A high-stability electric vehicle steering control method and control system

Abstract

The present invention discloses a high-stability electric vehicle steering control method and control system, which relates to the technical field of electric vehicle steering control. Partial state data of a vehicle in a driving steering state is collected to generate a stability coefficient for evaluating the vehicle steering; the suspension stiffness of the vehicle suspension system is adjusted. If the adjustment effect is less than expected, the vehicle driving steering state is adjusted continuously, and a controllability coefficient is generated according to the vehicle's response speed and center of gravity change. A trained vehicle driving model is established to predict the vehicle's steering state, and a prediction data set is established based on the prediction result to generate a risk coefficient. The vehicle's driving conditions are adjusted through simulation analysis, and the adjusted driving plan is output. After the vehicle executes the driving plan, if it does not meet expectations, a deceleration command is issued. By evaluating the vehicle's controllability, it is also convenient to adjust and improve it, thereby ensuring the safety of the vehicle's driving.

Description

Technical Field

[0001] The invention relates to the technical field of electric vehicle steering control, and in particular to an electric vehicle steering control method and control system with high stability. Background Art

[0002] An electric motorcycle is an electric vehicle that uses a battery to drive the motor. The electric drive and control system consists of a drive motor, a power supply, and a motor speed control device. Other devices are basically the same as those of an internal combustion engine. According to the motor power or maximum speed, electric motorcycles can be divided into electric mopeds and electric ordinary motorcycles. Both electric two-wheeled mopeds and electric two-wheeled ordinary motorcycles are motor vehicles.

[0003] In the Chinese invention patent with application number 201811445696.3, an active steering control method for a four-wheel independent drive electric vehicle is disclosed, which includes the following steps: presetting an ideal vehicle steering model, a state feedback observer, a front wheel steering angle controller and a yaw moment controller; taking the vehicle straight-ahead state as the initial moment, measuring the lateral velocity and yaw angular velocity in real time, inputting the unknown front wheel steering angle and yaw moment into the ideal vehicle steering model, constructing the expected mathematical model, comparing the expected values ​​of the lateral velocity and yaw angular velocity with the real-time values, and obtaining the real-time error; inputting the real-time error into the state observer to obtain a stable closed-loop system, and by solving the linear matrix inequality method, obtaining both the real-time vehicle lateral velocity and yaw angular velocity values ​​and the real-time front wheel steering angle and yaw moment values, thereby controlling the vehicle.

[0004] In the technical solution recorded in the above application, the problem of weak stability and economical steering strategy of electric vehicles is overcome, the flexible maneuverability of the vehicle for safe driving is improved, and it has the advantages of good control effect and high stability. However, in the above application, when the stability of the electric vehicle is low, it is impossible to quickly predict and evaluate the current steering risk of the vehicle, and it is difficult to quickly give a corresponding adjustment plan, so it is difficult to quickly reduce the driving risk of the vehicle.

[0005] To this end, the present invention provides an electric vehicle steering control method and control system with high stability. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the deficiencies in the prior art, the present invention provides a steering control method and control system for an electric vehicle with high stability. The method adjusts the suspension stiffness of the vehicle suspension system. If the adjustment effect is less than expected, the vehicle's driving steering state is further adjusted. A handling coefficient is generated based on the vehicle's response speed and center of gravity change. A trained vehicle driving model is established to predict the vehicle's steering state. A prediction data set is established based on the prediction result to generate a risk coefficient. The vehicle's driving conditions are adjusted through simulation analysis, and an adjusted driving plan is output. After the vehicle executes the driving plan, if the expectation is not met, a deceleration command is issued. This solves the technical problem raised in the background technology that the current steering risk of the vehicle cannot be predicted and evaluated quickly, and it is difficult to quickly provide a corresponding adjustment plan.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] A steering control method for an electric vehicle with high stability comprises the following steps:

[0011] Guide the vehicle to travel along the pre-planned driving path to the destination, collect partial status data of the vehicle in the driving and steering state, and establish a first steering data set after aggregation; generate a stability coefficient Ws(v,r) from the first steering data set, and if the obtained stability coefficient Ws(v,r) does not exceed the stability threshold, issue a first warning information to the outside;

[0012] After receiving the first warning information, the suspension stiffness of the vehicle suspension system is adjusted, and after the adjustment is completed, the current stability coefficient Ws(v,r) is obtained again. If the correlation between the adjustment range of the suspension stiffness and the change range of the stability coefficient Ws(v,r) is less than expected, the vehicle driving steering state is continued to be adjusted in a backup manner;

[0013] When the vehicle continues to be in a driving and turning state on the driving path, a second steering data set is established according to the response speed and center of gravity change of the vehicle, and a maneuverability coefficient Ck(o, t, v) is generated by the second steering data set. If the obtained maneuverability coefficient Ck(o, t, v) exceeds the corresponding maneuverability threshold, a third warning information is issued to the outside;

[0014] After receiving the third warning information, if at least one of the stability coefficient Ws(v,r) and the maneuverability coefficient Ck(o,t,v) does not exceed the corresponding threshold, a trained vehicle driving model is established to predict the steering state of the vehicle, and a prediction data set is established based on the prediction result to generate a risk coefficient Cy(v,v). If the risk coefficient Cy(v,v) exceeds the risk threshold, an alarm message is issued to the outside.

[0015] Using the trained vehicle driving model, the vehicle driving conditions are adjusted through simulation analysis, and the adjusted driving plan is output. After the vehicle executes the driving plan, if the execution effect does not meet the expectations, a deceleration command is issued to decelerate the vehicle.

[0016] Furthermore, the vehicle is guided to travel along the driving path toward the destination, and the driving process is divided into a number of driving intervals. When the vehicle is in a driving turning state, the current driving speed Sv of the vehicle and the average speed in each driving interval are obtained; the angle between the front wheels of the vehicle and the road surface is obtained to generate a driving angle Sr, and the average value of the driving angle Sr in each driving interval is obtained; the above data are continuously obtained several times along the time axis, and a first steering data set is established after being summarized.

[0017] Furthermore, the vehicle stability coefficient Ws(v,r) is generated from the first steering data set in the following manner: linear normalization is performed on the driving speed Sv and the driving angle Sr, and the corresponding data values ​​are mapped to [0, 1] according to the following formula:

[0018]

[0019] The parameters are: n is a positive integer greater than 1, i = 1, 2...n, weight coefficient: 0 ≤ F 1 ≤1,0≤F 2 ≤1, and F 2 +F 1 =1, is the historical mean of driving speed in each driving interval, is the historical mean of the driving angle in each driving interval; when the stability coefficient Ws(v,r) does not exceed the stability threshold, the first warning information is issued to the outside.

[0020] Further, after receiving the first warning information, a suspension adjustment instruction of the vehicle is generated to adjust the suspension stiffness of the vehicle suspension system, and the adjustment strategy is: the adjustment range of the suspension stiffness is positively correlated with the proportion of the stability coefficient Ws(v,r) exceeding the stability threshold, and after determining the adjustment range, the adjustment range is evenly completed within a number of driving intervals;

[0021] After the suspension stiffness adjustment is completed, after an observation period, the current stability coefficient Ws(v,r) is obtained again; if the stability coefficient Ws(v,r) changes compared with the previous value, the correlation coefficient between the adjustment amplitude and the change amplitude of the stability coefficient Ws(v,r) is obtained through correlation analysis, and this is used as the adjustment correlation Tz;

[0022] If the obtained adjustment correlation Tz does not exceed the expectation, after the adjustment request is issued, the vehicle's front wheel torque and steering angle are fine-tuned several times in succession, and the stability coefficient Ws(v,r) after the several adjustments is obtained; if the vehicle's adjusted stability coefficient Ws(v,r) still does not exceed the stability threshold, a second warning message is issued.

[0023] Furthermore, if the vehicle turns while driving, the response speed during the turning is obtained and determined as the first response speed Od; when the vehicle brakes, its braking response speed is obtained and determined as the second response speed Td; the center of gravity position of the vehicle in each driving interval and the current center of gravity position are obtained during the turning process, and through proximity centrality analysis, the center of gravity position with the highest centrality is obtained as the standard position, and the distance between the current center of gravity position and the standard position is obtained, which is used as the center of gravity offset distance Vp; after summarizing the above parameters, a second steering data set is established.

[0024] Furthermore, the controllability coefficient Ck(o, t, v) is generated from the second steering data set in the following manner: linear normalization is performed on the first response speed Od, the second response speed Td and the center of gravity offset distance Vp, and the corresponding data values ​​are mapped to [0, 1] according to the following formula:

[0025]

[0026] in, is the historical average of the first response speed Od, is the historical mean of the second response speed, is the historical mean of the center of gravity offset distance; n is a positive integer greater than 1, and the weight coefficient is: 0≤β≤1, 0≤α≤1, 0≤γ≤1 and α+β+γ=1;

[0027] If the obtained maneuverability coefficient Ck(o, t, v) does not exceed the corresponding maneuverability threshold, a third warning message is issued to the outside.

[0028] Furthermore, after receiving the third warning information, the current stability coefficient Ws(v,r) and the controllability coefficient Ck(o,t,v) of the vehicle are obtained. If at least one of the two does not exceed the corresponding threshold, the vehicle's specifications and performance parameters, driving state parameters and road condition information in the forward direction are collected and summarized to establish a modeling data set;

[0029] Select part of the data from the modeling data set, use the machine learning model to build a vehicle driving model, use the vehicle driving model to predict the steering state of the vehicle, and obtain the prediction results; query and obtain several driving speeds from the prediction results, marked as predicted speeds Y(s,v); obtain several center of gravity offset distances, marked as predicted offsets Y(v,p); after summarizing the above parameters, generate a prediction data set for the vehicle.

[0030] Furthermore, the predicted risk coefficient Cy(v,v) of the vehicle is generated from the predicted data set of the vehicle. The specific generation method is as follows: the predicted speed Y(s,v) and the predicted offset Y(v,p) are linearly normalized, and the corresponding data values ​​are mapped to [0, 1] according to the following formula:

[0031]

[0032] The meaning of the parameters is: 0≤ρ≤1, 0≤ζ≤1, and 0.6≤ρ+ζ≤1.2, where ρ and ζ are weight coefficients. If the predicted risk coefficient Cy(v,v) exceeds the risk threshold, an alarm message is issued to the outside.

[0033] Furthermore, a turning condition of the vehicle is preset, under which at least the predicted risk coefficient Cy(v,v) of the vehicle does not exceed the risk threshold, and the trained vehicle driving model is used to adjust the driving condition of the vehicle through simulation analysis, and at least one driving plan is output to enable the vehicle to meet the preset turning condition;

[0034] The driving plan obtained by the simulation analysis is output and executed by the vehicle. After an observation period, if the risk coefficient Cy(v,v) obtained by the prediction again still exceeds the risk threshold, a deceleration command is issued to decelerate the vehicle. The deceleration ratio is positively correlated with the ratio of the risk coefficient Cy(v,v) exceeding the risk threshold.

[0035] A high-stability electric vehicle steering control system, comprising:

[0036] The stability monitoring unit guides the vehicle to travel along the pre-planned driving path to the destination, collects partial status data of the vehicle in the driving and steering state, and generates a stability coefficient for evaluating the vehicle steering;

[0037] The adjustment unit adjusts the suspension stiffness of the vehicle suspension system, and if the correlation between the adjustment range of the suspension stiffness and the change range of the stability coefficient is less than expected, the vehicle driving steering state continues to be adjusted in a backup manner;

[0038] The maneuverability monitoring unit generates a maneuverability coefficient based on the vehicle's response speed and center of gravity change when the vehicle is in a turning state on the driving path. If it exceeds the corresponding maneuverability threshold, a warning message is issued to the outside;

[0039] The modeling unit establishes a trained vehicle driving model to predict the steering state of the vehicle, and establishes a prediction data set based on the prediction result to generate a risk coefficient. If the risk coefficient exceeds the risk threshold, an alarm message is issued to the outside.

[0040] The analysis unit adjusts the driving conditions of the vehicle through simulation analysis and outputs an adjusted driving plan. After the vehicle executes the driving plan, if the execution effect does not meet expectations, a deceleration command is issued.

[0041] (III) Beneficial effects

[0042] The present invention provides a steering control method and control system for an electric vehicle with high stability, which has the following beneficial effects:

[0043] 1. Adjust the suspension system of the vehicle and adjust the stability of the vehicle by changing the suspension stiffness. When it is difficult to achieve the expected effect by adjusting the suspension stiffness, adjust the torque and steering angle of the vehicle as a backup plan and adjust the vehicle, that is, make several fine adjustments to further improve the stability of the vehicle. If it is still difficult to achieve the expected effect after several fine adjustments, other adjustment plans are still needed to ensure the stability of the vehicle.

[0044] 2. Use the controllability coefficient Ck(o,t,v) to judge the current controllability of the vehicle. If the vehicle's controllability coefficient is worse, the vehicle is more difficult to control. When the vehicle's steering state is adjusted, the vehicle is less affected and the impact on the vehicle's stability is also small, making it difficult to ensure the stability of the vehicle. At the same time, evaluating the vehicle's controllability also facilitates its adjustment and improvement, thereby ensuring the safety of the vehicle's driving.

[0045] 3. Build a digital twin model of vehicle driving to predict the vehicle's steering state. Generate a risk coefficient Cy(v,v) based on the obtained prediction data set. Predict and evaluate the vehicle's current steering risk based on the risk coefficient Cy(v,v). If the vehicle's subsequent driving risk exceeds expectations, it means that the vehicle is currently facing a greater risk. At this time, an alarm message is sent to the outside to facilitate timely adjustment of the vehicle's steering state.

[0046] 4. After the target conditions are preset, the trained vehicle driving model is used for simulation analysis to obtain the corresponding adjustment plan, so that when the steering state of the vehicle needs to be adjusted, the adjustment plan can be quickly determined to improve the efficiency of the adjustment; when the adjustment plan is executed, after adjusting the steering state of the vehicle, the driving risk of the vehicle can be reduced on the basis of improving the stability of the vehicle, and the safety of the vehicle can be guaranteed by slowing down the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic flow chart of a high-stability electric vehicle steering control method according to the present invention;

[0048] Figure 2 The present invention is a schematic structural diagram of a steering control system for an electric vehicle with high stability. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1 The present invention provides a steering control method for an electric vehicle with high stability, comprising the following steps:

[0051] The partial state data of the vehicle are summarized and a first steering data set is established; a stability coefficient Ws(v,r) is generated from the first steering data set, and if the obtained stability coefficient Ws(v,r) does not exceed the stability threshold, a first warning information is issued to the outside;

[0052] The step 1 includes the following contents:

[0053] Step 101: before the vehicle is in a driving state, determine the destination of the vehicle, determine an area at least including the destination and the current position as an activity area, and obtain an electronic map covering at least the activity area;

[0054] After marking the destination on the electronic map and querying and acquiring the road condition information, a trained path planning model, for example, the path planning model can be trained by a path planning algorithm or a similar algorithm, plans a driving path from the current position to the destination for the vehicle in the activity area, and marks the driving path on the electronic map;

[0055] Step 102: guide the vehicle to travel along the driving path toward the destination, and divide the driving process into a number of driving intervals, for example, 30 seconds as one driving interval, and when the vehicle is in a driving and turning state, obtain the current driving speed Sv of the vehicle and the average speed in each driving interval; similarly, when the vehicle is in a forward state, obtain the angle between the front wheels of the vehicle and the road surface, generate a driving angle Sr, and obtain the average value of the driving angle Sr in each driving interval;

[0056] The above data are continuously acquired several times along the time axis, and the first steering data set is established after aggregation;

[0057] Step 103: Generate the vehicle stability coefficient Ws(v,r) from the first steering data set. The specific method is as follows: perform linear normalization processing on the driving speed Sv and the driving angle Sr, and map the corresponding data values ​​to [0, 1] according to the following formula:

[0058]

[0059] The parameters are: n is a positive integer greater than 1, i = 1, 2...n, weight coefficient: 0 ≤ F 1 ≤1,0≤F 2 ≤1, and F 2 +F 1 =1, the specific value is set by the user. is the historical mean of driving speed in each driving interval, is the historical mean of the driving angle in each driving interval;

[0060] Based on the historical driving data of the vehicle, under the condition that the vehicle can drive safely, a standard value of the stability coefficient is pre-set and determined as a stability threshold; when the obtained stability coefficient Ws(v,r) does not exceed the stability threshold, a first warning information is issued to the outside.

[0061] When using, combine the contents in steps 101 to 103:

[0062] After the vehicle enters the driving and steering state, the driving and steering state of the vehicle is monitored, and after setting the driving interval, the stability coefficient Ws(v,r) is generated according to the monitoring data, so as to make a preliminary judgment on the current stability of the vehicle based on the stability coefficient Ws(v,r). If the current stability of the vehicle is difficult to meet expectations, an early warning can be issued to the outside to facilitate timely processing to ensure the safety and stability of the vehicle.

[0063] Step 2: After receiving the first warning information, the suspension stiffness of the vehicle suspension system is adjusted, and after the adjustment is completed, the current stability coefficient Ws(v,r) is obtained again. If the correlation between the adjustment range of the suspension stiffness and the change range of the stability coefficient Ws(v,r) is less than expected, the vehicle driving steering state is continued to be adjusted in a backup manner;

[0064] The step 2 includes the following contents:

[0065] Step 201: after receiving the first warning information, a suspension adjustment instruction of the vehicle is generated to adjust the suspension stiffness of the vehicle suspension system, wherein the adjustment strategy is: the adjustment range of the suspension stiffness is positively correlated with the proportion of the stability coefficient Ws(v,r) exceeding the stability threshold, and after determining the adjustment range, the adjustment range is evenly completed within a number of driving intervals;

[0066] Step 202: After the suspension stiffness adjustment is completed, an observation period is set, wherein the length of the observation period is 5 to 10 driving intervals. After one observation period, the current stability coefficient Ws(v,r) is obtained again; if the stability coefficient Ws(v,r) changes from the previous value, the correlation coefficient between the adjustment amplitude and the change amplitude of the stability coefficient Ws(v,r) is obtained through correlation analysis, and this is used as the adjustment correlation Tz;

[0067] Step 203: If the obtained adjustment correlation Tz does not exceed the expectation, that is, when it is lower than the preset correlation threshold, in order to maintain the stability and safety of the vehicle, at this time, after the adjustment request is issued, the front wheel torque and steering angle of the vehicle are fine-tuned several times, and the stability coefficient Ws(v,r) after the several adjustments is obtained;

[0068] If the vehicle's adjusted stability coefficient Ws(v,r) still does not exceed the stability threshold, a second warning message is issued.

[0069] When using, combine the contents in steps 201 to 203:

[0070] After receiving the first warning information, in order to improve the driving stability of the vehicle, the suspension system of the vehicle is adjusted first, and the stability of the vehicle is adjusted by changing the suspension stiffness. When the expected effect cannot be achieved by adjusting the suspension stiffness, adjusting the torque and steering angle of the vehicle is used as a backup plan. The vehicle is adjusted with the backup plan, that is, several fine-tuning is performed to further improve the stability of the vehicle. If it is still difficult to achieve the expected effect after several fine-tuning, other adjustment plans are still needed to ensure the stability of the vehicle.

[0071] Step 3: When the vehicle is in a turning state on the driving path, a second turning data set is established according to the response speed and center of gravity change of the vehicle, and a maneuverability coefficient Ck(o, t, v) is generated from the second turning data set. If the obtained maneuverability coefficient Ck(o, t, v) exceeds the corresponding maneuverability threshold, a third warning information is issued to the outside;

[0072] The step three includes the following contents:

[0073] Step 301: when the vehicle continues to be in a driving turning state on the driving path, if the vehicle turns during driving, the response speed during turning is obtained and determined as a first response speed Od; when the vehicle brakes, the braking response speed is obtained and determined as a second response speed Td;

[0074] Furthermore, the center of gravity position and the current center of gravity position of the vehicle in each driving interval during the turning process are obtained, and the center of gravity position with the highest centrality is obtained as the standard position through close centrality analysis, and the distance between the current center of gravity position and the standard position is obtained as the center of gravity offset distance Vp; after summarizing the above parameters, a second turning data set is established;

[0075] Step 302: Generate a maneuverability coefficient Ck(o, t, v) from the second steering data set. The specific method is as follows: perform linear normalization processing on the first response speed Od, the second response speed Td and the center of gravity offset distance Vp, and map the corresponding data values ​​to [0, 1] according to the following formula:

[0076]

[0077] in, is the historical average of the first response speed Od, is the historical mean of the second response speed, is the historical mean of the center of gravity offset distance; n is a positive integer greater than 1, 0≤β≤1, 0≤α≤1, 0≤γ≤1 and α+β+γ=1, and its specific value is adjusted and set by the user;

[0078] A controllability threshold is preset, and if the obtained controllability coefficient Ck(o, t, v) does not exceed the corresponding controllability threshold, a third warning message is issued to the outside;

[0079] When using, combine the contents in steps 301 to 302:

[0080] After adjusting the stability of the vehicle, the vehicle's handling coefficient Ck(o,t,v) is generated through the vehicle's response speed and the degree of center of gravity offset. The vehicle's current handling is judged by the handling coefficient Ck(o,t,v). If the vehicle's handling coefficient is worse, the vehicle is more difficult to control. When the vehicle's steering state is adjusted, the vehicle is less affected and the impact on the vehicle's stability is also small, making it difficult to ensure the stability of the vehicle. At the same time, evaluating the vehicle's handling also facilitates its adjustment and improvement, thereby ensuring the safety of the vehicle's driving.

[0081] Step 4: After receiving the third warning information, if at least one of the stability coefficient Ws(v,r) and the maneuverability coefficient Ck(o,t,v) does not exceed the corresponding threshold, a trained vehicle driving model is established to predict the steering state of the vehicle, and a prediction data set is established based on the prediction result to generate a risk coefficient Cy(v,v); if the risk coefficient Cy(v,v) exceeds the risk threshold, an alarm message is issued to the outside;

[0082] The step 4 specifically includes the following contents:

[0083] Step 401: after receiving the third warning information, obtain the current stability coefficient Ws(v,r) and the controllability coefficient Ck(o,t,v) of the vehicle. If at least one of the two does not exceed the corresponding threshold, collect the vehicle's specifications and performance parameters, driving state parameters and road condition information in the forward direction, and summarize them to establish a modeling data set;

[0084] Select some data from the modeling data set as the training set and the test set respectively, use the machine learning model, and after training and testing, establish a digital twin model of vehicle driving, which is labeled as the vehicle driving model;

[0085] Step 402: obtain the current driving state data of the vehicle, such as the driving speed, driving direction and road condition information of the vehicle, form a prediction of the turning state of the vehicle by the vehicle driving model, and obtain the prediction result; query and obtain a number of driving speeds from the prediction result, marked as predicted speeds Y(s,v); at the same time, obtain a number of center of gravity offset distances, marked as predicted offsets Y(v,p); after summarizing the above parameters, generate a prediction data set of the vehicle;

[0086] Step 403: Generate the predicted risk coefficient Cy(v,v) of the vehicle from the predicted data set of the vehicle. The specific generation method is as follows: perform linear normalization processing on the predicted speed Y(s,v) and the predicted offset Y(v,p), and map the corresponding data values ​​to [0,1] according to the following formula:

[0087]

[0088] The meaning of the parameters is: 0≤ρ≤1, 0≤ζ≤1, and 0.6≤ρ+ζ≤1.2, ρ and ζ are weight coefficients, and their specific values ​​can be adjusted and set by the user, or obtained through simulation analysis by mathematical analysis software;

[0089] Based on the historical data of vehicle driving, the risk threshold is pre-set on the premise of ensuring vehicle safety. If the predicted risk coefficient Cy(v,v) exceeds the risk threshold, an alarm message is issued to the outside.

[0090] When used, combine the contents in steps 401 to 403:

[0091] When the stability and handling of the vehicle are difficult to meet expectations, a digital twin model of the vehicle's driving is established to predict the vehicle's steering state. The risk coefficient Cy(v,v) is generated based on the obtained prediction data set. The current steering risk of the vehicle is predicted and evaluated based on the risk coefficient Cy(v,v). If the subsequent driving risk of the vehicle exceeds expectations, it means that the vehicle is currently facing a greater risk. At this time, an alarm message is sent to the outside to facilitate timely adjustment of the vehicle's steering state.

[0092] Step 5: Using the trained vehicle driving model, adjusting the driving conditions of the vehicle through simulation analysis, and outputting the adjusted driving plan. After the vehicle executes the driving plan, if the execution effect does not meet expectations, a deceleration command is issued to decelerate the vehicle;

[0093] The step five includes the following contents:

[0094] Step 501: presetting a turning condition for the vehicle, under which at least the predicted risk coefficient Cy(v,v) of the vehicle does not exceed the risk threshold, so that the vehicle can maintain a low driving risk when driving; using the trained vehicle driving model, adjusting the driving condition of the vehicle through simulation analysis, for example, adjusting the torque of the vehicle, the stiffness of the suspension, the position of the center of gravity of the vehicle, etc., and outputting at least one driving plan so that the vehicle meets the pre-set turning condition;

[0095] Step 502: Output the driving plan obtained by the simulation analysis. After the vehicle executes the driving plan, after an observation period, if the risk coefficient Cy(v,v) obtained by the prediction again still exceeds the risk threshold, a deceleration command is issued to decelerate the vehicle. The deceleration ratio is positively correlated with the ratio of the risk coefficient Cy(v,v) exceeding the risk threshold.

[0096] When using, combine the contents in steps 501 and 502:

[0097] After the target conditions are preset, the trained vehicle driving model is used for simulation analysis to obtain the corresponding adjustment plan, so that when the steering state of the vehicle needs to be adjusted, the adjustment plan can be quickly determined to improve the efficiency of the adjustment; when the adjustment plan is executed, after adjusting the steering state of the vehicle, the driving risk of the vehicle can be reduced on the basis of improving the stability of the vehicle. At the same time, the safety of the vehicle is also guaranteed by slowing down the vehicle.

[0098] See also Figure 2 The present invention provides an electric vehicle steering control system with high stability, comprising:

[0099] The stability monitoring unit guides the vehicle to travel along the pre-planned driving path to the destination, collects partial status data of the vehicle in the driving and steering state, and establishes a first steering data set after aggregation; generates a stability coefficient Ws(v,r) from the first steering data set, and issues a first warning message if the obtained stability coefficient Ws(v,r) does not exceed the stability threshold;

[0100] The adjustment unit adjusts the suspension stiffness of the vehicle suspension system after receiving the first warning information, and obtains the current stability coefficient Ws(v,r) again after the adjustment is completed. If the correlation between the adjustment range of the suspension stiffness and the change range of the stability coefficient Ws(v,r) is less than expected, the vehicle driving steering state continues to be adjusted in a backup manner;

[0101] When the maneuverability monitoring unit and the vehicle are in a turning state on the driving path, a second steering data set is established according to the response speed and center of gravity change of the vehicle, and a maneuverability coefficient Ck(o,t,v) is generated from the second steering data set. If the obtained maneuverability coefficient Ck(o,t,v) exceeds the corresponding maneuverability threshold, a third warning information is issued to the outside;

[0102] The modeling unit, after receiving the third warning information, if at least one of the stability coefficient Ws(v,r) and the maneuverability coefficient Ck(o,t,v) does not exceed the corresponding threshold, establishes a trained vehicle driving model to predict the steering state of the vehicle, and establishes a prediction data set based on the prediction result to generate a risk coefficient Cy(v,v); if the risk coefficient Cy(v,v) exceeds the risk threshold, issues an alarm message to the outside;

[0103] The analysis unit uses the trained vehicle driving model to adjust the vehicle driving conditions through simulation analysis and outputs an adjusted driving plan. After the vehicle executes the driving plan, if the execution effect does not meet the expectations, a deceleration command is issued to decelerate the vehicle.

[0104] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0105] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A steering control method for an electric vehicle with high stability, characterized in that: The steps include: Guide the vehicle to drive along the pre-planned driving path to the destination, collect partial status data of the vehicle in the driving and steering state, and establish a first steering data set after aggregation; generate a stability coefficient from the first steering data set W ( v , r ), if the obtained stability coefficient W ( v , r ) does not exceed the stability threshold, and sends the first warning information to the outside; After receiving the first warning information, adjust the suspension stiffness of the vehicle suspension system, and obtain the current stability coefficient again after the adjustment is completed W ( v , r ), if the adjustment range of the suspension stiffness is equal to the stability coefficient W ( v , r ) is less than expected, the vehicle steering state is adjusted in a backup manner; When the vehicle continues to be in a driving and turning state on the driving path, a second steering data set is established according to the response speed and center of gravity change of the vehicle, and the maneuverability coefficient is generated from the second steering data set. C ( o , t , v ), if the controllability coefficient obtained C ( o , t , v ) exceeds the corresponding controllability threshold, a third warning message is issued to the outside; After receiving the third warning information, if the stability coefficient W ( v , r ) and controllability coefficient C ( o , t , v ) does not exceed the corresponding threshold, a trained vehicle driving model is established to predict the steering state of the vehicle, and a prediction data set is established based on the prediction results to generate a risk coefficient Cy ( v , v ), if the risk factor Cy ( v , v ) exceeds the risk threshold, an alarm message is sent to the outside; Using the trained vehicle driving model, the driving conditions of the vehicle are adjusted through simulation analysis, and the adjusted driving plan is output. After the vehicle executes the driving plan, if the execution effect does not meet the expectations, a deceleration instruction is issued to decelerate the vehicle; Guide the vehicle to drive along the driving path to the destination, and divide the driving process into several driving intervals. When the vehicle is in the driving and turning state, obtain the current driving speed of the vehicle Sv and the average speed in each driving interval; obtain the angle between the front wheels of the vehicle and the road surface to generate the driving angle Sr , and obtain the driving angle in each driving interval Sr The mean of The above data are continuously acquired several times along the time axis, and the first steering data set is established after aggregation; The stability coefficient of the vehicle is generated from the first steering data set W ( v , r ), the specific method is as follows: Sv and driving angle Sr Perform linear normalization and map the corresponding data values ​​to [0, 1] according to the following formula: The parameters have the following meanings: n is a positive integer greater than 1, , weight coefficient: , ,and , is the historical mean of driving speed in each driving interval, is the historical mean of the driving angle in each driving interval; The stability coefficient obtained W ( v , r ) does not exceed the stability threshold, a first warning message is issued to the outside.

2. The electric vehicle steering control method with high stability according to claim 1, characterized in that: After receiving the first warning information, the vehicle's suspension adjustment command is generated to adjust the suspension stiffness of the vehicle's suspension system. The adjustment strategy is: the adjustment range of the suspension stiffness and the stability coefficient W ( v , r ) is positively correlated with the proportion of exceeding the stability threshold, and after determining the adjustment range, the adjustment of the range is evenly completed within a number of driving intervals; After completing the suspension stiffness adjustment, after an observation period, obtain the current stability coefficient again W ( v , r ); if the stability coefficient W ( v , r ) changes from the previous value, the adjustment range and stability coefficient are obtained through correlation analysis W ( v , r ) as the correlation coefficient between the change ranges of Tz ; If the obtained adjusted relevance Tz After the adjustment request is issued, the front wheel torque and steering angle of the vehicle are fine-tuned several times, and the stability coefficient after the several adjustments is obtained. W ( v , r ); if the vehicle's adjusted stability coefficient W ( v , r ) still does not exceed the stability threshold, a second warning message is issued.

3. The electric vehicle steering control method with high stability according to claim 1, characterized in that: If the vehicle turns while driving, the response speed during the turn is obtained and determined as the first response speed. Od When the vehicle brakes, obtain its braking response speed and determine it as the second response speed Td ; Obtain the center of gravity position and current center of gravity position in each driving interval during the turning process of the vehicle. Through the proximity centrality analysis, the center of gravity position with the highest centrality is obtained as the standard position. The distance between the current center of gravity position and the standard position is obtained as the center of gravity offset distance. Vp ; After summarizing the above parameters, establish the second steering data set.

4. The electric vehicle steering control method with high stability according to claim 3, characterized in that: Generating the maneuverability coefficients from the second steering data set C ( o , t , v ), the specific method is as follows: the first response speed Od , Second response speed Td and center of gravity offset distance Vp Perform linear normalization and map the corresponding data values ​​to [0, 1] according to the following formula: in, First response speed Od The historical average of is the historical mean of the second response speed, is the historical mean of the center of gravity offset distance; n is a positive integer greater than 1, and the weight coefficient is: and ; If the controllability coefficient is obtained C ( o , t , v ) does not exceed the corresponding controllability threshold, a third warning message is issued to the outside.

5. The electric vehicle steering control method with high stability according to claim 1, characterized in that: After receiving the third warning information, obtain the current stability coefficient of the vehicle W ( v , r ) and controllability coefficient C ( o , t , v ), if at least one of the two does not exceed the corresponding threshold, collect the vehicle's specifications and performance parameters, driving state parameters and road condition information in the forward direction, and summarize them to establish a modeling data set; Select some data from the modeling data set, use the machine learning model to build a vehicle driving model, use the vehicle driving model to predict the vehicle's steering state, and obtain the prediction results; query and obtain several driving speeds from the prediction results, marked as predicted speeds Y ( s , v ); Get several centroid offset distances and mark them as predicted offsets Y ( v , p ) After summarizing the above parameters, a prediction data set for the vehicle is generated.

6. The electric vehicle steering control method with high stability according to claim 5, characterized in that: Generate the risk factor of vehicle prediction from the vehicle prediction dataset Cy ( v , v ), the specific generation method is as follows: the predicted speed Y ( s , v ), prediction offset Y ( v , p ) performs linear normalization processing and maps the corresponding data values ​​to [0, 1] according to the following formula: The meanings of the parameters are: is the weight coefficient; if the predicted risk coefficient is obtained Cy ( v , v ) exceeds the risk threshold, an alarm message is sent to the outside.

7. The electric vehicle steering control method with high stability according to claim 1, characterized in that: The turning condition of the vehicle is preset, under which the risk factor predicted by the vehicle is at least Cy ( v , v ) does not exceed the risk threshold, uses the trained vehicle driving model, adjusts the vehicle driving conditions through simulation analysis, and outputs at least one driving plan so that the vehicle reaches the pre-set turning condition; The driving plan obtained by the simulation analysis is output, and after being executed by the vehicle, after an observation period, if the risk coefficient obtained by the prediction is Cy ( v , v ) still exceeds the risk threshold, a deceleration command is issued to decelerate the vehicle. The deceleration ratio is related to the risk coefficient. Cy ( v , v ) is positively correlated with the proportion exceeding the risk threshold.

8. A high-stability electric vehicle steering control system, using the control method according to any one of claims 1 to 7, characterized in that: include: The stability monitoring unit guides the vehicle to travel along the pre-planned driving path to the destination, collects partial status data of the vehicle in the driving and steering state, and generates a stability coefficient for evaluating the vehicle steering; The adjustment unit adjusts the suspension stiffness of the vehicle suspension system, and if the correlation between the adjustment range of the suspension stiffness and the change range of the stability coefficient is less than expected, the vehicle driving steering state continues to be adjusted in a backup manner; The maneuverability monitoring unit generates a maneuverability coefficient based on the vehicle's response speed and center of gravity change when the vehicle is in a turning state on the driving path. If it exceeds the corresponding maneuverability threshold, a warning message is issued to the outside; The modeling unit establishes a trained vehicle driving model to predict the steering state of the vehicle, and establishes a prediction data set based on the prediction result to generate a risk coefficient. If the risk coefficient exceeds the risk threshold, an alarm message is issued to the outside. The analysis unit adjusts the driving conditions of the vehicle through simulation analysis and outputs an adjusted driving plan. After the vehicle executes the driving plan, if the execution effect does not meet expectations, a deceleration command is issued.

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