging a storage battery of the vehicle with the maximum output power of the photovoltaic battery. By adopting the technical scheme provided by the invention, the maximum power point of the vehicle can be quickly and accurately tracked under various illumination conditions, the charging efficiency is remarkably improved, and the service life of the storage battery is effectively prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of solar charging of vehicles, and in particular to a solar charging method, device, electronic equipment and storage medium for vehicles. Background Art

[0002] In the context of the global pursuit of green energy and pollution reduction, electric vehicles are developing rapidly due to their environmental and economic characteristics. Currently, electric vehicles can be recharged through plug-in solar charging. In plug-in solar electric vehicles, the operating state of the battery will vary depending on factors such as the state of charge of the vehicle battery, the connection to the AC power supply, and the intensity of light. In the process of switching between these states, it is crucial to ensure the maximum power point tracking of the photovoltaic cell, the efficient conversion of electrical energy, and the smooth switching between states.

[0003] However, despite the advantages of zero emission and cost-effectiveness, plug-in solar electric vehicles still have limited range and cannot meet the needs of long-distance travelers. In addition, plug-in solar electric vehicles cannot accurately track the maximum power point in different environments. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide a solar charging method, device, electronic device and storage medium for a vehicle. By adopting the technical solution provided by the present application, the vehicle can quickly and accurately track the maximum power point under various lighting conditions, significantly improve the charging efficiency, and effectively extend the service life of the battery.

[0005] This application mainly includes the following aspects:

[0006] In a first aspect, an embodiment of the present application provides a solar charging method for a vehicle, the solar charging method comprising:

[0007] (A) constructing a mathematical model representing a mapping relationship between an output voltage and an output current of a photovoltaic cell used to charge a vehicle using a first preset parameter and a second preset parameter;

[0008] (B) determining, based on the mathematical model and the particle swarm algorithm, a first array voltage corresponding to each particle in a particle swarm used to find the maximum output power of the photovoltaic cell;

[0009] (C) determining a maximum output power of the photovoltaic cell based on the first array voltage, a preset self-learning factor and a preset social learning factor;

[0010] (D) Charging a storage battery of the vehicle at the maximum output power of the photovoltaic cell.

[0011] Preferably, the first preset parameter is constructed based on at least one of the following items:

[0012] The maximum power point of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell, and the open-circuit voltage of the photovoltaic cell;

[0013] The second preset parameter is constructed based on at least one of the following items:

[0014] The maximum power point of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell, the open-circuit voltage of the photovoltaic cell, and a first preset parameter.

[0015] Preferably, the mathematical model characterizing the mapping relationship between the output voltage and the output current of the photovoltaic cell used to charge the vehicle is constructed by at least one parameter of the following items:

[0016] A first preset parameter, a second preset parameter, a current generated by the photovoltaic cell, and an open circuit voltage of the photovoltaic cell.

[0017] Preferably, the step (B) comprises:

[0018] Based on the mathematical model, determining particles in a particle group for finding the maximum output power of the photovoltaic cell;

[0019] Using a particle swarm algorithm, the number of particles is evenly distributed within a set voltage range according to the number of particles, and the voltage corresponding to each particle in the particle swarm is determined as a first array voltage.

[0020] Preferably, the preset self-learning factor is constructed based on at least one of the following items: a set maximum self-learning factor, a set minimum self-learning factor, a conversion ratio constant;

[0021] The preset social learning factor is constructed based on at least one of the following items: a set maximum social learning factor, a set minimum social learning factor, and a conversion ratio constant.

[0022] Preferably, the step (C) comprises:

[0023] When the particle group searches for the maximum output power of the photovoltaic cell within a first time period, determining a first self-learning factor of the particle and a first social learning factor of the particle;

[0024] Determining a first fitness value converged by each particle in the particle swarm based on the first array voltage, the first self-learning factor, and the first social learning factor;

[0025] When the particle group searches for the maximum output power of the photovoltaic cell in the second time period, determining a second self-learning factor of the particle and a second social learning factor of the particle;

[0026] Based on the first fitness value, the second self-learning factor and the second social learning factor, a second fitness value converged by each particle in the particle swarm is determined, and the second fitness value is determined as the maximum output power of the photovoltaic cell.

[0027] Preferably, after step (D), the solar charging method further comprises:

[0028] Obtaining the current output power of the photovoltaic cell;

[0029] Based on the output power and the maximum output power, determining a change in power of the photovoltaic cell;

[0030] When the change is within the first preset range, the process returns to step (B).

[0031] In a second aspect, an embodiment of the present application further provides a solar charging device for a vehicle, the solar charging device comprising:

[0032] A construction module, using the first preset parameter and the second preset parameter, constructs a mathematical model representing a mapping relationship between an output voltage and an output current of a photovoltaic cell used to charge a vehicle;

[0033] A first array voltage determination module, based on the mathematical model and the particle swarm algorithm, determines a first array voltage corresponding to each particle in a particle swarm used to find the maximum output power of the photovoltaic cell;

[0034] a maximum output power determination module, which determines the maximum output power of the photovoltaic cell based on the first array voltage, a preset self-learning factor and a preset social learning factor;

[0035] The charging module charges the storage battery of the vehicle with the maximum output power of the photovoltaic cell.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and when the computer program is executed by the processor, it implements the solar charging method as described in any one of the above items.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned solar charging methods are executed.

[0038] The embodiments of the present application provide a solar charging method, device, electronic device and storage medium for a vehicle. First, a mathematical model is constructed using a first preset parameter and a second preset parameter to characterize the mapping relationship between the output voltage and the output current of a photovoltaic cell used to charge the vehicle. Then, based on the mathematical model and the particle swarm algorithm, the first array voltage corresponding to each particle in the particle swarm is determined, and the maximum output power of the photovoltaic cell is found. Finally, the battery of the vehicle is charged based on the maximum output power. In this way, the technical solution provided by the present application is adopted to enable the vehicle to quickly and accurately track the maximum power point under various lighting conditions, significantly improve the charging efficiency, and effectively extend the service life of the battery.

[0039] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A flow chart of a solar charging method for a vehicle provided in an embodiment of the present application is shown;

[0042] Figure 2 An expanded top view of a solar charging device for a vehicle provided by an embodiment of the present application is shown;

[0043] Figure 3 A folded front view of a solar charging device for a vehicle provided by an embodiment of the present application is shown;

[0044] Figure 4 One of the structural schematic diagrams of a solar charging device for a vehicle provided in an embodiment of the present application is shown;

[0045] Figure 5 A second structural schematic diagram of a solar charging device for a vehicle provided in an embodiment of the present application is shown;

[0046] Figure 6 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0047] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0048] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0049] The following methods, devices, electronic devices or computer-readable storage media of the embodiments of the present application can be applied to any scenario requiring electronic device working mode control. The embodiments of the present application are not limited to specific application scenarios. Any solution using a vehicle solar charging method, device, electronic device and storage medium provided by the embodiments of the present application is within the protection scope of the present application.

[0050] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0051] In the context of the global pursuit of green energy and pollution reduction, electric vehicles are developing rapidly due to their environmental and economic characteristics. Currently, electric vehicles can be recharged through plug-in solar charging. In plug-in solar electric vehicles, the operating state of the battery varies depending on factors such as the state of charge of the car battery, the connection to the AC power supply, and the intensity of light. During the switching process between these states, it is crucial to ensure the maximum power point tracking of the photovoltaic cells, the efficient conversion of electrical energy, and the smooth switching between states. However, despite the advantages of zero emissions and cost-effectiveness, plug-in solar electric vehicles still have limited range and cannot meet the needs of long-distance travelers. In addition, plug-in solar electric vehicles cannot accurately track the maximum power point in different environments.

[0052] Based on this, the embodiments of the present application provide a solar charging method, device, electronic device and storage medium for a vehicle. An improved particle swarm algorithm based on a variable learning factor and a variable inertia weight enables the vehicle to quickly and accurately track the maximum power point under various lighting conditions, significantly improves the charging efficiency, and effectively extends the service life of the battery.

[0053] To facilitate the understanding of the present application, the technical solution provided by the present application is described in detail below in conjunction with specific embodiments.

[0054] See also Figure 1 , Figure 1 One of the flow charts of a solar charging method for a vehicle provided in an embodiment of the present application.

[0055] In the embodiments of the present application, the photovoltaic cell used to charge the vehicle is an important energy source for the electric vehicle charging system, and its performance is directly related to the charging efficiency and endurance of the electric vehicle. Therefore, in-depth analysis of the working characteristics of photovoltaic power generation is crucial to improving the utilization rate of photovoltaic output power.

[0056] like Figure 1 As shown in , the solar charging method provided by the embodiment of the present application includes:

[0057] Step S101 : constructing a mathematical model representing a mapping relationship between an output voltage and an output current of a photovoltaic cell used to charge a vehicle using a first preset parameter and a second preset parameter.

[0058] Here, the first preset parameter is constructed based on at least one of the following items: the maximum power point of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell, and the open-circuit voltage of the photovoltaic cell.

[0059] As an example, the specific calculation formula of the first preset parameter is shown in formula (1):

[0060]

[0061] Among them, C 2 is the first preset parameter, I m is the maximum power point current of the photovoltaic cell, U m is the maximum power point voltage of the photovoltaic cell, I sc is the short-circuit current of the photovoltaic cell, U oc is the open circuit voltage of the photovoltaic cell.

[0062] The second preset parameter is constructed based on at least one of the following items: the maximum power point of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell, the open-circuit voltage of the photovoltaic cell, and the first preset parameter.

[0063] As an example, the specific calculation formula of the second preset parameter is shown in formula (2):

[0064]

[0065] Among them, C 1 is the second preset parameter. Here, since photovoltaic cells are greatly affected by light and temperature, C is calculated. 1 and C 2 When the light amplitude difference and temperature difference are introduced to calculate the parameters in formula (1) and formula (2), specifically:

[0066] The specific calculation formula for the illumination amplitude difference of photovoltaic cells is shown in formula (3):

[0067] ΔT=TT ref (3)

[0068] Where ΔT is the temperature difference of the photovoltaic cell, T is the temperature of the photovoltaic cell, and T ref is the preset temperature reference value of the photovoltaic cell. As an example, T ref The value is 1000W / m 2 .

[0069] The specific calculation formula of the temperature difference of photovoltaic cells is shown in formula (4):

[0070] ΔS=SS ref (4)

[0071] Among them, ΔS is the illumination amplitude difference of the photovoltaic cell, S is the illumination temperature of the photovoltaic cell, and T ref is the preset light intensity value of the photovoltaic cell. As an example, T ref The value is 25℃.

[0072] The specific calculation formula of the maximum power point current of the photovoltaic cell is shown in formula (5):

[0073]

[0074] Among them, I′ m is the initial maximum power point current of the photovoltaic cell, a is the temperature compensation coefficient of the current generated by the photovoltaic cell, and as an example, the value of a is 0.00025.

[0075] The specific calculation formula of the maximum power point voltage of the photovoltaic cell is shown in formula (6):

[0076] U m =U′ m (1-cΔT)ln(1+bΔS) (6)

[0077] Among them, U′ m is the initial maximum power point voltage of the photovoltaic cell, b is the compensation coefficient of the photovoltaic cell voltage to light intensity, and c is the compensation coefficient of the photovoltaic cell voltage to temperature. As an example, the value of b is 0.5 and the value of c is 0.00288.

[0078] The specific calculation formula of the short-circuit current of the photovoltaic cell is shown in formula (7):

[0079]

[0080] Among them, I s ' c is the initial short-circuit current of the photovoltaic cell.

[0081] The specific calculation formula of the open circuit voltage of the photovoltaic cell is shown in formula (8):

[0082] U oc =U o ' c (1-cΔT)ln(1+bΔS) (8)

[0083] Among them, U o ' c is the initial open circuit voltage of the photovoltaic cell.

[0084] Here, substitute formula (3) and formula (4) into formula (5), formula (6), and formula (7):

[0085] The maximum power point current of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell and the open-circuit voltage of the photovoltaic cell are obtained by formula (5), formula (6), formula (7) and formula (8) into formula (1) and formula (2) to obtain the corresponding first preset parameter and second preset parameter.

[0086] In the embodiment of the present application, the mathematical model characterizing the mapping relationship between the output voltage and the output current of the photovoltaic cell used to charge the vehicle is constructed by at least one parameter of the following items: the first preset parameter, the second preset parameter, the current generated by the photovoltaic cell, and the open circuit voltage of the photovoltaic cell. As an example, the specific calculation formula of the mathematical model characterizing the mapping relationship between the output voltage and the output current of the photovoltaic cell used to charge the vehicle is shown in formula (9):

[0087]

[0088] Among them, I L is the output current of the photovoltaic cell, I ph is the current generated by the photovoltaic cell, U L is the output voltage of the photovoltaic cell.

[0089] In the embodiment of the present application, as an example, formula (1) and formula (2) are substituted into formula (9) to obtain a mapping relationship between the output voltage and output current of a photovoltaic cell used to charge a vehicle.

[0090] Step S102 : determining a first array voltage corresponding to each particle in a particle swarm for finding the maximum output power of the photovoltaic cell based on the mathematical model and the particle swarm algorithm.

[0091] Regarding step S102, as an example, during specific implementation, the following steps may be included:

[0092] First, based on the mathematical model, a particle in a particle group for finding the maximum output power of the photovoltaic cell is determined. Here, the output characteristic curve of the photovoltaic array m×n under partial shading produces at most n peaks. Therefore, the number of particles is set to n+1 to ensure that all potential peak points are fully covered, wherein the output characteristic curve of the photovoltaic array is a mapping relationship between the output voltage and the output current of the photovoltaic cell used to charge the vehicle.

[0093] Secondly, the particle swarm algorithm is used to evenly distribute the particles within the set voltage range according to the number of particles, and the voltage corresponding to each particle in the particle swarm is determined as the first array voltage. Here, as an example, the output characteristic curve of the photovoltaic array under partial shading is analyzed, and the particles are evenly distributed in the range of 0.9U oc The positions of n+1 particles correspond to the array voltage U 1 , U 2 , U 3 , …, U n+1 , array voltage U n The specific calculation formula is shown in formula (10):

[0094]

[0095] Step S103, determining the maximum output power of the photovoltaic cell based on the first array voltage, a preset self-learning factor and a preset social learning factor.

[0096] In an embodiment of the present application, the particle swarm algorithm is improved based on a method combining an asynchronous variable learning factor and an adaptive inertia weight.

[0097] Here, the preset self-learning factor is constructed by at least one parameter of the following items: a set maximum self-learning factor, a set minimum self-learning factor, and a conversion ratio constant. As an example, the specific calculation formula of the self-learning factor is shown in formula (11):

[0098] c 1 =c1min +c 1m2x ×cos(kπ / 2M) (11)

[0099] Among them, c 1 is the particle’s self-learning factor, c 1min is the minimum value of the particle’s self-learning factor, c 1max

[0100] is the maximum value of the particle's self-learning factor, and M is the conversion ratio constant.

[0101] The preset social learning factor is constructed based on at least one of the following items: a set maximum social learning factor, a set minimum social learning factor, and a conversion ratio constant. As an example, the specific calculation formula of the social learning factor is shown in formula (12):

[0102] c 2 =c 2min +c 2max ×sin(kπ / 2M) (12)

[0103] Among them, c 2 is the social learning factor of the particle, c 2min is the minimum social learning factor of the particle, c 2max

[0104] is the maximum social learning factor of the particle, and M is the conversion ratio constant.

[0105] In the embodiment of the present application, the self-learning factor reflects the self-summarizing ability of the particle, so that each particle can continuously optimize its own path during the exploration process; the social learning factor reflects the global search ability of the particle, so that the particle moves to the optimal position of the population.

[0106] Regarding step S103, as an example, during specific implementation, the following steps may be included:

[0107] First, when the particle swarm searches for the maximum output power of the photovoltaic cell in the first time period, the first self-learning factor of the particle and the first social learning factor of the particle are determined. As an example, in the early stage of the particle swarm algorithm, in order to ensure the diversity of particles, enable them to have good global search capabilities, avoid premature concentration on a certain point, and thus prevent premature convergence, a larger self-learning factor is selected, and a smaller social learning factor is selected accordingly. A larger self-learning factor prompts particles to move more toward their own optimal position in the early stage, while a smaller social learning factor can slow down the algorithm's movement speed toward the group's optimal position.

[0108] Secondly, based on the first array voltage, the first self-learning factor and the first social learning factor, the first fitness value converged by each particle in the particle swarm is determined. Here, the output power P=UI of the photovoltaic cell is used as the fitness function, and the goal of particle optimization is the fitness value.

[0109] Then, when the particle group searches for the maximum output power of the photovoltaic cell in the second time period, the second self-learning factor of the particle and the second social learning factor of the particle are determined. As an example, in order to have a good local search capability and to be able to quickly converge to the optimal position of the population, a larger self-learning factor and a smaller social learning factor are selected.

[0110] Finally, based on the first fitness value, the second self-learning factor and the second social learning factor, a second fitness value converged by each particle in the particle swarm is determined, and the second fitness value is determined as the maximum output power of the photovoltaic cell.

[0111] In the embodiment of the present application, the value of the inertia weight w has a significant impact on the global optimization ability of the particle, and its value determines the trend of the particle expansion space. When the inertia weight value is large, the global optimization ability is strong, but the local optimization ability is relatively weak; on the contrary, when the inertia weight value is small, the algorithm is strong in local optimization ability, but the global optimization ability is weak. Due to the nonlinearity and complexity of the optimization process, the use of a general linearly decreasing inertia weight is difficult to truly reflect the optimization process, and it is easy to cause premature convergence. In order to balance the global search ability and the local improvement ability, an adaptive inertia weight method is adopted, and the specific calculation formula of the inertia weight is shown in formula (13):

[0112]

[0113] Among them, w is the inertia weight of the particle, w max is the maximum value of the particle's inertia factor, w min is the minimum value of the particle's inertia factor, f is the particle's target fitness value, and f avg is the average target value of all particles at present, f min is the minimum target value of the particle.

[0114] Step S104: charging the battery of the vehicle according to the maximum output power.

[0115] After step S104, as an example, the solar charging method further includes:

[0116] First, the current output power of the photovoltaic cell is obtained.

[0117] Secondly, based on the output power and the maximum output power, the change in power of the photovoltaic cell is determined. Here, as an example, the specific calculation formula for the change in power of the photovoltaic cell is shown in formula (14):

[0118]

[0119] Among them, ΔP is the change in the power of the photovoltaic cell, P is the power detected by the photovoltaic cell in real time, and P m is the maximum power of the photovoltaic cell.

[0120] Finally, when the variation is within the first preset range, the process returns to step S102. Here, as an example, when ΔP>0..1, the particle swarm algorithm is restarted.

[0121] In the embodiment of the present application, a maximum power point tracking simulation model of an improved particle swarm algorithm is established to verify the above method. The specific simulation content and results are as follows:

[0122] Embodiment 1:

[0123] When the photovoltaic panel is partially shaded, the light intensity received by each part of the photovoltaic (PV) array is different. The light intensity distribution of the PV module under static shading is shown in Table 1. The temperature is set to 25°C and the simulation time is 2s.

[0124] Table 1 Light intensity distribution under static shading

[0125]

[0126] The simulation results of the traditional particle swarm algorithm and the improved particle swarm algorithm show that when tracking the same maximum power point, the tracking time of the improved particle swarm algorithm is 0.8 seconds shorter than that of the traditional particle swarm algorithm. The performance comparison of the two algorithms is shown in Table 2.

[0127] Table 2 Performance comparison of two algorithms under static shading

[0128]

[0129] Embodiment 2:

[0130] This embodiment aims to verify the maximum power point tracking under dynamic shading conditions. The distribution of light intensity under dynamic shading is shown in Table 3. As shown in Table 3, static shading situation 1 suddenly changes to static shading situation 2 after 0.8 seconds. The comparison of the simulation results of the traditional particle swarm algorithm and the improved particle swarm algorithm shows that at 0.8 seconds, due to the sudden change of the environment, both the traditional particle swarm algorithm and the improved particle swarm algorithm were restarted, and both successfully searched for the maximum power point before the sudden change of the environment. However, after the mutation, the traditional particle swarm algorithm fell into local optimization, and the tracked power was only 2447W, resulting in premature convergence, which caused the algorithm to fail. In contrast, the improved particle swarm algorithm can still quickly track the maximum power point. The specific performance comparison is shown in Table 4.

[0131] Table 3 Light intensity distribution under dynamic shading

[0132]

[0133] Table 4 Performance comparison of two algorithms under dynamic shading

[0134]

[0135] An embodiment of the present application provides a solar charging method for a vehicle. Through the method, the vehicle can quickly and accurately track the maximum power point under various lighting conditions, significantly improve the charging efficiency, and effectively extend the service life of the battery.

[0136] Based on the same application concept, the embodiments of the present application also provide a solar charging device for a vehicle corresponding to the solar charging method for a vehicle provided in the above embodiments. Since the principle of solving the problem by the device in the embodiments of the present application is similar to that of the solar charging method for a vehicle in the above embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0137] See also Figure 2 and Figure 3 , Figure 2 An expanded top view of a solar charging device for a vehicle provided in an embodiment of the present application; Figure 3 A folded front view of a solar charging device for a vehicle provided in an embodiment of the present application.

[0138] like Figure 2 and Figure 3 As shown, the solar charging device 410 includes: a solar panel and a charging interface.

[0139] In the embodiment of the present application, the solar charging device 410 is a plug-in type. When charging the vehicle, the solar charging device 410 is directly connected to the power battery port through the charging interface, and then the solar panel is manually or remotely unfolded for charging; after the vehicle is charged, the solar panel is manually or remotely folded to restore to its original state.

[0140] See also Figure 4 , Figure 4 This is one of the structural schematic diagrams of a solar charging device for a vehicle provided in an embodiment of the present application.

[0141] like Figure 4 As shown in , the solar charging device 410 also includes:

[0142] A construction module 411 constructs a mathematical model representing a mapping relationship between an output voltage and an output current of a photovoltaic cell used to charge a vehicle using a first preset parameter and a second preset parameter;

[0143] A first array voltage determination module 412, based on the mathematical model and the particle swarm algorithm, determines a first array voltage corresponding to each particle in the particle swarm used to find the maximum output power of the photovoltaic cell;

[0144] A maximum output power determination module 413 determines the maximum output power of the photovoltaic cell based on the first array voltage, a preset self-learning factor and a preset social learning factor;

[0145] The charging module 414 charges the storage battery of the vehicle with the maximum output power of the photovoltaic cell.

[0146] Preferably, the first preset parameter is constructed based on at least one of the following items: a maximum power point of the photovoltaic cell, a maximum power point voltage of the photovoltaic cell, a short-circuit current of the photovoltaic cell, and an open-circuit voltage of the photovoltaic cell.

[0147] The second preset parameter is constructed based on at least one of the following items: the maximum power point of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell, the open-circuit voltage of the photovoltaic cell, and the first preset parameter.

[0148] Preferably, the mathematical model characterizing the mapping relationship between the output voltage and output current of the photovoltaic cell used to charge the vehicle is constructed by at least one parameter of the following items: a first preset parameter, a second preset parameter, the current generated by the photovoltaic cell, and the open circuit voltage of the photovoltaic cell.

[0149] Preferably, the first array voltage determination module 412 is specifically used for:

[0150] Firstly, based on the mathematical model, particles in a particle group for finding the maximum output power of the photovoltaic cell are determined.

[0151] Then, a particle swarm algorithm is used to evenly distribute the number of particles within a set voltage range according to the number of particles, and the voltage corresponding to each particle in the particle swarm is determined as the first array voltage.

[0152] Preferably, the preset self-learning factor is constructed based on at least one of the following items: a set maximum self-learning factor, a set minimum self-learning factor, and a conversion ratio constant.

[0153] The preset social learning factor is constructed based on at least one of the following items: a set maximum social learning factor, a set minimum social learning factor, and a conversion ratio constant.

[0154] Preferably, the maximum output power determination module 413 is specifically used for:

[0155] First, when the particle group searches for the maximum output power of the photovoltaic cell within a first time period, a first self-learning factor of the particle and a first social learning factor of the particle are determined.

[0156] Secondly, based on the first array voltage, the first self-learning factor and the first social learning factor, a first fitness value converged by each particle in the particle group is determined.

[0157] Then, when the particle group searches for the maximum output power of the photovoltaic cell in the second time period, a second self-learning factor of the particle and a second social learning factor of the particle are determined.

[0158] Finally, based on the first fitness value, the second self-learning factor and the second social learning factor, a second fitness value converged by each particle in the particle swarm is determined, and the second fitness value is determined as the maximum output power of the photovoltaic cell.

[0159] See also Figure 5 , Figure 5 This is a second structural schematic diagram of a solar charging device for a vehicle provided in an embodiment of the present application.

[0160] Preferably, if Figure 5 As shown in , the solar charging device 410 also includes:

[0161] The acquisition module 415 acquires the current output power of the photovoltaic cell.

[0162] The change amount determination module 416 determines the change amount of the power of the photovoltaic cell based on the output power and the maximum output power.

[0163] Return module 417, when the variation is within the first preset range, returns to using the first array voltage determination module.

[0164] See also Figure 6 , Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0165] like Figure 6 As shown in , the electronic device 600 includes a processor 610 , a memory 620 and a bus 630 .

[0166] The memory 620 stores machine-readable instructions executable by the processor 610. When the electronic device 600 is running, the processor 610 communicates with the memory 620 via the bus 630. When the machine-readable instructions are executed by the processor 610, the above-mentioned Figure 1 The specific implementation of the steps of the solar charging method for a vehicle in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0167] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The specific implementation of the steps of the solar charging method for a vehicle in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0168] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0169] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0170] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0171] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0172] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0173] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A solar charging method for a vehicle, characterized in that: The solar charging method comprises: (A) constructing a mathematical model representing a mapping relationship between an output voltage and an output current of a photovoltaic cell used to charge a vehicle using a first preset parameter and a second preset parameter; (B) determining, based on the mathematical model and the particle swarm algorithm, a first array voltage corresponding to each particle in a particle swarm used to find the maximum output power of the photovoltaic cell; (C) determining a maximum output power of the photovoltaic cell based on the first array voltage, a preset self-learning factor, and a preset social learning factor; (D) Charging the vehicle's battery at the maximum output power of the photovoltaic cell.

2. The solar charging method according to claim 1, characterized in that: The first preset parameter is constructed based on at least one of the following items: The maximum power point of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell, and the open-circuit voltage of the photovoltaic cell; The second preset parameter is constructed based on at least one of the following items: The maximum power point of the photovoltaic cell, the maximum power point voltage of the photovoltaic cell, the short-circuit current of the photovoltaic cell, the open-circuit voltage of the photovoltaic cell, and a first preset parameter.

3. The solar charging method according to claim 1, characterized in that: The mathematical model representing the mapping relationship between the output voltage and the output current of the photovoltaic cell used to charge the vehicle is constructed by at least one parameter of the following items: A first preset parameter, a second preset parameter, a current generated by the photovoltaic cell, and an open circuit voltage of the photovoltaic cell.

4. The solar charging method according to claim 1, characterized in that: The step (B) comprises: Based on the mathematical model, determining particles in a particle group for finding the maximum output power of the photovoltaic cell; Using a particle swarm algorithm, the particles are evenly distributed within a set voltage range according to the number of the particles, and the voltage corresponding to each particle in the particle swarm is determined as the first array voltage.

5. The solar charging method according to claim 1, characterized in that: The preset self-learning factor is constructed based on at least one of the following items: a set maximum self-learning factor, a set minimum self-learning factor, and a conversion ratio constant; The preset social learning factor is constructed based on at least one of the following items: a set maximum social learning factor, a set minimum social learning factor, and a conversion ratio constant.

6. The solar charging method according to claim 1, characterized in that: The step (C) comprises: When the particle group searches for the maximum output power of the photovoltaic cell within a first time period, determining a first self-learning factor of the particle and a first social learning factor of the particle; Determining a first fitness value converged by each particle in the particle swarm based on the first array voltage, the first self-learning factor, and the first social learning factor; When the particle group searches for the maximum output power of the photovoltaic cell in the second time period, determining a second self-learning factor of the particle and a second social learning factor of the particle; Based on the first fitness value, the second self-learning factor and the second social learning factor, a second fitness value converged by each particle in the particle swarm is determined, and the second fitness value is determined as the maximum output power of the photovoltaic cell.

7. The solar charging method according to claim 1, characterized in that: After step (D), the solar charging method further comprises: Obtaining the current output power of the photovoltaic cell; Based on the output power and the maximum output power, determining a change in power of the photovoltaic cell; When the change is within the first preset range, the process returns to step (B).

8. A solar charging device for a vehicle, characterized in that: The solar charging device comprises: A construction module, using the first preset parameter and the second preset parameter, constructs a mathematical model representing a mapping relationship between an output voltage and an output current of a photovoltaic cell used to charge a vehicle; A first array voltage determination module, based on the mathematical model and the particle swarm algorithm, determines a first array voltage corresponding to each particle in a particle swarm used to find the maximum output power of the photovoltaic cell; a maximum output power determination module, which determines the maximum output power of the photovoltaic cell based on the first array voltage, a preset self-learning factor and a preset social learning factor; The charging module charges the storage battery of the vehicle with the maximum output power of the photovoltaic cell.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and when the computer program is executed by the processor, the solar charging method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the solar charging method according to any one of claims 1 to 7 are executed.

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