Electric bicycle controller parameter setting system and method

Abstract

The invention belongs to the technical field of electric bicycle control, and provides an electric bicycle controller parameter setting system and method, and the method comprises the steps: obtaining basic control parameters, power parameters, assistance parameters, motor state parameters and controller temperature parameters related to the operation of a controller; according to the controller temperature parameter and the motor operation state, in combination with the thermal state trend prediction model, outputting thermal states, including a cold state, a normal temperature state and a thermal state, of a period of time in the future; and respectively establishing and generating optimal parameter calibration data corresponding to different predicted thermal states according to different operation states. By constructing the thermal state trend prediction model, the thermal state trend of the controller can be predicted in a prospective manner, and the optimal parameter set calibrated for each state in advance is automatically called, so that the extensive mode of statically calibrating a set of parameters to cope with all working conditions is optimized, and the controller can be self-optimized according to the actual operation state; and the control accuracy and the intelligent level are improved.

Description

Technical Field

[0001] This invention belongs to the field of electric bicycle control technology, and in particular relates to an electric bicycle controller parameter setting system and method. Background Technology

[0002] As the core control unit of the electric bicycle, the performance of the controller directly determines the riding experience, energy efficiency and reliability of the vehicle. The controller manages the power output, response characteristics and protection mechanism of the motor through a large number of preset control parameters.

[0003] Currently, the commonly used controller parameter setting scheme is the static calibration method. That is, before the controller leaves the factory, engineers test and debug at room temperature to set a fixed and optimal set of parameters and write it into the controller. This scheme treats the controller's operating state as single and unchanging, and all control logic is based on a fixed set of parameters.

[0004] However, this static calibration method has significant limitations. First, the controller's state changes dynamically during actual operation. In particular, its internal temperature fluctuates drastically between cold, normal, and hot states depending on the load (such as climbing or accelerating) and ambient temperature. At different temperatures, motor characteristics, MOSFET on-resistance, and sensor accuracy will all change, but fixed parameters cannot adaptively adjust to these changes. This can lead to overly conservative parameters in cold states, resulting in weak start-up and slow response. In hot states, the controller has to perform abrupt power cuts due to heat dissipation requirements, causing a sudden drop in power and severely compromising the smoothness and predictability of the ride, thus affecting the user experience. Secondly, there are highly complex couplings and mutual interferences among the various parameters of the controller. For example, adjusting the start-up characteristic parameters may affect the smoothness at medium and high speeds, and optimizing the boost response may cause current oscillations. During calibration, it will fall into the problem of affecting the whole system with one change. In order to take into account various extreme working conditions, we often have to adopt a compromise and conservative strategy, which cannot fully release the performance potential of the controller. Therefore, the present invention provides a parameter setting system and method for an electric bicycle controller. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an electric bicycle controller parameter setting system and method to solve the aforementioned technical problems in the prior art.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows: A method for setting parameters of an electric bicycle controller, comprising: Acquire basic control parameters, power parameters, assist parameters, motor status parameters, and controller temperature parameters related to controller operation; Based on the controller temperature parameters and motor operating status, combined with the thermal state trend prediction model, the thermal state for a future period of time is output, including cold state, normal temperature state and hot state; Optimal parameter calibration data corresponding to different predicted thermal states are generated for different operating states. Among them, the optimal parameter calibration data corresponding to the cold state is used to determine the optimal values ​​of the current loop parameters, speed loop parameters, and basic power parameters; the optimal parameter calibration data corresponding to the normal temperature state is used to determine the optimal values ​​of the starting characteristic parameters and the assist response parameters; and the optimal parameter calibration data corresponding to the hot state is used to determine the optimal values ​​of the current limiting parameters and the torque attenuation parameters. During the operation of the controller, the corresponding optimal parameter calibration data is automatically called according to the operating status, and a dynamic compensation mechanism based on real-time control temperature is introduced to compensate the power parameters and assist parameters in real time.

[0007] An electric bicycle controller parameter setting system, the system comprising: Parameter acquisition module: Acquires basic control parameters, power parameters, assist parameters, motor status parameters, and controller temperature parameters related to controller operation; Status recognition module: Based on the controller temperature parameters and motor operating status, combined with the thermal status trend prediction model, outputs the thermal status for a future period of time, including cold state, normal temperature state and hot state; The split-state calibration module generates optimal parameter calibration data corresponding to different predicted thermal states for different operating states. Among them, the optimal parameter calibration data corresponding to the cold state is used to determine the optimal values ​​of the current loop parameters, speed loop parameters, and basic power parameters; the optimal parameter calibration data corresponding to the normal temperature state is used to determine the optimal values ​​of the starting characteristic parameters and the assist response parameters; and the optimal parameter calibration data corresponding to the hot state is used to determine the optimal values ​​of the current limiting parameters and the torque attenuation parameters. Dynamic compensation module: During the operation of the controller, the corresponding optimal parameter calibration data is automatically called according to the operating status, and a dynamic compensation mechanism based on real-time control temperature is introduced to compensate the power parameters and assist parameters in real time.

[0008] The beneficial effects of this invention are as follows: This invention constructs a thermal state trend prediction model, which can predict the future thermal state trend of the controller and automatically call the optimal parameter set pre-calibrated for each state. This optimizes the coarse mode of statically calibrating a set of parameters to deal with all operating conditions, enabling the controller to self-optimize according to the actual operating state and improve the accuracy and intelligence of control. This invention uses a split-state calibration method to independently optimize three sets of optimal parameters for different thermal states before leaving the factory. These parameters are self-consistent under their respective target states, thereby eliminating the contradiction of fixed parameter sets mutually restricting each other and being difficult to balance under different temperature states. This allows the controller to release its potential in the cold state, perform evenly at room temperature, and ensure safety in the hot state. This invention introduces a positive compensation mechanism in both cold and normal temperature states, which can temporarily improve the power response when high load demand is anticipated and there is sufficient heat dissipation margin. This fully utilizes the high performance potential of the controller at low temperatures, providing users with a more powerful power experience. In the cold state, through a dedicated parameter set, the problems of weak start-up and slow response are optimized, thereby fully utilizing the performance potential of the controller. Attached Figure Description

[0009] Figure 1 This is a flowchart showing the steps for setting parameters for an electric bicycle controller. Figure 2 This is a module architecture diagram of the electric bicycle controller parameter setting system. Detailed Implementation

[0010] The technical solution of the present invention will be further described in a non-limiting manner below with reference to specific embodiments.

[0011] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a method for setting parameters of an electric bicycle controller, which includes the following steps: Step S10: Obtain the basic control parameters, power parameters, assist parameters, motor status parameters, and controller temperature parameters related to controller operation; Specifically, in step S10, the basic control parameters include, but are not limited to: current loop proportional-integral-derivative (PID) parameters, speed loop PID parameters, and phase sequence control parameters; Basic parameters are used to maintain the basic control logic of the motor; Power parameters include, but are not limited to: motor output current, voltage, power limit, and torque output characteristics; Power parameters are used to reflect the power performance of the motor; Assist parameters include, but are not limited to: assist mode settings (such as economy mode, sport mode), assist response sensitivity, and assist torque curve; The assist parameter is used to define the assist power behavior of an electric bicycle during riding; Motor status parameters include, but are not limited to: monitoring the real-time operating status of the motor, including motor speed, rotor position, motor temperature, and vibration data; Motor status parameters are used to assess the current operating conditions of the motor; The controller temperature parameters are measured by temperature sensors and used as input for subsequent thermal state prediction and parameter compensation.

[0012] Step S20: Based on the controller temperature parameters and motor operating status, and combined with the thermal state trend prediction model, output the thermal state for a future period of time, including cold state, normal temperature state and hot state; Specifically, in step S20, control temperature parameters and motor operating status parameters are extracted, including motor phase current, motor speed, and DC bus voltage. The extracted data is then filtered (e.g., Kalman filtering or low-pass filtering). Calculate the approximate thermal load power of the controller based on the motor's operating status. The calculation formula is: ; in, This is the on-resistance of the MOSFET power device, which can be found in a table. It is the switching loss coefficient. It is the switching frequency. It is the phase current of the motor. It is the DC bus voltage; The current controller temperature, controller temperature change rate, current thermal load power, predicted workload, ambient temperature, and heat dissipation coefficient are combined to form a feature vector, which is used as the input to the thermal state trend prediction model. Furthermore, the temperature change rate is controlled by differential calculation based on current and historical temperature data. Within a fixed sampling period, the current temperature and the temperature at the previous moment are recorded and the difference is calculated. The difference is then compared with the sampling period to obtain the temperature change rate. The calculated temperature change rate is then filtered by moving average. Predict workload by forecasting future power demand trends based on the current operation of the motor and user input commands; For example, a sustained high throttle opening or a climbing state indicates that the load will remain high or continue to increase in the future; Calculate the output power based on the motor speed, motor phase current, and DC bus voltage. The calculation process for output power is as follows: the motor phase current is multiplied by the DC bus voltage to obtain the motor input power, and the motor input power is multiplied by the system total efficiency to obtain the output power. The overall system efficiency includes controller efficiency and motor efficiency, which is obtained by looking up a preset efficiency MAP chart. This MAP chart uses motor speed and load torque (and motor phase current) as input parameters. Based on user input, the system estimates future power demand trends as a quantified percentage using a predefined load forecasting algorithm. Among them, the predefined load prediction algorithm is: Extracting trend features: Continuously monitor and record the timing data of motor phase current and throttle / assist torque commands in the recent period (e.g., the past 3-5 seconds). By calculating the moving average and first-order difference (slope of change) of the data within the time window, the average level and trend of the current load (e.g., stable, rising or falling) are quantified. Operating condition pattern recognition: The algorithm combines the real-time speed of the motor and the acceleration of the vehicle to perform pattern matching for the current riding condition; For example, the high-load continuous mode is identified as maintaining a low speed (simulating climbing) or a continuous high acceleration state under a large throttle opening; the periodic fluctuation mode is identified as frequent starts and stops or acceleration and deceleration in urban riding; the low-load cruise mode is identified as maintaining a constant speed riding at a high speed under a small throttle opening. Future load projection: Based on extracted trend features and identified operating conditions, the load is projected for a future period of time (e.g., 5-10 seconds) using a pre-configured rule base. Specifically, if a high-load continuous mode is identified and the current is trending upward, it is predicted that the load will remain high or increase further from the current level in the future. If it is identified as a low-load cruise mode and the changes are smooth, the load is predicted to remain stable or decrease slightly in the future. Output quantified load forecast levels (such as low, medium, high) or percentage changes in power demand as one of the direct inputs to the thermal state trend forecasting model; Ambient temperature is obtained through an ambient temperature sensor; The heat dissipation coefficient is calibrated through the thermal balance test of the entire electric bicycle, and the threshold value is used to look up a table to characterize the heat dissipation efficiency in real time. The thermal state trend prediction model is built based on first-order thermodynamics principles. The received feature vectors are substituted into a calibrated differential equation, which describes the relationship between the controller's heat generation and heat dissipation. The differential equation is as follows: ; in, The model needs to calculate the output, namely the controller temperature at the next predicted time (t+Δt). The controller temperature measured at the current time t. It is the heat load power. It is the ambient temperature. It is the total thermal resistance from the controller to the environment, i.e., the heat dissipation coefficient. It is the heat capacity of the controller. It is a fixed calculation cycle; By solving the differential equation in real time, the thermal state trend prediction model can calculate the predicted temperature value at each moment in the future. The predicted temperature values ​​are compared with preset cold and hot thresholds; If both the peak value and the mean value in the predicted temperature data are less than the cold state threshold, the output is determined to be cold. If the predicted temperature value falls within or remains between the cold and hot thresholds, the output is determined to be at room temperature. If any value in the predicted temperature curve reaches or exceeds the hot state threshold, the output is determined to be hot. In order to prevent frequent state switching near the threshold, a hysteresis interval is introduced. Optionally, the temperature required to exit the hot state must be lower than the threshold for entering the hot state. It should be noted that the cold threshold is set based on the characteristic of the on-resistance of the power device changing with temperature. When the controller temperature is below a certain value, the on-resistance is significantly lower than the resistance at room temperature, the controller is more efficient, and it has the premise of being able to withstand a larger current without overheating. The hot threshold is the maximum allowable junction temperature of the power device and the overheat protection point of the controller. It must be set in a safe range far below the physical limits of the device to leave a buffer space for instantaneous fluctuations (for example, set at 75°C, while the absolute maximum junction temperature of the device is 150°C, and the hardware overheat protection point is 85°C).

[0013] Step S30: For different operating states, establish and generate optimal parameter calibration data corresponding to different predicted thermal states. Specifically: Cold calibration is used to determine the optimal values ​​of current loop parameters, velocity loop parameters, phase sequence parameters, and basic dynamic parameters; Room temperature calibration is used to determine start-up characteristic parameters, power assist response parameters, and mid-to-high speed ride comfort parameters. Hot calibration is used to determine current limiting parameters, torque attenuation parameters, and protection triggering parameters. Specifically, in step S30, the optimal parameter calibration is obtained through split-state testing, and the controller, motor and dynamometer related equipment are placed in a controllable environmental chamber or tested under specific climatic conditions through actual vehicle testing. Design standardized test cycle for each thermal state (cold, normal temperature, hot) to cover typical riding scenarios under that state; For example, cold-state test cycles: mainly in low-temperature environments (such as 0-15°C), frequent cold start, rapid acceleration, and hill-climb tests are performed; Normal temperature test cycle: At room temperature (e.g., 25°C), a comprehensive road condition simulation covering various speed and load changes including starting, cruising, acceleration, and deceleration is conducted; Hot test cycle: The controller is heated up by running under high temperature (e.g., 35-45°C) or high load continuously, and subjected to extreme tests of high power ramping and repeated start-stop. The controller's internal variables and system performance data are collected in real time using calibration tools. Based on the test data, the optimal parameters under each thermal state are determined through parameter adjustment and optimization iterations. The specific determination process is as follows: Cold calibration: The goal is to fully utilize the low on-resistance of power devices at low temperatures to maximize dynamic response and output capability. The parameter determination process is as follows: Current loop / speed loop parameters: In the cold test cycle, a set of conservative basic PID parameters are first set. By analyzing the current response speed and stability data (such as current overshoot and settling time) under rapid acceleration and ramp conditions, the proportional (P) coefficient is gradually increased to improve the response speed, and the integral (I) coefficient is adjusted to eliminate steady-state error until the fastest dynamic response is obtained while ensuring that the system is free from oscillation. Phase sequence parameters: At low temperatures, by monitoring the alignment between the motor position sensor signal and the back EMF waveform, the phase sequence compensation angle is finely adjusted to ensure the highest commutation accuracy and optimal motor operating efficiency. Basic power parameters: Given the high efficiency and heat dissipation margin of power devices at low temperatures, the peak current limit and power limit are gradually increased during testing until the allowable upper limit of the motor or battery is reached, or the vehicle-level power target is met, thereby determining their optimal values ​​under cold conditions. Room temperature calibration: The goal is to achieve the best balance between power, riding comfort, smoothness, and energy efficiency. The process of parameter determination is as follows: Starting characteristic parameters: By adjusting the slope and limit of the starting torque ramp function, and evaluating the vehicle's starting smoothness (such as impact) and acceleration time, the optimal starting curve that balances smoothness and speed is determined. Assist response parameters: In different assist modes, by changing the mapping relationship (gain and curve shape) of assist torque to pedal torque / speed, and combining objective data (such as the following of pedal force and vehicle speed), the set of parameters with the most natural and linear response is determined. Medium and high speed ride comfort parameters: Under cruise and acceleration conditions, by adjusting the low-pass filter time constant of the speed loop and introducing the speed feedforward coefficient, the fluctuations in speed and current are minimized, thereby determining the parameters that can effectively suppress vibration and noise. Hot calibration: The goal is to ensure system safety, prevent overheating damage, and maintain basic riding functionality through smooth and predictable power derating, avoiding sudden power interruption. This involves determining the parameters as follows: Current limiting parameters: In the hot limit test, based on the real-time monitored controller temperature, a current upper limit curve that decreases linearly with the temperature rise is set. By verifying whether the current upper limit curve can effectively suppress the excessive temperature rise and avoid abrupt power cut-off, the current limit value at each temperature point is determined. Torque decay parameter: In conjunction with current limiting, the output torque decay curve as temperature increases is set to ensure that the power has been smoothly and linearly reduced to a safe level before approaching the overheat protection point, thereby determining the starting temperature point and decay slope of the decay. Protection trigger parameters: Based on the maximum allowable junction temperature of the power device and the hardware design margin, set multiple protection thresholds. For example, set the power reduction start point at a temperature much lower than the hardware emergency shutdown point. Verify through testing whether this setting can provide sufficient buffer for the system, thereby reliably avoiding triggering hardware protection.

[0014] Step S40: During the operation of the controller, the corresponding optimal parameter calibration data is automatically called according to the operating status, and a dynamic compensation mechanism based on real-time control temperature is introduced to compensate the power parameters and assist parameters in real time, thereby eliminating the mutual influence between parameters under different states and reducing the performance degradation caused by controller overheating. Specifically, in step S40, the thermal status indicator output by the aforementioned step S20 is acquired in real time; Based on the acquired thermal state identifier, the optimal parameter calibration data established in the aforementioned step S30 is automatically called as the control basis for the current operating cycle. When the thermal state changes, a linear interpolation algorithm is used to smooth the parameter transition, thereby reducing sudden changes or oscillations in power output caused by parameter jumps due to thermal state switching. Furthermore, when the predicted thermal state switches from state A to state B, the system does not immediately switch all parameters from state A to state B. Instead, within a preset transition time (e.g., 2-5 seconds), it performs linear interpolation on key parameters (e.g., current loop P value, power limit, torque curve slope) between state A and state B values, so that the parameters smoothly transition to the target value over time, ensuring the continuity and smoothness of the riding experience. Based on the optimal parameter calibration data, a dynamic compensation mechanism based on real-time temperature control is introduced, mainly affecting the power parameters and assist parameters, including: Regarding power parameters: Under hot conditions, when the real-time temperature exceeds the hot threshold, a smoother and more linear power derating is achieved compared to the basic hot parameter set; In the controller's non-volatile memory, a preset temperature-compensation coefficient curve is used, with the difference between the real-time temperature and the hot threshold as the horizontal axis and the derating compensation coefficient as the vertical axis. The difference between the real-time temperature and the thermal threshold is used as the overtemperature value; The temperature-compensation coefficient curve is preset as a piecewise linear function. If the overtemperature value is less than or equal to 0, the derating compensation coefficient is 1, indicating that no additional derating is activated. If the overtemperature value is greater than 0 and less than or equal to the maximum allowable overtemperature value, the derating compensation coefficient is a linearly decreasing function from 1 to 0.7. If the over-temperature value exceeds the maximum allowable over-temperature value, the derating compensation factor remains at 0.7 until hardware protection is triggered; The maximum allowable over-temperature value is the maximum temperature rise range that the controller is allowed to withstand. It is set based on the thermal safety margin of the power devices and the need to maintain the basic riding function of the vehicle. In each control cycle, the real-time temperature is acquired to calculate the overtemperature value, and the corresponding derating compensation coefficient is obtained based on the overtemperature value. The current limiting parameter and torque attenuation parameter in the optimal calibration parameter data of the hot calibration are compensated and corrected: Multiply the current limiting parameter curve at each temperature point by the current limiting value by the derating compensation factor to obtain the current limiting correction value; Multiply the torque output value at each temperature point on the torque attenuation parameter curve by the derating compensation factor to obtain the torque attenuation correction value. Regarding the assist parameters: In cold and normal temperature conditions, when approaching high load but still at a low temperature, provide stronger power in advance, make full use of the heat dissipation margin, and positively compensate for the power response parameters and basic power parameters. Compensation is triggered when the predicted workload level is high or the percentage change in power demand exceeds a preset positive threshold. Set compensation weights based on the predicted future load level or percentage change and the current temperature margin of the controller. Preferably, the predicted load factor, temperature margin factor, and gain coefficient are multiplied together, and the resulting product is summed with 1 to obtain the compensation weight. The prediction load factor is the normalized prediction workload. The difference between the cold threshold and the real-time controller temperature is used as the controller temperature deviation value, and the difference between the cold threshold and the real-time ambient temperature is used as the ambient temperature deviation value. The ratio of the controller temperature deviation value and the ambient temperature deviation value is calculated as the temperature margin factor. The gain coefficient is a constant used to control the overall compensation amplitude. It is usually determined during the calibration phase based on the vehicle's dynamic performance target. For example, it can be 0.2. Based on the compensation weight, the gain of the assist torque curve, i.e., the power response parameter, is enhanced, along with the base power limit, i.e., the base power parameter. Multiply the output value of the assist torque curve by the compensation weight to obtain the assist torque correction value; Multiply the base power limit by the compensation weight to obtain the base power correction value.

[0015] It should be noted that compensation based on real-time temperature is because temperature is the most direct and critical factor causing changes in the characteristics of the internal components of the controller, thus affecting the validity of the parameters. Although other variables (such as voltage and speed) are also related, temperature changes directly alter the on-resistance, sensor accuracy, and system heat dissipation requirements, thereby constraining the power output characteristics and system safety. This implementation pre-optimizes three sets of parameters for different temperature ranges (cold, normal, and hot) through split-state calibration, fundamentally decoupling the mutual interference of parameters under different states. Then, fine-tuning is performed through real-time temperature compensation, achieving early and smooth derating under hot conditions, avoiding abrupt power cut-off caused by overheating, thereby minimizing the sudden drop in performance while ensuring safety.

[0016] Step S50: Write the dynamically compensated control parameter set into the controller; Specifically, in step S50, the real-time control parameter set after dynamic compensation and interpolation is encapsulated. The parameter set includes, but is not limited to: the current limiting correction value and torque attenuation correction value after derating compensation, and the assist torque correction value and base power correction value after positive compensation. Before writing, the parameter set is segmented and checked (such as cyclic redundancy check or summation check) to ensure the integrity of the data before transmission. A breakpoint protection mechanism is used to write the validated parameter set into the controller's non-volatile memory (such as FLASH). The specific process is as follows: First, a dedicated parameter backup area is allocated in the memory to store the currently effective parameter set; When writing a new parameter set, the original active parameter area is not directly overwritten; instead, the new parameter set is first written to a temporary storage area. After the temporary storage area is successfully written and verified, update a flag bit and switch the controller's running pointer to the new parameter set in the temporary storage area to make it effective; The new parameter set that has taken effect is transferred from the temporary storage area to the dedicated parameter backup area to complete the update. This process ensures that if any single-step write fails, it can still roll back to the previous set of valid parameters by using the flag bit. The system continuously monitors the controller's operating status after parameter updates. If a critical error is detected within a preset time (such as the motor failing to start, communication interruption, or abnormal temperature spikes in the controller), the rollback mechanism is automatically triggered. Through a flag, the controller is automatically switched back to the previous stable and valid parameter set stored in the dedicated parameter backup area, and fault information is recorded, thereby ensuring the vehicle's basic riding functions and system safety.

[0017] This embodiment constructs a thermal state trend prediction model, which can predict the future thermal state trend (cold state, normal temperature state, hot state) of the controller in advance, and automatically call the optimal parameter set pre-calibrated for each state. This optimizes the coarse mode of statically calibrating a set of parameters to deal with all operating conditions, and enables the controller to optimize itself according to the actual operating state, thereby improving the accuracy and intelligence of the control. This embodiment uses a split-state calibration method to independently optimize three sets of optimal parameters for different thermal states before leaving the factory. These parameters are self-consistent under their respective target states, thereby eliminating the inherent contradiction of fixed parameter sets mutually restricting each other and being difficult to balance under different temperature states. This allows the controller to release its potential in cold states, perform evenly at room temperature, and ensure safety in hot states. This embodiment avoids sudden changes and oscillations in power output caused by thermal state switching through a parameter smooth transition (linear interpolation) mechanism. Through dynamic fine derating compensation under thermal conditions, the power decreases continuously and smoothly as the temperature rises, thereby effectively preventing the sudden interruption of power caused by overheating power cutting and ensuring the predictability and smoothness of riding. This embodiment introduces a positive compensation mechanism in both cold and normal temperature states. When high load demand is anticipated and there is sufficient heat dissipation margin, the power response can be temporarily improved, making full use of the controller's high performance potential at low temperatures and providing users with a more powerful power experience. In the cold state, the problem of weak start-up and slow response is optimized through a special parameter set, thereby making full use of the controller's performance potential. This embodiment provides the controller with forward-looking overheat protection through thermal state prediction and proactive, smooth derating under hot conditions, thereby reducing the risk of triggering the hardware emergency protection threshold and extending the controller's lifespan.

[0018] Example 2 Based on the same inventive concept as the electric bicycle controller parameter setting method in the foregoing embodiments, such as Figure 2 As shown, this application provides a parameter setting system for an electric bicycle controller, wherein the system specifically includes: The system includes a parameter acquisition module, a state recognition module, a split-state calibration module, a dynamic compensation module, and a parameter writing module. Parameter acquisition module: Acquires basic control parameters, power parameters, assist parameters, motor status parameters, and controller temperature parameters related to controller operation; Status recognition module: Based on the controller temperature parameters and motor operating status, combined with the thermal status trend prediction model, outputs the thermal status for a future period of time, including cold state, normal temperature state and hot state; The split-state calibration module generates optimal parameter calibration data corresponding to different predicted thermal states for different operating states. Specifically: Cold calibration is used to determine the optimal values ​​of current loop parameters, velocity loop parameters, phase sequence parameters, and basic dynamic parameters; Room temperature calibration is used to determine start-up characteristic parameters, power assist response parameters, and mid-to-high speed ride comfort parameters. Hot calibration is used to determine current limiting parameters, torque attenuation parameters, and protection triggering parameters. Dynamic compensation module: During the operation of the controller, the corresponding optimal parameter calibration data is automatically called according to the operating status. A dynamic compensation mechanism based on real-time control temperature is introduced to compensate the power parameters and assist parameters in real time, thereby eliminating the mutual influence between parameters under different states and reducing the performance degradation caused by controller overheating. Parameter writing module: Writes the dynamically compensated set of control parameters into the controller.

[0019] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and various changes and modifications can be made without departing from the spirit and scope of the invention; all such changes and modifications will fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for setting parameters of an electric bicycle controller, characterized in that, include: Acquire basic control parameters, power parameters, assist parameters, motor status parameters, and controller temperature parameters related to controller operation; Based on the controller temperature parameters and motor operating status, combined with the thermal state trend prediction model, the thermal state for a future period of time is output, including cold state, normal temperature state and hot state; Optimal parameter calibration data corresponding to different predicted thermal states are generated for different operating states. Among them, the optimal parameter calibration data corresponding to the cold state is used to determine the optimal values ​​of the current loop parameters, velocity loop parameters and basic dynamic parameters. The optimal parameter calibration data corresponding to the normal temperature state is used to determine the optimal values ​​of the start-up characteristic parameters and the assist response parameters. The optimal parameter calibration data corresponding to the hot state is used to determine at least the optimal values ​​of the current limiting parameter and the torque attenuation parameter; During the operation of the controller, the corresponding optimal parameter calibration data is automatically called according to the operating status, and a dynamic compensation mechanism based on real-time control temperature is introduced to compensate the power parameters and assist parameters in real time.

2. The method for setting parameters of an electric bicycle controller according to claim 1, characterized in that, The process of outputting the thermal state over a future period of time is as follows: Extract control temperature parameters and motor operating status parameters, and calculate the approximate thermal load power of the controller based on the motor operating status; The current controller temperature, controller temperature change rate, current thermal load power, predicted workload, ambient temperature, and heat dissipation coefficient are combined to form a feature vector, which is used as the input of the thermal state trend prediction model and outputs the predicted temperature value at each moment in the future. If both the peak value and the mean value in the predicted temperature data are less than the cold state threshold, the output is determined to be cold. If the predicted temperature value falls within or remains between the cold and hot thresholds, the output is determined to be at room temperature. If any value in the predicted temperature curve reaches or exceeds the hot state threshold, the output is determined to be hot.

3. The method for setting parameters of an electric bicycle controller according to claim 2, characterized in that, The calculation process for controlling the rate of temperature change: Based on differential calculations using current and historical temperature data, the current temperature and the temperature at the previous moment are recorded within a fixed sampling period, and the difference is calculated. The difference is then compared with the sampling period to obtain the control temperature change rate.

4. The method for setting parameters of an electric bicycle controller according to claim 2, characterized in that, The process of predicting workload acquisition: The motor input power is obtained by multiplying the motor phase current and the DC bus voltage. The output power is obtained by multiplying the motor input power and the system overall efficiency. The overall system efficiency includes controller efficiency and motor efficiency, which is obtained by looking up a table using a preset efficiency MAP chart; Based on user input, a predefined load prediction algorithm is used: Continuously monitor and record the timing data of motor phase current and throttle / assist torque commands, and obtain trend characteristics by calculating the moving average and first-order difference of the data within the time window; By combining the real-time speed of the motor with the vehicle acceleration, operating mode identification is performed; Based on trend characteristics and operating condition pattern recognition, the system uses a pre-configured rule base to estimate the load over a future period and outputs a quantified load forecast level or percentage change in power demand.

5. The method for setting parameters of an electric bicycle controller according to claim 1, characterized in that, The process of obtaining optimal parameter calibration data: Through split-state testing, the controller, motor, and dynamometer were placed in a controlled environmental chamber, and standardized test cycle was designed for each thermal state. The controller's internal variables and system performance data are collected in real time using calibration tools. Based on the test data, the optimal parameters under each thermal state are determined through parameter adjustment and optimization iteration.

6. The method for setting parameters of an electric bicycle controller according to claim 5, characterized in that: Cold calibration process: Under low temperature test environment, through cold test cycle, with the goal of maximizing dynamic response and output capability, the current loop parameters, speed loop parameters, phase sequence parameters and basic dynamic parameters are optimized and determined; Normal temperature calibration process: Under normal temperature test environment, through normal temperature test cycle, with the goal of achieving the best balance between power, comfort and smoothness, the starting characteristic parameters, power assist response parameters and medium and high speed smoothness parameters are optimized and determined; Hot calibration process: Under high temperature or high load test environment, through hot test cycle, with the goal of ensuring system safety and maintaining basic riding by smooth derating, the current limiting parameters, torque attenuation parameters and protection trigger parameters are optimized and determined.

7. The method for setting parameters of an electric bicycle controller according to claim 1, characterized in that: In the step of automatically calling the corresponding optimal parameter calibration data according to the operating status during the operation of the controller, and introducing a dynamic compensation mechanism based on real-time control temperature, when the predicted thermal state changes, a linear interpolation algorithm is used to smoothly transition the called parameters.

8. The method for setting parameters of an electric bicycle controller according to claim 1, characterized in that: The dynamic compensation mechanism includes derating compensation for power parameters, the process of which is as follows: When the predicted thermal state is hot, calculate the over-temperature value between the real-time controller temperature and the hot state threshold. The derating compensation coefficient is obtained by querying the preset temperature-compensation coefficient curve based on the overtemperature value. The current limiting parameters and torque attenuation parameters calibrated for hot conditions are scaled and corrected using the derating compensation factor.

9. The method for setting parameters of an electric bicycle controller according to claim 1, characterized in that: The dynamic compensation mechanism includes positive compensation for the assist parameters, the process of which is as follows: When the predicted thermal state is cold or at room temperature, if it is determined that a high load is about to be entered based on the predicted workload, compensation is triggered. The compensation weight is obtained by multiplying the predicted load factor, temperature margin factor, and gain coefficient, and then summing the product with 1. The forecast load factor is the normalized forecast workload; The difference between the cold threshold and the real-time controller temperature is used as the controller temperature deviation value, the difference between the cold threshold and the real-time ambient temperature is used as the ambient temperature deviation value, and the ratio of the controller temperature deviation value to the ambient temperature deviation value is calculated as the temperature margin factor. By using compensation weights, the assist torque curve and power limit calibrated in the current state are enhanced.

10. A parameter setting system for an electric bicycle controller, characterized in that, The system is used to perform the method according to any one of claims 1-9, the system comprising: Parameter acquisition module: Acquires basic control parameters, power parameters, assist parameters, motor status parameters, and controller temperature parameters related to controller operation; Status recognition module: Based on the controller temperature parameters and motor operating status, combined with the thermal status trend prediction model, outputs the thermal status for a future period of time, including cold state, normal temperature state and hot state; The split-state calibration module generates optimal parameter calibration data corresponding to different predicted thermal states for different operating states. Among them, the optimal parameter calibration data corresponding to the cold state is used to determine the optimal values ​​of the current loop parameters, speed loop parameters, and basic power parameters; the optimal parameter calibration data corresponding to the normal temperature state is used to determine the optimal values ​​of the starting characteristic parameters and the assist response parameters; and the optimal parameter calibration data corresponding to the hot state is used to determine the optimal values ​​of the current limiting parameters and the torque attenuation parameters. Dynamic compensation module: During the operation of the controller, the corresponding optimal parameter calibration data is automatically called according to the operating status, and a dynamic compensation mechanism based on real-time control temperature is introduced to compensate the power parameters and assist parameters in real time.

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