Vehicle control method and device, electronic equipment, storage medium and program product

Abstract

Embodiments of the present application provide a vehicle control method and device, electronic equipment, storage medium and program product, relating to the technical field of vehicle control. The method comprises: in a current period of vehicle driving, determining a real-time demand torque based on a real-time opening degree of an accelerator pedal, a real-time stroke of a brake pedal and a real-time vehicle speed collected in real time; determining a target smooth torque based on the real-time demand torque; predicting a predicted impact degree of the vehicle in a next period based on a pre-collected real-time acceleration and real-time vehicle speed; correcting the target smooth torque based on the predicted impact degree to obtain a target torque of the current period, so as to control the vehicle to run based on the target torque of the current period in the current period. The embodiments of the present application realize dynamic adjustment of the target torque, improve the smoothness of the vehicle running, and reduce the car sickness probability of the members in the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle control method, device, electronic device, storage medium, and program product. Background Technology

[0002] Motion sickness, also known as kinetosis, is primarily caused by a mismatch between the changes in acceleration perceived by the vestibular system in the inner ear and visual information. During vehicle movement, the driver adapts to changes in the vehicle's posture based on anticipation, but passengers are extremely sensitive to sudden changes in acceleration, i.e., the impact, which is the main cause of motion sickness.

[0003] With the popularization of new energy vehicles, the rapid torque output of the motor, kinetic energy recovery, and the longitudinal impact generated during gear shifting can easily cause motion sickness in passengers. The severity of motion sickness is directly related to the vehicle's impact intensity. Existing vehicle smoothness control systems mostly intervene and adjust when the impact intensity exceeds the standard, resulting in poor vehicle smoothness and motion sickness prevention. Summary of the Invention

[0004] This application provides a vehicle control method, device, electronic device, storage medium, and program product to solve the problems of poor vehicle ride comfort and motion sickness prevention in the prior art.

[0005] According to one aspect of this application, a vehicle control method is provided, the method comprising: During the current cycle of vehicle operation, the real-time torque demand is determined based on the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed. The target smooth torque is determined based on the real-time demand torque; wherein, the rate of change of the target smooth torque relative to the historical target torque is less than a preset rate of change threshold; the historical target torque is the target torque of the vehicle in the previous cycle; the target torque of the first cycle is determined based on the preset torque. Based on pre-collected real-time acceleration and real-time vehicle speed, predict the predicted impact of the vehicle in the next cycle; The target smooth torque is corrected based on the predicted impact to obtain the target torque for the current cycle, so as to control the vehicle operation based on the target torque for the current cycle.

[0006] In one possible implementation, the determination of real-time torque demand based on real-time accelerator pedal opening, real-time brake pedal travel, and real-time vehicle speed includes: The system collects real-time data on the accelerator pedal opening, brake pedal travel, and vehicle speed. Determine the preset torque correspondence; wherein, the torque correspondence is used to characterize the correspondence between the accelerator pedal opening, the brake pedal travel, and the vehicle speed and the vehicle's required torque; The real-time torque requirement is determined based on the torque correspondence, real-time opening, real-time travel, and real-time vehicle speed.

[0007] In one possible implementation, the above-mentioned determination of the target smooth torque based on real-time demand torque includes: The real-time torque demand is filtered to obtain the first smooth torque; The target torque increment is determined based on the torque state and the first smooth torque; wherein, the torque state includes the torque rising state and the torque falling state; The target smooth torque is determined based on the target torque increment.

[0008] In one possible implementation, determining the target torque increment based on the torque state and the first smoothed torque includes: Determine the torque difference between the first smoothed torque and the historical target torque, and determine the first torque change rate based on the torque difference; Obtain the threshold for the rate of increase of torque and the threshold for the rate of decrease of torque. Based on the comparison results of the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, the target torque increment is determined; Determining the target smooth torque based on the target torque increment includes: The target smooth torque is the sum of the target torque increment and the historical target torque.

[0009] In one possible implementation, the determination of the target torque increment based on the comparison between the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, includes: If the torque state is a torque increase state, then the smaller value between the increase rate threshold and the first torque change rate is taken as the target torque increment. If the torque state is a torque decrease state, the larger of the decrease rate threshold and the first torque change rate will be used as the target torque increment.

[0010] In another possible implementation, the above-mentioned prediction of the vehicle's predicted impact in the next cycle based on pre-collected real-time acceleration and real-time vehicle speed includes: The current driving condition is determined based on real-time acceleration and real-time vehicle speed; the current driving condition includes any one of the following: low speed condition, medium speed condition, high speed condition, strong acceleration condition, slow acceleration condition, coasting condition, and braking condition. The query retrieves the baseline impact coefficient corresponding to the current driving conditions; the baseline impact coefficient is used to characterize the impact intensity of the vehicle in the current cycle. Calculation of measured impact intensity based on real-time acceleration; The target impact coefficient is obtained by updating the baseline impact coefficient based on the measured impact intensity. The predicted impact intensity is calculated based on the target impact coefficient and the target torque increment.

[0011] In another possible implementation, the above-mentioned correction of the target smoothing torque based on the predicted impact to obtain the target torque for the current cycle includes: Obtain the vehicle's safety baseline impact level; Based on the safety baseline impact and the predicted impact, the target smooth torque is corrected to obtain the target torque for the current cycle.

[0012] In another possible implementation, the target smoothing torque is corrected based on the safety baseline impact and the predicted impact to obtain the target torque for the current cycle, including: If the predicted impact is less than or equal to the safety baseline impact, the target smoothing torque will be used as the target torque for the current cycle. If the predicted impact is greater than the safety benchmark impact, the corrected torque increment is determined based on the target impact coefficient, and the target torque for the current cycle is determined based on the corrected torque increment.

[0013] In yet another possible implementation, the above-mentioned determination of the corrected torque increment based on the target impact coefficient includes: The second torque change rate is determined based on the target impact coefficient and the safety benchmark impact degree; Based on the comparison results of the rise rate threshold / fall rate threshold with the second torque change rate and the first torque change rate, as well as the torque state, the corrected torque increment is determined. The target torque for the current cycle is determined based on the corrected torque increment, including: The sum of the corrected torque increment and the historical target torque is used as the target torque for the current cycle.

[0014] In another possible implementation, the above-mentioned determination of the corrected torque increment based on the comparison results of the rise rate threshold / fall rate threshold with the second torque change rate and the first torque change rate, and the torque state, includes: If the torque state is a torque increase state, then the smaller of the increase rate threshold, the first torque change rate, and the second torque change rate will be used as the correction torque increment. If the torque state is a torque decrease state, the larger of the decrease rate threshold, the first torque change rate, and the second torque change rate will be used as the correction torque increment.

[0015] In another possible implementation, the above-mentioned method of obtaining the vehicle's safety baseline impact level includes: The baseline impact threshold is determined based on real-time vehicle speed; where real-time vehicle speed is positively correlated with the baseline impact threshold. It can identify the motion sickness risk level of vehicle occupants, power demand intensity, and road condition information ahead of the vehicle; The target coefficient is determined based on at least one of the following: the member's motion sickness risk level, power demand intensity, and road condition information ahead; The safety baseline impact level is determined based on the target coefficient and the baseline impact level threshold.

[0016] In another possible implementation, the target coefficient is determined based on at least one of the following: the member's motion sickness risk level, power demand intensity, and road condition information ahead, including: The first coefficient is determined based on the member's motion sickness risk level; The second coefficient is determined based on the intensity of energy demand. The third coefficient is determined based on road condition information; The target coefficient is determined based on the first, second, and third coefficients.

[0017] According to another aspect of the embodiments of this application, a vehicle control device is provided, the device comprising: The first determining module is used to determine the real-time required torque based on the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed collected in real time during the current cycle of vehicle driving. The second determining module is used to determine the target smooth torque based on the real-time demand torque; wherein, the torque change rate of the target smooth torque relative to the historical target torque is less than a preset change rate threshold; the historical target torque is the target torque of the vehicle in the previous cycle; the target torque of the first cycle is determined based on the preset torque. The prediction module is used to predict the vehicle's impact in the next cycle based on pre-collected real-time acceleration and real-time vehicle speed. The correction module is used to correct the target smooth torque based on the predicted impact to obtain the target torque for the current cycle, so as to control the vehicle operation based on the target torque for the current cycle.

[0018] According to another aspect of this application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method shown in the first aspect of this application.

[0019] According to another aspect of this application, a non-transitory computer-readable storage medium is provided that stores computer instructions for causing a computer to perform the steps of the method shown in the first aspect of this application.

[0020] According to another aspect of this application, a computer program product is provided, the computer program product including instructions that, when executed, cause a computer to perform the steps of the method shown in the first aspect of this application.

[0021] The beneficial effects of the technical solution provided in this application are: The vehicle control method provided in this application determines the real-time required torque based on real-time acquisition of the accelerator pedal opening, brake pedal travel, and vehicle speed during the current cycle of vehicle operation. It then determines a target smoothing torque based on this real-time required torque, ensuring that the rate of change of the target smoothing torque relative to the target torque of the previous cycle is less than a preset rate of change threshold, thus achieving the first smoothing process for the real-time required torque. Furthermore, it predicts the vehicle's expected impact in the next cycle based on pre-acquired real-time acceleration and vehicle speed, and corrects the target smoothing torque based on this predicted impact, achieving a second smoothing process based on the predicted impact of future cycles to obtain the target torque for the current cycle. The vehicle is then controlled based on this target torque during the current cycle. This application achieves dynamic adjustment of the target torque based on the rate of change of torque relative to the previous cycle and the predicted impact for the next cycle, improving the smoothness of vehicle operation and reducing the likelihood of motion sickness for passengers. Simultaneously, the pre-smoothing of torque based on the predicted impact in the next cycle further enhances the anti-motion sickness effect and effectively improves the user experience. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic flowchart of a vehicle control method provided in an embodiment of this application; Figure 2 A schematic flowchart illustrating the determination of real-time required torque in a vehicle control method provided in this application embodiment; Figure 3 A schematic flowchart illustrating the determination of a target smooth torque in a vehicle control method provided in this application embodiment; Figure 4 A schematic flowchart illustrating the determination of a target torque increment in a vehicle control method provided in this application embodiment; Figure 5 A schematic flowchart illustrating the prediction of impact magnitude in a vehicle control method provided in this application embodiment; Figure 6 A schematic flowchart illustrating the determination of a target torque in a vehicle control method provided in this application embodiment; Figure 7 A schematic diagram of the process for determining the corrected torque increment in a vehicle control method provided in an embodiment of this application; Figure 8 A schematic flowchart illustrating the determination of a safety reference impact level in a vehicle control method provided in this application embodiment; Figure 9 A flowchart illustrating the determination of target coefficients in a vehicle control method provided in this application embodiment; Figure 10 A schematic flowchart illustrating an example vehicle control method provided in an embodiment of this application; Figure 11 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application; Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] Embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0025] The accompanying drawings illustrate various structural schematics according to embodiments of the present disclosure. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0026] In the context of this disclosure, when a layer / element is referred to as being "above" another layer / element, the layer / element may be directly above the other layer / element, or there may be an intermediate layer / element between them. Additionally, if a layer / element is "above" another layer / element in one orientation, then when the orientation is reversed, the layer / element may be "below" the other layer / element.

[0027] During vehicle operation, motion sickness primarily stems from the conflict between motion signals perceived by the vestibular system in the inner ear and visual input signals. This conflict is compounded by sudden changes in longitudinal acceleration and irregular pitching / jerking of the vehicle body, inducing discomfort such as dizziness, nausea, and vomiting. Compared to traditional gasoline vehicles, electric motors in new energy vehicles possess millisecond-level torque response characteristics. During start-up, energy recovery from releasing the accelerator, and shifting gears in multi-speed transmissions, instantaneous torque surges are easily generated, creating significant longitudinal impacts. Simultaneously, the lack of engine vibration and noise as a motion reference due to the vehicle's noise reduction design significantly exacerbates vestibular sensory disturbances, making motion sickness more pronounced. Furthermore, under conditions such as frequent start-stop in congested urban traffic, driving on slopes, heavy loads, low-traction surfaces, and aging transmission components, the vehicle's dynamic characteristics undergo significant shifts. The same torque adjustment slope can produce vastly different impact levels, causing particularly intense discomfort for the elderly, children, and other vestibular-sensitive individuals. This has become a core pain point affecting the driving experience of family vehicles.

[0028] The inventors discovered that, in the existing technology, although some car models have a "comfort mode," most of them adjust fixed parameters, which has the following shortcomings: 1. Post-event remedies: Most solutions only intervene after the impact has exceeded the limit, by which time motion sickness has already occurred; 2. Model Dependence: Some solutions rely on accurate vehicle dynamics models (such as mass, gradient, and wind resistance), but actual working conditions are complex and variable, and model mismatch can lead to control failure. 3. Neglecting response lag: The system delay between torque request and actual acceleration response is not fully considered, resulting in insufficient control accuracy; 4. Incomplete consideration of state variables: The impact of current vehicle speed, current acceleration, and other states on the vehicle's dynamic response is not fully considered, resulting in limited adaptability; 5. Conflict between power and comfort: When rapid acceleration is needed to overtake, motion sickness control may unnecessarily limit power response, affecting driving safety and user experience.

[0029] To address the aforementioned technical issues, some embodiments of this application determine the real-time required torque based on real-time data collected from the accelerator pedal opening, brake pedal travel, and vehicle speed during the current cycle of vehicle operation. A target smoothing torque is then determined based on this real-time required torque, ensuring that the rate of change of the target smoothing torque relative to the target torque of the previous cycle is less than a preset rate of change threshold. This achieves the first smoothing process for the real-time required torque. Furthermore, based on pre-collected real-time acceleration and vehicle speed, the predicted impact of the vehicle in the next cycle is predicted. The target smoothing torque is then corrected based on this predicted impact, achieving a second smoothing process based on the predicted impact of future cycles. This yields the target torque for the current cycle, allowing vehicle operation to be controlled based on this target torque within the current cycle. This application achieves real-time, dynamic adjustment of the target torque based on the rate of change of torque relative to the previous cycle and the predicted impact for the next cycle, improving vehicle smoothness and reducing the likelihood of motion sickness for passengers. Simultaneously, the pre-smoothing of torque based on the predicted impact for the next cycle further enhances the anti-motion sickness effect and effectively improves the user experience.

[0030] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0031] This application provides a vehicle control method, such as... Figure 1 As shown, this method, which can be applied to a vehicle control terminal, includes: S101 determines the real-time torque requirement based on the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed during the current cycle of vehicle operation.

[0032] The aforementioned real-time torque demand can be the torque demanded under vehicle driving or energy recovery conditions; the aforementioned real-time torque demand is used for vehicle torque adjustment, shock suppression, and motion sickness control.

[0033] Specifically, the system can collect real-time accelerator pedal opening signal, real-time brake pedal travel signal, and vehicle current speed based on the vehicle controller; then, based on the above three driving state parameters, torque mapping lookup table and dynamic correction calculation are performed to obtain the real-time required torque.

[0034] Optionally, the aforementioned vehicle control terminal can be a mobile internet device, an in-vehicle navigation terminal, an in-vehicle computer, or other in-vehicle equipment, without specific limitations. It is understood that the vehicle control terminal can connect directly or indirectly to the vehicle controller via wired or wireless communication, but is not limited to these methods.

[0035] Optionally, the current cycle mentioned above is the vehicle control cycle, for example, the duration of each control cycle can be 10ms.

[0036] S102 determines the target smooth torque based on real-time demand torque.

[0037] Among them, the rate of change of the target smooth torque relative to the historical target torque is less than the preset rate of change threshold; the historical target torque is the target torque of the vehicle in the previous cycle; the target torque of the first cycle is determined based on the preset torque.

[0038] Specifically, the torque increment within the current cycle can be determined based on the real-time demand torque and the preset rate of change threshold, and then the target smoothing torque can be determined based on the torque increment and historical torque.

[0039] Optionally, the target torque for the first cycle can be a preset torque; the preset torque can be an initial torque, for example, the initial torque can be 0.

[0040] S103 predicts the vehicle's impact in the next cycle based on pre-collected real-time acceleration and real-time vehicle speed.

[0041] Specifically, an impact prediction model can be pre-built, and then based on the real-time acceleration and real-time vehicle speed collected by the vehicle controller, the impact prediction model can be used to predict the impact of the vehicle in the next control cycle.

[0042] S104, based on the predicted impact, corrects the target smooth torque to obtain the target torque for the current cycle, so as to control the vehicle operation based on the target torque for the current cycle.

[0043] Specifically, the vehicle's safety baseline impact can be predetermined, and the target smooth torque can be corrected based on the difference between the safety baseline impact and the predicted impact to obtain the target torque for the current cycle.

[0044] This application embodiment determines the real-time required torque based on real-time data collected during the current cycle of vehicle operation, including the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed. A target smoothing torque is then determined based on this real-time required torque, ensuring that the rate of change of the target smoothing torque relative to the target torque of the previous cycle is less than a preset rate of change threshold. This achieves the first smoothing process for the real-time required torque. Furthermore, based on pre-collected real-time acceleration and real-time vehicle speed, the predicted impact of the vehicle in the next cycle is predicted. The target smoothing torque is then corrected based on this predicted impact, achieving a second smoothing process based on the predicted impact of future cycles. This yields the target torque for the current cycle, allowing vehicle operation to be controlled based on this target torque. This application achieves dynamic adjustment of the target torque based on the rate of change of torque relative to the previous cycle and the predicted impact for the next cycle, improving the smoothness of vehicle operation and reducing the likelihood of motion sickness for passengers. Simultaneously, the pre-smoothing of torque based on the predicted impact for the next cycle further enhances the anti-motion sickness effect and effectively improves the user experience.

[0045] This application provides one possible implementation method, such as... Figure 2 As shown, the above method for determining the real-time torque demand based on real-time accelerator pedal opening, real-time brake pedal travel, and real-time vehicle speed includes: S201 can collect real-time data on the accelerator pedal opening, brake pedal travel, and vehicle speed.

[0046] Specifically, the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the current real-time vehicle speed can be obtained based on the vehicle controller.

[0047] S202, determine the preset torque correspondence.

[0048] Among them, the torque correspondence is used to characterize the relationship between the opening of the accelerator pedal, the travel of the brake pedal, and the vehicle speed and the torque required by the vehicle.

[0049] Optionally, the above correspondence can be a torque mapping table or a torque calculation formula.

[0050] Furthermore, the torque mapping table is used to characterize the correspondence between accelerator pedal opening, vehicle speed and the torque required for vehicle driving, as well as the matching relationship between brake pedal travel, vehicle speed and regenerative braking torque. This mapping table is generated and fixed in the vehicle controller after offline calibration under standard operating conditions, multi-driving resistance compensation and torque smoothing. During vehicle driving, the driver's basic torque requirement is obtained by interpolating the table under real-time operating conditions.

[0051] Specifically, taking the torque mapping table as an example, the table uses the accelerator pedal opening and real-time vehicle speed as dual horizontal axis input variables, and stores the basic drive torque required by the whole vehicle under the corresponding working conditions; under braking conditions, the brake pedal travel and real-time vehicle speed are used as inputs, and the energy recovery feedback torque is obtained by looking up the table.

[0052] S203 determines the real-time torque requirement based on the torque correspondence, real-time opening, real-time travel, and real-time vehicle speed.

[0053] Specifically, when the vehicle is running, the pedal opening and vehicle speed can be collected in real time; if the collected value falls exactly on a node in the table, the corresponding torque can be read directly; if it is between two nodes, a linear interpolation algorithm is used to calculate the intermediate transition torque to ensure that the torque output is continuous without step jumps.

[0054] In this embodiment, the accelerator pedal opening and brake pedal travel represent the driver's driving intention, and the corresponding real-time required torque is obtained by combining the torque correspondence. The torque correspondence can be a torque mapping table, which can be calibrated on a real vehicle to match the driving experience of the general public. The torque is sufficient for low-speed start-up, and the power convergence is smooth at high speeds. The acceleration and deceleration feel is linear and consistent, and the depth of the driver's pedal travel corresponds one-to-one with the vehicle's acceleration force. The real-time required torque obtained by looking up the table can lay a good foundation for subsequent torque slope limitation and impact prediction in the next cycle, which can significantly improve the accuracy of impact prediction and reduce prediction deviation. At the same time, the real-time required torque can serve as the basis for torque output in the unlearned operating condition range, ensuring smooth driving in all road conditions.

[0055] This application provides one possible implementation method, such as... Figure 3 As shown, the above method for determining the target smooth torque based on real-time demand torque includes: S301 filters the torque required in real time to obtain the first smooth torque.

[0056] Specifically, a first-order inertial low-pass filter algorithm can be used to filter the real-time required torque to obtain the first smooth torque.

[0057] S302, determine the target torque increment based on the torque state and the first smooth torque; and determine the target smooth torque based on the target torque increment.

[0058] The torque state includes torque increase state and torque decrease state.

[0059] Specifically, the first smoothed torque can be subjected to different smoothing processes based on the torque state; the specific processing method for the target smoothed torque will be explained in detail below.

[0060] In this embodiment, the real-time demand torque output from the lookup table is digitally filtered to produce a stable and smooth torque value as the first smoothing torque. This effectively eliminates the instantaneous high-frequency fluctuations in demand torque caused by pedal vibration, sensor signal noise, and road bumps. Simultaneously, the first smoothing torque is further smoothed and corrected based on the torque status, further improving the ride comfort. This effectively avoids frequent fluctuations in small torque values ​​that could continuously excite the vehicle body to produce subtle reciprocating impacts and induce motion sickness and discomfort for passengers.

[0061] This application provides one possible implementation method, such as... Figure 4 As shown, the above determination of the target torque increment based on the torque state and the first smooth torque includes: S401, determine the torque difference between the first smooth torque and the historical target torque, and determine the first torque change rate based on the torque difference.

[0062] The first smoothed torque of the current cycle can be T_req(n), and the historical target torque, i.e. the target torque of the previous cycle, can be T_filt(n-1).

[0063] The first torque change rate dT / dt can be calculated based on the following formula (1): dT / dt=(T_req(n)-T_filt(n-1)) / dt; (1) S402, obtain the threshold for the rate of increase of torque and the threshold for the rate of decrease of torque.

[0064] Among them, the torque rise rate is used to characterize how quickly the acceleration torque increases; the torque fall rate is used to characterize how quickly the torque drops when the pedal is released or the brake is retracted. Optionally, the rise rate threshold ΔT_max_up and the fall rate threshold ΔT_max_dwn are the maximum critical slopes for the allowable torque change. If the thresholds are exceeded, the amplitude is forcibly limited and smoothed to suppress shock and prevent motion sickness.

[0065] Specifically, the threshold for the rate of increase of torque and the threshold for the rate of decrease of torque can be obtained based on subjective evaluation of actual vehicles and impact tests. The specific methods for determining the benchmark thresholds can be found in existing technologies and will not be elaborated here.

[0066] S403, based on the comparison results of the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, determine the target torque increment.

[0067] The specific method for determining the target torque increment will be explained in detail below.

[0068] The above method for determining the target smooth torque based on the target torque increment includes: The target smooth torque is the sum of the target torque increment and the historical target torque.

[0069] Specifically, the target smooth torque T_filt(n) can be calculated based on the following formula (2): T_filt(n)=T_filt(n-1)+ΔT_filt(n)(2) Where ΔT_filt(n) is the target torque increment for the current cycle.

[0070] This application provides a possible implementation method in which the target torque increment is determined based on the comparison result of the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, including: If the torque state is a torque increase state, then the smaller value between the increase rate threshold and the first torque change rate is taken as the target torque increment. If the torque state is a torque decrease state, the larger of the decrease rate threshold and the first torque change rate will be used as the target torque increment.

[0071] Specifically, the torque state can be determined based on the change in torque in the current cycle relative to the previous cycle.

[0072] The torque change dT relative to the previous cycle can be calculated based on the following formula (3): dT=T_req(n)-T_filt(n-1)(3) In some implementations, if dT≥0, the torque state is a torque-increasing state; the target torque increment ΔT_filt(n) can be determined based on the following formula (4): ΔT_filt(n)=min(dT / dt,ΔT_max_up)(4) In other implementations, if dT < 0, the torque state is a torque decrease state; the target torque increment ΔT_filt(n) can be determined based on the following formula (5): ΔT_filt(n)=max(dT / dt,ΔT_max_dwn)(5) This application embodiment distinguishes between two working states: torque increase and torque decrease. It uses the corresponding rate threshold and the first torque change rate to compare and filter the target torque increment, which can specifically constrain the torque increase and decrease. This avoids excessively rapid torque increase during acceleration and excessively sharp torque decrease during deceleration, thus reducing vestibular stimulation from the source and effectively reducing the probability of motion sickness for passengers. The differentiated value logic of taking the smaller value of the two in the rising condition and the larger value of the two in the falling condition can accurately limit the instantaneous change of torque without rudely cutting off the driver's power demand, thus taking into account both driving responsiveness and driving smoothness.

[0073] This application provides one possible implementation method, such as... Figure 5 As shown, the above-mentioned prediction of vehicle impact in the next cycle based on pre-collected real-time acceleration and real-time vehicle speed includes: S501 determines the current driving conditions based on real-time acceleration and real-time vehicle speed.

[0074] The current driving conditions include any one of the following: low speed, medium speed, high speed, strong acceleration, slow acceleration, coasting, and braking.

[0075] S502 determines the current driving conditions based on real-time acceleration and real-time vehicle speed.

[0076] The baseline impact coefficient is used to characterize the impact intensity of a vehicle during the current cycle.

[0077] Specifically, the reference impact coefficient corresponding to the current driving condition can be obtained by querying a pre-built reference gain table.

[0078] Optionally, the aforementioned benchmark gain table may include a benchmark gain K_base(v,a) that covers the entire vehicle speed and acceleration range, established through bench testing and real vehicle calibration during the vehicle development phase.

[0079] Furthermore, the specific method for constructing the benchmark gain table may include: applying torque steps of different amplitudes on a flat, dry road surface at a fixed vehicle speed, recording the measured impact J_measured, and calculating the benchmark gain K_base based on formula (6): K_base=J_measured / Δt(6) S503 calculates the measured impact force based on real-time acceleration; updates the benchmark impact coefficient based on the measured impact force to obtain the target impact coefficient.

[0080] Specifically, the Recursive Least Squares (RLS) method can be used to update the baseline impact coefficient based on the measured impact intensity to obtain the target impact coefficient.

[0081] Alternatively, the above RLS update formula (7) is as follows: K_new=K_base+P*Δt*(J_act-K_base*Δt) (7) Where Δt is the duration of torque adjustment, J_act is the measured impact intensity, P is the adaptive gain used to control the learning rate, and K_new is the updated target impact coefficient.

[0082] S504 calculates the predicted impact intensity based on the target impact coefficient and the target torque increment.

[0083] Specifically, the final target impact coefficient, i.e., the target gain K_used, can be calculated based on the target impact coefficient: K_used=α*K_new +(1-α)* K_base(8) Where α = 1 - 1 / (M+1), and M is the number of learning iterations.

[0084] Furthermore, the predicted impact J_pred can be calculated based on the following formula (9): J_pred=K_used×Δt(9) Optionally, for work conditions that have not yet been learned, a hierarchical strategy can be used to determine the current K_new: First, find the nearest adjacent learned working condition interval K_new value and perform linear interpolation to obtain the current interval K_new; If there is no adjacent learning data, the offline baseline value K_base is used directly as K_new for the current interval.

[0085] This application embodiment collects real-time data pairs (Δt, J_act, v, a) during vehicle operation and matches the corresponding operating condition intervals based on the current vehicle speed v and acceleration a. It then uses RLS to online correct the control gain K corresponding to this interval, enabling the control strategy to adapt to time-varying conditions such as load, gradient, road surface adhesion coefficient, and vehicle aging. In other words, by using the current vehicle speed and acceleration as model state variables, the model can implicitly learn complex physical characteristics such as tire nonlinearity, transmission clearance, and wind resistance, improving the accuracy of impact prediction and laying a solid foundation for subsequent anti-motion sickness torque control.

[0086] This application provides one possible implementation method, such as... Figure 6 As shown, the above correction of the target smoothing torque based on the predicted impact intensity to obtain the target torque for the current cycle includes: S601, obtains the vehicle's safety baseline impact level.

[0087] The aforementioned safety benchmark impact level can be determined based on information such as the current risk level of motion sickness among vehicle occupants, power demand intensity, and road condition information ahead. The specific calculation steps will be explained in detail below.

[0088] S602, based on the safety benchmark impact and the predicted impact, corrects the target smooth torque to obtain the target torque for the current cycle.

[0089] Specifically, the safety baseline impact level and the predicted impact level can be compared, and the target smoothing torque can be corrected based on the comparison results to obtain the target torque for the current cycle. The specific torque correction steps are described below.

[0090] This application embodiment corrects the target smooth torque by using a safety benchmark impact and a predicted impact, thereby achieving a safe limit on the final torque command, ensuring that it does not exceed the driver's original torque demand T_req and does not exceed the physical limits of the power system, generating the target torque for the current cycle and outputting it to the motor controller.

[0091] Understandably, the RLS model can be updated online in real time. That is, the actual vehicle acceleration a can be collected in real time, the actual impact J_act can be calculated, and (Δt,J_act,v,a) can be used as new data for RLS update. This can realize online update of the RLS model for the corresponding dynamic range, forming a closed-loop adaptive model.

[0092] This application provides a possible implementation method in which the target smoothing torque is corrected based on the safety benchmark impact and the predicted impact to obtain the target torque for the current cycle, including: If the predicted impact J_pred is less than or equal to the safe baseline impact J_safe, then the target smoothing torque is used as the target torque for the current cycle. If the predicted impact J_pred is greater than the safe baseline impact J_safe, then the corrected torque increment is determined based on the target impact coefficient, and the target torque for the current cycle is determined based on the corrected torque increment.

[0093] In some implementations, if J_pred≤J_safe, it indicates that the current operation is safe, and the target smooth torque T_filt can be used as the final output torque to achieve torque control for the current cycle.

[0094] In other implementations, if J_pred > J_safe, it indicates that if the current trend continues, the vehicle impact will be too large, and the risk of motion sickness will also be high; torque smoothing correction can be further performed based on the following method.

[0095] This application's embodiments compare the predicted impact level J_pred with a preset safety impact threshold J_safe, thus predicting the impact level generated during subsequent torque adjustment in advance. This achieves proactive impact risk identification, unlike existing technologies that passively adjust torque after the actual impact occurs. This application prevents problems before they occur from the control logic, effectively reducing the probability of motion sickness caused by excessive longitudinal impact. Simultaneously, when the predicted impact level does not exceed the safety threshold, the target smooth torque is directly used for torque output without additional torque correction, preserving the driver's power operation intention to the maximum extent and ensuring power response responsiveness. When the predicted impact level exceeds the safety threshold, deep torque smoothing correction is initiated, balancing ride comfort and achieving a dynamic trade-off between power performance and motion sickness prevention. This hierarchical control strategy avoids continuously and indiscriminately restricting torque changes. While suppressing motion sickness triggers and ensuring ride comfort, it also considers vehicle power response performance and can adapt to changes in vehicle operating conditions and aging dynamics. The algorithm is simple and easy for the onboard controller to operate in real time.

[0096] This application provides one possible implementation method, such as... Figure 7 As shown, the above determination of the corrected torque increment based on the target impact coefficient includes: S701 determines the second torque change rate based on the target impact coefficient and the safety benchmark impact degree.

[0097] Specifically, the third torque change rate can be determined based on the target impact coefficient and the safety benchmark impact degree; then the second torque change rate can be determined based on the third torque change rate and the rise rate threshold and fall rate threshold.

[0098] Alternatively, the maximum allowable rate of torque change, i.e., the third rate of torque change ΔT_lim, can be derived from the relationship J=K_used×Δt: ΔT_lim=|J_safe| / |K_used|(10) Furthermore, the rising rate threshold ΔT_max_up, the falling rate threshold ΔT_max_dwn, and the third torque change rate ΔT_lim are compared, and the one with the smallest absolute value is taken as the second torque change rate ΔT, that is: ΔT=min(|ΔT_max_up|,|ΔT_max_dwn|,|ΔT_lim|) (11) S702 determines the corrected torque increment based on the comparison results of the rise rate threshold / fall rate threshold with the second torque change rate and the first torque change rate, as well as the torque state.

[0099] This application provides a possible implementation method in which the above-mentioned determination of the corrected torque increment based on the comparison results of the rise rate threshold / fall rate threshold with the second torque change rate and the first torque change rate, and the torque state, includes: If the torque state is a torque increase state, then the smaller of the increase rate threshold, the first torque change rate, and the second torque change rate will be used as the correction torque increment. If the torque state is a torque decrease state, the larger of the decrease rate threshold, the first torque change rate, and the second torque change rate will be used as the correction torque increment.

[0100] In some implementations, when the torque increases (dT≥0), the torque increment ΔT is corrected. ’ =min(dT / dt,ΔT_max_up,ΔT); In other implementations, when the torque decreases (dT < 0), the torque increment ΔT is corrected. ’ =max(dT / dt,ΔT_max_dwn,-ΔT).

[0101] The above method for determining the target torque for the current cycle based on the corrected torque increment includes: The sum of the corrected torque increment and the historical target torque is used as the target torque for the current cycle.

[0102] Specifically, T_pred(n) = T_filt(n-1) + ΔT ’ (12) Where T_pred(n) is the target torque for the current cycle.

[0103] This application's embodiments introduce a second torque change rate to participate in the selection of corrective torque increments, forming a multi-constraint mechanism. For ascending conditions, the minimum value of the ascending rate threshold, the first torque change rate, and the second torque change rate is selected; for descending conditions, the maximum value of the descending rate threshold, the first torque change rate, and the second torque change rate is selected. This simultaneously considers the driver's original torque requirements, basic smoothness constraints, and the deep smoothing requirements after exceeding the impact limit. It limits the instantaneous torque change amplitude at multiple levels, effectively avoiding severe longitudinal impacts caused by exceeding the predicted impact limit, and reducing the risk of motion sickness for passengers. Simultaneously, it employs differentiated screening rules based on torque state, with independent constraint logic for ascending and descending processes. This allows for adaptation to the impact characteristics of different scenarios such as acceleration and pedal release energy recovery, avoiding the problem of a single constraint mode being unable to simultaneously address acceleration and deceleration smoothness. The combined effect of multiple limits prevents sudden torque changes caused by the failure of any single indicator, improving the robustness of the control strategy.

[0104] This application provides one possible implementation method, such as... Figure 8 As shown, the above-mentioned acquisition of the vehicle's safety benchmark impact intensity includes: S801 determines the baseline impact threshold based on real-time vehicle speed.

[0105] Among them, the real-time vehicle speed is positively correlated with the baseline impact threshold.

[0106] Specifically, a function or table can be pre-built to show how the baseline impact threshold J_base(v) changes with vehicle speed. Generally speaking, people are more sensitive to impacts at low speeds (such as 0-30km / h), and the baseline impact threshold is lower; the baseline impact threshold is higher at medium and high speeds.

[0107] S802 identifies the motion sickness risk level of vehicle occupants, power demand intensity, and road condition information ahead of the vehicle.

[0108] The system can use seat pressure sensors, in-vehicle cameras (visual recognition), or physiological sensors to determine whether there are passengers in the vehicle, the number of passengers, and the passengers' current behavior (such as looking at their phones, sleeping with their eyes closed, or simply looking out the window). Based on this information, the risk level of motion sickness for each passenger can be determined. Understandably, the risk level is lower when only the driver is in the vehicle; higher when there are passengers; and further increases when passengers are in the vehicle and are using their phones, sleeping, or otherwise engaging in similar activities.

[0109] Optionally, the intensity of power demand can be determined based on data such as accelerator pedal opening or required torque, and driving style on the vehicle's infotainment system; the greater the intensity of power demand and the more aggressive the driving style, the higher the baseline impact threshold.

[0110] Optionally, the road conditions ahead can be identified based on the external camera. If there are no abnormal conditions such as bumps, the baseline impact threshold is higher; if there are curves or bumps ahead, the baseline impact threshold can be further reduced.

[0111] S803 determines the target coefficient based on at least one of the following: the member's motion sickness risk level, power demand intensity, and road condition information ahead.

[0112] The specific calculation method for the target coefficient will be explained in detail below.

[0113] S804 determines the safety baseline impact level based on the target coefficient and the baseline impact level threshold.

[0114] Specifically, the product of the target coefficient and the baseline impact threshold can be used as the safety baseline impact.

[0115] This application provides one possible implementation method, such as... Figure 9 As shown, the target coefficient is determined based on at least one of the following: the member's motion sickness risk level, power demand intensity, and road condition information ahead: S901, determine the first coefficient k_occupant based on the member's motion sickness risk level.

[0116] Optionally, if only the driver is in the vehicle, then k_occupant = 1.0; If there are passengers in the vehicle and the passengers are in a normal state, then k_occupant = 0.9; If there are passengers in the vehicle, and those passengers are in a state of motion sickness risk, such as looking at their phones or sleeping, then k_occupant = 0.8.

[0117] S902, the second coefficient k_power is determined based on the intensity of power demand.

[0118] Optionally, the power demand intensity can be determined based on data such as accelerator pedal opening or required torque, and driving style on the vehicle's infotainment system; the greater the power demand intensity and the more aggressive the driving style, the higher the baseline impact threshold. k_power∈(1.0,1.5).

[0119] S903, the third coefficient k_road is determined based on road condition information.

[0120] Optionally, the road conditions ahead can be identified based on the external camera. If there are no abnormal conditions such as bumps, the baseline impact threshold is higher; if there are curves or bumps ahead, the baseline impact threshold can be further reduced. k_road∈(0.8,1.0).

[0121] S904, based on the first coefficient, the second coefficient, and the third coefficient, determine the target coefficient.

[0122] Specifically, the product of the first, second, and third coefficients can be used as the target coefficient.

[0123] In some implementations, the safety baseline impact strength can be calculated based on the following formula: J_safe = J_base(v) × k_power × k_occupant × k_road (13) In other implementations, additional control for emergency scenarios can be set. If the pedal opening and / or the rate of change exceeds a threshold, for example, the opening is above 60% and the rate of change of the opening is greater than 40% per second, then the vehicle is in an emergency scenario such as emergency overtaking or hazard avoidance, and the anti-motion sickness control needs to be discontinued and J_safe=∞ is set.

[0124] This application's embodiments combine dynamic threshold adjustment based on vehicle speed and passenger status, enabling the anti-motion sickness control to intelligently balance comfort and power according to different driving scenarios and passenger needs, thereby enhancing the user experience. By sensing passenger behavior, it provides differentiated comfort protection for different passengers, especially catering to those prone to motion sickness. Furthermore, when rapid acceleration is needed for overtaking, the anti-motion sickness control disengages, further ensuring vehicle safety.

[0125] To better understand the vehicle control methods described above, the following will combine... Figure 10 A detailed example of the control method described herein, applied to a vehicle control terminal, is provided. The method includes the following steps: S1001 collects real-time accelerator pedal opening signal, real-time brake pedal travel signal, and vehicle current speed from the vehicle controller; then, based on the above three driving state parameters, it performs torque mapping lookup and dynamic correction calculation to obtain the real-time required torque.

[0126] S1002, a first-order inertial low-pass filter algorithm is used to filter the real-time demand torque to obtain the first smooth torque; the torque difference between the first smooth torque and the historical target torque is determined, and the first torque change rate is determined based on the torque difference.

[0127] Among them, the historical target torque is the target torque of the vehicle in the previous cycle; the target torque of the first cycle is determined based on the preset torque.

[0128] S1003, obtain the threshold for the rate of increase of torque and the threshold for the rate of decrease of torque.

[0129] S1004, if the torque state is a torque increase state, then the smaller value between the increase rate threshold and the first torque change rate is taken as the target torque increment. If the torque state is a torque decrease state, the larger of the decrease rate threshold and the first torque change rate will be used as the target torque increment.

[0130] S1005 uses the sum of the target torque increment and the historical target torque as the target smooth torque.

[0131] S1006, determine the current driving condition based on real-time acceleration and real-time vehicle speed; obtain the reference impact coefficient corresponding to the current driving condition by querying the pre-built reference gain table.

[0132] S1007 calculates the measured impact intensity based on real-time acceleration; using the recursive least squares method, the benchmark impact coefficient is updated based on the measured impact intensity to obtain the target impact coefficient.

[0133] S1008, the final target impact coefficient is calculated based on the target impact coefficient; and the predicted impact intensity is calculated based on the target impact coefficient.

[0134] S1009 determines the vehicle's safety baseline impact level based on information such as the current risk level of motion sickness among vehicle occupants, power demand intensity, and road condition information ahead.

[0135] S1010, if the predicted impact is less than or equal to the safety benchmark impact, the target smoothing torque is used as the target torque for the current cycle; if the predicted impact is greater than the safety benchmark impact, the corrected torque increment is determined based on the target impact coefficient, and the target torque for the current cycle is determined based on the corrected torque increment.

[0136] This application provides a vehicle control device, such as... Figure 11 As shown, the vehicle control device 110 may include: a first determining module 1101, a second determining module 1102, a prediction module 1103, and a correction module 1104; The first determining module 1101 is used to determine the real-time required torque based on the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed collected in real time during the current cycle of vehicle driving. The second determining module 1102 is used to determine the target smooth torque based on the real-time demand torque; wherein, the torque change rate of the target smooth torque relative to the historical target torque is less than a preset change rate threshold; the historical target torque is the target torque of the vehicle in the previous cycle; the target torque of the first cycle is determined based on the preset torque. The prediction module 1103 is used to predict the vehicle's predicted impact in the next cycle based on the pre-acquired real-time acceleration and real-time vehicle speed. The correction module 1104 is used to correct the target smooth torque based on the predicted impact to obtain the target torque for the current cycle, so as to control the vehicle operation based on the target torque for the current cycle.

[0137] This application embodiment provides a possible implementation method in which the first determining module 1101, when determining the real-time required torque based on the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed, is used to: The system collects real-time data on the accelerator pedal opening, brake pedal travel, and vehicle speed. Determine the preset torque correspondence; wherein, the torque correspondence is used to characterize the correspondence between the accelerator pedal opening, the brake pedal travel, and the vehicle speed and the vehicle's required torque; The real-time torque requirement is determined based on the torque correspondence, real-time opening, real-time travel, and real-time vehicle speed.

[0138] This application embodiment provides a possible implementation method in which the second determining module 1102, when determining the target smooth torque based on the real-time demand torque, is used for: The real-time torque demand is filtered to obtain the first smooth torque; The target torque increment is determined based on the torque state and the first smooth torque; wherein, the torque state includes the torque rising state and the torque falling state; The target smooth torque is determined based on the target torque increment.

[0139] This application embodiment provides a possible implementation method in which the second determining module 1102, when determining the target torque increment based on the torque state and the first smooth torque, is used to: Determine the torque difference between the first smoothed torque and the historical target torque, and determine the first torque change rate based on the torque difference; Obtain the threshold for the rate of increase of torque and the threshold for the rate of decrease of torque. Based on the comparison results of the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, the target torque increment is determined; The second determining module 1102 described above, when determining the target smooth torque based on the target torque increment, is used for: The target smooth torque is the sum of the target torque increment and the historical target torque.

[0140] This application embodiment provides a possible implementation method in which the second determining module 1102, when determining the target torque increment based on the comparison result of the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, is used to: If the torque state is a torque increase state, then the smaller value between the increase rate threshold and the first torque change rate is taken as the target torque increment. If the torque state is a torque decrease state, the larger of the decrease rate threshold and the first torque change rate will be used as the target torque increment.

[0141] This application embodiment provides a possible implementation method in which the prediction module 1103, when predicting the predicted impact of a vehicle in the next cycle based on pre-collected real-time acceleration and real-time vehicle speed, is used to: The current driving condition is determined based on real-time acceleration and real-time vehicle speed; the current driving condition includes any one of the following: low speed condition, medium speed condition, high speed condition, strong acceleration condition, slow acceleration condition, coasting condition, and braking condition. The query retrieves the baseline impact coefficient corresponding to the current driving conditions; the baseline impact coefficient is used to characterize the impact intensity of the vehicle in the current cycle. Calculation of measured impact intensity based on real-time acceleration; The target impact coefficient is obtained by updating the baseline impact coefficient based on the measured impact intensity. The predicted impact intensity is calculated based on the target impact coefficient and the target torque increment.

[0142] This application embodiment provides a possible implementation method. When the correction module 1104 corrects the target smooth torque based on the predicted impact to obtain the target torque of the current cycle, it is used to: Obtain the vehicle's safety baseline impact level; Based on the safety baseline impact and the predicted impact, the target smooth torque is corrected to obtain the target torque for the current cycle.

[0143] This application embodiment provides a possible implementation method. When the above-mentioned correction module 1104 corrects the target smooth torque based on the safety benchmark impact and the predicted impact to obtain the target torque of the current cycle, it is used to: If the predicted impact is less than or equal to the safety baseline impact, the target smoothing torque will be used as the target torque for the current cycle. If the predicted impact is greater than the safety benchmark impact, the corrected torque increment is determined based on the target impact coefficient, and the target torque for the current cycle is determined based on the corrected torque increment.

[0144] This application embodiment provides a possible implementation method in which the correction module 1104, when determining the correction torque increment based on the target impact coefficient, is used for: The second torque change rate is determined based on the target impact coefficient and the safety benchmark impact degree; Based on the comparison results of the rise rate threshold / fall rate threshold with the second torque change rate and the first torque change rate, as well as the torque state, the corrected torque increment is determined. When determining the target torque for the current cycle based on the corrected torque increment, the aforementioned correction module 1104 is used for: The sum of the corrected torque increment and the historical target torque is used as the target torque for the current cycle.

[0145] This application embodiment provides a possible implementation method. When the correction module 1104 determines the correction torque increment based on the comparison results of the rise rate threshold / fall rate threshold with the second torque change rate and the first torque change rate, and the torque state, it is used to: If the torque state is a torque increase state, then the smaller of the increase rate threshold, the first torque change rate, and the second torque change rate will be used as the correction torque increment. If the torque state is a torque decrease state, the larger of the decrease rate threshold, the first torque change rate, and the second torque change rate will be used as the correction torque increment.

[0146] This application embodiment provides a possible implementation method in which the above-mentioned correction module 1104, when obtaining the vehicle's safety benchmark impact degree, is used for: The baseline impact threshold is determined based on real-time vehicle speed; where real-time vehicle speed is positively correlated with the baseline impact threshold. It can identify the motion sickness risk level of vehicle occupants, power demand intensity, and road condition information ahead of the vehicle; The target coefficient is determined based on at least one of the following: the member's motion sickness risk level, power demand intensity, and road condition information ahead; The safety baseline impact level is determined based on the target coefficient and the baseline impact level threshold.

[0147] This application embodiment provides a possible implementation method in which the above-mentioned correction module 1104, when determining the target coefficient based on at least one of the member's motion sickness risk level, power demand intensity, and road condition information ahead, is used to: The first coefficient is determined based on the member's motion sickness risk level; The second coefficient is determined based on the intensity of energy demand. The third coefficient is determined based on road condition information; The target coefficient is determined based on the first, second, and third coefficients.

[0148] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0149] This application embodiment determines the real-time required torque based on real-time data collected during the current cycle of vehicle operation, including the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed. A target smoothing torque is then determined based on this real-time required torque, ensuring that the rate of change of the target smoothing torque relative to the target torque of the previous cycle is less than a preset rate of change threshold. This achieves the first smoothing process for the real-time required torque. Furthermore, based on pre-collected real-time acceleration and real-time vehicle speed, the predicted impact of the vehicle in the next cycle is predicted. The target smoothing torque is then corrected based on this predicted impact, achieving a second smoothing process based on the predicted impact of future cycles. This yields the target torque for the current cycle, allowing vehicle operation to be controlled based on this target torque. This application achieves dynamic adjustment of the target torque based on the rate of change of torque relative to the previous cycle and the predicted impact for the next cycle, improving the smoothness of vehicle operation and reducing the likelihood of motion sickness for passengers. Simultaneously, the pre-smoothing of torque based on the predicted impact for the next cycle further enhances the anti-motion sickness effect and effectively improves the user experience.

[0150] This application provides an electronic device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of a vehicle control method. Compared with related technologies, this application can achieve the following: In the current cycle of vehicle operation, this application determines the real-time required torque based on real-time acquisition of the accelerator pedal opening, brake pedal travel, and vehicle speed, and determines a target smoothing torque based on the real-time required torque, such that the torque change rate of the target smoothing torque relative to the target torque of the previous cycle is less than a preset change rate threshold, thereby achieving the first smoothing processing of the real-time required torque; furthermore, based on pre-acquired real-time acceleration... This invention predicts the vehicle's impact in the next cycle based on real-time vehicle speed and adjusts the target smoothing torque accordingly. This achieves a second smoothing process based on the predicted impact in future cycles, yielding the target torque for the current cycle. The vehicle is then controlled based on this target torque within the current cycle. This application dynamically adjusts the target torque based on the torque change rate relative to the previous cycle and the predicted impact in the next cycle, improving vehicle smoothness and reducing the likelihood of motion sickness for passengers. Furthermore, the pre-smoothing of torque based on the predicted impact in the next cycle further enhances the anti-motion sickness effect and effectively improves the user experience.

[0151] In one alternative embodiment, an electronic device is provided, such as Figure 12 As shown, Figure 12The illustrated electronic device 120 includes a processor 1201 and a memory 1203. The processor 1201 and the memory 1203 are connected, for example, via a bus 1202. Optionally, the electronic device 120 may further include a transceiver 1204, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 1204 is not limited to one type, and the structure of the electronic device 120 does not constitute a limitation on the embodiments of this application.

[0152] Processor 1201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 1201 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0153] Bus 1202 may include a pathway for transmitting information between the aforementioned components. Bus 1202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 1202 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0154] The memory 1203 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation herein.

[0155] The memory 1203 is used to store computer programs that execute the embodiments of this application, and the execution is controlled by the processor 1201. The processor 1201 is used to execute the computer programs stored in the memory 1203 to implement the steps shown in the foregoing method embodiments.

[0156] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, and tablets, as well as fixed terminals such as digital TVs and desktop computers.

[0157] This application provides a non-transitory computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the steps of the method shown in the first aspect of this application.

[0158] The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access, or it can include one or more data storage devices such as servers or data centers that can be integrated with media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media, or semiconductor media (e.g., solid-state disks (SSDs)).

[0159] This application provides a computer program product including instructions that, when executed, cause a computer to perform the steps of the method shown in the first aspect of this application.

[0160] The computer storage medium and computer program products provided in the embodiments of this application are used to execute the methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects corresponding to the cleaning equipment provided above, and will not be repeated here.

[0161] The above description does not provide detailed technical specifications regarding the structure of each layer. However, those skilled in the art should understand that layers and regions of desired shapes can be formed using various technical means. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be advantageously combined.

[0162] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0163] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A vehicle control method, characterized in that, include: During the current cycle of vehicle operation, the real-time torque demand is determined based on the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed. The target smoothing torque is determined based on the real-time demand torque; wherein the rate of change of the target smoothing torque relative to the historical target torque is less than a preset rate of change threshold; the historical target torque is the target torque of the vehicle in the previous cycle; the target torque of the first cycle is determined based on a preset torque. The predicted impact of the vehicle in the next cycle is predicted based on the pre-collected real-time acceleration and the real-time vehicle speed. The target smoothing torque is corrected based on the predicted impact to obtain the target torque for the current cycle, so as to control the vehicle operation based on the target torque for the current cycle.

2. The method according to claim 1, characterized in that, The determination of real-time torque demand based on real-time accelerator pedal opening, real-time brake pedal travel, and real-time vehicle speed includes: The real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed of the vehicle are collected in real time. Determine a preset torque correspondence; wherein the torque correspondence is used to characterize the correspondence between the accelerator pedal opening, the brake pedal travel, and the vehicle speed and the vehicle's required torque; Based on the torque correspondence, the real-time required torque is determined according to the real-time opening, the real-time travel, and the real-time vehicle speed.

3. The method according to claim 1, characterized in that, The determination of the target smooth torque based on the real-time demand torque includes: The real-time required torque is filtered to obtain the first smooth torque; The target torque increment is determined based on the torque state and the first smoothed torque; wherein the torque state includes a torque increase state and a torque decrease state; The target smooth torque is determined based on the target torque increment.

4. The method according to claim 3, characterized in that, The step of determining the target torque increment based on the torque state and the first smoothed torque includes: Determine the torque difference between the first smoothed torque and the historical target torque, and determine the first torque change rate based on the torque difference; Obtain the threshold for the rate of increase of torque and the threshold for the rate of decrease of torque. Based on the comparison between the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, the target torque increment is determined; The step of determining the target smooth torque based on the target torque increment includes: The sum of the target torque increment and the historical target torque is used as the target smooth torque.

5. The method according to claim 4, characterized in that, The step of determining the target torque increment based on the comparison result of the rise rate threshold / fall rate threshold and the first torque change rate, and the torque state, includes: If the torque state is a torque increase state, then the smaller value between the increase rate threshold and the first torque change rate is taken as the target torque increment. If the torque state is a torque decrease state, then the larger of the decrease rate threshold and the first torque change rate is taken as the target torque increment.

6. The method according to claim 4, characterized in that, The prediction of the vehicle's predicted impact in the next cycle based on the pre-collected real-time acceleration and the real-time vehicle speed includes: The current driving condition is determined based on the real-time acceleration and the real-time vehicle speed; wherein, the current driving condition includes any one of the following: low speed condition, medium speed condition, high speed condition, strong acceleration condition, slow acceleration condition, coasting condition, and braking condition. The reference impact coefficient corresponding to the current driving condition is obtained by querying; wherein, the reference impact coefficient is used to characterize the impact intensity of the vehicle in the current cycle; The measured impact intensity is calculated based on the real-time acceleration. The target impact coefficient is obtained by updating the baseline impact coefficient based on the measured impact intensity. The predicted impact intensity is calculated based on the target impact coefficient and the target torque increment.

7. The method according to claim 6, characterized in that, The step of correcting the target smoothing torque based on the predicted impact to obtain the target torque for the current period includes: Obtain the safety baseline impact level of the vehicle; Based on the safety benchmark impact and the predicted impact, the target smooth torque is corrected to obtain the target torque for the current cycle.

8. The method according to claim 7, characterized in that, The step of correcting the target smooth torque based on the safety benchmark impact and the predicted impact to obtain the target torque for the current cycle includes: If the predicted impact is less than or equal to the safety benchmark impact, then the target smoothing torque is taken as the target torque for the current cycle. If the predicted impact is greater than the safety benchmark impact, then the corrected torque increment is determined based on the target impact coefficient, and the target torque for the current cycle is determined based on the corrected torque increment.

9. The method according to claim 8, characterized in that, The step of determining the corrected torque increment based on the target impact coefficient includes: The second torque change rate is determined based on the target impact coefficient and the safety benchmark impact degree; Based on the comparison results between the rise rate threshold / fall rate threshold and the second torque change rate and the first torque change rate, and the torque state, the corrected torque increment is determined; Determining the target torque for the current cycle based on the corrected torque increment includes: The sum of the corrected torque increment and the historical target torque is used as the target torque for the current cycle.

10. The method according to claim 9, characterized in that, The step of determining the corrected torque increment based on the comparison result of the rise rate threshold / fall rate threshold with the second torque change rate and the first torque change rate, and the torque state, includes: If the torque state is a torque increase state, then the smaller of the increase rate threshold, the first torque change rate, and the second torque change rate is used as the corrected torque increment. If the torque state is a torque decrease state, then the larger of the decrease rate threshold, the first torque change rate, and the second torque change rate is used as the correction torque increment.

11. The method according to claim 7, characterized in that, The process of obtaining the safety baseline impact strength of the vehicle includes: A baseline impact threshold is determined based on the real-time vehicle speed; wherein the real-time vehicle speed is positively correlated with the baseline impact threshold. The system identifies the motion sickness risk level of the vehicle's occupants, their power demand intensity, and the road conditions ahead of the vehicle. The target coefficient is determined based on at least one of the member's motion sickness risk level, power demand intensity, and road condition information ahead; The safety benchmark impact is determined based on the target coefficient and the benchmark impact threshold.

12. The method according to claim 11, characterized in that, The determination of the target coefficient based on at least one of the member's motion sickness risk level, power demand intensity, and road condition information ahead includes: The first coefficient is determined based on the members' motion sickness risk level; The second coefficient is determined based on the aforementioned power demand intensity; The third coefficient is determined based on the aforementioned road condition information; The target coefficient is determined based on the first coefficient, the second coefficient, and the third coefficient.

13. A vehicle control device, characterized in that, The device includes: The first determining module is used to determine the real-time required torque based on the real-time opening of the accelerator pedal, the real-time travel of the brake pedal, and the real-time vehicle speed collected in real time during the current cycle of vehicle driving. The second determining module is used to determine a target smooth torque based on the real-time demand torque; wherein the torque change rate of the target smooth torque relative to the historical target torque is less than a preset change rate threshold; the historical target torque is the target torque of the vehicle in the previous cycle; the target torque of the first cycle is determined based on a preset torque. The prediction module is used to predict the vehicle's impact in the next cycle based on the pre-collected real-time acceleration and the real-time vehicle speed. The correction module is used to correct the target smooth torque based on the predicted impact to obtain the target torque for the current cycle, so as to control the vehicle operation based on the target torque for the current cycle.

14. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-12.

15. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-12.

16. A computer program product, characterized in that, The computer program product includes instructions that, when executed, cause a computer to perform the method of any one of claims 1-12.

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