Adaptive torque control method for ECO mode of pure electric vehicle based on fuzzy algorithm

Abstract

The invention relates to the field of vehicle control of pure electric vehicles, in particular to a pure electric vehicle ECO mode self-adaptive torque control method based on a fuzzy algorithm, which comprises the following steps: acquiring a reference boundary curve of driving and feedback torque of an ECO mode; according to a motor rotating speed interval, a plurality of fuzzy control areas are predefined between the reference boundary curves, and each fuzzy control area is associated with a fuzzy gain strategy which aims at a set driving scene and takes a vehicle real-time state as input; vehicle state parameters including the motor rotating speed, the whole vehicle load, the road gradient and accelerator pedal information are obtained in real time; according to the vehicle state parameters, judging a current driving scene and matching the current driving scene to a corresponding fuzzy control area; and performing real-time compensation on the reference torque of the ECO mode by applying a fuzzy gain strategy associated with the fuzzy control region, and outputting a final torque instruction. And on the premise of meeting the power performance and the comfort performance, the cruising ability is further improved.

Description

Technical Field

[0001] This application relates to the field of pure electric vehicle control, specifically to an adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithms. Background Technology

[0002] The energy efficiency and driving range of pure electric vehicles have always been the core focus of industry research and development and user attention. To alleviate users' range anxiety, one of the current mainstream technical solutions is to introduce an ECO (economic) driving mode into the vehicle. The basic principle of this mode is to use electronic control strategies to globally and fixedly limit the output power and torque of the drive motor, thereby actively reducing the vehicle's instantaneous energy consumption and average power consumption, in order to fundamentally extend the driving range on a single charge.

[0003] However, this traditional ECO mode, based on simple limitations, reveals a series of significant drawbacks in real-world, complex driving scenarios. When the vehicle is heavily loaded or climbing hills, requiring significant driving force, the limited torque output may fail to meet dynamic driving demands, resulting in sluggish acceleration, difficulty climbing hills, and even the potential safety risk of stalling mid-slope due to insufficient power. Conversely, on long downhill sections, the energy recovery intensity, typically set conservatively to ensure driving smoothness, may lead to insufficient braking force, requiring frequent use of mechanical braking to control speed. This not only increases energy loss but also accelerates wear on the braking system.

[0004] The aforementioned contradictions force drivers to frequently switch manually between ECO mode and other power modes based on real-time load and road conditions. This not only distracts drivers and affects the driving experience, but also, because the timeliness and accuracy of the switching depend entirely on the driver's subjective judgment, it is prone to delays or misjudgments in actual driving. This significantly reduces the energy-saving effect of ECO mode, and may even lead to its abandonment by users due to inconvenience, ultimately defeating its original energy-saving design purpose. Therefore, developing an adaptive ECO control strategy that can intelligently adapt to various complex operating conditions and balance economy and power without manual intervention has become an urgent technical problem to be solved in this field. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithms. By intelligently compensating for the maximum torque limit in ECO mode, it achieves normal driving under different loads and road conditions.

[0006] The present invention provides an adaptive torque control method for ECO mode of pure electric vehicles based on fuzzy algorithm, comprising the following steps: Obtain the baseline boundary curves of drive and regenerative torque in ECO mode; Based on the motor speed range, multiple fuzzy control regions are predefined between the reference boundary curves. Each fuzzy control region is associated with a fuzzy gain strategy that takes the real-time vehicle status as input for a given driving scenario. Real-time acquisition of vehicle status parameters, including motor speed, vehicle load, road gradient, and accelerator pedal information; Based on the vehicle status parameters, determine the current driving scenario and match it to the corresponding fuzzy control area; The fuzzy gain strategy associated with the fuzzy control region is applied to compensate the reference torque of the ECO mode in real time, and the final torque command is output.

[0007] By first establishing the reference boundary curves for drive and feedback torque, defining a dedicated fuzzy control region, and then combining this with the complete logic of matching scenarios based on the vehicle's real-time status and executing torque compensation, adaptive adjustment of torque control in ECO mode is achieved. On the one hand, this solves the problem of insufficient power in special scenarios such as heavy-load climbing and temporary overtaking in traditional ECO mode due to fixed torque limitations, eliminating the need for drivers to frequently switch driving modes and improving driving safety and convenience. On the other hand, through precise scenario-based torque compensation, while ensuring the driving needs of special operating conditions, the low-energy consumption advantage of ECO mode is preserved to the greatest extent, effectively balancing vehicle range and driving performance, and avoiding the dilemma of sacrificing power for energy saving or sacrificing energy saving for power in traditional ECO mode.

[0008] As a further limitation of the technical solution of the present invention, the step of obtaining the reference boundary curve of the drive and feedback torque in ECO mode includes: Obtain the drive external characteristic curve L1 and the feedback external characteristic curve L5 of the electric drive system; Based on the vehicle coasting resistance model, the power requirements in ECO mode, and the aforementioned drive external characteristic curve L1, the ECO drive maximum torque external characteristic reference curve L2 and the ECO drive minimum torque external characteristic reference curve L3 are fitted. Based on the vehicle coasting resistance model, the comfort requirements in ECO mode, and the feedback external characteristic curve L5, the ECO feedback minimum torque external characteristic reference curve L6 and the ECO feedback maximum torque external characteristic reference curve L4 are fitted. The reference boundary curves include curves L2, L3, L4, and L6.

[0009] It provides a precise control baseline that fits the vehicle's basic performance for subsequent fuzzy torque compensation: the drive-side reference curves (L2, L3) ensure low-energy power output in ECO mode under normal operating conditions, meeting basic driving power requirements; the feedback-side reference curves (L4, L6) define a reasonable range for energy recovery while ensuring braking comfort, avoiding excessive feedback from affecting the driving experience, and reserving adjustment space for feedback enhancement in subsequent scenarios such as slopes.

[0010] As a further limitation of the technical solution of the present invention, the method for determining the external characteristic reference curve L2 of the ECO drive maximum torque includes: For a given motor speed Determine the rotational speed The efficiency of the electric drive system is higher than the target drive efficiency value. Maximum available drive torque ; Calculate the minimum required torque to meet the power performance requirements in ECO mode. ; Pick With the electric drive system at this speed Maximum available drive torque The minimum value in the range is used as the rotational speed. Maximum torque under ECO drive ; Fitting motor speeds at all speeds The reference curve L2 for the maximum torque external characteristic of ECO drive was obtained.

[0011] By introducing an efficiency threshold for the electric drive system, it is ensured that the drive torque in ECO mode is always within the efficient operating range of the electric drive system, thereby reducing the vehicle's energy consumption and increasing the driving range from the energy conversion perspective. By anchoring the minimum torque required for power in ECO mode, it avoids excessively lowering the upper limit of torque in pursuit of ultimate energy saving, thus ensuring the vehicle's basic power response under normal operating conditions.

[0012] As a further limitation of the technical solution of the present invention, the method for determining the ECO feedback minimum torque external characteristic reference curve L6 includes: For a given motor speed Determine the rotational speed The efficiency of the electric drive system is higher than the feedback target efficiency value. Maximum available feedback torque absolute value ; Calculate the absolute value of the minimum feedback torque that meets the comfort requirements in ECO mode. ; Pick With the electric drive system at this speed The absolute value of the maximum available feedback torque. The minimum value in the range is taken as the negative value, and this negative value is taken as the rotational speed. ECO feedback minimum torque ; Fitting motor speeds at all speeds The minimum torque external characteristic reference curve L6 for ECO feedback is obtained.

[0013] By using a feedback efficiency threshold as a constraint, the high efficiency of the energy recovery process is ensured, the loss in the energy recovery process is reduced, and the energy utilization rate is improved. The minimum absolute value of the feedback torque is limited based on comfort requirements, which avoids problems such as vehicle jerking and excessive drag caused by excessive feedback torque, thus balancing energy recovery efficiency and driving comfort. Combined with the maximum feedback capability of the electric drive system, the feasibility of torque commands is ensured, preventing failures caused by exceeding the system hardware limits.

[0014] As a further limitation of the technical solution of the present invention, the step of pre-defining multiple fuzzy control regions between the reference boundary curves according to the motor speed range includes: The motor operating range is divided into a low-speed range [0, ...]. ), medium speed range [ , ), high speed range [ , ),in , The threshold values ​​are calibrated based on the high-efficiency operating range of the motor and the commonly used speed range of the vehicle. Within the low-speed range, a first fuzzy control region A1 is predefined between the ECO drive maximum torque external characteristic reference curve L2 and the electric drive system drive external characteristic curve L1. Within the medium-speed and high-speed ranges, a second fuzzy control region A2 is predefined between the ECO drive maximum torque external characteristic reference curve L2 and the electric drive system drive external characteristic curve L1. Within the medium-speed and high-speed ranges, a third fuzzy control region A3 is predefined between the ECO drive minimum torque external characteristic reference curve L3 and the ECO feedback maximum torque external characteristic reference curve L4. Within the low-speed range, a fourth fuzzy control region A4 is predefined between the ECO feedback minimum torque external characteristic reference curve L6 and the electric drive system feedback external characteristic curve L5.

[0015] The system divides the motor speed range into four dedicated fuzzy control regions (A1-A4), each precisely corresponding to a specific interval of the baseline boundary curve. This enables refined, zoned management of torque control in ECO mode, dividing the speed range according to the motor's high-efficiency range and commonly used speed segments. This allows the torque control strategy to better align with the operating characteristics of the electric drive system and the vehicle's actual driving conditions. Dedicated control domains are defined for torque compensation in different scenarios, avoiding mutual interference between torque strategies under different operating conditions. For example, the torque compensation regions for low-speed heavy-load hill climbing and medium-to-high-speed overtaking are separated, ensuring accurate torque response under special operating conditions.

[0016] As a further limitation of the technical solution of the present invention, the correspondence between the driving scenario, the fuzzy gain strategy, and the fuzzy control region is configured as follows: The driving scenario associated with the first fuzzy control region A1 is a heavy-load or hill-climbing condition at low speed. The fuzzy gain strategy is configured to be based on the real-time vehicle load M and the road gradient. Compensate for the upper limit of drive torque; The driving scenario associated with the second fuzzy control region A2 is a temporary overtaking situation at medium to high speeds. The fuzzy gain strategy is configured to temporarily increase the upper limit of the driving torque based on the real-time accelerator pedal opening PA and its rate of change RA. The driving scenario associated with the third fuzzy control region A3 is coasting or braking at medium to high speeds. The fuzzy gain strategy is configured to set the torque command at L3 or L4 to skip the inefficient torque range. The driving scenario associated with the fourth fuzzy control region A4 is a downhill driving condition with a set slope at low speed. The fuzzy gain strategy is configured to adjust based on the downhill slope. The upper limit of feedback torque has been enhanced.

[0017] It achieves precise binding of scenarios and strategies, and customizes exclusive torque compensation strategies for scenarios where traditional ECO mode is lacking, such as heavy-load climbing, temporary overtaking, coasting braking, and steep downhill driving. This directly solves the performance defects of traditional ECO mode under special operating conditions. The strategy takes the real-time status of the vehicle as input, ensuring the dynamic adaptability of torque compensation. It can adjust the torque in real time according to changes in load, gradient, and pedal status, improving the flexibility and accuracy of control. Different scenario strategies perform their respective functions. While ensuring power or braking needs, they also achieve added value such as energy saving and extending the life of mechanical brakes. For example, the A3 zone skips the inefficient torque range to save energy, and the A4 zone enhances feedback to reduce mechanical brake wear.

[0018] As a further limitation of the technical solution of the present invention, the fuzzy gain strategy of the first fuzzy control region A1 is specifically as follows: When the motor speed When in the low speed range: If satisfied Under the given conditions, the upper limit of the output torque is L2; If satisfied Under the given conditions, the upper limit of the output torque is ; in, For real-time vehicle load, For the vehicle's curb weight, The load change threshold, The slope threshold, This is the load gain coefficient. This is the slope gain coefficient; The specific fuzzy gain strategy for the second fuzzy control region A2 is as follows: When the motor speed When in the medium or high speed range: If the accelerator pedal opening PA is less than the opening threshold PA1 and the rate of change RA is less than the rate of change threshold RA1, then the upper limit of the output torque is L2. If the accelerator pedal opening PA is greater than or equal to the opening threshold PA1 and the rate of change RA is greater than or equal to the rate of change threshold RA1, then the upper limit of the output torque will be increased to L1 and maintained for a predetermined time T, after which it will return to L2.

[0019] When driving at low speeds under heavy loads or climbing hills, the system automatically increases the upper limit of drive torque, meeting the power demands for climbing and heavy loads without needing to switch driving modes. This eliminates the problem of insufficient power and potential safety hazards associated with traditional ECO mode in such conditions. Simultaneously, the gain coefficient limits the torque increase, preventing excessive energy consumption. For the A2 zone, overtaking behavior is determined by the pedal opening and rate of change, and torque is temporarily increased to meet the instantaneous power demands for temporary overtaking at medium to high speeds, ensuring driving safety. Furthermore, by setting the duration of the torque increase, the system prevents the driver from maintaining high torque output for extended periods.

[0020] As a further limitation of the technical solution of the present invention, determining the current driving scenario and matching it to the corresponding fuzzy control region based on vehicle state parameters includes: The driving condition and the non-driving condition are distinguished by comparing the real-time accelerator pedal opening PA with the preset non-pressed threshold PA2. If PA≥PA2, it is determined to be a driving condition, and then the following processing is performed according to the speed range to which the current motor speed S belongs: When the motor speed When the speed range is low, obtain the vehicle load. With road slope ; If satisfied If it meets one of the conditions, it is determined as an overload or climbing scenario and matched to the first fuzzy control region A1; otherwise, it is determined as a normal driving scenario, and the torque demand is directly limited by the reference boundary curve; When the motor speed belongs to the medium or high speed range, obtain the acceleration pedal opening change rate RA; If it simultaneously meets the conditions of PA≥PA1 and RA≥RA1, it is determined as a temporary overtaking scenario and matched to the second fuzzy control region A2; otherwise, it is determined as a normal driving scenario, and the torque demand is directly limited by the reference boundary curve; If PA < PA2, it is determined as a non-driving working condition, and then according to the motor speed The speed range it belongs to is processed as follows: When the motor speed belongs to the medium or high speed range, it is determined as a coasting or braking scenario and matched to the third fuzzy control region A3; When the motor speed belongs to the low speed range, obtain the road slope ; If , it is determined as a set slope downhill scenario and matched to the fourth fuzzy control region A4; otherwise, it is determined as a normal slow driving or downhill scenario, and the torque demand is directly limited by the reference boundary curve; Where, PA1 is the overtaking pedal opening threshold, and RA1 is the overtaking pedal change rate threshold.

[0021] A three-layer scenario determination system of working condition - speed - state parameters is established, which realizes the accurate identification of vehicle driving scenarios, avoids the failure of torque strategies caused by misjudgment of scenarios. For example, it distinguishes between driving and non-driving working conditions through the pedal opening, and then locks the specific scenario by combining parameters such as speed and slope; matches the corresponding fuzzy control regions for different scenarios, enables the torque compensation strategy to be accurately implemented, ensures that the torque demand in special working conditions is promptly responded, and at the same time, the normal working conditions still follow the reference curve to maintain the energy-saving attribute of the ECO mode; the determination logic is clear and executable, provides a clear algorithm basis for the strategy deployment of the vehicle controller, and reduces the engineering implementation difficulty.

[0022] As a further limitation of the technical solution of the present invention, apply the fuzzy gain strategy associated with the fuzzy control region to compensate the reference torque of the ECO mode in real time and output the final torque command, specifically including: Step (1) Determine the reference value of the current torque command; If it is currently in the driving working condition, based on the current motor speed <00001​​If the current operating condition is non-driving, then based on the current motor speed... The torque reference value is obtained from the ECO feedback minimum torque external characteristic reference curve L6; Step (2) Calculate the fuzzy gain compensation amount; The gain function associated with the matched fuzzy control region is called, and the vehicle state parameters used in the scene are used as input to calculate the real-time torque gain coefficient or compensation torque value. Step (3) Perform compensation and output; The gain coefficient is multiplied by the reference value, or the compensated torque value is added to the reference value to obtain the final torque command after real-time compensation, and then sent to the electric drive system controller.

[0023] A standardized torque compensation execution chain has been established, ensuring a closed-loop control logic from the reference value to the final command, thus improving the stability and reliability of torque control. The reference torque is determined by distinguishing between driving and non-driving conditions, ensuring the rationality of the reference value. Flexible compensation methods, such as multiplying the gain coefficient or adding the compensation torque, adapt to the strategy requirements of different fuzzy regions, ensuring the accuracy of torque adjustment. Commands are sent directly to the electric drive system controller, shortening the control chain, improving the real-time performance of torque response, and ensuring the timeliness of torque adjustment during driving.

[0024] As a further limitation of the technical solution of the present invention, when matching to the first fuzzy control region A1, the specific step of calculating the fuzzy gain compensation amount is as follows: Calculate the dynamic gain coefficient ; Then the final torque command in step (3) ; When the match reaches the second fuzzy control region A2, the determined benchmark value is The gain strategy in step (2) is to trigger a temporary boost flag, and step (3) is as follows: Within a duration T after the overtaking conditions are met, the final torque command is output. ; After the duration T ends, the final torque command is resumed. ; When the match reaches the third fuzzy control region A3, steps (1) and (2) are executed together, and the output is determined directly based on the accelerator pedal opening PA: If PA is less than the threshold PA2 for determining if the pedal is not depressed, then the final torque command output will be... or This allows us to skip the inefficient torque range between L3 and L4. When the match reaches the fourth fuzzy control region A4, the reference value determined in step (1) is Steps (2) and (3) are combined and executed as follows: If the downhill slope angle α satisfies The final torque command output will then be... ; Otherwise, the final torque command output .

[0025] For zone A1, torque is precisely amplified through a dynamic gain coefficient, satisfying the power requirements for heavy-load climbing without wasting energy. For zone A2, temporary boost signs and duration control balance overtaking power with energy-saving goals. For zone A3, L3 or L4 torque is directly locked, efficiently skipping inefficient zones, significantly improving the efficiency of the electric drive system and reducing overall vehicle power consumption. For zone A4, the feedback curve is switched according to the downhill gradient, enhancing energy recovery during steep downhill descents, improving range, reducing the frequency of mechanical braking, and extending the life of the braking system.

[0026] As can be seen from the above technical solutions, this application has the following advantages: By predefining a fuzzy control region and gain strategy bound to a specific driving scenario, intelligent dynamic compensation for the baseline torque in ECO mode is achieved. This overcomes the shortcomings of traditional ECO mode, which suffers from insufficient power or requires frequent manual switching under complex operating conditions due to fixed torque limitations. While ensuring basic energy-saving goals, it improves the vehicle's adaptive driving performance and safety in various real-world scenarios such as heavy loads, hill climbing, overtaking, and downhill driving, while also optimizing overall vehicle energy efficiency and driving experience. Attached Figure Description

[0027] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention.

[0029] Figure 2 This is a schematic diagram of the external characteristic curve of the traditional ECO drive torque.

[0030] Figure 3 This is a schematic diagram of the torque external characteristic curve of a pure electric vehicle based on a fuzzy algorithm. Detailed Implementation

[0031] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this application and in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0033] like Figure 1 As shown, this embodiment of the invention provides an adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithm, including: S1. Obtain the baseline boundary curves of drive and regenerative torque in ECO mode; this step specifically includes: Obtain the drive external characteristic curve L1 and the feedback external characteristic curve L5 of the electric drive system; Based on the vehicle coasting resistance model, the power requirements in ECO mode, and the aforementioned drive external characteristic curve L1, the ECO drive maximum torque external characteristic reference curve L2 and the ECO drive minimum torque external characteristic reference curve L3 are fitted. Based on the vehicle coasting resistance model, the comfort requirements in ECO mode, and the feedback external characteristic curve L5, the ECO feedback minimum torque external characteristic reference curve L6 and the ECO feedback maximum torque external characteristic reference curve L4 are fitted. The reference boundary curves include curves L2, L3, L4, and L6.

[0034] The vehicle's sliding resistance model is expressed as follows:

[0035] in, The sliding resistance of the vehicle at the motor speed. This refers to the motor speed. , , To achieve the resistance calibrated through testing, The resistance is a constant term independent of speed, mainly including rolling resistance (such as friction between the tire and the road surface). This refers to the resistance related to the first-order term of velocity, such as the frictional resistance of a transmission system (gears, bearings, etc.). The drag is related to the quadratic term of velocity, mainly air resistance (wind resistance, etc.).

[0036] like Figure 2 As shown, L7 is the external characteristic curve of traditional ECO drive torque, L8 is the external characteristic curve of traditional ECO feedback torque, and L9 is the curve of traditional ECO drive / feedback 0 torque.

[0037] In this embodiment of the invention, the method for determining the external characteristic reference curve L2 of the ECO drive maximum torque includes: For a given motor speed Determine the rotational speed The efficiency of the electric drive system is higher than the target drive efficiency value. Maximum available drive torque ; Calculate the minimum required torque to meet the power performance requirements in ECO mode. ; The total mass of the vehicle. In ECO mode, the minimum acceleration required to meet the vehicle's dynamic performance on a level road.

[0038] Pick With the electric drive system at this speed Maximum available drive torque The minimum value in the range is used as the rotational speed. Maximum torque under ECO drive ; Fitting motor speeds at all speeds The reference curve L2 for the maximum torque external characteristic of ECO drive was obtained. At actual speed... Under these conditions, ECO drive minimum torque Fit the minimum torque external characteristic reference curve L3 of ECO drive; The methods for determining the ECO feedback minimum torque external characteristic reference curve L6 include: For a given motor speed Determine the rotational speed The efficiency of the electric drive system is higher than the feedback target efficiency value. Maximum available feedback torque absolute value ; Calculate the absolute value of the minimum feedback torque that meets the comfort requirements in ECO mode. ; Among them, the torque that meets the comfort performance requirements in ECO mode , At actual vehicle speed, the minimum torque required for ECO mode to meet comfort performance requirements ( (negative value) In ECO mode, this refers to the maximum absolute value of deceleration on a level road that satisfies the vehicle's comfort performance.

[0039] Pick With the electric drive system at this speed The absolute value of the maximum available feedback torque. The minimum value in the range is taken as the negative value, and this negative value is taken as the rotational speed. ECO feedback minimum torque ; Fitting motor speeds at all speeds The minimum torque external characteristic reference curve L6 for ECO feedback is obtained.

[0040] At actual speed Under these conditions, ECO drive minimum torque The external characteristic curve L4 of the maximum torque feedback of ECO is fitted.

[0041] like Figure 3 As shown, L2 is the reference curve for the maximum torque external characteristic of ECO drive; L3 is the reference curve for the minimum torque external characteristic of ECO drive; L6 is the reference curve for the minimum torque external characteristic of ECO feedback (feedback torque is negative); L4 is the reference curve for the maximum torque external characteristic of ECO drive (feedback torque is negative); L1 is the external characteristic curve of electric drive system; and L5 is the external characteristic curve of electric drive system feedback.

[0042] The efficiency MAP of the electric drive system was obtained through bench testing and calibration; and the fitted curves L1 and L5 were obtained. S2. Based on the motor speed range, multiple fuzzy control regions are predefined between the reference boundary curves. Each fuzzy control region is associated with a fuzzy gain strategy for a given driving scenario, taking the real-time vehicle status as input. This step of predefining multiple fuzzy control regions between the reference boundary curves based on the motor speed range includes: The motor operating range is divided into a low-speed range [0, ...]. ), medium speed range [ , ), high speed range [ , ),in , The threshold values ​​are calibrated based on the high-efficiency operating range of the motor and the commonly used speed range of the vehicle. This is the first RPM value, used to demarcate the low and medium RPM range, for example, 2000 rpm; This is the second RPM value, used to demarcate the medium and high RPM range, such as 7000 rpm.

[0043] Within the low-speed range, a first fuzzy control region A1 is predefined between the ECO drive maximum torque external characteristic reference curve L2 and the electric drive system drive external characteristic curve L1. Within the medium-speed and high-speed ranges, a second fuzzy control region A2 is predefined between the ECO drive maximum torque external characteristic reference curve L2 and the electric drive system drive external characteristic curve L1. Within the medium-speed and high-speed ranges, a third fuzzy control region A3 is predefined between the ECO drive minimum torque external characteristic reference curve L3 and the ECO feedback maximum torque external characteristic reference curve L4. Within the low-speed range, a fourth fuzzy control region A4 is predefined between the ECO feedback minimum torque external characteristic reference curve L6 and the electric drive system feedback external characteristic curve L5.

[0044] Define the correspondence between driving scenarios, fuzzy gain strategies, and fuzzy control regions: The driving scenario associated with the first fuzzy control region A1 is a heavy-load or hill-climbing condition at low speed. The fuzzy gain strategy is configured to be based on the real-time vehicle load M and the road gradient. Compensate for the upper limit of drive torque; The driving scenario associated with the second fuzzy control region A2 is a temporary overtaking situation at medium to high speeds. The fuzzy gain strategy is configured to temporarily increase the upper limit of the driving torque based on the real-time accelerator pedal opening PA and its rate of change RA. The driving scenario associated with the third fuzzy control region A3 is coasting or braking at medium to high speeds. The fuzzy gain strategy is configured to set the torque command at L3 or L4 to skip the inefficient torque range. The driving scenario associated with the fourth fuzzy control region A4 is a downhill driving condition with a set slope at low speed. The fuzzy gain strategy is configured to adjust based on the downhill slope. The upper limit of feedback torque has been enhanced.

[0045] The fuzzy gain strategy for the first fuzzy control region A1 is as follows: When the motor speed When in the low speed range: If satisfied Under the given conditions, the upper limit of the output torque is L2; If satisfied Under the given conditions, the upper limit of the output torque is ; in, For real-time vehicle load, For the vehicle's curb weight, The load change threshold, The slope threshold, This is the load gain coefficient. This is the slope gain coefficient; The specific fuzzy gain strategy for the second fuzzy control region A2 is as follows: The fuzzy algorithm A2 region is used in ECO mode, specifically in the mid-speed and high-speed ranges, to enhance the external torque characteristics of the driving system during temporary overtaking. Its strategy is as follows: The minimum opening of the accelerator pedal when calibrated to the default depth: ； The minimum rate of change when the accelerator pedal is pressed rapidly under calibration: ； when ,like ; when ,like When the conditions are met, The response time is 60 seconds (this 60-second duration is to ensure a rapid response to overtaking maneuvers even after briefly releasing the accelerator pedal during overtaking). After 60 seconds... ; This refers to the actual accelerator pedal opening. The pedal opening threshold (e.g., 50%) is used to determine overtaking behavior. This represents the actual rate of change of the accelerator pedal. The threshold for the rate of change of pedal opening (e.g., 5% / 200ms) is used to determine the overtaking behavior.

[0046] The fuzzy algorithm A3 region is used in ECO mode to add torque to the driving external characteristics during vehicle coasting and braking in the mid-speed and high-speed ranges. Its strategy is as follows: when ,like ; when ,like ; To determine the threshold for the degree of accelerator pedal not being depressed (e.g., 2%). The fuzzy curve of the minimum torque external characteristic of ECO drive; The external characteristic fuzzy curve for the maximum torque feedback of ECO (the feedback torque is negative, and the maximum torque here actually has the smallest feedback force).

[0047] The fuzzy algorithm in region A4 is used in the low-speed range of ECO mode to enhance the external torque of the drive characteristics when the vehicle is descending a steep slope. The strategy is as follows: when ; when ; S3. Real-time acquisition of vehicle status parameters, including motor speed, vehicle load, road gradient, and accelerator pedal information; S4. Determine the current driving scenario based on the vehicle state parameters and match it to the corresponding fuzzy control region, specifically including: Based on the comparison between the real-time accelerator pedal opening PA and the preset non-pressed determination threshold PA2, distinguish between the driving condition and the non-driving condition; the non-pressed determination threshold (PA2) is a calibrated value close to zero, which is used to determine that the vehicle enters the non-driving condition when the accelerator pedal opening (PA) is lower than this threshold.

[0048] If PA ≥ PA2, it is determined as the driving condition, and then process according to the speed range where the current motor speed S belongs as follows: When the motor speed belongs to the low-speed range, obtain the vehicle load and the road gradient ; If one of the conditions is satisfied, it is determined as the heavy-load or climbing scenario and matched to the first fuzzy control region A1; otherwise, it is determined as the normal driving scenario, and the torque demand is directly limited by the reference boundary curve; When the motor speed belongs to the medium or high-speed range, obtain the accelerator pedal opening change rate RA; If the conditions PA ≥ PA1 and RA ≥ RA1 are both satisfied, it is determined as the temporary overtaking scenario and matched to the second fuzzy control region A2; otherwise, it is determined as the normal driving scenario, and the torque demand is directly limited by the reference boundary curve; If PA < PA2, it is determined as the non-driving condition, and then process according to the speed range where the motor speed belongs as follows: When the motor speed belongs to the medium or high-speed range, it is determined as the coasting or braking scenario and matched to the third fuzzy control region A3; When the motor speed belongs to the low-speed range, obtain the road gradient ; If , it is determined as the set-gradient downhill scenario and matched to the fourth fuzzy control region A4; otherwise, it is determined as the normal slow driving or downhill scenario, and the torque demand is directly limited by the reference boundary curve; Among them, PA1 is the overtaking pedal opening threshold, and RA1 is the overtaking pedal change rate threshold.

[0049] S5. Apply the fuzzy gain strategy associated with the fuzzy control region to perform real-time compensation on the reference torque in the ECO mode and output the final torque command. Specifically including: Step (1) Determine the reference value of the current torque command; If currently in drive mode, then based on the current motor speed The torque reference value is obtained from the external characteristic reference curve L2 of the ECO drive maximum torque; If the current operating condition is non-driving, then based on the current motor speed... The torque reference value is obtained from the ECO feedback minimum torque external characteristic reference curve L6; Step (2) Calculate the fuzzy gain compensation amount; The gain function associated with the matched fuzzy control region is called, and the vehicle state parameters used in the scene are used as input to calculate the real-time torque gain coefficient or compensation torque value. Step (3) Perform compensation and output; The gain coefficient is multiplied by the reference value, or the compensated torque value is added to the reference value to obtain the final torque command after real-time compensation, and then sent to the electric drive system controller.

[0050] When the first fuzzy control region A1 is matched, the specific steps for calculating the fuzzy gain compensation are as follows: Calculate the dynamic gain coefficient ; Then the final torque command in step (3) ; When the match reaches the second fuzzy control region A2, the determined benchmark value is The gain strategy in step (2) is to trigger a temporary boost flag, and step (3) is as follows: Within a duration T after the overtaking conditions are met, the final torque command is output. ; After the duration T ends, the final torque command is resumed. ; When the match reaches the third fuzzy control region A3, steps (1) and (2) are executed together, and the output is determined directly based on the accelerator pedal opening PA: If PA is less than the threshold PA2 for determining if the pedal is not depressed, then the final torque command output will be... or This allows us to skip the inefficient torque range between L3 and L4. When the match reaches the fourth fuzzy control region A4, the reference value determined in step (1) is Steps (2) and (3) are combined and executed as follows: If the downhill slope angle α satisfies The final torque command output will then be... ; Otherwise, the final torque command output .

[0051] This represents a function or lookup table operation. The input is the current real-time motor speed S, and the output is the maximum available drive torque value of the electric drive system at that speed S. The actual action is performed in the vehicle controller, which is usually achieved by querying the L1 curve MAP table pre-stored in memory.

[0052] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or other media capable of storing program code. It includes several instructions to cause a computer terminal (which may be a personal computer, server, or a second terminal, network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0053] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0054] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0055] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0056] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adaptive torque control in ECO mode of a pure electric vehicle based on fuzzy algorithm, characterized in that, include: Obtain the baseline boundary curves of drive and regenerative torque in ECO mode; Based on the motor speed range, multiple fuzzy control regions are predefined between the reference boundary curves. Each fuzzy control region is associated with a fuzzy gain strategy that takes the real-time vehicle status as input for a given driving scenario. Real-time acquisition of vehicle status parameters, including motor speed, vehicle load, road gradient, and accelerator pedal information; Based on the vehicle status parameters, determine the current driving scenario and match it to the corresponding fuzzy control area; The fuzzy gain strategy associated with the fuzzy control region is applied to compensate the reference torque of the ECO mode in real time, and the final torque command is output.

2. The adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithm according to claim 1, characterized in that, The steps to obtain the baseline boundary curves of drive and regenerative torque in ECO mode include: Based on the vehicle coasting resistance model, the power requirements under ECO mode and the drive external characteristic curve L1, the reference curves for the maximum torque external characteristic of ECO drive L2 and the minimum torque external characteristic of ECO drive L3 are obtained by fitting. Based on the vehicle coasting resistance model, the comfort requirements in ECO mode, and the feedback external characteristic curve L5, the minimum feedback torque external characteristic reference curve L6 and the maximum feedback torque external characteristic reference curve L4 of ECO are obtained by fitting. The reference boundary curves include curves L2, L3, L4, and L6.

3. The adaptive torque control method for ECO mode of pure electric vehicles based on fuzzy algorithm according to claim 2, characterized in that, The methods for determining the ECO drive maximum torque external characteristic reference curve L2 include: For a given motor speed Determine the rotational speed The efficiency of the electric drive system is higher than the target drive efficiency value. Maximum available drive torque ; Based on the vehicle coasting resistance model, calculate the minimum required torque to meet the power performance requirements in ECO mode. ; The speed can be obtained by querying the external characteristic curve L1 of the drive. Maximum available drive torque ; Pick With the electric drive system at this speed Maximum available drive torque The minimum value in the range is used as the rotational speed. Maximum torque under ECO drive ; Fitting motor speeds at all speeds The reference curve L2 for the maximum torque external characteristic of ECO drive is obtained. At actual speed Next, calculate And fit the motor at all speeds The minimum torque external characteristic reference curve L3 of ECO drive is obtained.

4. The adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithm according to claim 2, characterized in that, The methods for determining the ECO feedback minimum torque external characteristic reference curve L6 include: For a given motor speed Determine the rotational speed The efficiency of the electric drive system is higher than the feedback target efficiency value. Maximum available feedback torque absolute value ; Based on the vehicle's coasting resistance model, calculate the absolute value of the minimum feedback torque required to meet the comfort requirements in ECO mode. ; Query the feedback external characteristic curve L5 to obtain the rotational speed. Maximum available feedback torque ; Pick With the electric drive system at this speed The absolute value of the maximum available feedback torque. The minimum value in the range is taken as the negative value, and this negative value is taken as the rotational speed. ECO feedback minimum torque ; Fitting motor speeds at all speeds The minimum torque external characteristic reference curve L6 for ECO feedback is obtained; At actual speed Calculate the minimum torque for ECO drive. And fit the motor speeds at all speeds The ECO feedback maximum torque external characteristic reference curve L4 is obtained.

5. The adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithm according to claim 1, characterized in that, The step of pre-defining multiple fuzzy control regions between the reference boundary curves based on the motor speed range includes: The motor operating range is divided into a low-speed range [0, ...]. ), medium speed range [ , ), high speed range [ , ),in , The threshold values ​​are calibrated based on the high-efficiency operating range of the motor and the commonly used speed range of the vehicle. Within the low-speed range, a first fuzzy control region A1 is predefined between the ECO drive maximum torque external characteristic reference curve L2 and the electric drive system drive external characteristic curve L1. Within the medium-speed and high-speed ranges, a second fuzzy control region A2 is predefined between the ECO drive maximum torque external characteristic reference curve L2 and the electric drive system drive external characteristic curve L1. Within the medium-speed and high-speed ranges, a third fuzzy control region A3 is predefined between the ECO drive minimum torque external characteristic reference curve L3 and the ECO feedback maximum torque external characteristic reference curve L4. Within the low-speed range, a fourth fuzzy control region A4 is predefined between the ECO feedback minimum torque external characteristic reference curve L6 and the electric drive system feedback external characteristic curve L5.

6. The adaptive torque control method for ECO mode of pure electric vehicles based on fuzzy algorithm according to claim 5, characterized in that, Define the correspondence between driving scenarios, fuzzy gain strategies, and fuzzy control regions: The driving scenario associated with the first fuzzy control region A1 is a heavy-load or hill-climbing condition at low speed. The fuzzy gain strategy is configured to be based on the real-time vehicle load M and the road gradient. Compensate for the upper limit of drive torque; The driving scenario associated with the second fuzzy control region A2 is a temporary overtaking situation at medium to high speeds. The fuzzy gain strategy is configured to temporarily increase the upper limit of the driving torque based on the real-time accelerator pedal opening PA and its rate of change RA. The driving scenario associated with the third fuzzy control region A3 is coasting or braking at medium to high speeds. The fuzzy gain strategy is configured to set the torque command at L3 or L4 to skip the inefficient torque range. The driving scenario associated with the fourth fuzzy control region A4 is a downhill driving condition with a set slope at low speed. The fuzzy gain strategy is configured to adjust based on the downhill slope. The upper limit of feedback torque has been enhanced.

7. The adaptive torque control method for ECO mode of pure electric vehicles based on fuzzy algorithm according to claim 6, characterized in that, The fuzzy gain strategy for the first fuzzy control region A1 is as follows: When the motor speed When in the low speed range: If satisfied Under the given conditions, the upper limit of the output torque is ; If satisfied Under the given conditions, the upper limit of the output torque is ; in, For real-time vehicle load, For the vehicle's curb weight, The load change threshold, The slope threshold, This is the load gain coefficient. This is the slope gain coefficient; The specific fuzzy gain strategy for the second fuzzy control region A2 is as follows: When the motor speed When in the medium or high speed range: If the accelerator pedal opening PA is less than the opening threshold PA1 and the rate of change RA is less than the rate of change threshold RA1, then the upper limit of the output torque is L2. If the accelerator pedal opening PA is greater than or equal to the opening threshold PA1 and the rate of change RA is greater than or equal to the rate of change threshold RA1, then the upper limit of the output torque will be increased to L1 and maintained for a predetermined time T, after which it will return to L2.

8. The adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithm according to claim 7, characterized in that, Based on vehicle status parameters, determine the current driving scenario and match it to the corresponding fuzzy control region, including: The driving condition and the non-driving condition are distinguished by comparing the real-time accelerator pedal opening PA with the preset non-pressed threshold PA2. If PA≥PA2, it is determined to be a driving condition, and then the following processing is performed according to the speed range to which the current motor speed S belongs: When the motor speed When the speed range is low, obtain the vehicle load. With road slope ; If satisfied If one of the conditions is met, it is determined to be a heavy-load or hill-climbing scenario and matched to the first fuzzy control region A1; otherwise, it is determined to be a normal driving scenario, and the torque requirement is directly limited by the reference boundary curve. When the motor speed When the engine speed is in the medium or high speed range, obtain the accelerator pedal opening change rate RA; If both PA≥PA1 and RA≥RA1 are met, it is determined to be a temporary overtaking scenario and matched to the second fuzzy control region A2; otherwise, it is determined to be a normal driving scenario, and the torque requirement is directly limited by the reference boundary curve. If PA < PA2, it is determined as a non-driving condition, and then it is processed as follows according to the speed range to which the motor speed belongs: When the motor speed When the speed range is medium or high, it is determined to be a coasting or braking scenario and matched to the third fuzzy control region A3; When the motor speed When the engine speed is in the low speed range, obtain the road slope. ; like If the slope is set, it is determined to be a downhill scenario with a set gradient, and it is matched to the fourth fuzzy control area A4; otherwise, it is determined to be a normal slow-moving or downhill scenario, and the torque requirement is directly limited by the reference boundary curve. PA1 is the overtaking pedal opening threshold, and RA1 is the overtaking pedal change rate threshold.

9. The adaptive torque control method for pure electric vehicles in ECO mode based on fuzzy algorithm according to claim 8, characterized in that, The fuzzy gain strategy associated with the fuzzy control region is applied to compensate the reference torque of the ECO mode in real time, and the final torque command is output, specifically including: Step (1) Determine the reference value for the current torque command; If currently in drive mode, then based on the current motor speed The torque reference value is obtained from the external characteristic reference curve L2 of the ECO drive maximum torque; If the current operating condition is non-driving, then based on the current motor speed... The torque reference value is obtained from the ECO feedback minimum torque external characteristic reference curve L6; Step (2) Calculate the fuzzy gain compensation amount; The gain function associated with the matched fuzzy control region is called, and the vehicle state parameters used in the scene are used as input to calculate the real-time torque gain coefficient or compensation torque value. Step (3) Perform compensation and output; The gain coefficient is multiplied by the reference value, or the compensated torque value is added to the reference value to obtain the final torque command after real-time compensation, and then sent to the electric drive system controller.

10. The adaptive torque control method for ECO mode of pure electric vehicles based on fuzzy algorithm according to claim 9, characterized in that, When the first fuzzy control region A1 is matched, the specific steps for calculating the fuzzy gain compensation are as follows: Calculate the dynamic gain coefficient ; Then the final torque command in step (3) ; When the match reaches the second fuzzy control region A2, the determined benchmark value is The gain strategy in step (2) is to trigger a temporary boost flag, and step (3) is as follows: Within a duration T after the overtaking conditions are met, the final torque command is output. ; After the duration T ends, the final torque command is resumed. ; When the match reaches the third fuzzy control region A3, steps (1) and (2) are executed together, and the output is determined directly based on the accelerator pedal opening PA: If PA is less than the threshold PA2 for determining if the pedal is not depressed, then the final torque command output will be... or This allows us to skip the inefficient torque range between L3 and L4. When the match reaches the fourth fuzzy control region A4, the reference value determined in step (1) is Steps (2) and (3) are combined and executed as follows: If the downhill slope angle α satisfies The final torque command output will then be... ; Otherwise, the final torque command output .

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