CN111806449A - Method for estimating total vehicle mass and road surface gradient of pure electric vehicle - Google Patents

Method for estimating total vehicle mass and road surface gradient of pure electric vehicle Download PDF

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CN111806449A
CN111806449A CN202010582720.9A CN202010582720A CN111806449A CN 111806449 A CN111806449 A CN 111806449A CN 202010582720 A CN202010582720 A CN 202010582720A CN 111806449 A CN111806449 A CN 111806449A
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vehicle
road surface
gradient
mass
road
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崔晓龙
严鉴铂
刘义
臧恬
文宇航
杨鹏
王鹏
范浩
宋峰伟
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Xian Fast Auto Drive Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/08—Electric propulsion units
    • B60W2510/083—Torque
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/10—Change speed gearings
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/12—Lateral speed
    • B60W2520/125—Lateral acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/20—Tyre data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/10—Accelerator pedal position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/12—Brake pedal position

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Abstract

The invention discloses a method for estimating the whole vehicle mass and the road surface gradient of a pure electric vehicle, and belongs to the field of vehicle mass and road gradient calculation. A method for estimating the whole vehicle mass and the road surface gradient of a pure electric vehicle comprises the following steps: 1) acquiring vehicle running state information and vehicle parameters; 2) acquiring acceleration values of a vehicle in three directions; 3) establishing an estimation model of the vehicle mass and the gradient by adopting a least square method with dynamic forgetting factors for the data of 1) and 2); establishing an estimation model of the gradient by using Kalman filtering on the data of 1) and 2); 4) respectively inputting the running state information of the vehicle into two estimation models to obtain the real-time vehicle weight and two road surface gradients; 5) and (4) adopting a fusion algorithm to bring the two road surface gradients into a preset time factor, and outputting the final road surface gradient. The algorithm overcomes the defects that the slope estimation in the prior art is seriously dependent on the precision of a vehicle model and is greatly influenced by the static error of an acceleration sensor.

Description

Method for estimating total vehicle mass and road surface gradient of pure electric vehicle
Technical Field
The invention belongs to the field of vehicle mass and road gradient calculation, and particularly relates to a method for estimating the whole vehicle mass and road gradient of a pure electric vehicle.
Background
Currently, vehicle mass and road grade estimation is of increasing interest to vehicle manufacturers and component manufacturers. At present, two main research methods for obtaining road slope angle and vehicle mass are based on sensors and vehicle longitudinal dynamics. The sensor-based identification method is to directly measure the inclination angle by adding a sensor to the vehicle, for example, using an inclination displacement sensor, an inertial navigator, a GPS, or the like, and further calculate the vehicle mass. In the running process of a conventional vehicle, an accurate value of the road gradient cannot be obtained by using an angular displacement sensor under the influence of the longitudinal acceleration of a vehicle body, the deformation of a suspension and the jolt of the road; the slope angle measured by using the inertial navigator is relatively serious in lag and high in cost, and is not beneficial to common use of a real vehicle; however, the GPS has a low frequency and a positioning error, and when positioning is performed continuously in a special area, there is a problem that a signal cannot be received or a signal deviation is large, and a small speed error causes a large gradient estimation error.
The vehicle longitudinal dynamics or kinematics based identification method uses a longitudinal dynamics model of the vehicle plus data obtained from the vehicle CAN bus to estimate unknown system parameters. Although there are many methods in this respect, a common difficulty is the decoupling of the variation of the vehicle's own parameters (mass, etc.) and the external resistance (gradient), and furthermore the time-varying nature of the road increases the complexity of the estimation process.
The dynamics and kinematics methods are mostly used for joint estimation of mass and slope and for separate estimation of mass. The concept of comprehensive resistance and relative running gradient of a road is highlighted by the king jade sea and the like of a vehicle technology center, the output torque and the torque of an automobile engine are obtained through the CAN on the basis of SAE1939, then the running acceleration and the comprehensive resistance of the road are obtained by differentiating the rotating speed, and the identification of the road gradient is preliminarily realized. VahidiA proposes a method of estimating mass and slope simultaneously using a least squares method. McIntyremL develops the method, adopts the adaptive observer to simultaneously observe the mass and the gradient, designs an adaptive law, ensures the observation stability, and obtains a certain observation effect when the gradient of the road surface fluctuates sharply, such as step change and the like. Lingmann utilizes a Kalman filter to jointly estimate mass and gradient, a Kalman filtering principle is applied to carry out filtering processing on automobile driving force, automobile speed and viscous resistance required by gradient identification, and a result is obtained through calculation. The learner also establishes an observation equation by using a longitudinal dynamics method when the gradient is known, and estimates the mass of the whole vehicle by using Kalman filtering, and the estimation precision of the method depends on the road gradient estimation precision.
Disclosure of Invention
The invention aims to overcome the defects that the slope estimation in the prior art is seriously dependent on the precision of a vehicle model and is greatly influenced by the static error of an acceleration sensor, and provides a method for estimating the finished vehicle mass and the road slope of a pure electric vehicle.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a method for estimating the whole vehicle mass and the road surface gradient of a pure electric vehicle comprises the following steps:
1) acquiring vehicle running state information and vehicle parameters;
the running state information of the vehicle comprises the vehicle speed, the motor torque, the opening degree of an accelerator pedal, the state of a hand brake and a current gear;
the vehicle parameters comprise the tire radius, the rear axle speed ratio and the windward area;
2) monitoring the vehicle control physical installation deviation by using the existing acceleration sensor in the gear shifting controller and re-giving change to obtain the acceleration values of the vehicle in three directions;
3) establishing an estimation model of the vehicle mass and the gradient by adopting a least square method with a dynamic forgetting factor for the data in the step 1) and the step 2);
establishing a slope estimation model by using Kalman filtering on the data in the step 1) and the step 2);
4) respectively inputting the running state information of the vehicle into the two estimation models in the step 3) to obtain the real-time vehicle weight and two road surface gradients;
5) and (4) adopting a fusion algorithm to bring the two road surface gradients into a preset time factor, and outputting the final road surface gradient.
Further, the specific process of establishing the vehicle mass and gradient estimation model in step 3) is as follows:
according to a complete vehicle dynamics model Ft＝Ff+Fw+Fi+Fj；
Wherein, FtAs a driving force of the entire vehicle, FfAs air resistance, FwTo rolling resistance, FiFor braking resistance, FjIs acceleration resistance;
Ft＝τi0igwhere τ is the motor torque, i0igR is the tire radius for the reduction ratio;
Ff＝1/2*CdρA2wherein, CdIs the wind resistance coefficient, rho is the air density, A is the windward area;
Fwu is a road rolling resistance coefficient, M is a mass, g is a gravitational acceleration, and θ is a road gradient;
Fjmgsin θ, where M is mass, g is gravity acceleration th, and θ is road grade;
the above equation is collated to obtain M × (a)1+u*a2)＝Ft-Ff；
Wherein, a1For the sensor to measure the longitudinal acceleration value, a2Actually measuring a vertical acceleration value for the sensor;
obtaining the estimated vehicle mass at the next moment by using a least square method with dynamic forgetting factors;
based on the estimated vehicle mass at each moment, the estimated vehicle mass is brought into a vehicle dynamics model again to obtain the road surface gradient at the current moment;
and obtaining a real-time slope estimated value by using a least square method with a forgetting factor.
Further, Kalman filtering is used to estimate the road slope.
Further, the Kalman filtering algorithm in the step 3) is based on a1＝ax+gsinθ＝ax+aθ
A is toxAnd aθEstablishing a state transition matrix A and a transformation matrix H as state quantities;
wherein the content of the first and second substances,
Figure BDA0002553669750000041
vehicle speed V for obtaining observed quantityx,sensorWith longitudinal acceleration a1(i.e. a)x,sensor) Substituting Kalman filtering to obtain speed, acceleration and g ∙ sin thetaroadReal-time estimate of (a);
then according to g ∙ sin thetaroadAnd resolving the current gradient.
Compared with the prior art, the invention has the following beneficial effects:
according to the method for estimating the finished automobile mass and the road surface gradient of the pure electric vehicle, the dynamic forgetting factor is adopted to improve the automobile weight convergence time, and the fusion algorithm is adopted to improve the real-time performance and the accuracy of the road surface gradient; according to the method, a dynamic relation among longitudinal driving force, longitudinal acceleration and mass is established by analyzing a dynamic model, a mass calculation model is established by adopting a least square method, and the road surface gradient is subjected to multi-method combined estimation by adopting a kinematics and a dynamic method, so that the defects that the gradient estimation is seriously dependent on the precision of a vehicle model and is greatly influenced by a static error of an acceleration sensor are overcome; the method for estimating the vehicle mass and the road surface gradient of the pure electric vehicle is good in robustness, high in convergence speed and accurate in result.
Furthermore, a filter is adopted to extract the effective part of the acceleration information, and a Kalman filter is used to process the input acceleration signal, so that the low-frequency noise of the sensor and other noises of the road and the vehicle can be effectively removed.
Drawings
FIG. 1 is a comparison graph of a slope estimation result and an actually measured slope during the running process of three different vehicle types.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The existing finished automobile mass estimation method is greatly influenced by the gradient of a road surface and has more quantity to be calibrated, the characteristic that the longitudinal driving force of an electrically driven vehicle is accurate is considered, the mass and the gradient are decoupled, the finished automobile mass is obtained by using a Kalman filtering method and a least square method, the dynamic relation of the longitudinal driving force, the longitudinal acceleration and the mass is established by analyzing a dynamic model, then the multiple-method combined estimation is carried out on the gradient of the road surface by adopting a kinematics and dynamics method, and the defects that the gradient estimation is seriously dependent on the precision of a vehicle model and is greatly influenced by the static error of an acceleration sensor are overcome. The method for estimating the vehicle mass and the road surface gradient of the pure electric commercial vehicle is good in robustness, high in convergence speed and accurate in estimation.
The invention is described in further detail below with reference to the accompanying drawings:
examples
The method estimates the weights of the vehicles of three different vehicle types by utilizing the algorithm, the calculated data of the weights are shown in the following table 1, the precision of real-time estimation of the weights by each large OEM is generally 10%, the convergence time has no clear requirement, the precision of other algorithms is more than 5% -10% of errors, the convergence time is 200 and 500s and is unequal, the precision of the actual vehicle test algorithm is far higher than the requirement of each main engine plant under different weights, the convergence time is less than 100s, and the method has important significance for actual vehicle application, gear shifting quality optimization and driving experience improvement.
TABLE 1 comparison of the vehicle weight results obtained with the algorithm of the present invention with the actual values
Vehicle model Vehicle weight Calculating vehicle weight Error of the measurement
Light truck 3000 3160 5.33％
Heavy truck 8570 8800 2.68％
Muck truck 17000 17684 4.02％
Referring to fig. 1, fig. 1 is a comparison graph of an estimated result of a gradient and an actually measured gradient in the running process of three different vehicle types, wherein a white line in the graph is the actually measured gradient, and a gray line is the estimated gradient, and data show that when a road is flat and a vehicle has small jolt, the algorithm has high stability, and does not generate gradient jump, and when the gradient of the road changes, the gradient can be well predicted in real time, so that the requirement of a TCU (train control unit) optimization gear shifting strategy is met.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (4)

1. The method for estimating the finished automobile mass and the road surface gradient of the pure electric vehicle is characterized by comprising the following steps of:
1) acquiring vehicle running state information and vehicle parameters;
the running state information of the vehicle comprises the vehicle speed, the motor torque, the opening degree of an accelerator pedal, the state of a hand brake and a current gear;
the vehicle parameters comprise the tire radius, the rear axle speed ratio and the windward area;
2) monitoring the vehicle control physical installation deviation by using the existing acceleration sensor in the gear shifting controller and re-giving change to obtain the acceleration values of the vehicle in three directions;
3) establishing an estimation model of the vehicle mass and the gradient by adopting a least square method with a dynamic forgetting factor for the data in the step 1) and the step 2);
establishing a slope estimation model by using Kalman filtering on the data in the step 1) and the step 2);
4) respectively inputting the running state information of the vehicle into the two estimation models in the step 3) to obtain the real-time vehicle weight and two road surface gradients;
5) and (4) adopting a fusion algorithm to bring the two road surface gradients into a preset time factor, and outputting the final road surface gradient.
2. The method for estimating the total vehicle mass and the road surface gradient of the pure electric vehicle according to claim 1, wherein the specific process of establishing the estimation model of the vehicle mass and the road surface gradient in the step 3) is as follows:
according to a complete vehicle dynamics model Ft＝Ff+Fw+Fi+Fj；
Wherein, FtAs a driving force of the entire vehicle, FfAs air resistance, FwTo rolling resistance, FiFor braking resistance, FjIs acceleration resistance;
Ft＝τi0igwhere τ is the motor torque, i0igR is the tire radius for the reduction ratio;
Ff＝1/2*CdρA2wherein, CdIs the wind resistance coefficient, rho is the air density, A is the windward area;
Fwu is a road rolling resistance coefficient, M is a mass, g is a gravitational acceleration, and θ is a road gradient;
Fjmgsin θ, where M is mass, g is gravity acceleration th, and θ is road grade;
the above equation is collated to obtain M × (a)1+u*a2)＝Ft-Ff；
Wherein, a1For the sensor to measure the longitudinal acceleration value, a2Actually measuring a vertical acceleration value for the sensor;
obtaining the estimated vehicle mass at the next moment by using a least square method with dynamic forgetting factors;
based on the estimated vehicle mass at each moment, the estimated vehicle mass is brought into a vehicle dynamics model again to obtain the road surface gradient at the current moment;
and obtaining a real-time slope estimated value by using a least square method with a forgetting factor.
3. The method for estimating the vehicle mass and the road surface gradient of the pure electric vehicle according to claim 1, wherein the Kalman filtering algorithm in the step 3) is based on a1＝ax+gsinθ＝ax+aθ
A is toxAnd aθEstablishing a state transition matrix A and a transformation matrix H as state quantities;
wherein the content of the first and second substances,
Figure FDA0002553669740000021
vehicle speed V for obtaining observed quantityx,sensorMeasuring the longitudinal acceleration a with the sensorx,sensorSubstituting Kalman filtering to obtain speed, acceleration and g ∙ sin thetaroadReal-time estimate of (a);
then according to g ∙ sin thetaroadAnd calculating the real-time gradient.
4. The method for estimating the vehicle mass and the road surface gradient of the pure electric vehicle according to claim 2 is characterized in that Kalman filtering is adopted to carry out filtering processing on the longitudinal acceleration and the vertical acceleration.
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CN111506958A (en) * 2019-01-30 2020-08-07 南京汽车集团有限公司 Load estimation scheme design and evaluation system based on multiple related signal quantities of whole vehicle in intelligent networking environment
CN112550297A (en) * 2020-12-16 2021-03-26 陕西法士特齿轮有限责任公司 Weight and gradient calculation method of pure electric commercial vehicle based on three-axis accelerometer
CN112613253A (en) * 2021-01-06 2021-04-06 东南大学 Vehicle mass and road gradient combined self-adaptive estimation method considering environmental factors
CN112926140A (en) * 2021-03-25 2021-06-08 交通运输部公路科学研究所 Freight vehicle quality estimation method based on vehicle-road cooperation and TBOX
CN112937596A (en) * 2021-03-22 2021-06-11 潍柴动力股份有限公司 Static vehicle weight measuring method and vehicle starting method
CN113085876A (en) * 2021-03-23 2021-07-09 浙江吉利控股集团有限公司 Vehicle mass estimation method and device based on recursive gradient correction method
CN113119980A (en) * 2021-03-24 2021-07-16 西安法士特汽车传动有限公司 Road gradient estimation method, system and equipment for electric vehicle
CN113147768A (en) * 2021-05-13 2021-07-23 东北大学 Multi-algorithm fusion prediction-based automobile road surface state online estimation system and method
CN113232664A (en) * 2021-06-23 2021-08-10 博雷顿科技有限公司 Method and system for measuring real-time gradient of road condition of electric vehicle during driving
CN113264056A (en) * 2021-05-25 2021-08-17 三一汽车制造有限公司 Vehicle weight estimation method, device, vehicle and readable storage medium
CN113428153A (en) * 2021-08-03 2021-09-24 启迪云控(北京)科技有限公司 Control method and device for electric automobile
CN114379568A (en) * 2022-01-24 2022-04-22 广州瑞立科密汽车电子股份有限公司 Vehicle control method, device, medium and vehicle based on vehicle mass estimation
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CN114655224A (en) * 2022-03-21 2022-06-24 潍柴动力股份有限公司 Road gradient estimation method, electronic device and storage medium
CN114684159A (en) * 2022-03-21 2022-07-01 潍柴动力股份有限公司 Vehicle mass estimation method and device, electronic equipment and storage medium
CN114919585A (en) * 2022-07-22 2022-08-19 杭州宏景智驾科技有限公司 Vehicle weight and road gradient estimation method based on vehicle dynamics model
WO2022236529A1 (en) * 2021-05-10 2022-11-17 威刚科技股份有限公司 System and method for estimating weight of electric vehicle
WO2023001289A1 (en) * 2021-07-22 2023-01-26 中国第一汽车股份有限公司 Vehicle rolling resistance acquisition method and module, and storage medium
TWI806670B (en) * 2021-08-02 2023-06-21 劉偉鋒 A dynamic calculation method and device for the mass of an electric vehicle
CN116729399A (en) * 2023-07-11 2023-09-12 长春一东离合器股份有限公司苏州研发中心 Vehicle ramp, vehicle weight dynamic identification method, device, equipment and medium
TWI823659B (en) * 2022-11-03 2023-11-21 威剛科技股份有限公司 Slope estimation system and method for an electric vehicle

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Application publication date: 20201023