Method and device for adjusting assist ratio of electric moped and electric moped

Abstract

The embodiment of the application is suitable for the technical field of electric moped, and provides a moped power assistance ratio adjustment method, device and electric moped, the method comprises the following steps: determining the expected energy efficiency based on the expected mileage and the expected power consumption input by the user; introducing the expected energy efficiency and the target energy efficiency of the electric moped into a preset error algorithm to calculate the energy efficiency error between the expected energy efficiency and the target energy efficiency; determining the corresponding power assistance ratio change amount of the electric moped based on the energy efficiency error; and adjusting the power assistance ratio of the electric moped based on the power assistance ratio change amount. Through the method provided by the embodiment, the electric moped can automatically adjust the power assistance ratio according to the riding demand of the user, and provide a better riding experience for the user.

Description

Technical Field

[0001] This application relates to the field of electric-assisted bicycle technology, and in particular to a method, device and electric-assisted bicycle for adjusting the assist ratio. Background Technology

[0002] In the existing technology, an electric-assisted bicycle is a bicycle equipped with an auxiliary power source and a motor drive system. During riding, the electric-assisted bicycle provides auxiliary power to the bicycle's transmission system through the motor drive system, thereby reducing the pedaling stress on the user and providing a better riding experience.

[0003] In existing technology, users can select the appropriate gear according to the riding scenario and needs during the ride, and the electric-assisted bicycle can provide the corresponding assistance ratio through the motor drive system. However, electric-assisted bicycles cannot automatically adjust the assistance ratio based on the user's personalized riding data to automatically meet the user's personalized riding needs. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, device, and electric-assisted bicycle for adjusting the assist ratio, in order to meet the personalized riding needs of users.

[0005] The first aspect of this application provides a method for adjusting the power assist ratio of an electric-assisted bicycle, including:

[0006] Determine the expected energy efficiency based on the user's input of expected mileage and expected power consumption;

[0007] The desired energy efficiency and the target energy efficiency of the electric-assisted vehicle are imported into a preset error algorithm to calculate the energy efficiency error between the desired energy efficiency and the target energy efficiency.

[0008] The change in the assist ratio of the electric-assisted bicycle is determined based on the energy efficiency error.

[0009] The assist ratio of the electric-assisted vehicle is adjusted based on the change in the assist ratio and the current gear position of the electric-assisted vehicle.

[0010] A second aspect of this application provides a method for adjusting the power assist ratio of an electric-assisted bicycle, including:

[0011] Obtain the historical error corresponding to the energy efficiency error, and determine the energy efficiency change based on the energy efficiency error and the historical error;

[0012] The energy efficiency change and the energy efficiency error are input into a preset linear function to calculate the change in the assist ratio of the electric-assisted vehicle.

[0013] A third aspect of this application provides a method for adjusting the power assist ratio of an electric-assisted bicycle, including:

[0014] Obtain the historical error corresponding to the energy efficiency error, and determine the energy efficiency change rate based on the energy efficiency error and the historical error;

[0015] Based on preset fuzzy control rules, the fuzzy elements corresponding to the energy efficiency change rate and the energy efficiency error are determined;

[0016] The fuzzy elements are defuzzified based on a preset defuzzification algorithm to generate the change in the assist ratio of the electric-assisted vehicle.

[0017] A fourth aspect of this application provides a method for adjusting the power assist ratio of an electric-assisted bicycle, including:

[0018] The target energy efficiency and the energy efficiency error are input into a preset energy efficiency model to determine the energy efficiency change rate; the energy efficiency model is trained based on the expected energy efficiency change rate, the target energy efficiency, and the energy efficiency error.

[0019] The energy efficiency change rate and the energy efficiency error are input into a preset prediction model to generate the corresponding change in the assist ratio of the electric-assisted vehicle; the prediction model is trained based on the expected change in the assist ratio, the energy efficiency change rate, and the energy efficiency error.

[0020] The fifth aspect of this application provides a method for adjusting the power assist ratio of an electric-assisted bicycle, including:

[0021] If the electric-assisted vehicle is in a free gear state, the power assist ratio of the electric-assisted vehicle is adjusted based on the change in the power assist ratio.

[0022] If the gear position of the electric-assisted vehicle is fixed, then the range of assist ratio corresponding to the current gear position of the electric-assisted vehicle is determined, and the assist ratio of the electric-assisted vehicle is adjusted based on the change in assist ratio within the range of assist ratio.

[0023] A sixth aspect of this application provides a method for adjusting the power assist ratio of an electric-assisted bicycle, including:

[0024] Obtain the remaining battery percentage of the electric-assisted vehicle, and determine the corresponding remaining energy percentage based on the remaining battery percentage;

[0025] Based on the starting state of the electric-assisted vehicle, the target energy efficiency corresponding to the electric-assisted vehicle is determined;

[0026] The remaining energy ratio is converted into the remaining range of the electric-assisted vehicle using a conversion algorithm corresponding to the target energy efficiency.

[0027] A seventh aspect of this application provides a method for adjusting the power assist ratio of an electric-assisted bicycle, including:

[0028] If the electric-assisted vehicle is in motion, the distance traveled and the amount of electricity consumed by the electric-assisted vehicle within a preset power range are collected, and the current energy efficiency is determined based on the distance traveled and the amount of electricity consumed. If the current energy efficiency meets the preset effective conditions, the current energy efficiency is input into the filtering algorithm to generate the target energy efficiency corresponding to the electric-assisted vehicle.

[0029] If the electric-assisted vehicle is stationary, the current gear of the electric-assisted vehicle is obtained, and the target energy efficiency is determined based on the historical energy efficiency corresponding to the current gear.

[0030] An eighth aspect of this application provides a power assist ratio adjustment device for an electric-assisted bicycle, comprising:

[0031] The expected energy efficiency determination module is used to determine the expected energy efficiency based on the user's input of expected mileage and expected power consumption.

[0032] The error determination module is used to input the expected energy efficiency and the target energy efficiency of the electric-assisted vehicle into a preset error algorithm to calculate the energy efficiency error between the expected energy efficiency and the target energy efficiency.

[0033] The change determination module is used to determine the change in the assist ratio of the electric-assisted vehicle based on the energy efficiency error.

[0034] An adjustment module is used to adjust the power assist ratio of the electric-assisted vehicle based on the change in the power assist ratio and the current gear position of the electric-assisted vehicle.

[0035] A ninth aspect of this application provides an electric-assisted bicycle, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the assist ratio adjustment method for the electric-assisted bicycle as described in the first aspect above.

[0036] A tenth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the assist ratio adjustment method for an electric-assisted bicycle as described in the first aspect above.

[0037] The eleventh aspect of this application provides a computer program product that, when run on a computer, causes the computer to execute the electric assist ratio adjustment method for an electric-assisted bicycle described in the first aspect.

[0038] Compared with the prior art, the embodiments of this application have the following advantages:

[0039] In this embodiment, the electric-assisted bicycle can determine its desired energy efficiency based on the user's input of expected mileage and expected power consumption. After determining the desired energy efficiency, the electric-assisted bicycle can import the desired energy efficiency and target energy efficiency into a user-preset error algorithm. The electric-assisted bicycle can calculate the energy efficiency error between the desired energy efficiency and the target energy efficiency using the error algorithm, and determine the current change in the assist ratio based on the energy efficiency error. After determining the change in assist ratio, the electric-assisted bicycle can adjust its assist ratio based on the change in assist ratio and the current gear position. Through the method provided in this embodiment, the electric-assisted bicycle can determine the current change in assist ratio based on the energy efficiency error between the desired energy efficiency and the target energy efficiency, and adjust the assist ratio based on the change in assist ratio and the gear position. Specifically, when the electric-assisted bicycle determines that its target energy efficiency is greater than the user's desired energy efficiency, the electric-assisted bicycle can increase its assist ratio based on the change in assist ratio, providing the user with a more relaxed and comfortable riding experience. When the electric-assisted bicycle determines that its target energy efficiency is lower than the user's expected energy efficiency, it can reduce the assist ratio based on the change in assist ratio to increase range and achieve better riding performance. Therefore, in this embodiment, the electric-assisted bicycle can automatically adjust the assist ratio based on the expected and target energy efficiency to provide the user with a better riding experience. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of a method for adjusting the power assist ratio of an electric-assisted bicycle according to an embodiment of this application;

[0042] Figure 2 This is a flowchart illustrating the specific implementation of the assist ratio adjustment method S101 for an electric-assisted bicycle provided in the second embodiment of this application.

[0043] Figure 3 This is a flowchart illustrating the specific implementation of the assist ratio adjustment method S1012 for an electric-assisted bicycle provided in the third embodiment of this application.

[0044] Figure 4 This is a flowchart illustrating the specific implementation of the assist ratio adjustment method S103 for an electric-assisted bicycle provided in the fourth embodiment of this application.

[0045] Figure 5This is a flowchart illustrating the specific implementation of the assist ratio adjustment method S103 for an electric-assisted bicycle provided in the fifth embodiment of this application.

[0046] Figure 6 This is a flowchart illustrating the specific implementation of the assist ratio adjustment method S103 for an electric-assisted bicycle provided in the sixth embodiment of this application.

[0047] Figure 7 This is a flowchart illustrating the specific implementation of the assist ratio adjustment method S104 for an electric-assisted bicycle provided in the seventh embodiment of this application.

[0048] Figure 8 This is a schematic diagram of the calculation process for an electric-assisted bicycle provided in an embodiment of this application;

[0049] Figure 9 This is a schematic diagram of a power assist ratio adjustment device for an electric-assisted bicycle provided in an embodiment of this application;

[0050] Figure 10 This is a schematic diagram of an electric-assisted bicycle provided in an embodiment of this application. Detailed Implementation

[0051] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0052] The technical solution of this application will be described below through specific embodiments.

[0053] Reference Figure 1 This diagram illustrates a method for adjusting the assist ratio of an electric-assisted bicycle according to an embodiment of this application. This method can be applied to electric-assisted bicycles. The electric-assisted bicycle may be equipped with a control module, which controls the assist ratio and calculates the remaining mileage. This control module can be connected to multiple units of the electric-assisted bicycle, such as a battery management unit, positioning unit, and measurement unit, to obtain the bicycle's battery level and travel path information. The above-described method for determining the assist ratio of an electric-assisted bicycle can also be applied to a terminal device communicating with the electric-assisted bicycle. The terminal device can transmit data with the electric-assisted bicycle through a data transmission channel. In this case, the terminal device can be a computer, smartphone, tablet computer, microcontroller, or other devices. The method for determining the assist ratio of an electric-assisted bicycle may specifically include the following steps:

[0054] S101. Determine the expected energy efficiency based on the user's input of expected mileage and expected power consumption.

[0055] In this embodiment, after determining the remaining mileage of the electric-assisted bicycle, the user-inputted expected mileage and expected power consumption can be obtained. The electric-assisted bicycle can then calculate the expected energy efficiency based on the expected mileage and expected power consumption. The expected energy efficiency can be used to represent the expected distance the user expects the electric-assisted bicycle to travel per unit of electrical energy consumed.

[0056] In one possible implementation, the expected mileage and expected power consumption during the current ride can be input by the user before starting the ride via mileage and power consumption controls on the e-bike's input unit. Alternatively, the user can input the expected mileage and expected power consumption via an electronic device equipped with an e-bike management system user terminal. The electronic device can encapsulate the expected mileage and expected power consumption into a data packet using a pre-set data transmission protocol and send the data packet to the e-bike through a data transmission channel.

[0057] In one possible implementation, after receiving the user's input of the initial expected mileage and initial expected power consumption, the electric-assisted bicycle can determine whether the initial expected mileage is less than or equal to the bicycle's current remaining mileage. If the electric-assisted bicycle determines that the initial expected mileage is less than or equal to the remaining mileage, it can confirm that the current initial expected mileage is the expected mileage. If the electric-assisted bicycle determines that the initial expected mileage is greater than the remaining mileage, it can generate a first alarm message. The first alarm message can be used to indicate to the user that the currently input initial expected mileage is incorrect. The electric-assisted bicycle can also determine whether the initial expected power consumption is less than or equal to the bicycle's remaining power. If the electric-assisted bicycle determines that the initial expected power consumption is less than or equal to the remaining power, it can confirm that the current initial expected power consumption is the expected power consumption. If the electric-assisted bicycle determines that the initial expected power consumption is greater than the remaining power, it can generate a second alarm message. The second alarm message can be used to indicate to the user that the currently input initial expected power consumption is incorrect.

[0058] In one possible implementation, after obtaining the expected power consumption, the electric-assisted bicycle can convert the expected power consumption into expected energy consumption according to an energy consumption conversion table. The electric-assisted bicycle can input the expected energy consumption and expected mileage into a user-preset expected energy efficiency algorithm, and the expected energy efficiency is calculated through the algorithm. The specific calculation formula for the expected energy efficiency algorithm can be as follows:

[0059]

[0060] Where η0 can represent the expected energy efficiency of the electric-assisted vehicle; S0 can represent the expected mileage input by the user; and ΔSOE0 can represent the expected energy consumption.

[0061] S102. The expected energy efficiency and the target energy efficiency are imported into a preset error algorithm to calculate the energy efficiency error between the expected energy efficiency and the target energy efficiency.

[0062] In this embodiment, after determining the user's desired energy efficiency, the electric-assisted bicycle can import the desired energy efficiency and target energy efficiency into a user-preset error algorithm to calculate the energy efficiency error between the desired and target energy efficiency. The target energy efficiency of the electric-assisted bicycle can be used to represent the actual distance the bicycle can travel per unit of electrical energy consumed under current road conditions.

[0063] In one possible implementation, the error algorithm used in this embodiment of the electric-assisted bicycle may include, but is not limited to, various algorithms such as absolute error algorithm, average error algorithm, relative error algorithm, and standard deviation algorithm. Those skilled in the art can calculate the energy efficiency error between the expected energy efficiency and the target energy efficiency using any error algorithm known in the field. It should be noted that this application does not specifically limit the method for calculating the energy efficiency error.

[0064] S103. Determine the change in the assist ratio of the electric-assisted vehicle based on the energy efficiency error.

[0065] In this embodiment, after determining the energy efficiency error between the expected energy efficiency and the expected energy efficiency, the change in the assist ratio of the electric-assisted bicycle can be determined based on the energy efficiency error. In this embodiment, the assist ratio can be the ratio between the motor output torque and the pedal input torque in the electric-assisted bicycle, and the assist ratio can represent the amount of assistance provided by the assist system of the electric-assisted bicycle.

[0066] S104. Adjust the power assist ratio of the electric-assisted vehicle based on the change in the power assist ratio.

[0067] In this embodiment, after determining the change in the assist ratio of the electric-assisted bicycle, the assist ratio of the electric-assisted bicycle can be adjusted according to the change in the assist ratio and the current gear status of the electric-assisted bicycle.

[0068] In one possible implementation, after calculating the change in assist ratio, the electric-assisted bicycle can adjust its assist ratio based on the relationship between the desired energy efficiency and the target energy efficiency. When the electric-assisted bicycle determines that the target energy efficiency is greater than the user's desired energy efficiency, it can increase the assist ratio based on the change in assist ratio. When the electric-assisted bicycle determines that the target energy efficiency is less than the user's desired energy efficiency, it can decrease the assist ratio based on the change in assist ratio. When the electric-assisted bicycle determines that the target energy efficiency is equal to the user's desired energy efficiency, it can maintain the current assist ratio.

[0069] In this embodiment, the electric-assisted bicycle can adjust its assist ratio based on the energy efficiency error between the desired energy efficiency and the target energy efficiency. Since the target energy efficiency represents the actual distance the electric-assisted bicycle can travel per unit of electrical energy consumed, and the desired energy efficiency represents the expected distance the user hopes to travel per unit of electrical energy consumed, when the desired energy efficiency is greater than the target energy efficiency, the current range of the electric-assisted bicycle is insufficient to complete the user's expected mileage. In this case, the electric-assisted bicycle can appropriately reduce its assist ratio to reduce power consumption and increase range, achieving a better riding effect. When the target energy efficiency is greater than the desired energy efficiency, the current range of the electric-assisted bicycle is excessive. In this case, the electric-assisted bicycle can appropriately increase its assist ratio to provide a more relaxed and comfortable riding experience while meeting range requirements, thus improving the user's riding experience. Therefore, the method provided in this embodiment can improve the usability of electric-assisted bicycles and the user's riding experience.

[0070] Figure 2 A flowchart illustrating the specific implementation of a power assist ratio adjustment method S101 for an electric-assisted bicycle according to a second embodiment of this application is shown. See also... Figure 2 Compared to Figure 1 In the embodiment described above, the method for adjusting the power assist ratio of an electric-assisted bicycle provided in this embodiment includes S1011 to S1013 before S101, which are detailed below:

[0071] S1011. Obtain the remaining battery ratio of the electric-assisted vehicle, and determine the corresponding remaining energy ratio based on the remaining battery ratio.

[0072] In this embodiment, when a user needs to obtain the remaining mileage of the electric-assisted bicycle, they can send a request to the bicycle. The electric-assisted bicycle can respond to the user's request and obtain the current State of Charge (SOC). After determining the current State of Charge, the electric-assisted bicycle can determine the current State of Energy (SOE) based on the State of Charge. The State of Charge represents the proportion of usable electrical energy in the electric-assisted bicycle's battery to its nominal capacity. The nominal capacity is the discharge capacity of the battery during discharge. The SOE represents the proportion of usable electrical energy in the electric-assisted bicycle's battery to its nominal electrical energy. The nominal electrical energy is the electrical energy that the battery can output under certain discharge conditions.

[0073] In this embodiment, after obtaining the remaining battery power ratio of the electric-assisted bicycle, the remaining energy ratio can be determined based on the remaining battery power ratio, and the remaining mileage of the electric-assisted bicycle can be calculated based on the remaining energy ratio. Since the remaining energy ratio can more accurately represent the battery's power supply capacity, the method provided in this application embodiment can improve the accuracy of the remaining mileage calculation compared to the prior art which only calculates the remaining mileage based on the battery's rated capacity and discharge current.

[0074] In one possible implementation, when the e-bike determines that it meets the pre-set mileage calculation conditions, it can obtain the remaining battery level and begin calculating the remaining mileage. When the e-bike is stationary, the mileage calculation condition can be whether a change in operating state has occurred; that is, when the e-bike detects that the user has started the e-bike, it can obtain the current remaining battery level and begin calculating the remaining mileage.

[0075] In one possible implementation, when the electric-assisted bicycle is in motion, the mileage calculation condition can be whether there has been a change in mileage data. During the bicycle's operation, it can collect mileage data at pre-set intervals. This mileage data can include the bicycle's energy efficiency rating, remaining energy ratio, and gear position. The bicycle can determine whether the current mileage data collected in the current interval is the same as the previous mileage data collected in the previous interval. If the bicycle determines that any of the three data points—energy efficiency rating, remaining energy ratio, or gear position—is different from the previous data, it can determine that the current mileage meets the data change condition. The bicycle can then calculate its remaining mileage based on the collected current mileage data.

[0076] In one possible implementation, after obtaining the current remaining battery percentage of the electric-assisted bicycle, the remaining battery percentage is converted into a remaining energy percentage using a user-preset energy conversion table. This energy conversion table can store multiple remaining battery percentages and their corresponding remaining energy percentages. The energy conversion table can be generated by researchers during the development process using constant current discharge. Researchers can continuously perform constant current discharge on the electric-assisted bicycle in a laboratory setting, continuously collecting multiple remaining battery percentages, multiple discharge currents, and multiple discharge voltages during the constant current discharge process. Based on the collected discharge current and discharge voltage corresponding to each remaining battery percentage, researchers can calculate the remaining energy percentage corresponding to that remaining battery percentage and generate the energy conversion table based on all remaining battery percentages and their corresponding remaining energy percentages.

[0077] In one possible implementation, the electric-assisted bicycle may include a battery management unit (BMS), which may include multiple battery packs, a control module, a data acquisition module, a data analysis module, and a communication module. Specifically, the BMS in the electric-assisted bicycle can determine the remaining charge ratio of the bicycle's batteries in real time using the current integration method. When using the current integration method, the BMS can collect the total current of all battery packs in the electric-assisted bicycle in real time through the data acquisition module, and then integrate the multiple total currents collected by the data acquisition module over time to calculate the state of charge (SOC) of all battery packs, i.e., to calculate the remaining charge ratio of all battery packs. The data analysis module can calculate the remaining charge ratio of all battery packs using an ampere-hour integration algorithm or a coulomb counting method. The BMS can also calculate the remaining charge ratio of the electric-assisted bicycle's batteries in real time using various methods such as electrochemical impedance spectroscopy, open-circuit voltage method, neural network algorithm, and filtering model algorithm. It should be noted that those skilled in the art can obtain the remaining charge ratio using any method known in the art, and the embodiments of this application do not limit the specific method for obtaining the remaining charge ratio.

[0078] S1012. Based on the starting state of the electric-assisted vehicle, determine the target energy efficiency corresponding to the electric-assisted vehicle.

[0079] In this embodiment, after determining the current remaining energy ratio of the electric-assisted bicycle, the target energy efficiency of the electric-assisted bicycle can be determined according to the starting state of the electric-assisted bicycle.

[0080] In one possible implementation, the electric-assisted bicycle can include multiple different gears, each with a different target energy efficiency. After determining the current remaining energy ratio of the electric-assisted bicycle, the target energy efficiency can be determined based on the current gear and the starting state to calculate the remaining range corresponding to the current gear. The electric-assisted bicycle can also obtain the target energy efficiency for each gear individually, and can calculate the remaining range for each gear separately based on multiple target energy efficiencies.

[0081] S1013. Using a conversion algorithm corresponding to the target energy efficiency, the remaining energy ratio is converted into the remaining mileage of the electric-assisted vehicle.

[0082] In this embodiment, after determining the current target energy efficiency of the electric-assisted bicycle, the remaining energy ratio can be input into the conversion algorithm corresponding to the target energy efficiency. The electric-assisted bicycle can then use the conversion algorithm to convert the current remaining energy ratio into the current remaining range.

[0083] In one possible implementation, the conversion algorithm can be represented as follows:

[0084] L = SOE * η1

[0085] Where L can represent the remaining range of the electric-assisted vehicle; SOE can represent the remaining energy ratio of the electric-assisted vehicle; and η1 can represent the current target energy efficiency of the electric-assisted vehicle.

[0086] In one possible implementation, after calculating the remaining mileage, the electric-assisted bicycle can display the current remaining mileage on its display unit. When a user queries the remaining mileage via an electronic device, the electric-assisted bicycle, after calculating the remaining mileage, can also encapsulate the remaining mileage into a data packet according to a pre-set data transmission protocol and send the data packet to the user's electronic device. This electronic device can have a user terminal with an electric-assisted bicycle management system installed. The electric-assisted bicycle can display its remaining mileage in real time on the electronic device by sending data packets. This electronic device can be any electronic device capable of installing applications, such as a smartphone, computer, or tablet.

[0087] In this embodiment, when an electric-assisted bicycle needs to calculate its remaining mileage, it can obtain its current remaining battery percentage and determine its current remaining energy ratio based on that percentage. After determining the remaining energy ratio, the electric-assisted bicycle can determine its target energy efficiency based on its current operating state. Finally, the electric-assisted bicycle can convert its current remaining energy ratio into its current remaining mileage using a conversion algorithm corresponding to the target energy efficiency. Through the method provided in this embodiment, the electric-assisted bicycle can determine its remaining mileage based on its current remaining energy ratio and target energy efficiency. Compared to existing technologies that calculate remaining mileage solely based on battery capacity, the method provided in this embodiment can calculate the remaining mileage not only based on the electric-assisted bicycle's current driving conditions but also on its remaining energy status. Therefore, the method provided in this embodiment can improve the accuracy of calculating the remaining mileage of an electric-assisted bicycle.

[0088] Figure 3 This diagram illustrates a detailed implementation flowchart of a power assist ratio adjustment method S1012 for an electric-assisted bicycle according to a third embodiment of this application. See also... Figure 3 Compared to Figure 2 In the embodiment described above, the method for adjusting the power assist ratio of an electric-assisted bicycle, step S1012 includes steps S301 to S302, which are detailed below:

[0089] S301. If the electric-assisted vehicle is in a driving state, the driving distance and energy consumption of the electric-assisted vehicle within a preset energy range are collected, and the current energy efficiency is determined based on the driving distance and energy consumption; if the current energy efficiency meets the preset effective conditions, the current energy efficiency is input into the filtering algorithm to generate the target energy efficiency corresponding to the electric-assisted vehicle.

[0090] In this embodiment, the starting state of the electric-assisted bicycle can include a driving state and a stopped state. After determining the remaining energy ratio of the electric-assisted bicycle, the current starting state can be obtained. If the electric-assisted bicycle determines that it is currently in a driving state, it can collect the driving distance and energy consumption according to the user-preset energy range. The electric-assisted bicycle can determine the current energy efficiency based on the driving distance and energy consumption collected within the energy range. After calculating the current energy efficiency corresponding to the energy range, the electric-assisted bicycle can determine whether the current energy efficiency meets the user-preset valid conditions. If the electric-assisted bicycle determines that the current energy efficiency meets the valid conditions, it can input the current energy efficiency into the user-preset filtering algorithm and generate the target energy efficiency corresponding to the electric-assisted bicycle through the filtering algorithm.

[0091] In one possible implementation, after generating the target energy efficiency, the electric-assisted bicycle can obtain the current gear within the current battery capacity range and write the target energy efficiency and the corresponding current gear into a storage unit. The target energy efficiency written into the storage unit can then serve as the historical energy efficiency corresponding to the current gear.

[0092] In one possible implementation, the electric-assisted bicycle can determine its starting state using wheel speed sensors. After starting, the electric-assisted bicycle can continuously monitor the wheel speed using the wheel speed sensors. If the wheel speed reported by the wheel speed sensor is greater than 0, the electric-assisted bicycle can determine that it is currently moving. If the wheel speed reported by the wheel speed sensor is equal to 0, the electric-assisted bicycle can determine that it is currently stationary. The electric-assisted bicycle can also obtain its location information in real time through its positioning unit and determine its starting state based on this location information. Alternatively, the electric-assisted bicycle can determine its starting state by checking whether its location information has changed.

[0093] In one possible implementation, once the electric-assisted bicycle determines that it has entered a driving state, it can record the initial remaining battery ratio and the initial travel distance at the moment of entering the driving state. During the driving process, the electric-assisted bicycle can continuously acquire multiple intermediate remaining battery ratios. Each time an intermediate remaining battery ratio is acquired, the electric-assisted bicycle can calculate the consumed electricity based on the intermediate remaining battery ratio and the initial remaining battery ratio, and determine whether the consumed electricity is greater than or equal to a user-preset battery threshold. If, at a certain moment, the electric-assisted bicycle determines that the current consumed electricity is greater than or equal to the user-preset battery threshold, then the electric-assisted bicycle can determine that the current moment meets the battery range requirement. The electric-assisted bicycle can then determine the intermediate remaining battery ratio corresponding to the consumed electricity as the ending remaining battery ratio corresponding to the current battery range, and collect the ending travel distance at the current moment.

[0094] Electric-assisted bicycles (EV-Bikes) can calculate the driving distance corresponding to the current battery level range based on the initial and final driving distances. They can also convert the initial and final remaining battery ratios into initial and final remaining energy ratios using an energy conversion table, and calculate the energy consumption within the current battery level range. EV-Bikes can determine the current energy efficiency corresponding to the current battery level range based on the driving distance and energy consumption. After determining the driving distance and energy consumption for the current battery level range, EV-Bikes can use the final driving distance and final remaining battery ratio of the current range as the initial driving distance and initial remaining battery ratio for the next battery level range. EV-Bikes can determine multiple target energy efficiencies during operation using multiple battery level ranges and continuously update the remaining range based on these target energy efficiencies.

[0095] In one possible implementation, the electric-assisted bicycle can collect wheel speed or acceleration data via wheel speed sensors or acceleration sensors during its operation. This data can then be input into a preset distance calculation algorithm to indirectly calculate the distance traveled. Alternatively, the electric-assisted bicycle can directly obtain its distance traveled via a positioning unit during operation.

[0096] In one possible implementation, the specific calculation formula of the current energy efficiency algorithm can be as follows:

[0097]

[0098] Where η1 can represent the current energy efficiency of the electric-assisted vehicle within the power range; S1 can represent the driving range of the electric-assisted vehicle within the power range; and ΔSOE1 can represent the energy consumption of the electric-assisted vehicle within the power range.

[0099] In one possible implementation, after determining the current energy efficiency corresponding to the current battery level range, the electric-assisted bicycle can obtain the effective energy efficiency range corresponding to the travel distance within the current battery level range. The electric-assisted bicycle can determine whether the current energy efficiency meets the effective conditions by judging whether it is within the effective energy efficiency range. If the electric-assisted bicycle determines that the current energy efficiency is within the effective energy efficiency range, it can confirm that the current energy efficiency meets the effective conditions. If the electric-assisted bicycle determines that the current energy efficiency is not within the effective energy efficiency range, it can confirm that the current energy efficiency does not meet the effective conditions. Through the method provided in this embodiment, the electric-assisted bicycle can filter out abnormal situations such as excessively long uphill or downhill sections within the battery level range leading to excessively low or high current energy efficiency, thus ensuring that the target energy efficiency calculated by the electric-assisted bicycle is within the effective range. The effective range corresponding to each travel distance can be obtained by researchers through experiments using loads of different weights according to the load-bearing capacity of the electric-assisted bicycle.

[0100] In one possible implementation, the electric-assisted bicycle can continuously determine whether its assist status and gear position remain unchanged within any given battery level range. If the electric-assisted bicycle determines at any moment that its assist status or gear position has changed, it can update the battery level range and use the remaining battery percentage and travel distance collected at the current moment as the initial remaining battery percentage and initial travel distance for the updated battery level range. If the electric-assisted bicycle determines at any moment that its assist status and gear position remain unchanged, it can maintain the current battery level range and continue to collect intermediate remaining battery percentages and intermediate travel distances.

[0101] In one possible implementation, the filtering algorithm used in the electric-assisted bicycle can be any of the following: sliding window filtering, weighted average filtering, median filtering, Kalman filtering, etc. Those skilled in the art can use any well-known filtering algorithm in the field to filter the current energy efficiency and generate a target energy efficiency. The embodiments in this application are not intended to specifically limit the filtering algorithm.

[0102] S302. If the electric-assisted vehicle is in a stopped state, the current gear of the electric-assisted vehicle is obtained, and the target energy efficiency is determined based on the historical energy efficiency corresponding to the current gear.

[0103] In this embodiment, if the electric-assisted bicycle determines that it is currently stationary, it can obtain its current gear and retrieve the historical energy efficiency corresponding to that gear from the storage unit. The electric-assisted bicycle can determine its target energy efficiency based on the historical energy efficiency corresponding to the current gear; that is, it can use the historical energy efficiency as the target energy efficiency to calculate its remaining mileage. The historical energy efficiency can be data collected and recorded in the storage unit when the user last used that gear. If the storage unit does not contain the historical energy efficiency corresponding to that gear, meaning that gear has not been used, the electric-assisted bicycle can query the initial energy efficiency corresponding to that gear from the user-preset initial energy efficiency table and determine the initial energy efficiency corresponding to the current gear as the current target energy efficiency. The initial energy efficiency table can store the initial energy efficiency corresponding to each gear.

[0104] The method provided in this embodiment allows the electric-assisted bicycle to retrieve its historical energy efficiency corresponding to the current gear from the storage unit when it is stationary, serving as its target energy efficiency. When the bicycle is in motion, it can collect its travel distance and energy consumption within a preset energy range, and calculate its target energy efficiency based on these data. Therefore, the method allows the electric-assisted bicycle to continuously learn and update its target energy efficiency during operation, and determine its remaining mileage based on the updated target energy efficiency. Thus, the method provides an improved accuracy in calculating the remaining mileage, enabling the electric-assisted bicycle to determine its remaining mileage based on its travel status.

[0105] Figure 4 This document illustrates a flowchart illustrating the specific implementation of a power assist ratio adjustment method S103 for an electric-assisted bicycle according to a fourth embodiment of this application. See also... Figure 4 Compared to Figure 1 In the embodiment described above, the method for adjusting the power assist ratio of an electric-assisted bicycle, step S103 includes steps S401 to S402, which are detailed below:

[0106] S401. Obtain the historical error corresponding to the energy efficiency error, and determine the energy efficiency change based on the energy efficiency error and the historical error.

[0107] In this embodiment, after determining the current energy efficiency error of the electric-assisted bicycle, it can retrieve multiple historical errors corresponding to the energy efficiency error from the storage unit based on the current gear position. The electric-assisted bicycle can then determine the energy efficiency change at the current gear position based on the current energy efficiency error and the multiple historical errors. This energy efficiency change can represent the change in the target energy efficiency of the electric-assisted bicycle at the current gear position.

[0108] In one possible implementation, after determining the current energy efficiency error of the electric-assisted bicycle, the energy efficiency error can be written into a storage unit according to the current gear of the electric-assisted bicycle when calculating the energy efficiency error, so that the current energy efficiency error becomes a historical error in the next calculation and is used to calculate the next energy efficiency change.

[0109] In one possible implementation, the formula for calculating the change in energy efficiency can be as follows:

[0110] ΔT=K p Δη+K i ∫Δη(t)dt

[0111] Where ΔT represents the current change in energy efficiency of the electric-assisted vehicle; Δη represents the current energy efficiency error of the electric-assisted vehicle; ∫Δη(t)dt represents the integral value of the current energy efficiency error and multiple historical errors over time; K p It can represent the proportionality coefficient; K i It can represent the integral coefficient.

[0112] S402. Input the energy efficiency change and the energy efficiency error into a preset linear function to calculate the change in the assist ratio of the electric-assisted vehicle.

[0113] In one possible implementation, after determining the change in energy efficiency, the proportional-integral loop control module can input the change in energy efficiency and the energy efficiency error into a linear function preset by the user, and calculate the change in the assist ratio of the electric-assisted bicycle through the linear function.

[0114] In one possible implementation, after determining the change in the assist ratio and the energy efficiency error of the electric-assisted bicycle, the change in assist ratio and the energy efficiency error can be input into the proportional-integral loop control module to adjust the assist ratio of the electric-assisted bicycle.

[0115] In this embodiment, the electric-assisted bicycle can determine the change in energy efficiency at the current gear by using the historical error and the current energy efficiency error, and then determine the change in the assist ratio based on the change in energy efficiency and the energy efficiency error. Using the method provided in this embodiment, the electric-assisted bicycle can calculate the change in assist ratio by considering the user's riding habits. Therefore, the change in assist ratio calculated by the method provided in this embodiment has high usability.

[0116] Figure 5 This document illustrates a flowchart illustrating the specific implementation of a power assist ratio adjustment method S103 for an electric-assisted bicycle according to the fifth embodiment of this application. See also... Figure 5 Compared to Figure 1 In the embodiment described above, the method for adjusting the power assist ratio of an electric-assisted bicycle provided in this embodiment includes S103, which comprises S501 to S503, as detailed below:

[0117] S501. Obtain the historical error corresponding to the energy efficiency error, and determine the energy efficiency change rate based on the energy efficiency error and the historical error.

[0118] In this embodiment, after determining the current energy efficiency error of the electric-assisted bicycle, it can retrieve multiple historical errors corresponding to the energy efficiency error from the storage unit based on the current gear position. The electric-assisted bicycle can then determine the energy efficiency change rate at the current gear position based on the current energy efficiency error and the multiple historical errors. The energy efficiency change rate can be calculated by the electric-assisted bicycle by differentiating the energy efficiency error and the multiple historical errors corresponding to the current gear position over time. The energy efficiency change rate can be used to represent the rate of change of the target energy efficiency of the electric-assisted bicycle per unit time at the current gear position.

[0119] S502. Based on preset fuzzy control rules, determine the fuzzy elements corresponding to the energy efficiency change rate and the energy efficiency error.

[0120] In this embodiment, after determining the energy efficiency change rate, the electric-assisted bicycle can determine the fuzzy elements corresponding to the energy efficiency change rate and energy efficiency error according to the fuzzy control rules preset by the user.

[0121] In one possible implementation, after calculating the energy efficiency change rate, the electric-assisted bicycle can calculate the change membership degree and energy efficiency membership degree corresponding to the energy efficiency change rate and energy efficiency error, respectively, using a membership function. Specifically, the membership function in the electric-assisted bicycle can be a trigonometric membership function. The energy efficiency membership degree can be calculated using the absolute value of the energy efficiency error. The electric-assisted bicycle can determine the change element corresponding to the energy efficiency change rate from the change rate set based on the change membership degree. The electric-assisted bicycle can determine the error element corresponding to the energy efficiency error from the error set based on the energy efficiency membership degree. After determining the change element and error element, the electric-assisted bicycle can determine the corresponding fuzzy element by looking up a table according to the user-preset fuzzy control rules.

[0122] The set of change rates can contain 5 variable elements, which can be negative large (NB), negative small (NS), zero (Z), small (PS), and large (PB). The universe of discourse for the set of change rates can be [-1, 1]. Here, -1 represents a rapid decrease in energy efficiency error over time, 0 represents no change in energy efficiency error, and 1 represents a rapid increase in energy efficiency error over time. The set of errors can contain 3 error elements, which can be small (S), medium (M), and large (B). The universe of discourse for the set of errors can be [0, 1]. Here, 0 represents that the target energy efficiency is relatively close to the expected energy efficiency, and 1 represents that the target energy efficiency is completely unsatisfactory. The set of contribution ratios can contain 5 fuzzy elements, which can be negative large (NB), negative small (NS), zero (MT), small (PS), and large (PB). The universe of discourse for the set of contribution ratios can be [-1, 1]. Here, -1 represents minimizing the contribution ratio to the maximum extent, 0 represents keeping the contribution ratio constant, and 1 represents maximizing the contribution ratio.

[0123] It should be noted that, in this embodiment, the membership function can also be any type of membership function, such as a Gaussian membership function, a generalized bell-shaped membership function, a S-shaped membership function, a trapezoidal membership function, or a Z-shaped membership function. Those skilled in the art can calculate it using any membership function known in the field. This embodiment is not intended to specifically limit the membership function.

[0124] In one possible implementation, the fuzzy control rules can be specifically shown in the table below.

[0125]

[0126] S503. Defuzzify the fuzzy elements based on a preset defuzzification algorithm to generate the change in the assist ratio corresponding to the electric-assisted vehicle.

[0127] In this embodiment, after the electric-assisted bicycle determines the fuzzy elements corresponding to the energy efficiency change rate and energy efficiency error through fuzzy control rules, it can defuzzify the fuzzy elements based on the user-preset defuzzification algorithm to generate the corresponding change in the assist ratio of the electric-assisted bicycle.

[0128] In one possible implementation, the defuzzification algorithm in the electric-assisted bicycle can be the centroid method. When the electric-assisted bicycle defuzzifies fuzzy elements using the centroid method, it can multiply the membership functions corresponding to the fuzzy elements and the change in assist ratio to generate a distribution curve for each fuzzy element. Then, the electric-assisted bicycle can input the distribution curve into a pre-set weighted average function. The electric-assisted bicycle can calculate the area and centroid position of the distribution curve using the weighted average function. The electric-assisted bicycle can then use the centroid position as the change in assist ratio after defuzzifying the fuzzy element.

[0129] It should be noted that the defuzzification algorithm in this application can also be a variety of defuzzification methods, such as the maximum membership degree averaging method, the area equal division method, the minimum maximum membership degree method, and the maximum maximum membership degree method. Those skilled in the art can defuzzify fuzzy elements using any defuzzification method known in the field. The embodiments in this application are not intended to specifically limit the defuzzification algorithm.

[0130] The method provided in this embodiment allows for the calculation of the change in the assist ratio of an electric-assisted bicycle based on the user's riding habits. Therefore, the change in the assist ratio calculated using the method provided in this embodiment is more accurate.

[0131] Figure 6 A flowchart illustrating the specific implementation of a power assist ratio adjustment method S103 for an electric-assisted bicycle according to the sixth embodiment of this application is shown. See also... Figure 6 Compared to Figure 3 In the embodiment described above, the method for adjusting the power assist ratio of an electric-assisted bicycle, step S103 includes steps S601 to S602, which are detailed below:

[0132] S601. Input the target energy efficiency and the energy efficiency error into a preset energy efficiency model to determine the energy efficiency change rate through the energy efficiency model; the energy efficiency model is trained based on the expected energy efficiency change rate, the target energy efficiency, and the energy efficiency error.

[0133] In this embodiment, the electric-assisted bicycle may include an energy efficiency model and a prediction model. After determining the target energy efficiency and energy efficiency error, the electric-assisted bicycle can input the target energy efficiency and energy efficiency error into a preset energy efficiency model. The energy efficiency model can be trained using the expected energy efficiency change rate, the target energy efficiency, and the energy efficiency error. The electric-assisted bicycle can determine the energy efficiency change rate through the energy efficiency model.

[0134] In one possible implementation, the energy efficiency model can be any machine learning model or statistical model. The user can input the energy efficiency model to be trained into the electric-assisted bicycle. Simultaneously, the user can also input the expected energy efficiency change rate, target energy efficiency, and energy efficiency error as training samples into the electric-assisted bicycle to train the energy efficiency model. During training, after each initial energy efficiency change rate is generated, the electric-assisted bicycle can calculate a first loss value using a preset first loss function. Specifically, the electric-assisted bicycle can calculate the mean squared error between the initial energy efficiency change rate and the expected energy efficiency change rate corresponding to the sample as the first loss value. The electric-assisted bicycle can backpropagate the calculated first loss value to the energy efficiency model to be trained and update the parameters in the model based on the first loss value. When the first loss value reaches a user-defined first stopping condition, the electric-assisted bicycle can use the current energy efficiency model to be trained as the energy efficiency model.

[0135] S602. Input the energy efficiency change rate and the energy efficiency error into a preset prediction model to generate the corresponding change in the assist ratio of the electric-assisted vehicle; the prediction model is trained based on the expected change in the assist ratio, the energy efficiency change rate and the energy efficiency error.

[0136] In this embodiment, after determining the energy efficiency change rate, the electric-assisted bicycle can input the energy efficiency change rate and energy efficiency error into a preset prediction model. The prediction model can be trained using the expected change in assist ratio, the energy efficiency change rate, and the energy efficiency error. The electric-assisted bicycle can then determine the change in assist ratio using the prediction model.

[0137] In one possible implementation, the prediction model can be any machine learning model or statistical model. The user can input the prediction model to be trained into the electric-assisted bicycle. Simultaneously, the user can also input the expected change in assist ratio, energy efficiency change rate, and energy efficiency error as training samples into the electric-assisted bicycle to train the prediction model. During training, after each initial change in assist ratio is generated, the electric-assisted bicycle can calculate a second loss value using a preset second loss function. Specifically, the electric-assisted bicycle can calculate the mean squared error between the initial change in assist ratio and the expected change in assist ratio corresponding to the sample as the second loss value. The electric-assisted bicycle can backpropagate the calculated second loss value to the prediction model to be trained and update the parameters in the prediction model based on the second loss value. When the second loss value reaches a second stopping condition set by the user, the electric-assisted bicycle can use the current prediction model to be trained as the prediction model.

[0138] The method provided in this embodiment allows electric-assisted bicycles to calculate the change in assist ratio using energy efficiency and prediction models, and adjust the assist ratio of the electric-assisted bicycle according to the change in assist ratio, so as to provide users with a more intelligent driving experience and efficient range performance.

[0139] Figure 7 A flowchart illustrating the specific implementation of a power assist ratio adjustment method S104 for an electric-assisted bicycle according to the seventh embodiment of this application is shown. See also... Figure 7 Compared to Figure 1 In the embodiment described above, the method for adjusting the power assist ratio of an electric-assisted bicycle, step S104 includes steps S701 to S702, which are detailed below:

[0140] S701. If the gear position of the electric-assisted vehicle is in a free state, the power assist ratio of the electric-assisted vehicle is adjusted based on the change in the power assist ratio.

[0141] In this embodiment, the electric-assisted bicycle can include multiple different gears, each gear corresponding to a different range of assist ratios. After determining the current change in the assist ratio, the electric-assisted bicycle can adjust its assist ratio based on the current gear position. The gear position can be either a free state or a fixed state. A free state indicates that the electric-assisted bicycle can freely change gears. A fixed state indicates that the electric-assisted bicycle cannot change gears. If the electric-assisted bicycle determines that its gear position is free, it can adjust the assist ratio based on the change in assist ratio, and can also update its current gear based on the range of assist ratios to which the adjusted assist ratio belongs.

[0142] S702. If the gear position of the electric-assisted vehicle is fixed, then determine the range of assist ratio corresponding to the current gear position of the electric-assisted vehicle, and adjust the assist ratio of the electric-assisted vehicle based on the change in assist ratio within the range of assist ratio.

[0143] In this embodiment, if the electric-assisted bicycle determines that its current gear is fixed, it can obtain the current gear and consult a range table to determine the corresponding range of assist ratios. This range table stores all the electric-assisted bicycle's gears and their corresponding assist ratio ranges. After determining the range, the electric-assisted bicycle can adjust its assist ratio within that range based on any changes in assist ratio.

[0144] The method provided in this embodiment allows the electric-assisted bicycle to adjust its assist ratio according to the gear position, thereby meeting user needs while providing a more intelligent driving experience and efficient range performance.

[0145] like Figure 8 The diagram shown is a schematic representation of the calculation process for an electric-assisted bicycle according to an embodiment of this application. See also... Figure 8 When a user starts the electric-assisted bicycle, the bicycle can obtain its current energy efficiency and remaining energy ratio based on its current gear, and calculate its remaining mileage. Then, the bicycle can obtain the user's input desired mileage and desired energy consumption, and determine the desired energy efficiency based on these. The bicycle can compare the desired energy efficiency with the target energy efficiency and adjust the assist ratio accordingly. When the bicycle starts moving, it can obtain the initial battery level, initial remaining energy ratio, and initial travel distance for the current battery range. Within the current battery range, the bicycle continuously obtains the assist ratio and checks whether it is non-zero and whether it remains constant. If the assist ratio is zero and / or changes within the current battery range, the bicycle updates the current battery range, using the current battery level, remaining energy ratio, and travel distance as the initial battery level, initial remaining energy ratio, and initial travel distance for the updated current battery range.

[0146] If the assist ratio is not zero and remains unchanged within the current battery range, the electric-assisted bicycle can periodically collect the intermediate battery level, intermediate remaining energy ratio, and intermediate travel distance according to the user-preset collection interval. The electric-assisted bicycle can calculate the energy consumption value based on the initial and intermediate battery levels and determine if the energy consumption value is greater than or equal to a preset battery threshold. If the electric-assisted bicycle determines that the energy consumption value is less than the battery threshold, it can continue to collect the intermediate battery level, intermediate remaining energy ratio, and intermediate travel distance at the specified intervals. If the electric-assisted bicycle determines that the energy consumption value is greater than or equal to the battery threshold, it can determine that the currently collected intermediate battery level, intermediate remaining energy ratio, and intermediate travel distance are the end battery level, end remaining energy ratio, and end travel distance of the current battery range, respectively. The electric-assisted bicycle can calculate the current energy efficiency of the current battery range based on the initial remaining energy ratio, initial travel distance, end remaining energy ratio, and end travel distance. The electric-assisted bicycle can filter the current energy efficiency using a filtering algorithm to generate a target energy efficiency and calculate the current remaining mileage based on the target energy efficiency.

[0147] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0148] Reference Figure 9 The diagram illustrates a power assist ratio adjustment device for an electric-assisted bicycle according to an embodiment of this application. Specifically, it may include a desired energy efficiency determination module 901, an error determination module 902, a change determination module 903, and an adjustment module 904, wherein:

[0149] The expected energy efficiency determination module 901 is used to determine the expected energy efficiency based on the expected mileage and expected power consumption input by the user.

[0150] Error determination module 902 is used to input the expected energy efficiency and the target energy efficiency of the electric-assisted vehicle into a preset error algorithm to calculate the energy efficiency error between the expected energy efficiency and the target energy efficiency.

[0151] The change determination module 903 is used to determine the change in the assist ratio of the electric-assisted vehicle based on the energy efficiency error.

[0152] The adjustment module 904 is used to adjust the power assist ratio of the electric-assisted vehicle based on the change in the power assist ratio and the current gear position of the electric-assisted vehicle.

[0153] The change determination module can also be used to obtain the historical error corresponding to the energy efficiency error, determine the energy efficiency change based on the energy efficiency error and the historical error, and input the energy efficiency change and the energy efficiency error into a preset linear function to calculate the change in the assist ratio corresponding to the electric-assisted vehicle.

[0154] The change determination module can also be used to obtain the historical error corresponding to the energy efficiency error, determine the energy efficiency change rate based on the energy efficiency error and the historical error; determine the fuzzy elements corresponding to the energy efficiency change rate and the energy efficiency error based on preset fuzzy control rules; and defuzzify the fuzzy elements based on preset defuzzification algorithm to generate the change in the assist ratio corresponding to the electric-assisted vehicle.

[0155] The change determination module can also be used to input the target energy efficiency and the energy efficiency error into a preset energy efficiency model to determine the energy efficiency change rate through the energy efficiency model; the energy efficiency model is trained based on the expected energy efficiency change rate, the target energy efficiency, and the energy efficiency error; the energy efficiency change rate and the energy efficiency error are input into a preset prediction model to generate the change in the assist ratio corresponding to the electric-assisted vehicle; the prediction model is trained based on the expected change in the assist ratio, the energy efficiency change rate, and the energy efficiency error.

[0156] The adjustment module can also be used to adjust the assist ratio of the electric-assisted vehicle based on the change in assist ratio if the gear position of the electric-assisted vehicle is in a free state; and to determine the assist ratio range corresponding to the current gear position of the electric-assisted vehicle if the gear position of the electric-assisted vehicle is in a fixed state, and to adjust the assist ratio of the electric-assisted vehicle based on the change in assist ratio within the range of the assist ratio.

[0157] The expected energy efficiency determination module can also be used to obtain the remaining battery ratio of the electric-assisted vehicle and determine the corresponding remaining energy ratio based on the remaining battery ratio; determine the target energy efficiency of the electric-assisted vehicle based on the starting state of the electric-assisted vehicle; and convert the remaining energy ratio into the remaining mileage of the electric-assisted vehicle through a conversion algorithm corresponding to the target energy efficiency.

[0158] The expected energy efficiency determination module can also be used to: if the electric-assisted vehicle is in a driving state, collect the driving distance and energy consumption of the electric-assisted vehicle within a preset energy range, and determine the current energy efficiency based on the driving distance and energy consumption; if the current energy efficiency meets preset effective conditions, input the current energy efficiency into a filtering algorithm to generate the target energy efficiency corresponding to the electric-assisted vehicle; if the electric-assisted vehicle is in a stationary state, obtain the current gear of the electric-assisted vehicle, and determine the target energy efficiency based on the historical energy efficiency corresponding to the current gear.

[0159] As the apparatus embodiments are basically similar to the method embodiments, they are described in a relatively simple manner. For relevant details, please refer to the description in the method embodiment section.

[0160] Reference Figure 10 The diagram illustrates an electric-assisted bicycle according to an embodiment of this application. Figure 10 As shown, the electric-assisted bicycle 1000 in this embodiment includes: a processor 1010, a memory 1020, and a computer program 1021 stored in the memory 1020 and executable on the processor 1010. When the processor 1010 executes the computer program 1021, it implements the steps in various embodiments of the above-described electric-assisted bicycle remaining mileage adjustment method, for example... Figure 1 Steps S101 to S104 are shown. Alternatively, when the processor 1010 executes the computer program 1021, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 9 The functions of modules 901 to 904 are shown.

[0161] For example, the computer program 1021 can be divided into one or more modules / units, which are stored in the memory 1020 and executed by the processor 1010 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which can be used to describe the execution process of the computer program 1021 in the electric-assisted bicycle 1000. For example, the computer program 1021 can be divided into a desired energy efficiency determination module, an error determination module, a change determination module, and an adjustment module, wherein:

[0162] The expected energy efficiency determination module is used to determine the expected energy efficiency based on the user's input of expected mileage and expected power consumption.

[0163] The error determination module is used to input the expected energy efficiency and the target energy efficiency of the electric-assisted vehicle into a preset error algorithm to calculate the energy efficiency error between the expected energy efficiency and the target energy efficiency.

[0164] The change determination module is used to determine the change in the assist ratio of the electric-assisted vehicle based on the energy efficiency error.

[0165] An adjustment module is used to adjust the power assist ratio of the electric-assisted vehicle based on the change in the power assist ratio and the current gear position of the electric-assisted vehicle.

[0166] The electric-assisted bicycle 1000 may be the electric-assisted bicycle described in the foregoing embodiments. The electric-assisted bicycle 1000 may include, but is not limited to, a processor 1010 and a memory 1020. Those skilled in the art will understand that... Figure 10 This is merely one example of an electric-assisted bicycle 1000 and does not constitute a limitation on the electric-assisted bicycle 1000. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electric-assisted bicycle 1000 may also include input / output devices, network access devices, buses, etc.

[0167] The processor 1010 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0168] The memory 1020 can be an internal storage unit of the electric-assisted bicycle 1000, such as a hard drive or memory of the electric-assisted bicycle 1000. The memory 1020 can also be an external storage device of the electric-assisted bicycle 1000, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electric-assisted bicycle 1000. Furthermore, the memory 1020 can include both internal storage units and external storage devices of the electric-assisted bicycle 1000. The memory 1020 is used to store the computer program 1021 and other programs and data required by the electric-assisted bicycle 1000. The memory 1020 can also be used to temporarily store data that has been output or will be output.

[0169] This application also discloses an electric-assisted bicycle, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the electric-assisted bicycle's assist ratio adjustment method as described in the foregoing embodiments.

[0170] This application also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the electric assist ratio adjustment method for an electric-assisted bicycle as described in the foregoing embodiments.

[0171] This application also discloses a computer program product that, when run on a computer, causes the computer to execute the electric assist ratio adjustment method for the electric assist bicycle described in the foregoing embodiments.

[0172] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for adjusting the assist ratio of an electric-assisted bicycle, characterized in that, include: Obtain the remaining battery percentage of the electric-assisted vehicle, and determine the corresponding remaining energy percentage based on the remaining battery percentage; Based on the starting state of the electric-assisted vehicle, the target energy efficiency corresponding to the electric-assisted vehicle is determined; The start-up state includes a driving state and a stopped state. Determining the target energy efficiency of the electric-assisted vehicle based on its start-up state includes: if the electric-assisted vehicle is in a driving state, collecting the driving distance and energy consumption within a preset energy range, and determining the current energy efficiency based on the driving distance and energy consumption; if the current energy efficiency meets preset validity conditions, inputting the current energy efficiency into a filtering algorithm to generate the target energy efficiency corresponding to the electric-assisted vehicle; if the electric-assisted vehicle is in a stopped state, obtaining the current gear of the electric-assisted vehicle, and determining the target energy efficiency based on the historical energy efficiency corresponding to the current gear. Determine the expected energy efficiency based on the user's input of expected mileage and expected power consumption; The desired energy efficiency and the target energy efficiency of the electric-assisted vehicle are imported into a preset error algorithm to calculate the energy efficiency error between the desired energy efficiency and the target energy efficiency. The change in the assist ratio of the electric-assisted bicycle is determined based on the energy efficiency error. The power assist ratio of the electric-assisted bicycle is adjusted based on the change in the power assist ratio.

2. The method according to claim 1, characterized in that, The step of determining the change in the assist ratio of the electric-assisted bicycle based on the energy efficiency error includes: Obtain the historical error corresponding to the energy efficiency error, and determine the energy efficiency change based on the energy efficiency error and the historical error; The energy efficiency change and the energy efficiency error are input into a preset linear function to calculate the change in the assist ratio of the electric-assisted vehicle.

3. The method according to claim 1, characterized in that, The step of determining the change in the assist ratio of the electric-assisted bicycle based on the energy efficiency error includes: Obtain the historical error corresponding to the energy efficiency error, and determine the energy efficiency change rate based on the energy efficiency error and the historical error; Based on preset fuzzy control rules, the fuzzy elements corresponding to the energy efficiency change rate and the energy efficiency error are determined; The fuzzy elements are defuzzified based on a preset defuzzification algorithm to generate the change in the assist ratio of the electric-assisted vehicle.

4. The method according to claim 1, characterized in that, The step of determining the change in the assist ratio of the electric-assisted bicycle based on the energy efficiency error further includes: The target energy efficiency and the energy efficiency error are input into a preset energy efficiency model to determine the energy efficiency change rate; the energy efficiency model is trained based on the expected energy efficiency change rate, the target energy efficiency, and the energy efficiency error. The energy efficiency change rate and the energy efficiency error are input into a preset prediction model to generate the corresponding change in the assist ratio of the electric-assisted vehicle; the prediction model is trained based on the expected change in the assist ratio, the energy efficiency change rate, and the energy efficiency error.

5. The method according to claim 1, characterized in that, The adjustment of the electric-assisted bicycle's assist ratio based on the change in assist ratio includes: If the electric-assisted vehicle is in a free gear state, the power assist ratio of the electric-assisted vehicle is adjusted based on the change in the power assist ratio. If the gear position of the electric-assisted vehicle is fixed, then the range of assist ratio corresponding to the current gear position of the electric-assisted vehicle is determined, and the assist ratio of the electric-assisted vehicle is adjusted based on the change in assist ratio within the range of assist ratio.

6. The method according to any one of claims 1-5, characterized in that, Before determining the expected energy efficiency based on the user-input expected mileage and expected power consumption, the method further includes: The remaining energy ratio is converted into the remaining range of the electric-assisted vehicle using a conversion algorithm corresponding to the target energy efficiency.

7. A power assist ratio adjustment device for an electric-assisted bicycle, characterized in that, include: The expected energy efficiency determination module is used to obtain the remaining battery ratio of the electric-assisted vehicle and determine the corresponding remaining energy ratio based on the remaining battery ratio. Based on the starting state of the electric-assisted vehicle, the target energy efficiency corresponding to the electric-assisted vehicle is determined; The startup state includes a driving state and a stopped state. Determining the target energy efficiency of the electric-assisted vehicle based on its startup state includes: if the electric-assisted vehicle is in a driving state, collecting the driving distance and energy consumption within a preset energy range, and determining the current energy efficiency based on the driving distance and energy consumption; if the current energy efficiency meets preset validity conditions, inputting the current energy efficiency into a filtering algorithm to generate the target energy efficiency corresponding to the electric-assisted vehicle; if the electric-assisted vehicle is in a stopped state, obtaining the current gear of the electric-assisted vehicle, and determining the target energy efficiency based on the historical energy efficiency corresponding to the current gear; and determining the expected energy efficiency based on the user-input expected mileage and expected energy consumption. The error determination module is used to input the expected energy efficiency and the target energy efficiency of the electric-assisted vehicle into a preset error algorithm to calculate the energy efficiency error between the expected energy efficiency and the target energy efficiency. The change determination module is used to determine the change in the assist ratio of the electric-assisted vehicle based on the energy efficiency error. An adjustment module is used to adjust the power assist ratio of the electric-assisted vehicle based on the change in the power assist ratio and the current gear position of the electric-assisted vehicle.

8. An electric-assisted bicycle, the electric-assisted bicycle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the assist ratio adjustment method for an electric-assisted bicycle as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the assist ratio adjustment method for the electric-assisted bicycle as described in any one of claims 1-6.

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