Real-time machine learning and physics-based hybrid method for performing ELSD torque estimation

Abstract

A method for determining electronic limited slip differential (eLSD) clutch torque in real time includes receiving vehicle data in real time, where the vehicle data includes a torque request; determining an initial eLSD clutch torque using a neural network and the vehicle data; determining a clutch torque limit for the eLSD using a physics-based model; determining whether the initial eLSD clutch torque is outside a clutch torque limit of the eLSD; in response to determining that the initial eLSD clutch torque is outside a clutch torque limit of the eLSD, adjusting the initial eLSD clutch torque using the clutch torque limit to determine a final clutch torque of the eLSD; and commanding the eLSD in real-time to apply the final clutch torque to a clutch of the eLSD.

Description

Technical Field

[0001] The present disclosure relates to a real-time machine learning and physics-based hybrid approach for estimating eLSD clutch torque. Background Art

[0002] This section generally presents the background of the present disclosure. The extent to which the work performed by the currently named inventors described in this section and in various aspects of the specification is not an indication that it was prior art at the time of filing the application, and it is never explicitly or implicitly admitted that it is prior art to the present disclosure.

[0003] An electronic limited slip differential (eLSD) system includes a clutch. Some eLSD systems use a physics-based clutch torque estimation algorithm to estimate the clutch torque. However, when the eLSD system is in a locked state, the physics-based clutch torque estimation algorithm is not always accurate due to the inherent limitations of the physics-based model. In particular, the physics-based model makes certain assumptions, such as, the vehicle is on a high friction coefficient surface and is not experiencing a breakaway friction coefficient condition, which may lead to incorrect estimates under other conditions. These inaccuracies may cause the eLSD controller to confuse and cause incorrect actions to be taken. Therefore, it is necessary to consider the limitations of the physics-based model used in the clutch torque estimation algorithm in the locked state and adopt other methods to improve the accuracy of the estimation. Summary of the invention

[0004] The present disclosure.

[0005] The present disclosure also describes a vehicle including a sensor, a steer-by-wire system, and a controller in communication with the sensor and the steer-by-wire system. The controller is programmed to perform the above method. The present disclosure also describes a tangible, non-transitory machine-readable medium including machine-readable instructions, which, when executed by a processor, causes the processor to perform the above method.

[0006] Other application areas of the present disclosure will become apparent from the detailed description provided below.It should be understood that the detailed description and specific examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure.

[0007] The above features and advantages and other features and advantages of the systems and methods of the present disclosure will become apparent from the detailed description, including the claims and exemplary embodiments, when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present disclosure will be more fully understood from the detailed description and accompanying drawings, in which:

[0009] Figure 1 is a block diagram depicting an embodiment of a vehicle including an eLSD;

[0010] Figure 2 is a flow chart of a method for estimating clutch torque of an eLSD of a vehicle. DETAILED DESCRIPTION

[0011] Reference will now be made in detail to several examples of the present disclosure illustrated in the accompanying drawings. Whenever possible, the same or similar reference numerals are used in the drawings and the description to refer to the same or similar parts or steps.

[0012] refer to Figure 1 , the vehicle 10 generally includes a chassis 12, a body 14, front wheels and rear wheels 17, and may be referred to as a vehicle system. In the depicted embodiment, the vehicle 10 includes two front wheels 17a and two rear wheels 17b. The body 14 is disposed on the chassis 12 and substantially surrounds the components of the vehicle 10. The body 14 and the chassis 12 may together form a frame. The wheels 17 are each rotatably coupled to the chassis 12 near a respective corner of the body 14. The vehicle 10 includes a front axle 19 coupled to the front wheels 17a and a rear axle 25 coupled to the rear wheels 17b.

[0013] The vehicle 10 is an autonomous vehicle, and the control system 98 is incorporated into the vehicle 10. The system 98 may be referred to as a system. For example, the vehicle 10 is a vehicle that is automatically controlled to transport passengers from one location to another. Although the vehicle 10 is depicted as a pickup truck in the illustrated embodiment, it should be understood that other vehicles may also be used, including trucks, sedans, coupes, sport utility vehicles (SUVs), recreational vehicles (RVs), etc. In an embodiment, the vehicle 10 may include a so-called secondary, tertiary, tertiary or tertiary driving automation system. The tertiary system represents "high automation", which refers to the driving mode-specific performance of the autonomous driving system for all aspects of the dynamic driving task, even if the human driver does not respond appropriately to the intervention request. The tertiary system represents "full automation", which refers to the full-time execution of the autonomous driving system for all aspects of the dynamic driving task under a variety of road and environmental conditions that can be managed by the human driver. In a level 3 vehicle, the system 98 performs the entire dynamic driving task (DDT) within its designed area. In a level 2 vehicle, the system provides steering, braking / acceleration support, lane centering and adaptive cruise control. However, even when these systems are activated, the vehicle operator at the steering wheel must steer and constantly supervise the automated functions.

[0014] As shown, the vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. The steering system 24 is a wire-controlled steering system. In various embodiments, the propulsion system 20 may include an electric motor such as a traction motor and / or a fuel cell propulsion system. The vehicle 10 may also include a battery (or battery pack) 21 electrically connected to the propulsion system 20. Therefore, the battery 21 is configured to store electrical energy and provide electrical energy to the propulsion system 20. In certain embodiments, the propulsion system 20 may include an internal combustion engine. The transmission system 22 is configured to transmit power from the propulsion system 20 to the wheels 17 according to a selectable speed ratio. According to various embodiments, the transmission system 22 may include a stepped automatic transmission, a continuously variable transmission, or other suitable transmission. The braking system 26 is configured to provide braking torque to the wheels 17. In various embodiments, the braking system 26 may include a friction brake, a wire control brake, a regenerative braking system (such as a motor), and / or other suitable braking systems. The steering system 24 affects the position of the wheels 17 and may include a steering wheel 33. Although depicted as including a steering wheel 33 for purposes of illustration, in some embodiments contemplated within the scope of the present disclosure, the steering system 24 may not include a steering wheel 33.

[0015] The sensor system 28 includes one or more sensors 40 (i.e., sensing devices) that sense observable conditions of the external environment and / or the internal environment of the vehicle 10. The sensors 40 communicate with the controller 34 and may include, but are not limited to, one or more steering wheel sensors 45, one or more radars, one or more light detection and ranging (lidar) sensors, one or more proximity sensors, one or more odometers, one or more ground penetrating radar (GPR) sensors, one or more steering angle sensors, global navigation satellite system (GNSS) transceivers (e.g., one or more global positioning system (GPS) transceivers), one or more tire pressure sensors, one or more cameras 41 (such as eye trackers), one or more gyroscopes, one or more accelerometers, one or more inclinometers, one or more speed sensors, one or more ultrasonic sensors, one or more inertial measurement units (IMUs), one or more night vision devices, thermal imaging sensors, and / or other sensors. Each sensor 40 is configured to generate a signal indicative of a sensed observable condition of the external environment and / or the internal environment of the vehicle 10. Because the sensor system 28 provides data to the controller 34, the sensor system 28 and its sensors 40 are considered to be information sources (or simply sources).

[0016] The actuator system 30 includes one or more actuators 42 that control one or more vehicle features, such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may also include interior and / or exterior vehicle features, such as, but not limited to, doors, trunk, and cabin features, such as air, music, lighting, etc. The actuator 42 may be part of the steering system 24 and include one or more wheel actuators (RWA) and hand wheel actuators (HWA).

[0017] The data storage device 32 stores data for automatically controlling the vehicle 10. In various embodiments, the data storage device 32 stores a defined map of a navigable environment. In various embodiments, the defined map may be predefined by a remote system and obtained from the remote system. For example, the defined map may be assembled by a remote system and transmitted to the vehicle 10 (wirelessly and / or by wire) and stored in the data storage device 32. The data storage device 32 may be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system.

[0018] The vehicle 10 may also include one or more airbags 35 in communication with the controller 34 or another controller of the vehicle 10. The airbags 35 include inflatable airbags and are configured to transition between a stowed configuration and a deployed configuration to cushion the effects of external forces applied to the vehicle 10. The sensors 40 may include airbag sensors, such as an IMU, configured to detect external forces and generate signals indicating the magnitude of such external forces. The controller 34 is configured to command the deployment of the airbags 35 based on signals from one or more sensors 40, such as airbag sensors. Thus, the controller 34 is configured to determine when the airbags 35 have been deployed.

[0019] The controller 34 includes at least one processor 44 and a non-transitory computer-readable storage device or medium 46. The processor 44 can be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor in several processors associated with the controller 34, a semiconductor-based microprocessor (in a microchip or chipset), a macroprocessor, a combination thereof, or a device generally used to execute instructions. For example, the computer-readable storage device or medium 46 can include volatile and non-volatile storage in read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operating variables when the processor 44 is powered off. The computer-readable storage device or medium 46 can be implemented using a variety of storage devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or other electrical, magnetic, optical, or combination storage devices capable of storing data, some of which represent executable instructions for controlling the vehicle 10 by the controller 34. The controller 34 of the vehicle 10 may be referred to as a vehicle controller and may be programmed to perform the method 100 described in detail below. Figure 2 ).

[0020] The instructions may include one or more separate programs, each of which includes an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, execute logic, calculations, methods and / or algorithms for automatically controlling components of the vehicle 10, and generate control signals to the actuator system 30 to automatically control components of the vehicle 10 based on the logic, calculations, methods and / or algorithms. Although Figure 1 , a single controller 34 is shown, but embodiments of the vehicle 10 may include multiple controllers 34 that communicate via a suitable communication medium or combination of communication mediums and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control features of the vehicle 10. In various embodiments, one or more instructions of the controller 34 are embodied in the control system 98.

[0021] The vehicle 10 includes a user interface 23, which may be a touch screen in the instrument panel. The user interface 23 may include, but is not limited to, an alarm, such as one or more speakers 27 for providing audible sounds, tactile feedback in a vehicle seat or other object, one or more displays 29, one or more microphones 31, and / or other devices suitable for providing notifications to a vehicle user of the vehicle 10. The user interface 23 is in electronic communication with a controller 34 and is configured to receive input from a vehicle occupant 11 (e.g., a vehicle driver or a vehicle passenger). For example, the user interface 23 may include a touch screen and / or buttons configured to receive input from a vehicle occupant 11. Thus, the controller 34 is configured to receive input from a user via the user interface 23. The vehicle 10 may include one or more displays 29, which are configured to display information to a vehicle occupant 11 (e.g., a vehicle operator or a passenger) and may be a head-up display (HUD).

[0022] The communication system 36 communicates with the controller 34 and is configured to wirelessly transmit information to and from other remote vehicles 48, such as, but not limited to, other vehicles ("V2V" communication), infrastructure ("V2I" communication), systems of remote call centers (such as GENERAL MOTORS' ON-STAR), and / or personal electronic devices, such as mobile phones. In the present disclosure, the term "remote vehicle" refers to a vehicle, such as an automobile, that is configured to send one or more signals to the vehicle 10 but is not physically connected to the vehicle 10. In certain embodiments, the communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or by using cellular data communications. However, additional or alternative communication methods such as dedicated short-range communication (DSRC) channels are also considered within the scope of the present disclosure. A DSRC channel refers to a one-way or two-way short-range to medium-range wireless communication channel designed specifically for automotive use and a set of corresponding protocols and standards. Therefore, the communication system 36 may include one or more antennas and / or communication transceivers 37 for receiving and / or transmitting signals, such as collaborative sensing messages (CSM). The communication transceiver 37 may be considered a sensor 40. The communication system 36 is configured to wirelessly transmit information between the vehicle 10 and another vehicle. Additionally, the communication system 36 is configured to wirelessly transmit information between the vehicle 10 and infrastructure or other vehicles.

[0023] The vehicle 10 includes one or more electronically controlled limited slip differentials (eLSD) 39. The eLSD 39 includes a slip control algorithm that provides a controlled variable coupling of vehicle drive torque to two or more wheels using an electrically actuated clutch 43. Under normal driving conditions, the eLSD 39 acts as an open differential, distributing torque evenly to the axles. However, when a loss of traction is sensed at one output of the differential, the clutch 43 of the eLSD 39 can be activated through a feedback control loop to maintain a speed difference between the differential outputs, which will cause the wheels to rotate at their natural speed, thereby improving the stability and comfort of the vehicle 10.

[0024] Figure 2 1 is a flow chart of a method 100 for determining electronic limited slip differential (eLSD) clutch torque in real time. The method 100 begins at box 102. Box 102 requires the development of a physics-based model for estimating the clutch torque of the eLSD 39 when the eLSD 39 is locked and when the eLSD is unlocked. However, the physics-based clutch torque estimate for the unlocked state is more reliable than the physics-based clutch torque estimate for the locked state. For the unlocked state of the eLSD 39, the clutch torque is a function of the requested torque, the speed difference between the wheels 17, and the clutch torque capacity. For the unlocked state of the eLSD 39, the controller 34 can estimate the clutch torque of the eLSD 39 using the following equation:

[0025]

[0026] in:

[0027] T c_acct is the clutch torque of the eLSD 39 in the unlocked state;

[0028] T c_rqst is the requested torque;

[0029] τ c is the model time constant;

[0030] s is the Laplace domain;

[0031] p1 is the first model coefficient;

[0032] p2 is the second model coefficient;

[0033] (dω) is the speed difference between the rear wheels; and

[0034] p3 is the third model coefficient.

[0035] When the clutch 43 and wheel speeds of the eLSD 39 are the same, for the locked state of the eLSD 39, the controller 34 may estimate the clutch torque of the eLSD 39 using the following equation:

[0036]

[0037] T c_act =R eff,r (F xrl -F xrr )

[0038] in:

[0039] R eff,r is the effective rolling radius of the rear tire;

[0040] F xrl is the left rear wheel traction;

[0041] T r_act It is rear engine driven;

[0042] I wr is the moment of inertia of the right rear wheel; and

[0043] is the angular acceleration of the right rear wheel.

[0044] When both wheels 17 are within the linear region, the following equation can be used:

[0045]

[0046]

[0047] T c_act to actuate the clutch;

[0048] F xrr is the right rear wheel traction;

[0049] μ rl is the friction coefficient of the left rear wheel;

[0050] F zrl is the normal force of the left rear wheel;

[0051] μ rr is the friction coefficient of the right rear wheel;

[0052] F zrr is the normal force of the right rear wheel;

[0053] F xrl is the left rear wheel traction;

[0054] K norm is the longitudinal tire stiffness normalized by the normal force;

[0055] l w is the wheelbase of the vehicle 10;

[0056] r is the yaw rate of the vehicle 10;

[0057] R eff,r is the effective rolling radius of the rear tire; and

[0058] ω rr is the angular velocity of the right rear wheel.

[0059] When both wheels 17 are in the nonlinear region, the following equation can be used:

[0060]

[0061] in:

[0062] F xrl for left rear traction;

[0063] F xrr for right rear traction;

[0064] μ rl is the left rear friction coefficient;

[0065] μ rr is the friction coefficient of the right rear;

[0066] F zrl is the left rear normal force; and

[0067] F zrr is the right rear normal force.

[0068] When one wheel 17 is in the linear region, the following equation can be used:

[0069]

[0070] in:

[0071] F xrl for left rear traction;

[0072] F xrr for right rear traction;

[0073] μ rl is the left rear friction coefficient;

[0074] μ rr is the friction coefficient of the right rear;

[0075] F zrl is the left rear normal force; and

[0076] F zrr is the right rear normal force.

[0077] After executing box 102, the method 100 proceeds to box 104. Box 104 requires analyzing the vehicle data to understand which signals can be used to predict the clutch torque of the eLSD 39. Certain input features (i.e., vehicle data) are selected for offline training. In order to reduce the complexity of the neural network, different combinations of possible features are selected to train the neural network. The final set of input features is then selected based on the following criteria: (a) the performance of the neural network (i.e., the features that result in the most accurate eLSD clutch torque estimation are selected); (b) the difficulty of production model implementation (i.e., input features that are more difficult to implement in the production model are not selected as inputs to the neural network). After analyzing the vehicle data, the neural network only requires the following inputs for offline training and later real-time execution, namely: the torque clutch capacity of the eLSD 39, the speed of the left rear wheel of the vehicle 10, the speed of the right rear wheel 17 of the vehicle 10, the axle torque of the vehicle 10, the longitudinal speed of the vehicle 10, the yaw rate of the vehicle 10, the normal force of the left rear wheel 17 of the vehicle 10, and the normal force of the right rear wheel 17 of the vehicle 10. The method 100 then proceeds to block 106 .

[0078] In box 106, a neural network model for eLSD clutch torque estimation is developed using the previously discussed inputs. The neural network structure can be based on the complexity of the data and physical phenomena. Features (i.e., neural network inputs) significantly affect the output (i.e., eLSD clutch torque estimation). In box 106, the appropriate neural network type (e.g., feedforward, feedback, etc.) is evaluated and selected. In box 106, the size and complexity of the neural network is determined. Specifically, the minimum number of layers and neurons that can represent the data is selected. As a non-limiting example, the neural network may include two hidden layers and a switch activation function. There may be thirteen neurons in each layer. After the neural network is developed, the neural network can be trained offline. As a non-limiting example, 225 sets of vehicle data can be used to train the neural network. Vehicle data can be collected in many different driving scenarios when the vehicle 10 is running with a surface friction coefficient from 0 to 1. The training data can be divided into training data, validation data, and test data with five-fold cross-validation data. Since the accuracy of the sign is very important for eLSD control, the cost function of training the neural network is adjusted to include the sign of the clutch torque. The cost function of the neural network can be expressed as follows:

[0079]

[0080] in:

[0081] W is the cost function weight;

[0082] T C outputting clutch torque to the network; and

[0083] is the ground true clutch torque.

[0084] Thus, the neural network uses the vehicle data as input and outputs an initial eLSD clutch torque. The method 100 then continues to box 108 .

[0085] At box 108, a constraint model (i.e., clutch torque bounds) is employed to constrain the clutch torque estimation during the unlocked state to a physics-based model, and the clutch torque estimation during the locked state to a data-driven approach, with a seamless transition between the two. To ensure that the clutch torque estimation does not exhibit any erratic behavior when the neural network is exposed to information that has never been analyzed before, constraint logic is used. The constraint logic differs depending on whether the eLSD 39 is in a locked state or an unlocked state. When the eLSD 39 is in the unlocked state, the neural network is constrained by a physics-based estimate of unlocking, which can be represented by the following equation:

[0086] Max=Tc+margin

[0087] Min=Tc-margin

[0088] in:

[0089] Tc is the clutch torque capacity;

[0090] margin is the predetermined margin value;

[0091] Max is the maximum limit; and

[0092] Min is the minimum limit.

[0093] When the eLSD 39 is in a locked state, a physics-based estimate of locking is used to constrain the output of the neural network. The physics-based model calculates the maximum and largest possible eLSD clutch torque using the following equation:

[0094] T cest_max_final =R eff (F zr +F zl )

[0095] T cest_min_final =-R eff (F zr +F zl )

[0096] Max=Min(T cest_max ,T c )

[0097] Min=Max(T cest_max ,T c )

[0098] in:

[0099] R efr is the effective rolling radius of the tire;

[0100] F zl is the normal force of the left rear wheel;

[0101] F zr is the normal force of the right rear wheel;

[0102] Max is the maximum limit; and

[0103] Min is the minimum limit;

[0104] T c is the clutch torque capacity;

[0105] T cest_max is the maximum torque coefficient; and

[0106] T cest_min_final Minimum torque coefficient.

[0107] Therefore, the minimum limit and the maximum limit of the clutch torque limit are each a function of the torque clutch capacity of the clutch 43 of the eLSD 39. Next, the controller 34 determines whether the initial eLSD clutch torque determined using the neural network is outside the clutch torque limit of the eLSD. If the initial eLSD clutch torque determined by the neural network is outside the clutch torque limit, the controller 34 uses the clutch torque limit to adjust the initial eLSD clutch torque to determine the constrained clutch torque of the eLSD and ultimately determine the final clutch torque of the eLSD 39. For example, if the initial eLSD clutch torque is greater than the maximum limit, the constrained eLSD clutch torque is set to the maximum limit. If the initial eLSD clutch torque is less than the minimum limit, the constrained eLSD clutch torque is set to the minimum limit. If the initial eLSD clutch torque is within the clutch range, no adjustment is required. Then, the method 100 proceeds to box 110.

[0108] At box 110, a digital low pass filter is used to ensure that the final signal sent to the eLSD control algorithm does not contain a large amount of noise. The selection of the digital low pass filter coefficients must be able to reduce a sufficient amount of noise, but not introduce a large lag in the estimate. Optimization using algorithms such as pattern search has been shown to produce the best results for calculating the filter coefficients. In addition, there may be discontinuities in the clutch torque estimate signal between the unlocked constraint scheme and the locked constraint scheme. The eLSD 39 is in a transition state when the eLSD switches between the locked state and the unlocked state. When the eLSD switches between the locked and unlocked states, heavy filtering is added for a calibrable time. Therefore, the controller 34 determines whether the eLSD 39 is in a transition state. If the eLSD 39 is in a transition state, the controller 34 sets the time constant of the digital low pass filter to have a first predetermined value. If the LSD 39 is not in a transition state, the controller 34 sets the time constant of the digital low pass filter to have a second predetermined value. The first predetermined value is greater than the second predetermined value to filter out discontinuities in the clutch torque estimate signal when the eLSD 39 is in a transition state. The digital low-pass filter then outputs the final clutch torque of the eLSD 39. The controller 34 can then command the vehicle 10 to perform a control action in real time by considering the final clutch torque of the eLSD 39. For example, the controller 34 can command the eLSD 39 to apply the final clutch torque to the clutch 43 of the eLSD 49 in real time.

[0109] Although exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms covered by the claims. The words used in the specification are descriptive rather than restrictive, and it should be understood that various changes can be made without departing from the spirit and scope of the present disclosure. As previously mentioned, the features of various embodiments can be combined to form further embodiments of the system and method of the present disclosure that may not be explicitly described or shown. Although various embodiments have been described by providing advantages for one or more desired characteristics or being preferred over other embodiments or prior art implementations, it should be recognized by those of ordinary skill in the art that one or more features or characteristics can be compromised to achieve the desired overall system properties that depend on specific applications and implementations. These properties may include, but are not limited to, cost, strength, life, life cycle cost, marketability, appearance, packaging, size, serviceability, weight, production, ease of assembly, etc. Therefore, embodiments described as inferior to other embodiments or prior art implementations for one or more characteristics are not outside the scope of the present disclosure and can be expected to be used for specific applications.

[0110] The drawings are in simplified form and not drawn to exact scale. Directional terms such as top, bottom, left, right, upward, over, above, below, behind, and in front may be used with respect to the drawings for convenience and clarity only. These and similar directional terms should not be construed to limit the scope of the present disclosure in any way.

[0111] Embodiments of the present disclosure are described herein. However, it should be understood that the disclosed embodiments are merely examples, and other embodiments may take various alternative forms. The drawings are not necessarily drawn to scale; certain features may be enlarged or minimized to show the details of specific components. Therefore, the specific structural and functional details disclosed herein should not be interpreted as restrictive, but merely as a representative basis for teaching those skilled in the art to adopt the systems and methods of the present disclosure in different ways. As will be understood by those of ordinary skill in the art, the various features shown and described with reference to any of the accompanying drawings may be combined with the features shown in one or more other drawings to produce embodiments that are not explicitly shown or described. The combination of features shown provides representative embodiments of typical applications. However, various combinations and modifications of features consistent with the teachings of the present disclosure may be required for specific applications or implementations.

[0112] Embodiments of the present disclosure may be described herein in terms of functional and / or logical block components and various processing steps. It should be understood that such block components may be implemented by any number of hardware, software, and / or firmware components configured to perform a specified function. For example, embodiments of the present disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of the present disclosure.

[0113] For the sake of brevity, this document may not describe in detail the technology related to signal processing, data fusion, signaling, control and other functional aspects of the system (and the various operating components of the system). In addition, the connecting lines shown in the various figures included herein are intended to represent example functional relationships and / or physical connections between various elements. It should be noted that there may be alternative or additional functional relationships or physical connections in the embodiments of the present disclosure.

[0114] This description is merely illustrative in nature and is in no way intended to limit the present disclosure, its application or use. The broad teachings of the present disclosure can be implemented in various forms. Therefore, although the present disclosure includes specific examples, the true scope of the present disclosure should not be limited thereto, because other modifications will become apparent after studying the drawings, the specification and the appended claims.

Claims

1. A method for determining electronic limited slip differential (eLSD) clutch torque in real time, comprising: receiving vehicle data in real time, wherein the vehicle data includes a torque request; determining an initial eLSD clutch torque using the neural network and the vehicle data; determining a clutch torque limit for the eLSD using a physics-based model; determining whether the initial eLSD clutch torque is outside the clutch torque limit of the eLSD; In response to determining that the initial eLSD clutch torque is outside the clutch torque limit of the eLSD, adjusting the initial eLSD clutch torque using the clutch torque limit to determine a final clutch torque of the eLSD; as well as The eLSD is commanded in real time to apply the final clutch torque to a clutch of the eLSD. 2 . The method of claim 1 , further comprising filtering the final clutch torque using a digital filter, wherein the digital filter has a time constant.

3. The method according to claim 2, further comprising: determining whether the eLSD is in a transition state, wherein the eLSD is in the transition state when the eLSD switches between a locked state and an unlocked state; as well as In response to determining that the eLSD is in the transition state, the time constant is set to a first predetermined value. 4 . The method of claim 3 , further comprising setting the time constant to a second predetermined value in response to determining that the eLSD is not in the transition state, wherein the first predetermined value is greater than the second predetermined value.

5. The method of claim 4, wherein the vehicle data includes a torque clutch capacity of the vehicle eLSD, a vehicle left wheel speed, a vehicle right wheel speed, a vehicle shaft torque, a vehicle longitudinal speed, a vehicle yaw rate, a vehicle left wheel normal force, and a vehicle right wheel normal force, and the method further comprises determining the initial eLSD clutch torque using the neural network and using only the vehicle eLSD torque clutch capacity, the vehicle left wheel speed, the vehicle right wheel speed, the vehicle shaft torque, the vehicle longitudinal speed, the vehicle yaw rate, the vehicle left wheel normal force, and the vehicle right wheel normal force.

6. The method of claim 5, wherein the clutch torque limits of the eLsSD include a maximum limit and a minimum limit, and each of the maximum limit and the minimum limit is a function of the clutch torque capacity of the eLSD.

7. The method of claim 6, wherein the neural network is trained offline.

8. A vehicle comprising: a plurality of sensors, each sensor configured to generate vehicle data; electronic limited-slip differential (eLSD); a controller in communication with the plurality of sensors and the eLSD, wherein the controller is programmed to: receiving vehicle data in real time, wherein the vehicle data includes a torque request; determining an initial eLSD clutch torque using the neural network and the vehicle data; determining a clutch torque limit for the eLSD using a physics-based model; determining whether the initial eLSD clutch torque exceeds the clutch torque limit of the eLSD; in response to determining that the eLSD clutch torque is outside the clutch torque limit of the eLSD, adjusting the initial eLSD clutch torque using the clutch torque limit to determine a final clutch torque of the eLSD; as well as The eLSD is commanded in real time to apply the final clutch torque to a clutch of the eLSD. 9 . The vehicle of claim 8 , wherein the controller is programmed to filter the final clutch torque using a digital filter, wherein the digital filter has a time constant.

10. The vehicle of claim 9, wherein the controller is programmed to determine whether the eLSD is in a transition state, wherein the eLSD is in the transition state when the eLSD switches between a locked state and an unlocked state.

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