A method for estimating vehicle speed in neutral coasting mode based on high-precision map

Abstract

The present invention discloses a method for estimating the speed of a vehicle in neutral coasting based on a high-precision map, comprising the following steps: 1) obtaining vehicle parameters and slope data in real time when the vehicle is in neutral coasting; 2) determining acceleration model parameters based on vehicle parameters and slope data when the neutral coasting time reaches a set time; 3) predicting the acceleration at the current moment according to the acceleration model parameters; 4) predicting the vehicle speed at the next moment based on the predicted acceleration at the current moment; 5) predicting the driving distance from the current moment to the next moment according to the predicted vehicle speed at the next moment; 6) searching the road slope at the driving distance ahead through a high-precision map, and predicting the acceleration at the next moment based on the searched road slope and the predicted vehicle speed at the next moment; 7) repeating steps 4)-6) to realize the prediction of the future vehicle speed during the neutral coasting process. The present invention determines acceleration model parameters based on vehicle information and high-precision map data, and predicts the vehicle speed during the neutral coasting process. The method is simple and the predicted vehicle speed has high accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of automobile technology, and in particular relates to a method for estimating the neutral coasting vehicle speed based on a high-precision map. Background Art

[0002] The vehicle speed prediction during neutral coasting is helpful for the development of intelligent energy management related functions. In the existing neutral coasting process, the rolling resistance coefficient is simplified to a constant value for longitudinal dynamics modeling, while the slope change is ignored. The previous vehicle speed is directly used to fit and predict the future vehicle speed. This method has the following defects:

[0003] When estimating vehicle speed, the rolling resistance coefficient is simplified to a constant value and its change with vehicle speed is ignored. Although this simplifies the calculation workload, the prediction accuracy is low when the vehicle speed changes greatly. The change in road slope is ignored, and the current vehicle speed is directly used to fit and predict the future vehicle speed. The prediction accuracy for short distances is relatively high, but the accuracy is low for long distances. Summary of the invention

[0004] The purpose of the present invention is to solve the deficiencies of the above-mentioned background technology and to provide a method for estimating the vehicle speed during coasting in neutral gear based on a high-precision map, so as to improve the accuracy of vehicle speed estimation.

[0005] The technical solution adopted by the present invention is: a method for estimating the neutral coasting vehicle speed based on a high-precision map, comprising the following steps:

[0006] 1) When the vehicle is coasting in neutral, obtain vehicle parameters and slope data of high-precision maps in real time;

[0007] 2) When the neutral coasting time reaches the set time, the acceleration model parameters are determined based on the vehicle parameters and slope data;

[0008] 3) Predict the acceleration a0 at the current moment based on the acceleration model parameters;

[0009] 4) Predict the vehicle speed v1 at the next moment based on the predicted acceleration a0 at the current moment;

[0010] 5) Predict the travel distance s1 from the current moment to the next moment based on the predicted vehicle speed v1 at the next moment and the acceleration a0 at the current moment;

[0011] 6) Find the road slope θ1 at the front driving distance s1 through the high-precision map, and predict the acceleration a1 at the next moment based on the found road slope θ1 and the predicted vehicle speed v1 at the next moment;

[0012] 7) Repeat steps 4)-6) to predict the future vehicle speed during the neutral coasting process.

[0013] Furthermore, when the neutral coasting time reaches the set time, the set time before the current moment is divided into three time intervals, each time interval has n sampling points, and the sampled data includes the slope and vehicle speed at each time point. The acceleration model parameters are determined based on the slope, vehicle speed and the mean acceleration model.

[0014] Furthermore, the coefficients of the acceleration model are determined by the following formula:

[0015]

[0016] Among them, among them, are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the first time interval; are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the second time interval; are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the third time interval, K0, K1, K2 are the first coefficient, second coefficient and third coefficient of the acceleration model respectively, and g is the acceleration due to gravity.

[0017] Furthermore, the acceleration at the current moment is

[0018] a0=gθ0+K0+K1v0+K2v0 2

[0019] Among them, a0 is the acceleration at the current moment, K0, K1, K2 are the first parameter, second parameter and third parameter of the acceleration model respectively, g is the acceleration of gravity, θ0 is the road slope at the current moment, and v0 is the vehicle speed at the current moment.

[0020] Furthermore, the vehicle speed at the next moment is predicted by the following formula:

[0021] v1=v0+a0ΔT

[0022] Among them, v1 is the predicted vehicle speed at the next moment, v0 is the vehicle speed at the current moment, a0 is the predicted acceleration at the current moment, and ΔT is the sampling period.

[0023] Furthermore, the sampling period is the least common multiple of the transmission periods of the vehicle speed signal, the gear position signal, the brake signal, the throttle signal and the map signal when the vehicle is in a neutral coasting state.

[0024] Furthermore, the travel distance from the current moment to the next moment is predicted by the following formula:

[0025]

[0026] Among them, S1 is the predicted driving distance, v1 is the predicted vehicle speed at the next moment, v0 is the vehicle speed at the current moment, and a0 is the predicted acceleration at the current moment.

[0027] Furthermore, during the neutral coasting process, the deviation between the actual vehicle speed and the predicted vehicle speed is monitored in real time, and a determination is made as to whether to correct the acceleration model parameters based on the deviation between the actual vehicle speed and the predicted vehicle speed. When it is determined that the acceleration model parameters should be corrected, a new acceleration is predicted based on the corrected parameters.

[0028] Furthermore, if the absolute value of the difference between the actual vehicle speed and the predicted vehicle speed at a plurality of consecutive moments is greater than a set value, it is determined that the acceleration model parameters are corrected, otherwise the acceleration model parameters are not corrected.

[0029] Furthermore, the correction process is: calculate the absolute value of the difference between the actual vehicle speed at the multiple moments and the predicted vehicle speed to obtain multiple vehicle speed deviations, calculate the ratio of each vehicle speed deviation to the sampling period to obtain multiple acceleration deviation values, calculate the average value of the multiple acceleration deviation values, and add the average value to the current first parameter of the acceleration model as the new first parameter of the acceleration model.

[0030] The beneficial effects of the present invention are:

[0031] The present invention determines acceleration model parameters based on vehicle information and high-precision map data, thereby predicting the vehicle speed during neutral coasting. The method is simple and the predicted vehicle speed has high accuracy.

[0032] During the vehicle speed prediction process, the present invention monitors the prediction error in real time and proposes a prediction parameter correction method to further improve the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0034] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0035] The present invention considers the change of rolling resistance coefficient with vehicle speed and establishes a new longitudinal dynamics model. At the same time, based on the high-precision map, accurate road slope data within a certain distance ahead is obtained, and an acceleration model for vehicle neutral coasting is established. The acceleration model parameters are iteratively calculated using the collected real vehicle data and high-precision slope data, and the future vehicle speed of neutral coasting is predicted using the acceleration model. At the same time, the deviation between the actual vehicle speed and the predicted vehicle speed is compared in real time. When the deviation is greater than a certain value within a certain period of time, the acceleration model parameters are corrected, and finally the accurate prediction of the vehicle speed within a long distance ahead is achieved.

[0036] According to test experience, on a straight road, the rolling resistance of the vehicle and the driving speed are approximately in a quadratic polynomial relationship. During the vehicle's neutral sliding process, the power chain is disengaged, and the vehicle only travels under the action of slope resistance, rolling resistance, and air resistance. The mathematical model of the vehicle's longitudinal dynamics can be established as follows:

[0037] ma=mgsinθ+mgcosθ(k0+k1v+k2v 2 )+k3v 2

[0038] Where m is the total mass of the vehicle (the mass equivalent to the rotational inertia of the rear axle and tires is relatively small and can be ignored), a is the vehicle acceleration, g is the acceleration due to gravity, θ is the road slope, and v is the average speed of the left and right wheels of the vehicle; k0+k1v+k2v 2 is the rolling resistance coefficient, and k0, k1, k2 are all constants; Where A is the frontal area of ​​the vehicle, ρ is the air density, C d is the drag coefficient, and A, ρ, C d are all constants, then k3 is a constant.

[0039] Since the road slope θ is generally small, cosθ≈1, sinθ≈θ. When the vehicle is moving, the total mass remains basically unchanged, so m is approximately a constant. Let K0=gk0, K1=gk1, Then K0, K1, and K2 are all constants, and the longitudinal dynamics model can be simplified to an acceleration model:

[0040] a=gθ+K0+K1v+K2v 2

[0041] When coasting in neutral gear begins, the parameters of the acceleration model are calculated using vehicle and map data.

[0042] like Figure 1 As shown, the present invention provides a method for estimating the neutral coasting vehicle speed based on a high-precision map, comprising the following steps:

[0043] 1) When the vehicle is coasting in neutral, obtain vehicle parameters and slope data of high-precision maps in real time;

[0044] 2) When the neutral coasting time reaches the set time, the acceleration model parameters are determined based on the vehicle parameters and slope data;

[0045] 3) Predict the acceleration a0 at the current moment based on the acceleration model parameters;

[0046] 4) Predict the vehicle speed v1 at the next moment based on the predicted acceleration a0 at the current moment;

[0047] 5) Predict the travel distance s1 from the current moment to the next moment based on the predicted vehicle speed v1 at the next moment and the acceleration a0 at the current moment;

[0048] 6) Find the road slope θ1 at the front driving distance s1 through the high-precision map, and predict the acceleration a1 at the next moment based on the found road slope θ1 and the predicted vehicle speed v1 at the next moment;

[0049] 7) Repeat steps 4)-6) to predict the future vehicle speed during the neutral coasting process.

[0050] In the above scheme, when the neutral coasting time reaches the set time, the set time before the current moment is evenly divided into 3 time intervals Δt. There are n sampling points in each time interval. The sampled data includes the slope and vehicle speed at each time point. According to the slope and vehicle speed at each sampling point, the average acceleration in the corresponding time interval Δt can be obtained. Average slope Average speed Average speed squared The acceleration model parameters are determined based on these data and the mean acceleration model.

[0051] Substitute the data of the above three time intervals into the mean acceleration model We get a system of three linear equations with K0, K1, and K2 as unknowns, as shown below. We can solve them to get K0, K1, and K2.

[0052]

[0053] Among them, among them, are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the first time interval; are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the second time interval; are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the third time interval, K0, K1, K2 are the first parameter, second parameter and third parameter of the acceleration model respectively, and g is the gravitational acceleration.

[0054] After determining the acceleration model parameters, combined with the current slope and vehicle speed, the current acceleration can be predicted.

[0055] α0=gθ0+K0+K1v0+K2v0 2

[0056] Among them, a0 is the acceleration at the current moment, K0, K1, and K2 are the first, second, and third parameters of the acceleration model respectively, g is the acceleration of gravity, θ0 is the road slope at the current moment, and v0 is the vehicle speed at the current moment. When predicting at the initial moment, v0 is the actual vehicle speed, and v0 is the predicted vehicle speed at each subsequent moment.

[0057] The vehicle speed at the next moment is predicted by the following formula

[0058] v1=v0+a0ΔT

[0059] Among them, v1 is the predicted vehicle speed at the next moment, v0 is the vehicle speed at the current moment, when predicting at the initial moment, v0 is the actual vehicle speed, and v0 is the predicted vehicle speed at each subsequent moment, a0 is predicted to be the acceleration at the current moment, ΔT is the sampling period, and the sampling period is the least common multiple of the vehicle speed signal, gear signal, brake signal, throttle signal, and map signal sending period when the vehicle is in neutral gliding state.

[0060] After v1 is determined, the travel distance s1 from the current moment to the next moment is predicted by the following formula

[0061]

[0062] Based on the map, we can find that the slope at S1 ahead is θ1, and the new acceleration is predicted to be a1=gθ1+K0+K1v1+K2v1 2 , a1 is used to predict the vehicle speed at the next moment.

[0063] In the above scheme, during the neutral gliding process, the deviation between the actual vehicle speed and the predicted vehicle speed is monitored in real time, and whether to correct the acceleration model parameters is determined based on the deviation between the actual vehicle speed and the predicted vehicle speed. When it is determined that the acceleration model parameters are to be corrected, the new acceleration is predicted based on the corrected parameters.

[0064] If the absolute value of the difference between the actual vehicle speed and the predicted vehicle speed at multiple consecutive moments is greater than the set value, it is determined that the acceleration model parameters are corrected, otherwise the acceleration model parameters are not corrected.

[0065] The correction process is as follows: calculating the absolute value of the difference between the actual vehicle speed at the multiple moments and the predicted vehicle speed to obtain multiple vehicle speed deviations, calculating the ratio of each vehicle speed deviation to the sampling period to obtain multiple acceleration deviation values, calculating the average value of the multiple acceleration deviation values, adding the average value to the current first parameter of the acceleration model as the new first parameter of the acceleration model, and continuing the vehicle speed prediction.

[0066] Example

[0067] When the neutral coasting time reaches the set time, the initial time is recorded as t0, and the acceleration model parameters are determined based on the vehicle parameters and slope data before t0 and the aforementioned mean acceleration model; the acceleration a0 at the current time t0 is predicted based on the acceleration model parameters, a0=gθ0+K0+K1v0+K2v0 2 , at this time v0 is the actual vehicle speed at the current time t0.

[0068] The vehicle speed prediction process is carried out periodically, and each cycle corresponds to a moment. In the first prediction cycle, that is, at moment t0, the vehicle speed v1 at the next moment t1 is predicted based on the predicted acceleration a0 at the current moment, where v1=v0+a0ΔT; the travel distance s1 from the current moment to the next moment is predicted based on the predicted vehicle speed v1 at the next moment t1 and the acceleration a0 at the current moment. The road slope θ1 at the front driving distance s1 is found through the high-precision map, and the acceleration a1 at the next time t1 is predicted based on the found road slope θ1 and the predicted vehicle speed v1 at the next time.

[0069] At the second cycle, i.e., time t1, the vehicle speed v2 at the next time t2 is predicted based on the acceleration a1 predicted at the previous time, v2 = v1 + a1ΔT, at which time v1 is the predicted vehicle speed; the travel distance s2 from time t1 to time t2 is predicted based on the predicted vehicle speed v2 at time t2 and the predicted acceleration a1, The road slope θ2 at the driving distance s2 ahead is searched through a high-precision map, and the acceleration a2 at time t2 is predicted based on the searched road slope θ2 and the predicted vehicle speed v2 at the next moment.

[0070] Repeating the second cycle process at each subsequent prediction can achieve the prediction of the future vehicle speed during the neutral coasting process. In actual operation, because all road slopes within the set distance (such as 2Km) ahead can be found through high-precision maps, all vehicle speeds (which may include multiple predicted vehicle speeds) within the set distance can be predicted at one time at the initial moment of operation. After driving a certain distance (such as 50m) at the next moment, the vehicle speed at the set distance + driving distance (i.e. 2050m) can be predicted. This ensures that the vehicle speed within the set distance can be predicted at every moment. Regardless of the method, the process of predicting the vehicle speed remains unchanged.

[0071] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0072] In order to make the description of the present disclosure more detailed and complete, the above description is provided for the implementation and specific embodiments of the present invention; however, this is not the only form of implementing or using the specific embodiments of the present invention. The implementation covers the features of multiple specific embodiments and the method steps and sequences used to construct and operate these specific embodiments. However, other specific embodiments can also be used to achieve the same or equivalent functions and step sequences.

[0073] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0074] The disclosed embodiments are described above to enable any person skilled in the art to implement or use the present invention. Various modifications of these embodiments are obvious to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

[0075] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field.

Claims

1. A method for estimating vehicle speed in neutral coasting mode based on a high-precision map, characterized in that: The following steps are involved: 1) When the vehicle is coasting in neutral, obtain vehicle parameters and slope data of high-precision maps in real time; 2) When the neutral coasting time reaches the set time, the acceleration model parameters are determined based on the vehicle parameters and slope data: the set time before the current moment is evenly divided into 3 time intervals, each time interval has n sampling points, and the sampled data includes the slope and vehicle speed at each time point. The acceleration model parameters are determined based on the slope, vehicle speed and mean acceleration model; 3) Predict the acceleration a0 at the current moment based on the acceleration model parameters; 4) Predict the vehicle speed v1 at the next moment based on the predicted acceleration a0 at the current moment; 5) Predict the travel distance s1 from the current moment to the next moment based on the predicted vehicle speed v1 at the next moment and the acceleration a0 at the current moment; 6) Find the road slope θ1 at the front driving distance s1 through the high-precision map, and predict the acceleration a1 at the next moment based on the found road slope θ1 and the predicted vehicle speed v1 at the next moment; 7) Repeat steps 4)-6) to achieve the prediction of the future vehicle speed during the neutral coasting process; During the process of coasting in neutral gear, the deviation between the actual vehicle speed and the predicted vehicle speed is monitored in real time. According to the deviation between the actual vehicle speed and the predicted vehicle speed, it is determined whether to correct the acceleration model parameters. When it is determined that the acceleration model parameters should be corrected, the new acceleration is predicted based on the corrected parameters.

2. The method for estimating vehicle speed in neutral coasting mode based on a high-precision map according to claim 1, characterized in that: The acceleration model parameters are determined by the following formula in, are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the first time interval; are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the second time interval; are the average acceleration, average slope, average vehicle speed and average vehicle speed square calculated in the third time interval, K0, K1, K2 are the first parameter, second parameter and third parameter of the acceleration model respectively, and g is the gravitational acceleration.

3. The method for estimating vehicle speed in neutral coasting mode based on a high-precision map according to claim 1, characterized in that: The acceleration at the current moment is a0=gθ0+K0+K1v0+K2v0 2 Among them, a0 is the acceleration at the current moment, K0, K1, K2 are the first parameter, second parameter and third parameter of the acceleration model respectively, g is the acceleration of gravity, θ0 is the road slope at the current moment, and v0 is the vehicle speed at the current moment.

4. The method for estimating vehicle speed in neutral coasting mode based on a high-precision map according to claim 1, characterized in that: Predict the vehicle speed at the next moment using the following formula v1=v0+a0ΔT Among them, v1 is the predicted vehicle speed at the next moment, v0 is the vehicle speed at the current moment, a0 is the predicted acceleration at the current moment, and ΔT is the sampling period.

5. The method for estimating vehicle speed in neutral coasting mode based on a high-precision map according to claim 4, characterized in that: The sampling period is the least common multiple of the transmission periods of the vehicle speed signal, the gear position signal, the brake signal, the throttle signal, and the map signal when the vehicle is in a neutral coasting state.

6. The method for estimating vehicle speed in neutral coasting mode based on a high-precision map according to claim 1, characterized in that: The travel distance from the current moment to the next moment is predicted by the following formula Among them, S1 is the predicted driving distance, v1 is the predicted vehicle speed at the next moment, v0 is the vehicle speed at the current moment, and a0 is the predicted acceleration at the current moment.

7. The method for estimating vehicle speed in neutral coasting mode based on a high-precision map according to claim 1, characterized in that: If the absolute value of the difference between the actual vehicle speed and the predicted vehicle speed at multiple consecutive moments is greater than the set value, it is determined that the acceleration model parameters are corrected, otherwise the acceleration model parameters are not corrected.

8. The method for estimating vehicle speed in neutral coasting mode based on a high-precision map according to claim 7, characterized in that: The correction process is as follows: calculating the absolute value of the difference between the actual vehicle speed at the multiple moments and the predicted vehicle speed to obtain multiple vehicle speed deviations, calculating the ratio of each vehicle speed deviation to the sampling period to obtain multiple acceleration deviation values, calculating the average value of the multiple acceleration deviation values, and adding the average value to the current first parameter of the acceleration model as the new first parameter of the acceleration model.

Images