Speed ratio control method for continuously variable transmission of electric automobile

Abstract

The invention relates to the technical field of electric vehicle power transmission system control, in particular to a speed ratio control method for a continuously variable transmission of an electric vehicle. A fuzzy PID algorithm is adopted to dynamically calculate the speed ratio adjustment amount and send the speed ratio adjustment amount to an execution mechanism for adjustment, a differential speed ratio strategy is set according to different driving modes (economy, standard and motion), pre-adjustment is achieved in combination with vehicle-mounted navigation road condition information, the response speed and energy efficiency performance are improved, and the control process is provided with speed ratio upper and lower limits and change rate limits. The driving stability is ensured; a feedback mechanism corrects control parameters in real time, improves precision and stability, supports OTA remote upgrade, can dynamically optimize an efficiency map and a control strategy, realizes intelligent collaborative optimization of high-efficiency operation and drivability of a motor, and is suitable for electric vehicles in various driving forms.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric vehicle power transmission system control, in particular to a speed ratio control method of electric vehicle continuously variable transmission. BACKGROUND

[0002] With the rapid development of electric vehicle technology, higher requirements are put forward for the efficiency and adaptability of the power system. Traditional electric vehicles generally use fixed speed ratio reducers, which are simple in structure and low in cost, but it is difficult to balance power and economy under different working conditions. Some models introduce multi-gear automatic transmission, which improves transmission efficiency, but there is a jerk feeling in the shifting process, which affects driving comfort.

[0003] Continuously variable transmission (CVT) has been widely used in fuel vehicles due to its continuous adjustable transmission ratio and smooth power output. However, the output characteristics of electric motor are significantly different from those of internal combustion engine, and the traditional CVT control strategy cannot be directly applied. The existing control method of electric vehicle CVT is mostly based on fixed mapping relationship or simple feedback control, which lacks dynamic matching ability for motor efficiency interval, resulting in limited improvement of vehicle energy efficiency.

[0004] In addition, most control strategies do not fully consider the differences in driving mode, battery SOC state and real-time road condition information, and cannot realize intelligent adjustment and predictive control of speed ratio. SUMMARY

[0005] (I) Technical problems solved In view of the shortcomings of the prior art, the present application provides a speed ratio control method of electric vehicle continuously variable transmission.

[0006] (II) Technical scheme To achieve the above purpose, the present application provides the following technical scheme: the speed ratio control method of electric vehicle continuously variable transmission of the present application comprises the following steps: Collecting vehicle operating parameters through multiple sensors, the vehicle operating parameters including vehicle speed, motor speed, throttle opening, battery SOC and driving mode selection; Based on the motor efficiency map, the motor target speed is determined in combination with the current vehicle operating parameters; Using fuzzy PID algorithm, the speed ratio adjustment amount is dynamically calculated in combination with the deviation between the actual motor speed and the target motor speed; Sending control instructions to the transmission actuator to execute speed ratio adjustment; Through the feedback mechanism, the adjustment effect is monitored in real time, and the control parameters are dynamically optimized to improve the control precision.

[0007] Preferably, the driving mode includes economy mode, standard mode and sport mode, and different speed ratio adjustment strategies are set for each mode: Economic mode: priority to the motor efficiency ≥90% of the interval as the goal, ratio adjustment bias to slow change; Standard mode: balance power response and economy, ratio adjustment change rate is 0.05-0.1 / s; Sport mode: priority to ensure power output, ratio adjustment response delay ≤0.2s, the maximum change rate ≤0.15 / s.

[0008] Further preferably, the input quantity of the fuzzy PID algorithm includes: Motor speed deviation = target speed - actual speed; Speed deviation change rate = current deviation - last time deviation; The algorithm dynamically adjusts the PID parameters through the fuzzy rule base, and the PID parameters include proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd, wherein: When the speed deviation is >500r / min, increase Kp, the value range is 1.2-1.5, and decrease Ki, the value range is 0.01-0.03; When the speed deviation is ≤500r / min, decrease Kp, the value range is 0.8-1.0, and increase Ki, the value range is 0.05-0.08.

[0009] Again preferably, the ratio adjustment step is specifically: Combine the road condition information of the vehicle navigation to calculate the pre-adjustment ratio 0.5-2s in advance, when the uphill road section is identified, pre-adjust small ratio, increase the transmission ratio, to improve the torque; when the straight acceleration section is identified, pre-adjust large ratio, reduce the transmission ratio, to improve the speed, the road condition information includes slope, bend curvature, speed limit sign, traffic congestion state.

[0010] Preferably, the limit conditions of the ratio adjustment include: Ratio upper and lower limit value: the lower limit value is limited to 0.8-1.0 based on the maximum motor speed, and the upper limit value is limited to 2.5-3.0 based on the maximum motor torque; Change rate limit: dynamically adjust according to vehicle speed, when vehicle speed <40km / h, the maximum change rate ≤0.15 / s; when vehicle speed ≥40km / h, the maximum change rate ≤0.1 / s, to prevent impact >8m / s caused by sudden change of ratio 3 .

[0011] Further preferably, when the battery SOC is lower than the set threshold, the energy-saving ratio control strategy includes: The target speed priority matches the motor efficiency ≥92% interval to avoid the motor working in the low-efficiency high-consumption area; Limit the maximum change rate of the ratio to 70% of the conventional mode, reduce the energy loss in the adjustment process; If the driving mode is the sports mode, the automatic degradation is the standard mode, and the unnecessary power output is reduced. The threshold value of the battery SOC setting ranges from 15% to 20%.

[0012] Further preferably, the feedback mechanism comprises: The deviation of the actual speed ratio from the target speed ratio is calculated in real time. If the deviation is greater than 0.05 for three consecutive control periods, the proportional factor of the fuzzy PID is automatically corrected by ±0.1. Based on historical adjustment data, the correction coefficient of the motor efficiency map is dynamically updated.

[0013] Preferably, the OTA upgrade module is further included. The fuzzy rule base and the initial value of the PID parameters of the fuzzy PID are updated remotely. Based on the differences in motor characteristics of different vehicle batches, the motor efficiency map is dynamically optimized. The speed ratio strategy of the driving mode is added or adjusted.

[0014] Further preferably, the motor efficiency map is a three-dimensional mapping relationship, with the motor speed and the output torque as inputs and the motor efficiency as output. The map data is calibrated through bench testing and supports online correction based on real vehicle data.

[0015] Further preferably, the calculation of the speed ratio adjustment amount further comprises: The adjustment amount is smoothed by combining the current speed ratio with the response characteristics of the transmission actuator, ensuring that the speed ratio transition has no step change.

[0016] (Three) Beneficial Effects Compared with the prior art, the present application provides a speed ratio control method for a continuously variable transmission of an electric vehicle, which has the following beneficial effects: The technical solution collects running parameters such as vehicle speed, motor speed, throttle opening, and battery SOC, dynamically determines the target speed in combination with the motor efficiency map, and calculates the speed ratio adjustment amount using the fuzzy PID algorithm, thereby realizing intelligent matching of the motor running state. The method sets different speed ratio strategies according to different driving modes (economic, standard, and sports), balances power performance and economy, makes the motor run in the efficiency ≥ 90% interval in the economic mode, and responds to the delay ≤ 0.2s in the sports mode, thereby significantly improving the vehicle energy efficiency and driving experience.

[0017] The system introduces a road condition prediction mechanism, adjusts the speed ratio in advance in combination with navigation information to improve response speed, sets upper and lower limits of the speed ratio and a change rate limit to prevent excessive impact and ensure driving stability. The feedback mechanism corrects control parameters in real time to improve accuracy and stability. When the battery SOC is low, the energy-saving strategy is automatically switched to extend the cruising range. OTA remote upgrade is supported to dynamically optimize the fuzzy rule base, efficiency atlas and driving mode strategy, enhance system adaptability and expandability. The overall method has high control accuracy, fast response and strong adaptability, and has good engineering application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is a flowchart of the speed ratio control method of the continuously variable transmission of the electric vehicle. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] Please refer to Figure 1 The speed ratio control method of the continuously variable transmission of the electric vehicle comprises the following steps: Collect vehicle operating parameters through multiple sensors, wherein the vehicle operating parameters include vehicle speed, motor speed, throttle opening, battery SOC and driving mode selection; Determine the target motor speed based on the motor efficiency atlas in combination with the current vehicle operating parameters; Use the fuzzy PID algorithm to dynamically calculate the speed ratio adjustment amount in combination with the deviation between the actual motor speed and the target motor speed; Send a control instruction to the transmission actuator to execute speed ratio adjustment; Monitor the adjustment effect in real time through the feedback mechanism to dynamically optimize the control parameters to improve control accuracy.

[0021] The technical solution realizes dynamic and accurate adjustment of the speed ratio through the cooperative mechanism of "multi-parameter perception-intelligent decision-making-pre-adjustment execution-closed-loop optimization", and the core logic is: Collect vehicle operating parameters (vehicle speed, motor state, driver's intention, etc.) in real time through multiple sensors to comprehensively perceive the vehicle and environmental state; Dynamically determine the target motor speed based on the motor efficiency atlas and the driving mode to balance efficiency and power demand; Use the fuzzy PID algorithm to calculate the speed ratio adjustment amount in real time according to the speed deviation, taking into account adjustment speed and accuracy; Combined with navigation and road conditions, the speed ratio is pre-adjusted to adapt to road conditions in advance. Through the feedback mechanism to continuously correct the deviation, combined with OTA upgrade module and special working condition strategy (such as low SOC energy saving), to ensure efficient and stable control in all scenarios.

[0022] Vehicle operating parameter acquisition Through multi-sensor collaborative acquisition of key parameters, input is provided for decision-making: Vehicle speed: Use wheel speed sensor or GPS module, sampling frequency 100Hz, accuracy ±0.5km / h, reflect vehicle driving state; Motor speed: Acquired through motor shaft end encoder, frequency 1000Hz, accuracy ±10r / min, real-time monitoring of motor operating state; Throttle opening: Detected by accelerator pedal position sensor, accuracy ±1%, quantifying driver's power demand (the larger the opening, the stronger the demand); Battery SOC: Obtained through battery management system (BMS), frequency 10Hz, accuracy ±2%, reflecting battery remaining capacity; Driving mode selection: Received by the vehicle controller from the driver's manual selection signal (economy / standard / sport), triggering the corresponding control strategy.

[0023] Target speed determination based on motor efficiency map Motor efficiency map structure: Three-dimensional mapping relationship (horizontal axis: motor speed 500-15000r / min; vertical axis: output torque 10-500N・m; z-axis: motor efficiency 0-100%), calibrated through bench test (covering full speed and torque range), and supporting online correction based on real vehicle data (updating correction coefficients every 100km).

[0024] Target speed matching logic: With current throttle opening (reflecting power demand) and battery SOC as constraints, filter "feasible efficiency interval" from the map; Economy mode: Prefer to select speed points with efficiency ≥90%, if there are multiple points, select the point close to the current speed (reduce adjustment amount); Standard mode: Balance efficiency (≥85%) and power response, select the weighted optimal solution of efficiency and speed change rate; Sport mode: Relax efficiency requirement (≥80%), prefer to select high-speed points that can quickly increase torque (meet the demand for sudden acceleration); Low SOC (≤20%): Force to lock the interval with efficiency ≥92%, avoid inefficient and high-consumption speed.

[0025] Fuzzy PID algorithm dynamically calculates speed ratio adjustment amount Through the fusion algorithm of "fuzzy reasoning + PID regulation", the rapid convergence and accurate control of the deviation are realized: Input processing: Speed deviation (e) = target speed - actual speed (unit: r / min); Speed deviation change rate (ec) = current deviation - last time deviation (unit: r / min·s), reflecting the trend of deviation change.

[0026] Fuzzy rule base and parameter adjustment: Fuzzy rules are designed based on expert experience, such as: "if e is positive and ec is small, then Kp is large and Ki is small" (accelerate convergence); "if e is small and ec is negative, then Kp is small and Ki is large" (reduce overshoot); PID parameter dynamic adjustment: When |e|>500r / min (large deviation): Kp=1.2-1.5 (enhance regulation strength), Ki=0.01-0.03 (reduce integral accumulation to avoid overshoot); When |e|≤500r / min (small deviation): Kp=0.8-1.0 (reduce regulation strength), Ki=0.05-0.08 (enhance integral effect to eliminate steady-state deviation); The differential coefficient Kd is fixed at 0.02-0.05, which is used to suppress high-frequency disturbance.

[0027] Speed ratio adjustment output: Based on the initial adjustment amount calculated by PID, combined with the current speed ratio and the characteristics of the transmission actuator (such as the pressure-displacement hysteresis of hydraulic type, the gear meshing gap of mechanical type), through first-order low-pass filtering (cutoff frequency 5Hz) for smoothing processing, to ensure that the speed ratio transition is smooth (impact degree ≤8m / s 3 ).

[0028] Differential strategy for driving mode For different driving needs, set differential speed ratio adjustment logic: Economy mode: energy saving as the core, speed ratio adjustment maximum change rate ≤0.05 / s (slow change, reduce energy loss), target speed strictly matches the efficiency ≥90% interval, preferentially prolongs the endurance; Standard mode: balance power and economy, change rate 0.05-0.1 / s, allow efficiency to drop to 85% for a short time (meet the demand for regular acceleration); Sport mode: power response as the core, response delay ≤0.2s (fast regulation), maximum change rate ≤0.15 / s, target speed deviates to high torque interval (even if the efficiency is slightly lower), to ensure the performance of sudden acceleration.

[0029] Speed ratio pre-regulation and road condition adaptation Combined with the road condition information of the car navigation, the speed ratio is optimized in advance to reduce the response lag: Road condition information analysis: extract slope, curve curvature (0-10° / m), speed limit (0-120km / h), congestion state (smooth / slow / congestion); Pre-adjustment logic: Uphill section (slope > 5°): adjust the speed ratio by 0.8-2s (increase the transmission ratio), increase the motor output torque (such as from 2.0 to 1.8); Straight acceleration section (speed limit > current speed 20km / h): adjust the speed ratio by 0.5-1s (reduce the transmission ratio), reserve high-speed capability for acceleration (such as from 2.0 to 2.2); Bend (curvature > 5° / m): adjust the speed ratio by 1-1.5s (reduce speed, improve handling stability).

[0030] Limiting conditions for speed ratio adjustment Ensure driving stability and component safety through hard constraints: Upper and lower limits of speed ratio: Lower limit 0.8-1.0: based on the maximum speed limit of the motor (to avoid overloading caused by exceeding the rated value 105%); Upper limit 2.5-3.0: based on the maximum torque limit of the motor (to avoid motor overheating caused by low speed and high load); Change rate limit: Vehicle speed < 40km / h (low speed): maximum change rate ≤ 0.15 / s (allow faster adjustment, suitable for frequent acceleration and deceleration); Vehicle speed ≥ 40km / h (high speed): maximum change rate ≤ 0.1 / s (slow down adjustment to avoid body impact); The above limits ensure that the impact is ≤8m / s 3 .

[0031] Low SOC energy saving strategy (SOC ≤ 15%-20%) When the battery is low, adjust the strategy to extend the endurance: Target speed lock: only select target speed from the interval where efficiency ≥ 92%, prohibit entering low efficiency area (efficiency < 80%); Adjustable energy consumption control: the maximum change rate of speed ratio is reduced to 70% of the normal mode (such as from 0.1 / s to 0.07 / s), reducing energy loss during adjustment; Mode degradation: if the current mode is sport mode, automatically switch to standard mode to avoid meaningless high power output.

[0032] Feedback correction mechanism Continuous optimization of control accuracy through closed-loop feedback: Deviation monitoring: Real-time calculation of the deviation between the actual speed ratio (input shaft speed / output shaft speed) and the target speed ratio, with a precision of ≤±0.02; Parameter correction: If the deviation is >0.05 for 3 consecutive control periods, automatically correct the proportional factor (±0.1) of the fuzzy PID, for example, Kp from 1.0 to 1.1 (increase the adjustment strength); Atlas update: Based on the adjustment data of the last 100km, dynamically update the correction coefficient of the motor efficiency atlas (e.g. if the efficiency deviates due to motor aging, correct the efficiency value in the atlas).

[0033] OTA upgrade module Support remote iterative optimization, adapt to long-term use requirements: Algorithm upgrade: remotely update the fuzzy rule library of the fuzzy PID (e.g. add the "snow mode" low speed high torque rule) and the initial value of the PID parameters; Atlas optimization: based on the differences in motor characteristics of different batches of vehicles (e.g. efficiency deviation of different batches of motors), dynamically correct the efficiency atlas; Function expansion: add new driving modes (e.g. "off-road mode") or adjust the speed ratio strategy of existing modes (e.g. increase the efficiency threshold of economic mode from 90% to 92%).

[0034] Motor efficiency atlas As the core basis for target speed calculation, its structure and application principle are: Three-dimensional structure: take motor speed (500-15000r / min) and output torque (10-500N・m) as input, and efficiency (0-100%) as output, forming a "speed-torque-efficiency" three-dimensional mapping table; Calibration method: obtain full working condition data (sample every 500r / min speed interval, 50N・m torque interval) through bench test, and generate complete atlas through interpolation processing; Online correction: based on real vehicle running data (statistical deviation between actual efficiency and atlas every 100km), correct the atlas efficiency value within ±2%, to ensure matching with the actual state of the motor.

[0035] The OTA upgrade module uses conventional related equipment in the automotive field, such as NXP Semiconductors' S32G2 Vehicle Network Processor, STMicroelectronics' Telemaco3P series processor, Quectel's AG550Q or RM500Q series module.

[0036] Detailed workflow Phase 1: System initialization (when the vehicle starts) Load pre-configured parameters: motor efficiency map (initial data from bench calibration), fuzzy PID rule base, speed ratio parameters for each driving mode (e.g. maximum change rate 0.05 / s for economy mode); Set constraints: upper and lower limits of speed ratio (0.8-3.0), SOC threshold (20%), pre-adjustment time window (0.5-2s); Initialize sensors and actuators: self-check vehicle speed sensor, motor encoder, hydraulic / mechanical actuators, ensure no faults.

[0037] Stage 2: Parameter acquisition and mode identification (real-time execution) Parameter acquisition: Vehicle speed v = 50 km / h (wheel speed sensor, 100 Hz); Motor speed n = 2800 r / min (encoder, 1000 Hz), output torque T = 120 N・m; Throttle opening α = 25% (pedal sensor, 100 Hz); Battery SOC = 70% (BMS, 10 Hz); Driving mode: standard mode (driver manually selects); Navigation conditions: 300m ahead is a 5° uphill, current road is clear.

[0038] Mode identification: based on throttle opening (25%) and mode selection, determine the current need to balance power and economy.

[0039] Stage 3: Target speed calculation Query from motor efficiency map: when the current torque T = 120 N・m, the speed interval with efficiency ≥ 85% is 2500-4000 r / min; Combine standard mode strategy, select "efficiency-speed change rate" weighted optimal solution: target speed n_target = 3200 r / min (efficiency 90%, deviation from current speed 400 r / min, adjustment amount moderate).

[0040] Stage 4: Fuzzy PID calculates speed ratio adjustment amount Calculate input: Speed deviation e = 3200-2800 = 400 r / min (≤500 r / min, small deviation); Deviation change rate ec = 400-300 = 100 r / min・s (positive small).

[0041] Fuzzy reasoning: e positive small, ec positive small → Kp = 0.9, Ki = 0.06, Kd = 0.03; PID output initial adjustment amount Δi0 = 0.08; Smooth processing: combined with the response lag of hydraulic actuators (0.1s), a first-order filter is used to correct Δi=0.07 (avoid overshoot), and the target speed ratio i_target= current speed ratio (2.0) +0.07=2.07.

[0042] Stage 5: Speed ratio pre-adjustment and execution Pre-adjustment: The navigation identifies a 300m uphill slope (5°), and the pre-adjustment is started 1s in advance, with the target speed ratio pre-adjusted from 2.07 to 1.95 (reduce the speed ratio and increase the transmission ratio to increase the torque); Actuator action: The transmission controller sends PWM instructions to the electric hydraulic actuator (duty cycle is linearly related to target speed ratio), adjusts at a rate of 0.08 / s (meets the standard mode 0.05-0.1 / s limit), and gradually increases the speed ratio from 2.0 to 1.95.

[0043] Stage 6: Feedback correction Real-time monitoring of actual speed ratio i_actual=1.94 (deviation 0.01≤0.02, accuracy meets the standard); No need to correct PID parameters, record this adjustment data (speed deviation, adjustment amount, efficiency) for subsequent atlas correction.

[0044] Stage 7: Special working condition processing (example) Low SOC scenario: When the SOC drops to 18%, trigger energy-saving strategy: Target speed is locked to 2600r / min with efficiency ≥92% (original 3200r / min adjusted to 2600r / min); Speed ratio change rate limit to 0.056 / s (70% of standard mode); Bend scenario: The navigation identifies a sharp bend (curvature 8° / m), and the speed ratio is reduced to 1.8 1.2s in advance, reducing the vehicle speed to 30km / h and improving the stability of the bend.

[0045] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle, characterized in that, Includes the following steps: Vehicle operating parameters are collected by multiple sensors, including vehicle speed, motor speed, throttle opening, battery SOC, and driving mode selection. The target motor speed is determined based on the motor efficiency graph and the current vehicle operating parameters. The fuzzy PID algorithm is used to dynamically calculate the speed ratio adjustment based on the deviation between the actual motor speed and the target speed. Send control commands to the transmission actuator to perform speed ratio adjustment; The feedback mechanism monitors the adjustment effect in real time and dynamically optimizes control parameters to improve control accuracy.

2. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, The driving modes include Eco, Standard, and Sport modes, each with a different gear ratio adjustment strategy: Economic mode: Prioritize the range where motor efficiency is ≥90%, and adjust the speed ratio with a tendency for slow changes; Standard mode: Balances power response and economy, with a speed ratio adjustment rate of 0.05-0.1 / s; Sport mode: Prioritizes power output, speed ratio adjustment response delay ≤0.2s, maximum change rate ≤0.15 / s.

3. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, The inputs to the fuzzy PID algorithm include: Motor speed deviation = target speed - actual speed; Rate of change of rotational speed deviation = Current deviation - Deviation at the previous moment; The algorithm dynamically adjusts the PID parameters using a fuzzy rule base. The PID parameters include a proportional coefficient Kp, an integral coefficient Ki, and a derivative coefficient Kd, where: When the speed deviation is >500 r / min, increase Kp, with a value range of 1.2-1.5, and decrease Ki, with a value range of 0.01-0.

03. When the speed deviation is ≤500r / min, decrease Kp, with a value range of 0.8-1.0, and increase Ki, with a value range of 0.05-0.

08.

4. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, The speed ratio adjustment steps are as follows: The vehicle navigation system calculates and adjusts the gear ratio 0.5-2 seconds in advance based on road condition information. When an uphill section is detected, the gear ratio is adjusted to a smaller value to increase the transmission ratio and improve torque. When a straight acceleration section is detected, the gear ratio is adjusted to a larger value to decrease the transmission ratio and improve vehicle speed. The road condition information includes gradient, curve curvature, speed limit signs, and traffic congestion status.

5. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, The limiting conditions for speed ratio adjustment include: Speed ​​ratio upper and lower limits: The lower limit is based on the maximum motor speed and is limited to 0.8-1.0, and the upper limit is based on the maximum motor torque and is limited to 2.5-3.0; Rate of change limit: Dynamically adjusted according to vehicle speed. When vehicle speed < 40 km / h, the maximum rate of change is ≤ 0.15 / s; when vehicle speed ≥ 40 km / h, the maximum rate of change is ≤ 0.1 / s, to prevent shocks > 8 m / s caused by sudden changes in speed ratio. 3 .

6. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, When the battery SOC is below a set threshold, the energy-saving speed ratio control strategy includes: The target speed should be matched with the range where the motor efficiency is ≥92% to avoid the motor operating in the inefficient and high-consumption range; Limit the maximum rate of change of speed ratio to 70% of the normal mode to reduce energy loss during the adjustment process; If the driving mode is Sport mode, it will automatically downgrade to Standard mode to reduce unnecessary power output; The threshold value for the battery SOC is set to be between 15% and 20%.

7. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, The feedback mechanism includes: Calculate the deviation between the actual speed ratio and the target speed ratio in real time; If the deviation is greater than 0.05 for three consecutive control cycles, the proportional factor of the fuzzy PID will be automatically corrected, with a correction amount of ±0.

1. The correction coefficients of the motor efficiency graph are dynamically updated based on historical adjustment data.

8. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, It also includes an OTA upgrade module: Remotely update the fuzzy rule base and initial values ​​of fuzzy PID parameters; Dynamically optimize motor efficiency maps based on differences in motor characteristics between different vehicle batches; Add or adjust the speed ratio strategy for driving modes.

9. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 8, characterized in that, The motor efficiency graph is a three-dimensional mapping relationship, with motor speed and output torque as inputs and motor efficiency as output. The graph data is calibrated through bench tests and supports online correction based on real vehicle data.

10. The speed ratio control method for a continuously variable transmission (CVT) in an electric vehicle according to claim 1, characterized in that, The calculation of the speed ratio adjustment also includes: By combining the current speed ratio with the response characteristics of the transmission actuator, the adjustment amount is smoothed to ensure that the speed ratio transition is seamless.

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