An electric bicycle power optimization system

Abstract

This invention discloses an electric bicycle power optimization system, comprising: a power monitoring module that monitors the remaining power of the electric bicycle battery, as well as the battery's power consumption data and instantaneous output current in real time; a lifespan prediction module that collects several battery parameters and predicts the battery life based on these parameters; a vehicle detection module that monitors the electric bicycle's load-bearing data and tire pressure data in real time; a calculation module that processes the tire pressure and load-bearing data to obtain riding resistance; a processing module that obtains the battery's internal resistance based on the power consumption data and instantaneous output current, and inputs the remaining power, instantaneous output current, battery internal resistance, battery lifespan, and riding resistance into a power prediction model to predict the remaining power at the next moment; and a power control module that adjusts the electric bicycle controller's current output strategy in advance based on the predicted remaining power. This invention effectively extends the driving range of electric bicycles.

Description

Technical Field

[0001] This invention relates to the field of electric bicycle technology, and in particular to an electric bicycle power optimization system. Background Technology

[0002] Electric bicycles are vehicles that use batteries as auxiliary power and are based on ordinary bicycles, but are equipped with motors, controllers, batteries, throttles, brake levers, and display instrument systems.

[0003] Due to the weight and size restrictions imposed by the new national standard on electric bicycles, the battery compartments of these bicycles are typically designed to be smaller, limiting their capacity and resulting in shorter ranges. Frequent charging is inconvenient for users, and the long charging times are further exacerbated by the lack of fast charging support for most models. Therefore, improving the range of these new standard electric bicycles, while maintaining the same battery capacity, is a crucial issue that needs to be addressed to ensure a better user experience. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide an electric bicycle power optimization system for dynamically adjusting battery output and extending the electric bicycle's range.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an electric bicycle power optimization system, comprising:

[0006] The power monitoring module is used to monitor the remaining power of the electric bicycle battery in real time, as well as the power consumption data and instantaneous output current of the electric bicycle battery within a preset time period.

[0007] The lifespan prediction module is used to collect several battery parameters of the electric bicycle battery in real time and predict the battery lifespan based on each of the battery parameters.

[0008] The vehicle detection module is used to detect the load-bearing data and tire pressure data of electric bicycles in real time.

[0009] The calculation module, connected to the vehicle detection module, is used to process the tire pressure data and load data to obtain the riding resistance.

[0010] The processing module is connected to the power monitoring module, the life prediction module and the calculation module respectively. It is used to process the power consumption data and the instantaneous output current to obtain the battery internal resistance of the electric bicycle battery in the preset time period, and input the remaining power, the instantaneous output current, the battery internal resistance, the battery life and the riding resistance into the pre-trained power prediction model to predict the predicted remaining power at the next moment.

[0011] The power control module, connected to the processing module, is used to adjust the current output strategy of the electric bicycle controller in advance based on the predicted remaining power.

[0012] Furthermore, it also includes:

[0013] An environmental acquisition module is used to acquire several environmental interference parameters in real time, including environmental temperature data and environmental humidity data.

[0014] An optimization module, connected to the environmental acquisition module and the processing module respectively, is used to adjust the connection weights between the input layer and the hidden layer in the power prediction model based on the environmental temperature data and the environmental humidity data, and to retrain the power prediction model to obtain the optimized power prediction model.

[0015] Furthermore, the optimization module includes:

[0016] The disturbance generation unit is used to establish an environmental disturbance equation based on the ambient temperature data and the ambient humidity data, so as to output the environmental disturbance quantity;

[0017] An adjustment unit, connected to the disturbance generation unit, is used to adjust the connection weights between the battery internal resistance and the hidden layer in the power prediction model based on the amount of environmental disturbance.

[0018] Furthermore, the environmental disturbance equation is configured as follows:

[0019] E d 2 =ε1(T e -T0) 2 +ε2(H e -H0) 2

[0020] T min ≤T e ≤T max

[0021] H min ≤H e ≤H max

[0022] ε1ε2=1

[0023] Among them, E d T is used to represent the amount of environmental disturbance. e H is used to represent the ambient temperature data. e The ambient humidity data is used to represent the environmental humidity data. ε1 and ε2 represent the preset first and second disturbance coefficients, respectively. T0 and H0 represent the preset temperature and humidity standard values, respectively. min T max H is used to represent the preset lower temperature limit and upper temperature limit, respectively. min H max These are used to represent the preset lower and upper humidity limits, respectively.

[0024] Furthermore, the lifetime prediction module includes:

[0025] The data acquisition unit is used to collect the number of cycles, historical voltage, historical current and battery temperature of the electric bicycle battery during use, as battery parameters.

[0026] The prediction unit, connected to the acquisition unit, is used to input the battery parameters into a preset power battery life prediction model to predict the battery life.

[0027] Furthermore, it also includes a kinetic energy recovery module connected to the power monitoring module, the kinetic energy recovery module comprising:

[0028] The first recycling unit is used to convert the mechanical energy of the electric bicycle when the pedal is pedaled into electrical energy and store it in the electric bicycle battery.

[0029] The second recovery unit is used to convert the mechanical energy of the electric bicycle during gliding and braking into electrical energy and store it in the electric bicycle battery.

[0030] Furthermore, it also includes a wind speed detection module, connected to the calculation module, for detecting the relative wind speed on the electric bicycle in real time during riding;

[0031] The calculation module includes:

[0032] The first calculation unit is used to input the relative wind speed into a preset wind resistance calculation formula to calculate the cycling wind resistance.

[0033] The second calculation unit, connected to the first calculation unit, is used to obtain the corresponding tire friction coefficient by matching the tire pressure data in a preset storage device, calculate the riding ground friction force based on the tire friction coefficient and the load data, and then calculate the riding resistance based on the riding wind resistance and the riding ground friction force.

[0034] Furthermore, the wind resistance calculation formula is configured as follows:

[0035]

[0036] Among them, F f The values ​​are used to represent the wind resistance during cycling, p to represent air density, S to represent the pre-obtained windward area during cycling, and C. d The coefficient Δv is used to represent the preset drag coefficient, and Δv is used to represent the relative wind speed.

[0037] Furthermore, the power prediction model is an LSTM neural network model.

[0038] The beneficial effects of this invention are:

[0039] This invention monitors the remaining battery power, power consumption data, and instantaneous output current of an electric bicycle in real time. It also detects the bicycle's load-bearing data and tire pressure data. Riding resistance is calculated based on the load-bearing and tire pressure data. Battery lifespan is predicted based on collected battery parameters. Furthermore, battery internal resistance is calculated based on power consumption data and instantaneous output current. The remaining battery power, instantaneous output current, battery internal resistance, battery lifespan, and riding resistance are input into a power prediction model to predict the remaining battery power at the next moment. Finally, the current output strategy of the electric bicycle controller is adjusted in advance based on the predicted remaining battery power. This achieves dynamic optimization of the electric bicycle's power output based on battery internal data, vehicle data, and environmental interference parameters, effectively extending the electric bicycle's range and improving the user experience. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the electric bicycle power optimization system of the present invention;

[0041] Figure 2 This is a schematic diagram of the life prediction module in this invention;

[0042] Figure 3 This is a schematic diagram of the computing module in this invention;

[0043] Figure 4 This is a schematic diagram of the structure of the optimization module in this invention;

[0044] Figure 5 This is a schematic diagram of the kinetic energy recovery module in this invention.

[0045] Reference numerals in the attached diagram: 1. Power monitoring module; 2. Lifespan prediction module; 21. Acquisition unit; 22. Prediction unit; 3. Vehicle detection module; 4. Calculation module; 41. First calculation unit; 42. Second calculation unit; 5. Processing module; 6. Power control module; 7. Environmental acquisition module; 8. Optimization module; 81. Disturbance generation unit; 82. Adjustment unit; 9. Kinetic energy recovery module; 91. First recovery unit; 92. Second recovery unit; 10. Wind speed detection module. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0049] Please also see Figure 1 This embodiment provides an electric bicycle power consumption optimization system, including:

[0050] The power monitoring module 1 is used to monitor the remaining power of the electric bicycle battery in real time, as well as the power consumption data and instantaneous output current of the electric bicycle battery within a preset time period.

[0051] The lifespan prediction module 2 is used to collect several battery parameters of the electric bicycle battery in real time and predict the battery life based on each battery parameter.

[0052] Vehicle detection module 3 is used to detect the load-bearing data and tire pressure data of electric bicycles in real time.

[0053] Calculation module 4, connected to vehicle detection module 3, is used to process tire pressure data and load data to obtain riding resistance.

[0054] The processing module 5 is connected to the power monitoring module 1, the life prediction module 2 and the calculation module 4 respectively. It is used to obtain the battery internal resistance of the electric bicycle battery within a preset time period based on the power consumption data and the instantaneous output current. The remaining power, instantaneous output current, battery internal resistance, battery life and riding resistance are input into the pre-trained power prediction model to predict the predicted remaining power at the next moment.

[0055] The power control module 6 is connected to the processing module 5 and is used to adjust the current output strategy of the electric bicycle controller in advance based on the predicted remaining power.

[0056] Specifically, in this embodiment, the power monitoring module 1 includes several power sensors and a processing chip. Each power sensor is installed in the battery compartment of the electric bicycle battery and is used to collect several battery parameters within a preset time period from the current time and send them to the processing chip. The processing chip processes the battery parameters to obtain the remaining power, power consumption data, and instantaneous output current at the current time within the preset time period. The preset time period can be 1 minute. The battery parameters include historical voltage, historical current, and battery temperature at various times within 1 minute. The life prediction module 2 can be battery life prediction software in the processing chip, used to predict the battery life based on the battery parameters. The vehicle detection module 3 includes a gravity sensor and a tire pressure monitoring sensor. The gravity sensor is used to detect the load data of the electric bicycle in real time, and the tire pressure monitoring sensor is used to detect the tire pressure data of the wheels in real time. The calculation module 4 can be a resistance calculation program pre-configured in the processing chip, used to calculate and process the riding resistance based on the tire pressure data and load data. Processing module 5 can be the power prediction software in the control chip. This power prediction software is configured with a pre-trained power prediction model. This model can predict the remaining power at the next moment based on the remaining power, instantaneous output current, battery internal resistance, battery life, and riding resistance. Preferably, the power prediction model is an LSTM neural network model. The battery internal resistance can be calculated based on power consumption data and instantaneous output current. The calculation process includes: calculating the power consumption W according to W = P * T, where W represents the power consumption data per unit time T. The unit time can be 1 second. Simultaneously, P = I... 2 R and I represent the instantaneous output current, and R represents the battery's internal resistance. Therefore, R = W / I 2T. The power control module 6 can be a control chip connected to the processing chip. It is used to control the controller of the electric bicycle and adjust the current output strategy of the electric bicycle controller in advance according to the predicted remaining power. It realizes dynamic optimization of the power output of the electric bicycle based on the internal battery data and vehicle data, effectively extending the range of the electric bicycle and improving the user experience.

[0057] In this implementation plan, refer to Figure 1 As shown, it also includes:

[0058] The environmental acquisition module 7 is used to collect several environmental interference parameters in real time, including environmental temperature data and environmental humidity data.

[0059] The optimization module 8 is connected to the environmental acquisition module 7 and the processing module 5 respectively. It is used to adjust the connection weights between the input layer and the hidden layer in the power prediction model according to the environmental temperature data and environmental humidity data, and to retrain the power prediction model to obtain the optimized power prediction model.

[0060] Specifically, in this embodiment, as the temperature rises, the resistance of the internal components of the battery increases, leading to an increase in the battery's internal resistance. In this technical solution, the electric bicycle uses a lithium battery. As the humidity of the lithium battery's operating environment increases, moisture decomposes during discharge, resulting in higher internal pressure. High humidity also easily causes the battery to rust, increasing its resistance. Simultaneously, the electrolyte in the lithium battery contains LiPF6, which, upon encountering moisture, produces hydrogen fluoride gas, causing problems such as bulging, reduced thickness, and incomplete SEI film formation leading to capacity decay. To filter out the influence of ambient temperature and humidity on the battery's internal resistance, environmental interference parameters are first collected in real time by the environmental acquisition module 7. This module includes a temperature sensor and a humidity sensor; the temperature sensor collects ambient temperature data in real time, and the humidity sensor collects ambient humidity data in real time. The optimization module 8 optimizes the pre-trained power prediction model. This module 8 can be a model optimization program set in the processing chip, used to optimize the power prediction model. Since ambient temperature and humidity data affect battery internal resistance, and the original power prediction model did not consider this interference, its prediction of remaining power was biased due to the influence of ambient temperature and humidity data. Optimization module 8 adjusts the connection weights between the input and hidden layers of the power prediction model based on ambient temperature and humidity data. This optimizes the model to avoid the impact of these data on prediction accuracy, effectively improving its accuracy.

[0061] Preferred, such as Figure 4 As shown, optimization module 8 includes:

[0062] The disturbance generation unit 81 is used to establish an environmental disturbance equation based on ambient temperature data and ambient humidity data, so as to output the environmental disturbance quantity.

[0063] The adjustment unit 82 is connected to the disturbance generation unit 81 and is used to adjust the connection weight between the battery internal resistance in the input layer and the hidden layer in the power prediction model according to the amount of environmental disturbance.

[0064] Specifically, in this embodiment, the model optimization program of the optimization module 8 first needs to generate an environmental disturbance quantity based on the ambient temperature data and ambient humidity data, and then adjust the connection weight between the battery internal resistance in the input layer and the hidden layer in the power prediction model according to the environmental disturbance quantity, so as to optimize the prediction accuracy of the power prediction model.

[0065] Preferably, the environmental disturbance equation is configured as follows:

[0066] E d 2 =ε1(T e -T0) 2 +ε2(H e -H0) 2

[0067] T min ≤T e ≤T max

[0068] H min ≤H e ≤H max

[0069] ε1ε2=1

[0070] Among them, E d T is used to represent the amount of environmental disturbance. e Used to represent ambient temperature data, H e Used to represent environmental humidity data, ε1 and ε2 represent the preset first and second disturbance coefficients, respectively, and T0 and H0 represent the preset temperature and humidity standard values, respectively. min T max H is used to represent the preset lower temperature limit and upper temperature limit, respectively. min H max These are used to represent the preset lower and upper humidity limits, respectively.

[0071] Specifically, in this embodiment, both the environmental disturbance amount and the environmental temperature data are proportional to the environmental disturbance amount. The first disturbance coefficient and the second disturbance coefficient are preset initial coefficients, which can also be adjusted according to the actual environment of the electric bicycle riding location to improve the calculation accuracy of the environmental disturbance amount. The temperature standard value and humidity standard value are based on the average temperature and average humidity of the electric bicycle riding location over the course of a year. By limiting the upper and lower limits of the environmental temperature and humidity data, incorrect and invalid data are avoided from being input into the environmental disturbance amount equation, which would cause errors in the calculation of the environmental disturbance amount, thus ensuring the accuracy of the environmental disturbance amount calculation.

[0072] In this implementation plan, refer to Figure 2 As shown, the lifetime prediction module 2 includes:

[0073] The data acquisition unit 21 is used to collect the number of cycles, historical voltage, historical current and battery temperature of the electric bicycle battery during use, as battery parameters.

[0074] The prediction unit 22 is connected to the acquisition unit 21 and is used to input the battery parameters into the preset power battery life prediction model to predict the battery life.

[0075] Specifically, in this embodiment, the acquisition unit 21 acquires the number of cycles, historical voltage, historical current and battery temperature of the electric bicycle battery during use through the power sensor, as battery parameters. The prediction unit 22 inputs each battery parameter into the existing battery life prediction software (i.e. power battery life prediction model) to predict the battery life.

[0076] In this implementation plan, refer to Figure 1 As shown, it also includes a kinetic energy recovery module 9, which is connected to the power monitoring module 1, such as... Figure 5 As shown, the kinetic energy recovery module 9 includes:

[0077] The first recycling unit 91 is used to convert the mechanical energy of pedaling an electric bicycle into electrical energy and store it in the electric bicycle battery.

[0078] The second recovery unit 92 is used to convert the mechanical energy of the electric bicycle during gliding and braking into electrical energy and store it in the electric bicycle battery.

[0079] Specifically, in this embodiment, since the new national standard electric bicycles are required to be equipped with pedals, during the riding process, the kinetic energy recovery module 9 can convert the mechanical energy of pedaling, as well as the mechanical energy of gliding and braking, into electrical energy and store it in the electric bicycle battery, thereby realizing kinetic energy recovery during riding and extending the electric bicycle's range. At the same time, the power monitoring module 1 also needs to monitor the battery power after kinetic energy recovery to ensure monitoring accuracy.

[0080] In this implementation plan, refer to Figure 1 As shown, it also includes a wind speed detection module 10, which is connected to the calculation module 4, and is used to detect the relative wind speed on the electric bicycle in real time during riding.

[0081] like Figure 3 As shown, calculation module 4 includes:

[0082] The first calculation unit 41 is used to input the relative wind speed into the preset wind resistance calculation formula to calculate the riding wind resistance.

[0083] The second calculation unit 42 is connected to the first calculation unit 41. It is used to match the tire friction coefficient in a preset storage device based on the tire pressure data, calculate the riding ground friction force based on the tire friction coefficient and load data, and then calculate the riding resistance based on the riding wind resistance and the riding ground friction force.

[0084] Specifically, in this embodiment, wind resistance is also an important factor affecting the riding range of an electric bicycle during riding. The wind speed detection module 10 can be a wind speed sensor, configured on the electric bicycle, used to detect the relative wind speed on the electric bicycle in real time during riding. The calculation module 4 can be a resistance calculation program in the processing chip. First, it needs to calculate the riding wind resistance based on the relative wind speed. This process includes: inputting the relative wind speed into the wind resistance calculation formula to calculate the riding wind resistance, wherein the wind resistance calculation formula is configured as follows:

[0085] Among them, F f Used to represent wind resistance during cycling, p represents air density, S represents the pre-obtained frontal area of ​​the rider, and C... d The value is used to represent the preset drag coefficient, and Δv is used to represent the relative wind speed.

[0086] The memory configured in the processing chip stores several tire pressure data points, each associated with a corresponding tire friction coefficient. The resistance calculation program then inputs the tire friction coefficient and load data into the friction calculation formula to calculate the friction force on the riding surface. Finally, the riding wind resistance and the riding surface friction force are added together to calculate the riding resistance. Therefore, this riding resistance includes both riding wind resistance and riding surface friction force, resulting in a more comprehensive calculation and more accurate predictions from the battery power prediction model.

[0087] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An electric bicycle power consumption optimization system, characterized in that, include: The power monitoring module is used to monitor the remaining power of the electric bicycle battery in real time, as well as the power consumption data and instantaneous output current of the electric bicycle battery within a preset time period. The lifespan prediction module is used to collect several battery parameters of the electric bicycle battery and predict the battery lifespan based on each of the battery parameters. The vehicle detection module is used to detect the load-bearing data and tire pressure data of electric bicycles in real time. The calculation module is connected to the vehicle detection module and is used to process the tire pressure data and load data to obtain the riding resistance. The processing module is connected to the power monitoring module, the life prediction module and the calculation module respectively. It is used to process the power consumption data and the instantaneous output current to obtain the battery internal resistance of the electric bicycle battery in the preset time period, and input the remaining power, the instantaneous output current, the battery internal resistance, the battery life and the riding resistance into the pre-trained power prediction model to predict the predicted remaining power at the next moment. The power control module, connected to the processing module, is used to adjust the current output strategy of the electric bicycle controller in advance based on the predicted remaining power. Also includes: An environmental acquisition module is used to acquire several environmental interference parameters in real time, including environmental temperature data and environmental humidity data. An optimization module, connected to the environmental acquisition module and the processing module respectively, is used to adjust the connection weights between the input layer and the hidden layer in the power prediction model according to the environmental temperature data and the environmental humidity data, and to retrain the power prediction model to obtain the optimized power prediction model. It also includes a wind speed detection module, which is connected to the calculation module and is used to detect the relative wind speed on the electric bicycle in real time during riding; The calculation module includes: The first calculation unit is used to input the relative wind speed into a preset wind resistance calculation formula to calculate the cycling wind resistance. The second calculation unit, connected to the first calculation unit, is used to obtain the corresponding tire friction coefficient by matching the tire pressure data in a preset storage device, calculate the riding ground friction force based on the tire friction coefficient and the load data, and then calculate the riding resistance based on the riding wind resistance and the riding ground friction force.

2. The electric bicycle power consumption optimization system according to claim 1, characterized in that, The optimization module includes: The disturbance generation unit is used to establish an environmental disturbance equation based on the ambient temperature data and the ambient humidity data, so as to output the environmental disturbance quantity; An adjustment unit, connected to the disturbance generation unit, is used to adjust the connection weights between the battery internal resistance and the hidden layer in the power prediction model based on the amount of environmental disturbance.

3. The electric bicycle power consumption optimization system according to claim 2, characterized in that, The environmental disturbance equation is configured as follows: ； ； ； ； in, Used to represent the amount of environmental disturbance. Used to represent the ambient temperature data. Used to represent the ambient humidity data. , These are used to represent the preset first disturbance coefficient and the second disturbance coefficient, respectively. , These are used to represent preset temperature and humidity standard values, respectively. , These are used to represent the preset lower temperature limit and upper temperature limit, respectively. , These are used to represent the preset lower and upper humidity limits, respectively.

4. The electric bicycle power consumption optimization system according to claim 1, characterized in that: The lifetime prediction module includes: The data acquisition unit is used to collect the number of cycles, historical voltage, historical current and battery temperature of the electric bicycle battery during use, as battery parameters. The prediction unit, connected to the acquisition unit, is used to input the battery parameters into a preset power battery life prediction model to predict the battery life.

5. The electric bicycle power consumption optimization system according to claim 1, characterized in that, It also includes a kinetic energy recovery module connected to the power monitoring module, the kinetic energy recovery module comprising: The first recycling unit is used to convert the mechanical energy of the electric bicycle when the pedal is pedaled into electrical energy and store it in the electric bicycle battery. The second recovery unit is used to convert the mechanical energy of the electric bicycle during gliding and braking into electrical energy and store it in the electric bicycle battery.

6. The electric bicycle power consumption optimization system according to claim 1, characterized in that: The wind resistance calculation formula is configured as follows: in, Used to represent the wind resistance during cycling Used to indicate air density Used to represent the pre-acquired windward area of ​​the riding position. Used to indicate the preset drag coefficient. Used to represent the relative wind speed.

7. The electric bicycle power consumption optimization system according to claim 1, characterized in that: The power prediction model is an LSTM neural network model.

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