The subject disclosure relates to vehicle actuation commands to affect transient handling.
Vehicles (e.g., automobiles, trucks, motorcycles) can generally be thought of as being in steady state or transient motion. Unlike in steady state, during transient motion, one or more variables (e.g., lateral acceleration) are changing. Vehicles are increasingly available in both a traditional gas-only version and a battery version (e.g., hybrid vehicle, electrified vehicle). The battery version of a vehicle is heavier than the gas-only version. As a result, the transient response, which is subjectively perceived by a driver as the handling of the vehicle, may be sluggish. Accordingly, it is desirable to provide vehicle actuation commands to affect transient handling.
In one exemplary embodiment, a method of controlling a vehicle includes obtaining a linear representation of a vehicle dynamics model that include actuator dynamics u integrated with vehicle dynamics x. The actuator dynamics u include a road wheel angle at rear wheels δr and a torque Mz. The method also includes obtaining an objective function based on a function of the vehicle dynamics x and the actuator dynamics u and formulating a cost function to minimize the objective function. The actuator dynamics u including the torque Mz are determined for a next time sample based on minimizing the objective function. The vehicle is controlled to implement the torque Mz.
In addition to one or more of the features described herein, the determining the actuator dynamics u including the torque Mz includes obtaining the road wheel angle at the rear wheels δr as an input.
In addition to one or more of the features described herein, the determining the actuator dynamics u including the torque Mz includes also determining the road wheel angle at the rear wheels δr.
In addition to one or more of the features described herein, the method also includes formulating a constraint to be solved with minimization of the cost function, wherein the constraint minimizes the actuator dynamics u.
In addition to one or more of the features described herein, the method also includes representing the cost function and the constraint as:
where
Rt and Qt are weights, utref is a reference provided by a supervisory algorithm, and yt−ytdes is the objective function with yt being a vector that is the function of the vehicle dynamics x and the actuator dynamics u and ytdes being a vector of desired values.
In addition to one or more of the features described herein, the vehicle dynamics x include lateral velocity Vy and yaw rate r.
In addition to one or more of the features described herein, the formulating the cost function is as a quadratic programming problem.
In addition to one or more of the features described herein, the determining the actuator dynamics u for the next time sample includes using a linear quadratic regulator as a solver for the quadratic programming problem.
In addition to one or more of the features described herein, the determining the actuator dynamics u includes using a feedforward and feedback algorithm.
In another exemplary embodiment, a system in a vehicle includes memory configured to store parameters, and processing circuitry to obtain a linear representation of a vehicle dynamics model that includes actuator dynamics u integrated with vehicle dynamics x. The actuator dynamics u include a road wheel angle at rear wheels δr and a torque Mz. The processing circuitry also obtains an objective function based on a function of the vehicle dynamics x and the actuator dynamics u and formulates a cost function to minimize the objective function. The actuator dynamics u including the torque Mz are determined for a next time sample based on minimizing the objective function. The vehicle is controlled to implement the torque Mz.
In addition to one or more of the features described herein, the processing circuitry determining the actuator dynamics u including the torque Mz includes the processing circuitry obtaining the road wheel angle at the rear wheels r as an input.
In addition to one or more of the features described herein, the processing circuitry determining the actuator dynamics u including the torque Mz includes the processing circuitry also determining the road wheel angle at the rear wheels δr.
In addition to one or more of the features described herein, the processing circuitry formulates a constraint to be solved with minimization of the cost function, wherein the constraint minimizes the actuator dynamics u.
In addition to one or more of the features described herein, the processing circuitry represents the cost function and the constraint as:
where
Rt and Qt are weights, utref is a reference provided by a supervisory algorithm, and yt-ytdes is the objective function with yt being a vector that is the function of the vehicle dynamics x and the actuator dynamics u and ytdes being a vector of desired values.
In addition to one or more of the features described herein, the vehicle dynamics x include lateral velocity Vy and yaw rate r.
In addition to one or more of the features described herein, the processing circuitry formulates the cost function as a quadratic programming problem.
In addition to one or more of the features described herein, the processing circuitry determining the actuator dynamics u for the next time sample includes the processing circuitry using a linear quadratic regulator as a solver for the quadratic programming problem.
In addition to one or more of the features described herein, the processing circuitry determining the actuator dynamics u includes using a feedforward and feedback algorithm.
The above features and advantages, and other features and advantages of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings.
Other features, advantages and details appear, by way of example only, in the following detailed description, the detailed description referring to the drawings in which:
The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features.
As previously noted, a driver of a heavier version of a given vehicle (e.g., battery hybrid as compared with gas-only version) may experience a relatively sluggish response. One manifestation of the sluggishness of the heavier version of the vehicle is a longer time between a torque input (e.g., driver depressing the accelerator pedal) and achieving the torque command. However, simply making the response time faster may lead to instability.
Embodiments of the systems and methods detailed herein relate to vehicle actuation commands to affect transient handling. Existing torque vectoring and active rear steering (ARS) mechanisms may be the vehicle actuation mechanisms for which commands are determined to improve the agility of a vehicle. Specifically, a cost function may be developed to formulate routine agility (RA) and routine stability (RS) metrics in terms of vehicle states. Minimizing the cost function results in a faster response balanced with a feeling of stability. Minimization of the cost function is used to determine the commands for the torque vectoring and, when available, ARS mechanisms.
In accordance with an exemplary embodiment,
A controller 110 is indicated in the vehicle 100. This controller 110 may be a collection of systems that together control different aspects of vehicle dynamics and operation. For example, the controller 110 may control the rear motors M-R1 and M-R2 that achieve the torque Mz that is determined according to the processes discussed with reference to
{dot over (x)}(t)=f(x(t),u(t)) [EQ. 1]
In EQ. 1, x(t) represents the vehicle dynamics and u(t) represents the actuator dynamics. To facilitate optimization, the representation is linearized such that the processes at block 210 include obtaining a linearized representation of a vehicle dynamics model that integrates actuator dynamics:
xt+1=Axt+But+Cδf [EQ. 2]
EQ. 2 includes the road wheel angle at the front wheels δf. In addition:
In EQ. 5, Vy is the lateral velocity and δr is the yaw rate. These indicate the current vehicle state. The road wheel angle at the rear wheels δr and the torque Mz, which are the actuator commands represented by u, as indicated at EQ. 6, are optimized as further discussed. While determination of both the road wheel angle at the rear wheels δr and the torque Mz is discussed herein for explanatory purposes, the road wheel angle at the rear wheels δr may be an input, as previously noted. In that case, only the torque Mz is the only one of the actuation commands u determined according to the processes discussed with reference to
At block 220, obtaining an objective function to enhance RA and RS refers to obtaining the function to be minimized. The objective function is a function of x and u (y(x, u)) and is given by yt-ytdes, where yt designates the predicted objective value and ytdes designates the desired objective value. At block 230, formulating the cost function and constraint results in the following:
The second part of EQ. 7, involving the objective function, can be regarded as the cost function portion (i.e., transient handling error), while the optional first part of EQ. 7 can be regarded as the constraint portion (i.e., control action error). The weights Rt and Qt facilitate control of how much each of the portions affects J. The optional first part of EQ. 7 limits the value of ut based on a reference utref that may be provided by a supervisory algorithm or system that supervises the transient handling. The supervisory algorithm may be implemented by the controller 110 like other aspects of the transient handling. Alternately, utref may be 0.
In addition to the constraint based on the reference utref, the actuator dynamics ut are also subject to the following constraints:
umin≤ut≤umax [EQ. 8]
Δumin≤Δut≤Δumax [EQ. 9]
And, the predicted objective value yt is subject to the following constraint:
ymin≤yt≤ymax [EQ. 10]
Each of the min and max values are calibration values that may be fixed or whose selection from tables may be updated based on actuator capacities, road condition, or vehicle status (e.g., yaw rate).
Table 1 details four components of yt and ytdes. That is, the objective function yt-ytdes is a 1-by-4 matrix. Based on the transpose, indicated by T, each part of EQ. 7 results in a single value.
In Table 1, Fy
In EQ. 11, m is mass, and in EQ. 12, Izz is moment of inertia around the z axis, which is perpendicular to the x and y axes indicated in
At block 240, the processes include minimizing the cost function to obtain control commands for vehicle actuation mechanisms. Any known procedure may be used to optimize the cost function to determine the actuator commands represented by u, as indicated at EQ. 6. That is:
ut(xt)argminutj(xt,yt) [EQ. 13]
The optimal solution ut for the current state xt may indicate the road wheel angle at the rear wheels δr and the torque Mz needed to execute the transient state of the vehicle 100 with agility. Once the actuation commands (e.g., δr, Mz) are obtained according to one or more embodiments, using the ARS functionality to achieve the road wheel angle at the rear wheels δr and controlling the motors M-R1 and M-R2 to achieve the torque Mz are known mechanisms. That is, when two rear motors M-R1 and M-R2 are present, as in the exemplary case, the rear motors M-R1 and M-R2 may be used to achieve torque Mz. For example, the torque Mz is given by:
In EQ. 14, TR1 and TR2 are the torques produced by the motors M-R1 and M-R2, respectively. Based on determining the torque Mz, the motors M-R1 and M-R2 may then be controlled to produce the necessary TR1 and TR2. According to alternate embodiments, other known mechanisms may be used to achieve the torque Mz, once it is determined according to one or more embodiments. An exemplary method 300 to obtain the optimal solution ut for the current state xt involves a quadratic programming (QP) solver, which is discussed with reference to
At block 320, obtaining a vehicle dynamics model with actuator dynamics integrated refers to obtaining the linearized representation set out as EQ. 2. With u selected, EQ. 2 may be solved for the current time sample t. The selected u (at block 310) is also provided to blocks 330 and 340. At block 330, the objective function based on y(x, u) may be determined according to the selection of u at block 310. The selected u (from block 310) and the objective function (from block 330) are provided to determine the result of the formulated cost function and constraint at block 340. As indicated in
While the above disclosure has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from its scope. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiments disclosed, but will include all embodiments falling within the scope thereof.
Number | Name | Date | Kind |
---|---|---|---|
4679809 | Ito | Jul 1987 | A |
4690431 | Ito | Sep 1987 | A |
6453226 | Hac | Sep 2002 | B1 |
8271175 | Takenaka | Sep 2012 | B2 |
8775006 | Moshchuk | Jul 2014 | B2 |
8903607 | Lee | Dec 2014 | B2 |
9037353 | Di Cairano | May 2015 | B2 |
9573623 | Moshchuk | Feb 2017 | B2 |
10937107 | Shalev-Shwartz | Mar 2021 | B2 |
10970790 | Shalev-Shwartz | Apr 2021 | B2 |
11052757 | Kasaiezadeh Mahabadi | Jul 2021 | B2 |
11097743 | Kasaiezadeh Mahabadi | Aug 2021 | B2 |
11164264 | Shalev-Shwartz | Nov 2021 | B2 |
11332152 | Moshchuk | May 2022 | B2 |
11416943 | Shalev-Shwartz | Aug 2022 | B2 |
11459029 | Ishihara | Oct 2022 | B2 |
11491970 | Nahidi | Nov 2022 | B2 |
20030042790 | Amberkar | Mar 2003 | A1 |
20040002795 | Tanimoto | Jan 2004 | A1 |
20040007412 | Neef | Jan 2004 | A1 |
20040158375 | Tsutsumi | Aug 2004 | A1 |
20040193345 | Chen | Sep 2004 | A1 |
20060061464 | Okada | Mar 2006 | A1 |
20060091727 | Motoyama | May 2006 | A1 |
20070256484 | Imanishi | Nov 2007 | A1 |
20080059021 | Lu | Mar 2008 | A1 |
20080059034 | Lu | Mar 2008 | A1 |
20080082246 | Brown | Apr 2008 | A1 |
20080086248 | Lu | Apr 2008 | A1 |
20080086251 | Lu | Apr 2008 | A1 |
20080100332 | Tracht | May 2008 | A1 |
20090024269 | Shorten | Jan 2009 | A1 |
20090024293 | Takenaka | Jan 2009 | A1 |
20100211261 | Sasaki | Aug 2010 | A1 |
20120173039 | Yokota | Jul 2012 | A1 |
20120253608 | Aoki | Oct 2012 | A1 |
20130179036 | Lee | Jul 2013 | A1 |
20130218396 | Moshchuk | Aug 2013 | A1 |
20140350818 | Obermuller | Nov 2014 | A1 |
20140371992 | Di Cairano | Dec 2014 | A1 |
20150298694 | Park | Oct 2015 | A1 |
20160200360 | Moshchuk | Jul 2016 | A1 |
20180265055 | Hasegawa | Sep 2018 | A1 |
20180356819 | Mahabadi | Dec 2018 | A1 |
20190235499 | Kazemi | Aug 2019 | A1 |
20190283746 | Shalev-Shwartz | Sep 2019 | A1 |
20190291728 | Shalev-Shwartz | Sep 2019 | A1 |
20190295179 | Shalev-Shwartz | Sep 2019 | A1 |
20190329768 | Shalev-Shwartz | Oct 2019 | A1 |
20200062069 | Sorniotti | Feb 2020 | A1 |
20200216085 | Bobier-Tiu | Jul 2020 | A1 |
20200317048 | Kasaiezadeh Mahabadi | Oct 2020 | A1 |
20210094577 | Shalev-Shwartz | Apr 2021 | A1 |
20210110483 | Shalev-Shwartz | Apr 2021 | A1 |
20210110484 | Shalev-Shwartz | Apr 2021 | A1 |
20210221386 | Quirynen | Jul 2021 | A1 |
20210245732 | Nahidi | Aug 2021 | A1 |
20210291801 | Westerhof | Sep 2021 | A1 |
20220258723 | Hu | Aug 2022 | A1 |
20220289288 | Hultén | Sep 2022 | A1 |
20230042818 | Moshchuk | Feb 2023 | A1 |
20230044869 | Asadi | Feb 2023 | A1 |
20230082309 | Boutron | Mar 2023 | A1 |
20230084461 | Koehler | Mar 2023 | A1 |
Number | Date | Country |
---|---|---|
1522915 | Aug 2004 | CN |
103978971 | Aug 2014 | CN |
110228462 | Sep 2019 | CN |
109204317 | May 2020 | CN |
109849899 | Jan 2021 | CN |
111497826 | Aug 2021 | CN |
113753080 | Dec 2021 | CN |
114889446 | Aug 2022 | CN |
3816486 | Dec 1988 | DE |
3905811 | Sep 1989 | DE |
3929994 | Mar 1990 | DE |
3932361 | Apr 1990 | DE |
10016343 | Oct 2000 | DE |
10015682 | Feb 2001 | DE |
10015682 | Feb 2007 | DE |
0278366 | Aug 1988 | EP |
0473112 | Mar 1992 | EP |
0476372 | Mar 1992 | EP |
3111617 | Dec 2021 | FR |
2520144 | Jul 1996 | JP |
2623940 | Jun 1997 | JP |
H107010 | Jan 1998 | JP |
2000289637 | Oct 2000 | JP |
3546423 | Jul 2004 | JP |
2014159269 | Sep 2014 | JP |
WO-2022113249 | Jun 2022 | WO |
Entry |
---|
“A Fault-Tolerant Integrated Vehicle Stability Control Using Adaptive Control Allocation;” Temiz et al.; ARXIV ID: 2008.05697; Aug. 13, 2020. (Year: 2020). |
“Investigating Functional Redundancies in the Context of Vehicle Automation—A Trajectory Tracking Perspective;” Stolt et al.; ARXIV ID: 1805.02028; May 5, 2018. (Year: 2018). |
“A model predictive control allocation approach to hybrid braking of electric vehicles;” Satzger et al. ; 2014 IEEE Intelligent Vehicles Symposium Proceedings (pp. 286-292); Aug. 1, 2014. (Year: 2014). |
“Integrated control of in-wheel-motored electric vehicles using a model predictive control method;” Ren et al.; Proceeding of the 11th World Congress on Intelligent Control and Automation (pp. 1676-1681); Jun. 1, 2014. (Year: 2014). |
Number | Date | Country | |
---|---|---|---|
20230044869 A1 | Feb 2023 | US |