Charging control method of two-wheeled vehicle shared charging pile and charging pile

Abstract

The invention discloses a charging control method of a two-wheeled vehicle shared charging pile and a charging pile, and relates to the technical field of shared charging piles, and the method comprises the following steps: obtaining various parameter information generated by the two-wheeled vehicle shared charging pile in the process of charging a two-wheeled vehicle, the parameter information including energy management information and system compatibility information; the charging process of the two-wheeled vehicle shared charging pile is intelligently monitored, the charging hidden danger index is generated in time, the high-risk state is effectively recognized, especially, early warning is rapidly given out under the non-accidental condition, workers are reminded of inspection, battery overheating, expansion or explosion is prevented, and the safety of users and the environment is guaranteed. In addition, by analyzing energy management and system compatibility information, an energy conversion efficiency index and a charging adaptability index are generated, and it is ensured that the charging pile intelligently matches different electric vehicle requirements. Through the optimization, the utilization rate of electric energy is improved, the charging cost is reduced, data support is provided for subsequent maintenance, and the management efficiency and the service reliability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of shared charging piles, and in particular to a charging control method and a charging pile for a shared charging pile for two-wheeled vehicles. Background Art

[0002] The charging control method of shared charging piles for two-wheeled vehicles mainly refers to the technical means for realizing intelligent management and scheduling of shared electric vehicles during the charging process. These methods include status monitoring of charging piles, dynamic adjustment of charging strategies, and real-time response to user needs. By integrating IoT technology and data analysis, charging piles can monitor the battery power status, temperature, and charging efficiency in real time to ensure that the charging process is safe, fast, and efficient. In addition, the system can predict charging needs and optimize charging time based on user usage habits and historical data, avoiding waste of resources during peak hours, thereby improving overall charging efficiency.

[0003] Specifically, the charging control method also includes the intelligent scheduling of charging periods and the implementation of pricing strategies. During the low power load period, the system can encourage users to charge during low peak hours through dynamic pricing, thereby reducing grid pressure and charging costs. At the same time, by analyzing users' charging habits using machine learning algorithms, personalized charging plans can be developed for different users to improve user experience. This intelligent charging management not only improves the utilization rate of charging piles, but also promotes the rational allocation of power resources, and ultimately achieves the sustainable development of the shared electric vehicle ecosystem. Through these innovative methods, shared charging piles not only meet the needs of users, but also contribute to the green transformation of urban transportation.

[0004] The prior art has the following deficiencies:

[0005] If the two-wheeled shared charging pile fails to intelligently identify the charging power of the electric vehicle and provides an inappropriate charging power, and the system does not recognize this anomaly, serious consequences may occur. If the power provided by the charging pile is too high, the battery may overheat, causing the internal chemical reaction to run away, causing the battery to swell, leak, or even explode, which not only makes the electric vehicle unable to be used normally, but also poses a major safety threat to the surrounding environment and personnel. At the same time, the mismatch of charging power may cause the charging management system of the electric vehicle to overload, trigger the protection mechanism, cause system failure, damage software or hardware components, and increase subsequent maintenance costs. Therefore, it is crucial to ensure that the charging pile can accurately identify and match the charging power to ensure the safe and efficient charging of electric vehicles.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a charging control method and charging pile for a shared charging pile for two-wheeled vehicles. By intelligently monitoring the charging process of the shared charging pile for two-wheeled vehicles, a charging hidden danger index is generated in time, and high-risk charging states can be effectively identified. In particular, in non-accidental high-risk situations, the system can quickly issue an early warning to remind relevant staff to conduct necessary inspections, thereby preventing safety accidents such as battery overheating, expansion, leakage or explosion, and ensuring the safety of users and the surrounding environment. In addition, by analyzing energy management information and system compatibility information, an energy conversion efficiency index and a charging adaptability index are generated to ensure that the charging pile can intelligently match the charging needs of different electric vehicles. This optimization not only improves the utilization rate of electric energy and reduces the charging cost, but also provides data support for subsequent maintenance, enhances the management efficiency and scientificity of the charging pile, and achieves the reliability of charging services and the improvement of user satisfaction, so as to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: comprising the following steps:

[0009] Obtain various parameter information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle, including energy management information and system compatibility information;

[0010] Under the detection window, the acquired energy management information and system compatibility information are analyzed, and the analyzed energy management information and system compatibility information are input into the pre-trained machine model, and a charging hidden danger index is generated through the machine model. The charging hidden danger index is used to intelligently perceive the charging process of the two-wheeled vehicle by the two-wheeled vehicle shared charging pile;

[0011] Based on the charging risk index, the charging process of two-wheeled vehicles with shared charging piles is divided into high-risk charging state or low-risk charging state, and the abnormal charging process of two-wheeled vehicles with shared charging piles is intelligently analyzed;

[0012] For the assessed high-risk charging status, the status information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle is continuously obtained, and the high-risk charging status is divided into accidental and non-accidental types;

[0013] For the high-risk charging states after classification, different early warning methods are adopted according to different states.

[0014] Preferably, the energy management information includes energy conversion efficiency, which is a measure of the extent to which electrical energy is effectively utilized during the charging process. It represents the ratio of the electrical energy transmitted from the charging pile to the electric vehicle battery to the total input electrical energy. The system compatibility information includes charging adaptability, which refers to the ability of the charging system to intelligently identify and match charging requirements when facing electric vehicles of different brands, models and battery specifications.

[0015] Preferably, within the detection window, an abnormal analysis is performed on the energy conversion efficiency and system compatibility, and an energy conversion efficiency index and a charging adaptability index are generated respectively. The analyzed energy conversion efficiency index and charging adaptability index are input into a pre-trained machine model, and a charging hidden danger index is generated through the machine model. The charging hidden danger index is used to intelligently perceive the charging process of two-wheeled vehicles by the two-wheeled vehicle shared charging pile.

[0016] Preferably, in the detection window, the logic of performing abnormal analysis on the energy conversion efficiency to generate the energy conversion efficiency index is as follows:

[0017] Calculate the energy loss during the charging process, the calculation expression is as follows: E loss =E in -E out , where E loss Represents energy loss, E in Indicates the input electrical energy, that is, the total energy input from the charging pile to the battery, E out Indicates the output electrical energy, that is, the energy actually stored in the battery;

[0018] The energy loss rate is calculated by energy loss, and the calculation expression is as follows: Where, L rate represents the energy loss rate;

[0019] According to the input power and output power, the energy conversion efficiency is calculated. The calculation expression is as follows: In the formula, E eff represents the energy conversion efficiency, T env represents the ambient temperature, i.e. the ambient temperature during charging, and k is an adjustment parameter, which represents the influence of ambient temperature on energy conversion efficiency;

[0020] Through the energy loss rate L rate and energy conversion efficiency E eff Generate the energy conversion efficiency index, the calculation expression is as follows: In the formula, E CI represents the energy conversion efficiency index, and β is the weight of the energy conversion efficiency.

[0021] Preferably, in the detection window, the logic of performing abnormal analysis on charging adaptability to generate a charging adaptability index is as follows:

[0022] Collect relevant parameter data during the charging process, including charging current I, charging voltage V and ambient temperature T;

[0023] According to the charging current and voltage, the charging power is defined, and the calculation expression is as follows: P = V * I, and the battery adaptability parameter is defined at the same time, A = f (T, Battery Type, Max Power), where P represents the charging power, A represents the battery adaptability parameter, and f is a comprehensive function used to calculate the battery adaptability parameter A based on temperature, battery type and maximum charging power. Battery Type represents the battery type, and Max Power represents the maximum charging power.

[0024] The charging adaptability model CA is constructed using the charging power and battery adaptability parameters. The weight coefficient of the nonlinear combination is introduced to reflect the adaptability under different conditions. The calculation expression is as follows: Where W1 is the weight coefficient of charging power, and W2 is the weight coefficient of battery adaptability parameter;

[0025] Combined with the charging adaptability model, the charging adaptability index is generated, and the calculation expression is as follows: Where CAI represents the charging adaptability index, T opt represents the optimal operating temperature of the battery, σ T It is the temperature fluctuation coefficient, which reflects the impact of ambient temperature changes on adaptability.

[0026] Preferably, the energy conversion efficiency index E CI and charging adaptability index CAI are input into the pre-trained machine model, and the charging hidden danger index CH is generated by the machine model according to the formula:

[0027]

[0028] ; Where μ1 and μ2 are energy conversion efficiency index E CI And the charging adaptability index CAI preset proportional coefficient, and μ1 and μ2 are both greater than 0.

[0029] Preferably, the generated charging hidden danger index is compared and analyzed with a preset charging hidden danger index reference threshold, and the analysis results are as follows;

[0030] If the charging hidden danger index is greater than or equal to the preset charging hidden danger index reference threshold, the abnormal charging process of the two-wheeled vehicle at the two-wheeled vehicle shared charging pile is classified as a high-risk charging state;

[0031] If the charging hidden danger index is less than a preset charging hidden danger index reference threshold, the abnormal charging process of the two-wheeled vehicle at the two-wheeled vehicle shared charging pile is classified as a low-risk charging state.

[0032] Preferably, for the assessed high-risk charging state, a data set of several charging hidden danger indexes generated in several detection windows during the charging process of the two-wheeled vehicle shared charging pile is continuously obtained to establish a data set for comprehensive analysis, and the data set is calibrated as P, then P = {CH π}={CH1, CH2, CH3,..., CH n}, π=1, 2, 3, ………, n, n is a positive integer

[0033] Calculate the mean and standard deviation of several charging hidden danger indexes in the data set, and mark the mean and standard deviation of the charging hidden danger index as M 平均 and M 标准 but:

[0034] The average value M of the charging hidden danger index obtained in the data set 平均 and standard deviation M 标准 The high-risk charging status is further divided by comparing with the preset reference threshold of the average value of the charging hidden danger index and the reference threshold of the standard deviation of the charging hidden danger index. The comparison results are as follows:

[0035] If the average value of charging hidden danger index M 平均 Greater than or equal to the preset charging hazard index reference threshold and the charging hazard index standard deviation M 标准 Less than the preset standard deviation of the charging hidden danger index, or the average value M of the charging hidden danger index 平均 If the charging risk index is greater than or equal to the preset reference threshold, the high-risk charging state is classified as non-accidental;

[0036] If the average value of charging hidden danger index M 平均 Less than the preset reference threshold of charging hidden danger index and the standard deviation of charging hidden danger index M 标准 If it is smaller than the preset standard deviation of the charging hazard index, the high-risk charging state is classified as accidental.

[0037] Two-wheeled vehicle shared charging pile, including data collection module, intelligent analysis module, risk assessment module, status monitoring module and early warning management module;

[0038] The data acquisition module obtains various parameter information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle, including energy management information and system compatibility information;

[0039] The intelligent analysis module analyzes the acquired energy management information and system compatibility information in the detection window, inputs the analyzed energy management information and system compatibility information into the pre-trained machine model, generates a charging hidden danger index through the machine model, and uses the charging hidden danger index to intelligently perceive the charging process of the two-wheeled vehicle shared charging pile for the two-wheeled vehicle;

[0040] The risk assessment module divides the charging process of a two-wheeled vehicle with a shared charging pile into a high-risk charging state or a low-risk charging state based on the charging risk index, and performs intelligent analysis on the abnormal charging process of a two-wheeled vehicle with a shared charging pile;

[0041] The state monitoring module continuously obtains the state information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle for the assessed high-risk charging state, and divides the high-risk charging state into accidental and non-accidental types;

[0042] The early warning management module adopts different early warning methods according to different conditions for the high-risk charging conditions after classification.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] The present invention can timely identify high-risk charging states through intelligent monitoring of the charging process of two-wheeled shared charging piles and the generation of charging hidden danger indexes. This timeliness ensures the early discovery of potential problems during the charging process. In particular, when identifying non-accidental high-risk states, the system can quickly issue early warning prompts to remind relevant staff to conduct necessary inspections, thereby effectively preventing the occurrence of safety accidents such as battery overheating, expansion, leakage, and even explosion, and protecting the safety of users and the surrounding environment.

[0045] The present invention generates an energy conversion efficiency index and a charging adaptability index by analyzing energy management information and system compatibility information. It can not only quantitatively evaluate the efficiency during the charging process, but also ensure that the charging pile can be intelligently matched according to the charging needs of different electric vehicles. This optimization not only improves the utilization rate of electric energy and reduces charging costs, but also provides data support for subsequent maintenance and overhaul, making the management of charging piles more efficient and scientific. Through dynamic monitoring and classification of charging status, the system can achieve refined management and improve the reliability of the overall charging service and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0047] Figure 1 The present invention is a flow chart of the charging control method and the charging pile method of the two-wheeled vehicle shared charging pile.

[0048] Figure 2 It is a module schematic diagram of the charging control method of the shared charging pile for two-wheeled vehicles and the charging pile of the present invention. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0050] The present invention provides Figure 1 The charging control method and charging pile of the two-wheeled vehicle shared charging pile shown include the following steps:

[0051] Obtain various parameter information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle, including energy management information and system compatibility information;

[0052] Energy management information includes energy conversion efficiency, which is a measure of the degree to which electric energy is effectively used during the charging process. It represents the ratio of the electric energy transmitted from the charging pile to the electric vehicle battery to the total input electric energy. System compatibility information includes charging adaptability, which refers to the ability of the charging system to intelligently identify and match charging requirements when facing electric vehicles of different brands, models and battery specifications.

[0053] Under the detection window, the acquired energy management information and system compatibility information are analyzed, and the analyzed energy management information and system compatibility information are input into the pre-trained machine model, and a charging hidden danger index is generated through the machine model. The charging hidden danger index is used to intelligently perceive the charging process of the two-wheeled vehicle by the two-wheeled vehicle shared charging pile;

[0054] Under the detection window, an abnormal analysis is performed on the energy conversion efficiency and system compatibility, and an energy conversion efficiency index and a charging adaptability index are generated respectively. The analyzed energy conversion efficiency index and charging adaptability index are input into a pre-trained machine model, and a charging hidden danger index is generated through the machine model. The charging hidden danger index is used to intelligently perceive the charging process of two-wheeled vehicles by the two-wheeled vehicle shared charging pile;

[0055] When the energy conversion efficiency index of a shared charging pile for two-wheeled vehicles changes during the charging process, the power provided by the charging pile may be too high. In this case, the battery may overheat under high-power charging conditions. Battery overheating can cause the internal temperature to rise, which in turn causes the internal chemical reaction to get out of control, forming a thermal runaway phenomenon. This process may reduce the chemical stability of the battery material, resulting in serious consequences such as battery swelling, leakage, or even explosion. When the battery explodes, in addition to the inability to use the electric vehicle normally, the released high-temperature gas and harmful substances will also pose a major safety threat to the surrounding environment and personnel. In addition, the gas generated by the chemical reaction inside the battery, such as hydrogen or toxic gas, may accumulate in a confined space, causing a fire or poisoning incident, further endangering the safety of the user and surrounding personnel. Therefore, ensuring that the energy conversion efficiency of the charging pile is within the normal range is crucial to avoiding safety hazards during the charging process. It can effectively reduce the risk of battery overheating and the safety accidents caused by it, protect the life and property safety of users, and maintain the safety of the public environment.

[0056] Under the detection window, the logic of abnormal analysis of energy conversion efficiency to generate energy conversion efficiency index is as follows:

[0057] Calculate the energy loss caused by various factors during the charging process. The calculation expression is as follows: E loss =E in -E out , where E loss Represents energy loss, E in Indicates the input electrical energy, that is, the total energy input from the charging pile to the battery, E out Indicates the output electrical energy, that is, the energy actually stored in the battery;

[0058] It should be noted that energy loss during charging may be caused by multiple factors, including the following:

[0059] 1. Internal resistance: There is resistance inside the battery and charging pile, which will cause heat to be generated when current passes through, resulting in energy loss. High internal resistance will increase energy loss and affect charging efficiency.

[0060] 2. Heat loss: During the charging process, the battery and charging pile generate heat due to the flow of current, and part of the energy is converted into heat and dissipated into the environment, resulting in a reduction in effective charging energy. High temperature environment or poor heat dissipation design will exacerbate this loss.

[0061] 3. Chemical reaction efficiency: The chemical reaction inside the battery is the key process for energy storage and release. If the chemical reaction efficiency is not high, the loss in the energy conversion and storage process will increase, affecting the charging effect.

[0062] 4. Matching of charging voltage and current: If the output voltage and current of the charging pile fail to match the input characteristics of the electric vehicle battery, energy loss will occur. For example, too high a voltage may cause unnecessary energy waste, while too low a voltage will prolong the charging time.

[0063] 5. Poor contact: Poor contact between the charging connector and the battery will increase the contact resistance, resulting in energy loss during transmission. Regular inspection and maintenance of the connector can reduce the occurrence of this problem.

[0064] 6. Environmental factors: External ambient temperature, humidity, and air flow can all affect charging efficiency. Extreme temperatures can cause battery performance to deteriorate, while high humidity can cause corrosion and affect the quality of electrical connections.

[0065] 7. Charging strategy: Different charging strategies (such as constant voltage charging, constant current charging) will have different effects on energy loss. An unreasonable charging strategy may lead to excessive energy loss.

[0066] 8. Aging and deterioration: Batteries will gradually age with increasing usage time, and their performance and chemical reaction efficiency will decrease, resulting in increased energy loss.

[0067] The combined effect of the above factors may significantly affect the effective use of energy during the charging process, reduce charging efficiency, and increase charging time and cost.

[0068] The energy loss rate is calculated by energy loss, and the calculation expression is as follows: Where, L rate represents the energy loss rate;

[0069] According to the input power and output power, the energy conversion efficiency is calculated. The calculation expression is as follows: In the formula, E eff represents the energy conversion efficiency, T env represents the ambient temperature, i.e. the ambient temperature during charging, and k is an adjustment parameter, which represents the influence of ambient temperature on energy conversion efficiency;

[0070] It should be noted that the setting of the adjustment parameter (K) can be explained in detail through the following steps:

[0071] 1. Theoretical analysis

[0072] First, it is necessary to conduct a theoretical analysis of the effect of ambient temperature on energy conversion efficiency. Studies have shown that the performance and charging efficiency of batteries will change significantly at different temperatures, and usually perform best within a specific temperature range (such as 20°C to 25°C). Outside this range, especially at low and high temperatures, the internal resistance of the battery may increase and the chemical reaction rate decrease, thus affecting the energy conversion efficiency.

[0073] 2. Experimental Data Collection

[0074] When setting (K), a large number of experiments should be performed to collect data; these experiments may include:

[0075] Carry out charging test on the battery at different ambient temperatures and record the input power and output power;

[0076] Observe the effects of different temperatures on charging time, charging efficiency and battery temperature;

[0077] 3. Data Analysis

[0078] Use statistical analysis tools to analyze the collected data to find the relationship between temperature and energy conversion efficiency; regression analysis or other nonlinear fitting methods can be used to obtain the curve of the effect of temperature on energy conversion efficiency; this step can help determine the preliminary value of \(k\) at a specific temperature;

[0079] 4. Set initial value

[0080] According to the results of data analysis, set the initial value of (K); this value is usually the result of empirical values ​​and can be obtained in initial experiments; for example, if the energy conversion efficiency is highest at 25°C, (K) can be set to a constant based on this temperature to reflect the effect of other temperatures on efficiency;

[0081] 5. Dynamic Adjustment

[0082] In practical applications, k can be adjusted dynamically according to real-time data during the charging process. If the charging efficiency is found to be lower than expected, it may be that the ambient temperature affects the efficiency. Temporary adjustment of K can be considered to improve performance. A feedback mechanism is introduced to continuously optimize k according to the actual charging situation.

[0083] 6. Verification and optimization

[0084] After setting (K), a verification test is required to observe the change of charging efficiency under the set value; if it is found that it does not meet expectations, return to the previous steps for re-analysis and optimize the setting of (K); through multiple rounds of experiments and adjustments, a reasonable (K) value is finally determined;

[0085] Through the above steps, the setting of the adjustment parameter (K) will be more scientific and practical, which will help optimize the charging efficiency and ensure that electric vehicles can be charged safely and efficiently under various environmental conditions;

[0086] Through the energy loss rate L rate and energy conversion efficiency E eff Generate the energy conversion efficiency index, the calculation expression is as follows: In the formula, E CIrepresents the energy conversion efficiency index, β is the weight of energy conversion efficiency;

[0087] In the detection window, the calculation expression of the energy conversion efficiency index shows that the smaller the performance value of the energy conversion efficiency index, the greater the energy loss during the charging process, which may indicate that the two-wheeled vehicle shared charging pile has an abnormal problem during charging, such as charging power mismatch, equipment failure or poor battery status. In this case, the charging pile cannot effectively transmit electrical energy to the battery, increasing the probability of abnormal events. On the contrary, if the performance value of the energy conversion efficiency index is large, it means that the charging pile is efficiently converting and utilizing electrical energy, the charging process is relatively smooth, and the probability of abnormalities is correspondingly small.

[0088] When the charging adaptability index of the shared charging pile for two-wheeled vehicles changes, the power provided by the charging pile may not be suitable for the actual needs of the electric vehicle; if the power provided by the charging pile is too high, the battery will face excessive current during the charging process, which may cause the battery temperature to rise rapidly and exceed its safe operating range; the chemical reaction inside the battery may get out of control in a high temperature environment, generating excessive gas and heat, which in turn causes the battery to swell, and even cause serious consequences such as leakage and fire; in extreme cases, the battery may explode, causing the electric vehicle to be unable to be used normally, seriously affecting the user's travel safety; in addition, battery explosion or combustion will not only directly endanger the lives of people around, but may also cause great damage to the surrounding environment, generating toxic gases and the risk of fire spreading; even if there is no explosion, battery overheating and leakage may also cause damage to other equipment and facilities of the charging pile, increasing the cost of repair and replacement; therefore, ensuring that the charging pile has good charging adaptability to monitor and match the charging power in real time is crucial to ensuring the safety of electric vehicles and improving charging efficiency, and can effectively prevent safety hazards caused by insufficient charging adaptability;

[0089] In the detection window, the logic of abnormal analysis of charging adaptability and generating charging adaptability index is as follows:

[0090] Collect relevant parameter data during the charging process, including charging current I, charging voltage V and ambient temperature T;

[0091] According to the charging current and voltage, the charging power is defined, and the calculation expression is as follows: P = V * I, and the battery adaptability parameter is defined at the same time, A = f (T, Battery Type, Max Power), where P represents the charging power, A represents the battery adaptability parameter, and f is a comprehensive function used to calculate the battery adaptability parameter A based on temperature, battery type and maximum charging power. Battery Type represents the battery type, and Max Power represents the maximum charging power.

[0092] It should be noted that battery type refers to the type of battery used in electric vehicles, such as lithium-ion batteries, lead-acid batteries, nickel-metal hydride batteries, etc. Different types of batteries have different charging characteristics and safety. For example, lithium-ion batteries generally have higher energy density and faster charging speed, but are also more sensitive to charging current and temperature; therefore, charging adaptability needs to be optimized according to the battery type to ensure the safety and high efficiency of the charging process;

[0093] The maximum charging power is the maximum power that the battery can withstand when charging, as specified by the battery manufacturer. Exceeding the maximum charging power may cause the battery to overheat, swell, or be damaged. Therefore, the calculation of the charging adaptability index needs to take this parameter into account to ensure that the charging pile does not provide power that exceeds the battery's tolerance during the charging process, thereby ensuring charging safety and battery life.

[0094] The charging adaptability model CA is constructed using the charging power and battery adaptability parameters. The weight coefficient of the nonlinear combination is introduced to reflect the adaptability under different conditions. The calculation expression is as follows: Where W1 is the weight coefficient of charging power, and W2 is the weight coefficient of battery adaptability parameter;

[0095] It should be noted that in the process of generating the charging adaptability index, the weight coefficient W of the nonlinear combination is introduced to better reflect the actual situation under different conditions when calculating the adaptability; specifically, the introduction of the weight coefficient has the following significance and functions:

[0096] 1. Reflect the importance of parameters:

[0097] The weight coefficient W is used to quantify the relative importance of each parameter in charging adaptability. For example, the charging power P and the battery adaptability parameter A may have different effects on charging efficiency. By adjusting the weight coefficient, the importance of certain parameters under specific conditions can be highlighted, thereby improving the accuracy of the model.

[0098] 2. Nonlinear combination:

[0099] The introduction of nonlinear combination weight coefficients means that the relationship between different parameters may not be a simple linear relationship when calculating adaptability; this nonlinear processing can better simulate the complexity of the actual charging process; for example, the relationship between current and voltage may show different characteristics under different battery types or environmental conditions; through nonlinear combination, the model can adapt to these changes and provide a more accurate assessment of charging adaptability;

[0100] 3. Dynamic adaptability:

[0101] By dynamically adjusting the weight coefficient, the charging adaptability index can be adjusted in real time according to the actual situation. For example, in a high temperature environment, the battery's sensitivity to the charging current increases, and the weight coefficient related to the current can be increased accordingly. This flexibility enables the system to respond to environmental changes in a timely manner and improve the safety and efficiency of charging.

[0102] 4. Consider multiple factors:

[0103] The use of weight coefficients allows the combined effects of multiple factors to be considered simultaneously in the calculation of the charging adaptability index; for example, factors such as charging current, charging voltage, battery type and ambient temperature can be weighted by weight coefficients to form a comprehensive evaluation standard that reflects the adaptability of the battery under specific charging conditions;

[0104] 5. Formula performance:

[0105] In the formula, the weight coefficient W can be defined in different ways, such as based on expert experience, historical data analysis, or optimized through machine learning methods; this flexibility and adaptability makes the calculation of the charging adaptability index more scientific and accurate;

[0106] In summary, the introduction of the nonlinear combination weight coefficient W plays a vital role in the generation of the charging adaptability index, ensuring that the model can accurately reflect the charging adaptability under different conditions, thereby improving the safety and efficiency of the electric vehicle charging process;

[0107] Combined with the charging adaptability model, the charging adaptability index is generated, and the calculation expression is as follows: Where CAI represents the charging adaptability index, T opt represents the optimal operating temperature of the battery, σ T is the temperature fluctuation coefficient, which reflects the impact of ambient temperature changes on adaptability;

[0108] It should be noted that the optimal operating temperature of the battery can be obtained by the following methods:

[0109] 1. Manufacturer Specifications:

[0110] Battery manufacturers typically provide the optimal operating temperature range in product data sheets; these are based on laboratory testing and performance evaluation in real-world applications.

[0111] 2. Laboratory testing:

[0112] By testing the battery charge and discharge at different temperatures and recording the battery performance indicators (such as capacity, charging time, cycle life, etc.), the optimal temperature range can be determined;

[0113] 3. Thermal simulation:

[0114] Use thermal simulation software to analyze the thermal behavior of the battery under different operating conditions to identify the temperature range where performance is optimal;

[0115] 4. User feedback and field testing:

[0116] Collect user feedback on the battery and combine it with field test data to understand the most effective operating temperature of the battery in actual use;

[0117] 5. Literature research:

[0118] Consult relevant research literature and technical reports to understand the optimal operating temperature and influencing factors of different types of batteries;

[0119] These methods can be used in combination to obtain a more accurate and reliable optimal operating temperature of the battery;

[0120] Under the detection window, the calculation expression of the charging adaptability index shows that the smaller the performance value of the charging adaptability index, the greater the energy loss during the charging process, indicating that the charging pile has an abnormality in identifying and matching the charging needs of electric vehicles; this inefficiency may cause the charging power to be too high or too low, increasing the risk of charging abnormalities, and thus affecting the safety and charging effect of the battery; on the contrary, if the performance value of the energy conversion efficiency index is larger, it indicates that the charging pile can effectively manage energy conversion and maintain an efficient charging process, thereby reducing the probability of abnormalities in the charging process of the two-wheeled shared charging pile;

[0121] The energy conversion efficiency index E CI and charging adaptability index CAI are input into the pre-trained machine model, and the charging hidden danger index CH is generated by the machine model according to the formula:

[0122]

[0123] ; Where μ1 and μ2 are energy conversion efficiency index E CI and the preset proportional coefficient of the charging adaptability index CAI, and μ1 and μ2 are both greater than 0;

[0124] Under the detection window, it can be seen from the calculation expression of the charging hidden danger index that the smaller the performance value of the energy conversion efficiency index generated by the two-wheeled vehicle shared charging pile in the process of charging the two-wheeled vehicle, the smaller the performance value of the charging adaptability index, the smaller the performance value of the charging hidden danger index, indicating that the probability of abnormality in the charging process of the two-wheeled vehicle shared charging pile is greater, and the larger the performance value of the energy conversion efficiency index generated by the two-wheeled vehicle shared charging pile in the process of charging the two-wheeled vehicle, the larger the performance value of the charging adaptability index, the larger the performance value of the charging hidden danger index, indicating that the probability of abnormality in the charging process of the two-wheeled vehicle shared charging pile is smaller;

[0125] Based on the charging risk index, the charging process of two-wheeled vehicles with shared charging piles is divided into high-risk charging state or low-risk charging state, and the abnormal charging process of two-wheeled vehicles with shared charging piles is intelligently analyzed;

[0126] The generated charging hidden danger index is compared and analyzed with the preset charging hidden danger index reference threshold, and the analysis results are as follows;

[0127] If the charging hidden danger index is greater than or equal to the preset charging hidden danger index reference threshold, the abnormal charging process of the two-wheeled vehicle at the two-wheeled vehicle shared charging pile is classified as a high-risk charging state;

[0128] If the charging hidden danger index is less than a preset charging hidden danger index reference threshold, the abnormal charging process of the two-wheeled vehicle at the two-wheeled vehicle shared charging pile is classified as a low-risk charging state;

[0129] For the assessed high-risk charging status, the status information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle is continuously obtained, and the high-risk charging status is divided into accidental and non-accidental types;

[0130] For the assessed high-risk charging status, a data set of several charging risk indexes generated under several detection windows during the charging process of two-wheeled vehicles by two-wheeled shared charging piles is continuously obtained to establish a comprehensive analysis, and the data set is calibrated as P, then P = {CH π}={CH1, CH2, CH3,..., CH n}, π=1, 2, 3, ………, n, n is a positive integer

[0131] Calculate the mean and standard deviation of several charging hidden danger indexes in the data set, and mark the mean and standard deviation of the charging hidden danger index as M 平均 and M 标准 but:

[0132] The average value M of the charging hidden danger index obtained in the data set 平均 and standard deviation M 标准 The high-risk charging status is further divided by comparing with the preset reference threshold of the average value of the charging hidden danger index and the reference threshold of the standard deviation of the charging hidden danger index. The comparison results are as follows:

[0133] If the average value of charging hidden danger index M 平均 Greater than or equal to the preset charging hazard index reference threshold and the charging hazard index standard deviation M 标准 Less than the preset standard deviation of the charging hidden danger index, or the average value M of the charging hidden danger index 平均If the charging risk index is greater than or equal to the preset reference threshold, the high-risk charging state is classified as non-accidental;

[0134] It should be noted that if the average value of the electrical hazard index M 平均 Greater than or equal to the preset charging hazard index reference threshold and the charging hazard index standard deviation M 标准 Less than the preset standard deviation of the charging hidden danger index, or the average value M of the charging hidden danger index 平均 When the value is greater than or equal to the preset reference threshold value of the charging hidden danger index, it indicates that the situation in which the charging hidden danger index in the data set is greater than or equal to the reference threshold value of the charging hidden danger index is not accidental;

[0135] If the average value of charging hidden danger index M 平均 Less than the preset reference threshold of charging hidden danger index and the standard deviation of charging hidden danger index M 标准 If it is less than the preset standard deviation of the charging hazard index, the high-risk charging state is classified as accidental;

[0136] It should be noted that if the average value of the charging hidden danger index M 平均 Less than the preset reference threshold of charging hidden danger index and the standard deviation of charging hidden danger index M 标准 When it is less than the preset standard deviation of the charging hidden danger index, it indicates that the situation in which the charging hidden danger index in the data set is greater than or equal to the reference threshold of the charging hidden danger index is accidental;

[0137] When managing the classified high-risk charging status, for non-accidental situations, early warnings should be issued in a timely manner to remind relevant staff to check the charging status of the two-wheeled vehicle to prevent abnormal situations from causing battery combustion and safety accidents. For accidental status, considering that it may recover on its own in a short time, no early warning will be issued, but records must be kept for reference during subsequent maintenance;

[0138] The present invention can timely identify high-risk charging states through intelligent monitoring of the charging process of two-wheeled shared charging piles and generation of charging hidden danger indexes; this timeliness ensures early discovery of potential problems during the charging process, especially when non-accidental high-risk states are identified, the system can quickly issue early warning prompts to remind relevant staff to conduct necessary inspections, thereby effectively preventing the occurrence of safety accidents such as battery overheating, expansion, leakage and even explosion, and protecting the safety of users and the surrounding environment;

[0139] The present invention generates an energy conversion efficiency index and a charging adaptability index by analyzing energy management information and system compatibility information, which can not only quantitatively evaluate the efficiency during the charging process, but also ensure that the charging piles can be intelligently matched according to the charging requirements of different electric vehicles; this optimization not only improves the utilization rate of electric energy and reduces the charging cost, but also provides data support for subsequent maintenance and overhaul, making the management of charging piles more efficient and scientific; through dynamic monitoring and classification of the charging status, the system can achieve refined management and improve the reliability of the overall charging service and user satisfaction.

[0140] The present invention provides Figure 2 The two-wheeled vehicle shared charging pile shown includes a data acquisition module, an intelligent analysis module, a risk assessment module, a status monitoring module and an early warning management module;

[0141] The data acquisition module obtains various parameter information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle, including energy management information and system compatibility information;

[0142] The intelligent analysis module analyzes the acquired energy management information and system compatibility information in the detection window, inputs the analyzed energy management information and system compatibility information into the pre-trained machine model, generates a charging hidden danger index through the machine model, and uses the charging hidden danger index to intelligently perceive the charging process of the two-wheeled vehicle shared charging pile for the two-wheeled vehicle;

[0143] The risk assessment module divides the charging process of a two-wheeled vehicle with a shared charging pile into a high-risk charging state or a low-risk charging state based on the charging risk index, and performs intelligent analysis on the abnormal charging process of a two-wheeled vehicle with a shared charging pile;

[0144] The state monitoring module continuously obtains the state information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle for the assessed high-risk charging state, and divides the high-risk charging state into accidental and non-accidental types;

[0145] The early warning management module adopts different early warning methods according to different high-risk charging states after classification;

[0146] The charging control method for a two-wheeled vehicle shared charging pile provided in an embodiment of the present invention is implemented by the above-mentioned two-wheeled vehicle shared charging pile. The specific method and process of the two-wheeled vehicle shared charging pile are detailed in the embodiment of the charging control method for the above-mentioned two-wheeled vehicle shared charging pile, which will not be repeated here.

[0147] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A charging control method for a two-wheeled vehicle shared charging pile, characterized in that: The following steps are involved: Obtain various parameter information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle, including energy management information and system compatibility information; Under the detection window, the acquired energy management information and system compatibility information are analyzed, and the analyzed energy management information and system compatibility information are input into the pre-trained machine model, and a charging hidden danger index is generated through the machine model. The charging hidden danger index is used to intelligently perceive the charging process of the two-wheeled vehicle by the two-wheeled vehicle shared charging pile; Based on the charging risk index, the charging process of two-wheeled vehicles with shared charging piles is divided into high-risk charging state or low-risk charging state, and the abnormal charging process of two-wheeled vehicles with shared charging piles is intelligently analyzed; For the assessed high-risk charging status, the status information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle is continuously obtained, and the high-risk charging status is divided into accidental and non-accidental types; For the high-risk charging states after classification, different early warning methods are adopted according to different states.

2. The charging control method for a two-wheeled vehicle shared charging pile according to claim 1, characterized in that: Energy management information includes energy conversion efficiency, which is a measure of the extent to which electrical energy is effectively utilized during the charging process. It represents the ratio of the electrical energy transmitted from the charging pile to the electric vehicle battery to the total input electrical energy. System compatibility information includes charging adaptability, which refers to the ability of the charging system to intelligently identify and match charging requirements when facing electric vehicles of different brands, models and battery specifications.

3. The charging control method of the two-wheeled vehicle shared charging pile according to claim 1, characterized in that: Under the detection window, the energy conversion efficiency and system compatibility are analyzed for abnormalities, and the energy conversion efficiency index and charging adaptability index are generated respectively. The analyzed energy conversion efficiency index and charging adaptability index are input into the pre-trained machine model, and the charging hidden danger index is generated by the machine model. The charging hidden danger index is used to intelligently perceive the charging process of two-wheeled vehicles by the shared charging piles for two-wheeled vehicles.

4. The charging control method of the two-wheeled vehicle shared charging pile according to claim 3, characterized in that: Under the detection window, the logic of abnormal analysis of energy conversion efficiency to generate energy conversion efficiency index is as follows: Calculate the energy loss during the charging process, the calculation expression is as follows: E loss =E in -E out , where E loss Represents energy loss, E in Indicates the input electrical energy, that is, the total energy input from the charging pile to the battery, E out Indicates the output electrical energy, that is, the energy actually stored in the battery; The energy loss rate is calculated by energy loss, and the calculation expression is as follows: Where, L rate represents the energy loss rate; According to the input power and output power, the energy conversion efficiency is calculated. The calculation expression is as follows: In the formula, E eff represents the energy conversion efficiency, T env represents the ambient temperature, i.e. the ambient temperature during charging, and k is an adjustment parameter, which represents the influence of ambient temperature on energy conversion efficiency; Through the energy loss rate L rate and energy conversion efficiency E eff Generate the energy conversion efficiency index, the calculation expression is as follows: In the formula, E cI represents the energy conversion efficiency index, and β is the weight of the energy conversion efficiency.

5. The charging control method of the two-wheeled vehicle shared charging pile according to claim 3, characterized in that: In the detection window, the logic of abnormal analysis of charging adaptability and generating charging adaptability index is as follows: Collect relevant parameter data during the charging process, including charging current I, charging voltage V and ambient temperature T; According to the charging current and voltage, the charging power is defined, and the calculation expression is as follows: P = V * I, and the battery adaptability parameter is defined at the same time, A = f (T, Battery Type, Max Power), where P represents the charging power, A represents the battery adaptability parameter, and f is a comprehensive function used to calculate the battery adaptability parameter A based on temperature, battery type and maximum charging power. BatteryType represents the battery type, and Max Power represents the maximum charging power. The charging adaptability model CA is constructed using the charging power and battery adaptability parameters. The weight coefficient of the nonlinear combination is introduced to reflect the adaptability under different conditions. The calculation expression is as follows: Where W1 is the weight coefficient of charging power, and W2 is the weight coefficient of battery adaptability parameter; Combined with the charging adaptability model, the charging adaptability index is generated, and the calculation expression is as follows: Where CAI represents the charging adaptability index, T opt represents the optimal operating temperature of the battery, σ T It is the temperature fluctuation coefficient, which reflects the impact of ambient temperature changes on adaptability.

6. The charging control method of the two-wheeled vehicle shared charging pile according to claim 3, characterized in that: The energy conversion efficiency index E CI and the charging adaptability index CAI are input into the pre-trained machine model, and the charging hidden danger index CH is generated by the machine model according to the formula: Where μ1 and μ2 are energy conversion efficiency index E CI And the charging adaptability index CAI preset proportional coefficient, and μ1 and μ2 are both greater than 0.

7. The charging control method of the two-wheeled vehicle shared charging pile according to claim 6, characterized in that: The generated charging hidden danger index is compared and analyzed with the preset charging hidden danger index reference threshold, and the analysis results are as follows; If the charging hidden danger index is greater than or equal to the preset charging hidden danger index reference threshold, the abnormal charging process of the two-wheeled vehicle at the two-wheeled vehicle shared charging pile is classified as a high-risk charging state; If the charging hidden danger index is less than a preset charging hidden danger index reference threshold, the abnormal charging process of the two-wheeled vehicle at the two-wheeled vehicle shared charging pile is classified as a low-risk charging state.

8. The charging control method for a two-wheeled vehicle shared charging pile according to claim 7, characterized in that: For the assessed high-risk charging status, a data set of several charging risk indexes generated under several detection windows during the charging process of two-wheeled vehicles by two-wheeled shared charging piles is continuously obtained to establish a comprehensive analysis, and the data set is calibrated as P, then P = {CH π }={CH1, CH2, CH3,..., CH n }, π=1, 2, 3, ………, n, n is a positive integer Calculate the mean and standard deviation of several charging hidden danger indexes in the data set, and mark the mean and standard deviation of the charging hidden danger index as M 平均 and M 标准 but: The average value M of the charging hidden danger index obtained in the data set 平均 and standard deviation M 标准 The high-risk charging status is further divided by comparing with the preset reference threshold of the average value of the charging hidden danger index and the reference threshold of the standard deviation of the charging hidden danger index. The comparison results are as follows: If the average value of charging hidden danger index M 平均 Greater than or equal to the preset charging hazard index reference threshold and the charging hazard index standard deviation M 标准 Less than the preset standard deviation of the charging hidden danger index, or the average value M of the charging hidden danger index 平均 If the charging risk index is greater than or equal to the preset reference threshold, the high-risk charging state is classified as non-accidental; If the average value of charging hidden danger index M 平均 Less than the preset reference threshold of charging hidden danger index and the standard deviation of charging hidden danger index M 标准 If it is smaller than the preset standard deviation of the charging hazard index, the high-risk charging state is classified as accidental.

9. A two-wheeled vehicle shared charging pile, used to implement the charging control method of the two-wheeled vehicle shared charging pile as described in any one of claims 1 to 8, characterized in that: It includes data acquisition module, intelligent analysis module, risk assessment module, status monitoring module and early warning management module; The data acquisition module obtains various parameter information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle, including energy management information and system compatibility information; The intelligent analysis module analyzes the acquired energy management information and system compatibility information in the detection window, inputs the analyzed energy management information and system compatibility information into the pre-trained machine model, generates a charging hidden danger index through the machine model, and uses the charging hidden danger index to intelligently perceive the charging process of the two-wheeled vehicle shared charging pile for the two-wheeled vehicle; The risk assessment module divides the charging process of a two-wheeled vehicle with a shared charging pile into a high-risk charging state or a low-risk charging state based on the charging risk index, and performs intelligent analysis on the abnormal charging process of a two-wheeled vehicle with a shared charging pile; The state monitoring module continuously obtains the state information generated by the two-wheeled vehicle shared charging pile during the charging process of the two-wheeled vehicle for the assessed high-risk charging state, and divides the high-risk charging state into accidental and non-accidental types; The early warning management module adopts different early warning methods according to different high-risk charging states after classification.

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