DE102023005283A1 - System and method for torque management in a vehicle - Google Patents

Abstract

The present disclosure provides a system and method for torque management in a vehicle. It includes detecting (402) the presence of one or more anomalies on a road on which the ego vehicle is traveling; determining (404) real-time parameters of the ego vehicle; and calculating (406) the required target torque for one or more wheels of the ego vehicle to overcome the detected one or more anomalies based on the determined real-time parameters. It also includes controlling (408) the operation of one or more motors associated with the one or more wheels of the ego vehicle; wherein at least one of the one or more motors associated with the one or more wheels of the ego vehicle approaching the detected one or more anomalies is deactivated and the torque of the remaining motors is increased by a corresponding amount to enable the ego vehicle to efficiently traverse the detected one or more anomalies.

Description

The present disclosure relates to the field of advanced driver assistance systems (ADAS). More particularly, the present disclosure provides a system and method for controlling torque and improving the efficiency of a power source in a vehicle.

While driving on a track/road, the vehicle will pass through various anomalies on the track, including uneven surface conditions, speed bumps, potholes, etc. When passing through the anomalies, the speed of the vehicle will be reduced and the torque of the axle passing over the anomaly will be immediately reduced, which may affect the efficiency of the vehicle. In addition, sudden changes in driving behavior may also have a negative impact on the battery health and power consumption of the vehicle.

If the vehicle gets stuck in the anomaly, such as a pothole, then it takes a lot of time and energy/power for the vehicle to get out of the pothole safely. A driver moves the vehicle forward and backward several times, applies brakes and accelerator, and tries various ways to move the vehicle out of the pothole, which may also affect the vehicle's efficiency, battery health, and power consumption.

Many techniques have been developed to circumvent the problems mentioned above, for example, the patent document US10821968B2 a driving control method including performing road surface adaptation control in which, when an uneven road surface of a road on which a vehicle is driven is detected, wheel torque control of the vehicle is performed so that a squat effect and a dive effect are generated in the vehicle traveling over the uneven road surface. The performance of the road surface adaptability control includes: performing the road surface adaptability determination in which a road image of the road is photographed and the uneven road surface is detected in the road image. Further, the wheel torque control is performed based on the wheel torque so that the squat effect and the dive effect are generated in the vehicle while the vehicle approaches and travels over the uneven road surface to change the vehicle speed of the vehicle. The wheel torque control sets an acceleration of the vehicle to a value of zero at a half point of the road unevenness, which is converted from the squat effect to the dive effect.

The patent document US7152707B2 relates to a driving force control device and method for an automotive vehicle. It is an internal combustion engine that rotates the main drive wheels of the vehicle by the engine torque, a generator driven by the engine; a motor driven by the generator to rotate the auxiliary drive wheels of the vehicle by engine torque in a four-wheel drive state, a detection section for detecting a vehicle start failure in which the vehicle has not started in response to a vehicle start operation in the four-wheel drive state, and an engine torque characteristic changing section for changing a target torque characteristic of the engine to a higher level to increase the engine torque upon detection of the vehicle start failure.

However, the cited documents disclose various driving control techniques. However, the documents do not disclose a torque control algorithm that delivers a small torque pulse to the wheel of the vehicle stuck in the anomaly for each small change in the steering angle from the reference steering angle. Furthermore, the cited documents did not disclose the deactivation of the motor connected to an axle approaching the anomaly and the transmission of the torque.

A general object of the present disclosure is to provide an efficient and reliable system and method that eliminates the above-mentioned limitations of existing systems and methods and facilitates torque management in the vehicle.

An object of the present disclosure is to provide a system and method equipped with a torque control algorithm that delivers a small torque pulse to the wheel of the vehicle stuck in the anomaly for every small steering angle change from the reference steering angle.

Another object of the present disclosure is to provide a system and method that disables the motor associated with an axle that is about to approach the anomaly.

Another object of the present disclosure is to provide a system and method that transfers torque from the deactivated motor to the remaining motors.

Another object of the present disclosure is to provide a system and method that improves vehicle battery life.

Yet another object of the present disclosure is to provide a system and method that optimizes vehicle efficiency.

Yet another object of the present disclosure is to provide an intelligent and intelligent system and method that facilitates torque management in the vehicle along with energy conservation.

Aspects of the present disclosure relate to the field of advanced driver assistance systems (ADAS). In particular, the present disclosure provides a system and method for controlling torque and improving the efficiency of a power source in a vehicle.

One aspect of the present disclosure relates to a torque management system implemented in an ego vehicle. The system includes a controller comprising a processor coupled to a memory, the memory storing one or more instructions executable by the processor to: detect the presence of one or more anomalies on a road on which the ego vehicle is traveling; determine real-time parameters of the ego vehicle; calculate, based on the determined real-time parameters, the required target torque for one or more wheels of the ego vehicle to overcome the detected one or more anomalies; and control operation of one or more motors associated with the one or more wheels of the ego vehicle; wherein at least one of the one or more motors associated with the one or more wheels of the ego vehicle approaching the detected one or more anomalies is deactivated and the torque of the remaining motors is increased by a corresponding amount to enable the ego vehicle to efficiently traverse the detected one or more anomalies.

In one aspect, in the event that the vehicle becomes stuck in an anomaly, the controller may initiate a series of signals corresponding to a predefined change in steering angle with respect to a reference angle, wherein the predefined change in steering angle may result in pulses of positive and negative torque across the wheels stuck in the anomaly to enable the vehicle to overcome the anomaly.

In another aspect, the controller may be operatively coupled to a learning machine, wherein the learning machine provides data relating to a controlled change in steering angle and a controlled application of the accelerator pedal of the ego vehicle based on a training and test data set associated with the learning machine.

In one aspect, the system may be configured to: track the motion and parameters of an object vehicle running in front of the ego vehicle; detect the anomaly based on the tracked motion of the object vehicle; process the tracked parameters of the object vehicle as it crosses/overcomes the anomaly; and, by mapping the processed parameters with the parameters of the ego vehicle, trigger the set of signals related to the change in steering angle and the operation of the accelerator pedal to enable the ego vehicle to cross/overcome the anomaly.

In another aspect, the system may trigger an alarm upon detection of the anomaly, and wherein the system may calculate a translational torque and suggest an acceleration and a change in steering angle accordingly, and may also provide a suggestive crossing of torque distribution across various axles coupled to the one or more wheels required to cross/overcome the anomaly.

Another aspect of the present disclosure relates to a torque management method implemented in an ego vehicle. The method includes: detecting, at a controller, the presence of one or more anomalies on a road on which the ego vehicle is traveling; determining, at the controller, real-time parameters of the ego vehicle; calculating, at the controller, the required target torque for one or more wheels of the ego vehicle to overcome the detected one or more anomalies based on the determined real-time parameters; and controlling, by the controller, operation of one or more motors associated with the one or more wheels of the ego vehicle; wherein at least one of the one or more motors associated with the one or more wheels of the ego vehicle approaching the detected one or more anomalies is deactivated and the torque of the remaining motors is increased by an appropriate value to enable the ego vehicle to efficiently cross the detected one or more anomalies.

In one aspect, in the event that the vehicle becomes stuck in an anomaly, the method may include triggering a set of signals corresponding to a predefined change in steering angle with respect to a reference angle, wherein the predefined change in steering angle may result in pulses of positive and negative torque across the wheels stuck in the anomaly to enable the vehicle to overcome the anomaly.

In another aspect, the method may include providing data relating to a controlled change in steering angle and controlled actuation of the accelerator pedal of the ego vehicle, wherein the data may be provided based on a training and test data set associated with the learning machine.

In another aspect, the method may include tracking movements and parameters of an object vehicle running in front of the ego vehicle; detecting the anomaly based on the tracked movement of the object vehicle; processing the tracked parameters of the object vehicle while crossing/overcoming the anomaly; and triggering, by mapping the processed parameters with the parameters of the ego vehicle, the set of signals related to the change in steering angle and the operation of the accelerator pedal to enable the ego vehicle to cross/overcome the anomaly.

In another aspect, the method may comprise triggering an alarm upon detection of the anomaly, and wherein the method may also comprise calculating the translational torque, and accordingly suggesting an acceleration, a change in steering angle and also providing a suggestive override of the torque distributed over different axes coupled to the one or more wheels required to cross/overcome the anomaly.
Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments taken in conjunction with the accompanying drawing figures in which like numerals represent like parts.

• 1 veranschaulicht eine beispielhafte Netzwerkarchitektur des vorgeschlagenen Drehmomentmanagementsystems zur Verbesserung der Effizienz des Fahrzeugs, um seine Gesamtfunktionsweise gemäß einer Ausführungsform der vorliegenden Offenbarung zu veranschaulichen.
• 2 zeigt beispielhafte Funktionseinheiten eines Controllers, der mit dem vorgeschlagenen System verbunden ist, in Übereinstimmung mit einer beispielhaften Ausführungsform der vorliegenden Offenbarung.
• Die 3A und 3B veranschaulichen die Funktionsweise des vorgeschlagenen Systems in unterschiedlichen Szenarien in Übereinstimmung mit einer beispielhaften Ausführungsform der vorliegenden Offenbarung.
• 4 ist ein Flussdiagramm, das die Schritte darstellt, die an dem vorgeschlagenen Drehmomentmanagementverfahren gemäß einer Ausführungsform der vorliegenden Offenbarung beteiligt sind.
• 5 veranschaulicht ein beispielhaftes Computersystem, in dem oder mit dem Ausführungsformen der vorliegenden Erfindung in Übereinstimmung mit Ausführungsformen der vorliegenden Offenbarung verwendet werden können.

The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

• 1 illustrates an exemplary network architecture of the proposed torque management system for improving vehicle efficiency to illustrate its overall functionality according to an embodiment of the present disclosure.
• 2 shows exemplary functional units of a controller connected to the proposed system, in accordance with an exemplary embodiment of the present disclosure.
• The 3A and 3B illustrate the operation of the proposed system in different scenarios in accordance with an exemplary embodiment of the present disclosure.
• 4 is a flowchart illustrating the steps involved in the proposed torque management method according to an embodiment of the present disclosure.
• 5 illustrates an exemplary computer system in or with which embodiments of the present invention may be used in accordance with embodiments of the present disclosure.

Following is a detailed description of embodiments of the disclosure illustrated in the accompanying drawings. The embodiments are detailed enough to clearly communicate the disclosure. However, the level of detail offered is not intended to limit the expected variations of embodiments; on the contrary, the intent is to cover all modifications, equivalents, and alternatives that fall within the spirit and scope of the present disclosures as defined by the appended claims.

Embodiments discussed herein relate to the field of advanced driver assistance systems (ADAS). In particular, the present disclosure provides a system and method for controlling torque and improving the efficiency of an energy source in a vehicle.

Referring to FIG. 1, an exemplary network architecture of the proposed system 100 (referred to interchangeably herein as system 100) is shown, where the system 100 may be implemented in a vehicle (also referred to as an ego vehicle) to control torque and improve the efficiency of a power source in the ego vehicle.

According to an embodiment, the system 100 may include a monitoring unit 102 configured to identify the presence of an object within an area of interest (AOL) from the current position of the ego vehicle. In an exemplary embodiment, the monitoring unit 102 may include a plurality of different sensors positioned at predefined positions on the ego vehicle and configured to detect the presence of the object within the AOL. In one case, the monitoring unit 102 may be equipped with sensors including but not limited to proximity sensor, RADAR, camera, and LiDAR. In another case, the object may include object vehicles, obstacles, and people.

According to one embodiment, the monitoring unit 102 may detect various anomalies on the route, including uneven surface conditions such as broken roads, dirt roads, speed bumps, potholes, crowds, and the like.

According to an embodiment, the system 100 may include a controller 106 that may be in communication with the monitoring unit 102. The controller 106 may also be operatively coupled to a learning engine 104, wherein the learning engine 104 may be equipped with a CNN (Convolutional Neural Network) architecture and/or a RNN (Recurrent Neural Network) architecture. Furthermore, the learning engine 104 may perform its operations using random forest and support vector techniques.
In one embodiment, the controller 106 may detect, via the monitoring unit 102, the presence of one or more anomalies on a road on which the ego vehicle is traveling. In another embodiment, the controller 106 may receive the detected one or more anomalies from the monitoring unit 102.

In one embodiment, the controller 106 may determine real-time parameters of the ego vehicle, where the determined parameters may include, among other things, the type of vehicle, the speed, acceleration, de-acceleration of the vehicle, and the distance of the vehicle from the detected one or more anomalies. In another embodiment, the controller 106 may calculate the required target torque for one or more wheels of the ego vehicle to overcome the detected one or more anomalies, where the controller 106 may calculate the required target torque based on the determined real-time parameters.

Further, based on the calculated target torque, the controller 106 may control operation of one or more motors associated with the one or more wheels of the ego vehicle. In one implementation, at least one of the one or more motors associated with the one or more wheels of the ego vehicle approaching the detected one or more anomalies is deactivated and the torque of the remaining motors is increased by an appropriate amount to enable the ego vehicle to efficiently traverse the detected one or more anomalies.

In one embodiment, the system 100 may not be able to detect the one or more anomalies due to poor visibility conditions due to heavy rain, water-clogged roads, during the night, in foggy situations, and the like. This may result in the ego vehicle getting stuck in the anomalies, e.g., a pothole, on the road. In the scenario, the controller 106 may trigger a set of signals corresponding to a predefined change in steering angle with respect to a reference angle, such that the predefined change in steering angle results in pulses of positive and negative torque across the wheels stuck in the anomaly to enable the vehicle to overcome the anomaly.

In another embodiment, in the scenario, the learning engine 104 may provide data relating to a controlled change in steering angle and a controlled application of the accelerator pedal of the ego vehicle based on a training and test data set associated with the learning engine 104. The training and test data set may include data relating to previous such scenarios in which the ego vehicle got stuck in the anomaly and the manner in which it got out of the anomaly. In an exemplary embodiment, the data relating to refer to previous scenarios, provide a change in the steering angle, amount of acceleration/deceleration and braking application required in the previous scenarios.

In yet another embodiment, when the system 100 is unable to detect the one or more anomalies due to poor visibility conditions, the monitoring unit 102 may be configured to track the motion and parameters of an object vehicle traveling in front of the ego vehicle. According to one embodiment, the system 100 may detect an anomaly based on the tracked motion of the object vehicle. The system 100 may also process the tracked parameters of the object vehicle as it crosses/overcomes the anomaly. Further, the system 100 may map the processed parameters to the parameters of the ego vehicle, for example, the system 100 may compare the type of the object vehicle with that of the ego vehicle and accordingly process and convert the speed and acceleration of the object vehicle for the ego vehicle. Furthermore, the system 100 may trigger the set of signals related to the change in steering angle and the operation of the accelerator pedal to allow the ego vehicle to cross/overcome the anomaly, wherein the change in steering angle and the operation of the accelerator pedal are calculated taking into account the mapping of the processed parameters with the parameters of the ego vehicle.

In one embodiment, the system 100 may trigger an alarm upon detection of the anomaly, wherein the alarm may be in the form of an alarm, an audible signal that may be displayed on a display/display screen 108 of the ego vehicle, or an alarm text, audio-visual message, or email that may be transmitted to a mobile computing device, such as a smartphone or laptop of a user connected to the vehicle.

According to one embodiment, the system 100 may also be configured to calculate a translational torque and suggest an acceleration and a change in steering angle accordingly, and may also provide a suggestive pass of torque split across different axles coupled to the one or more wheels required to cross/overcome the anomaly. In an exemplary embodiment, the suggestion could be displayed on the ego vehicle's display screen 108 or transmitted as a text/audio-visual message or email to the user's mobile computing device.

According to one embodiment, the controller 106 may communicate with the monitoring unit 102, the learning engine 104, and the display 108 via a network 110. Further, the network 110 may be a wireless network, a wired network, or a combination thereof, which may be implemented as one of various types of networks, such as an intranet, local area network (LAN), wide area network (WAN), Internet, and the like. Further, the network 110 may be either a dedicated network or a shared network. The shared network may represent an association of various types of networks that may use a variety of protocols, e.g., Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.

In one embodiment, the controller 106 may be implemented using any one or a combination of hardware components and software components, such as a cloud, a server 112, a computer system, a computing device, a network device, and the like. Further, the controller 106 may interact with the monitoring unit 102, the learning engine 104, and the display 108 via a website or application that may reside in the proposed system 100. In one implementation, the system 100 may be accessed via a website or application that may be configured with any operating system, including but not limited to Android™, iOS™, and the like.

Referring to FIG. 2, block diagram 200 illustrates example functional units of controller 106, which may include one or more processors 202. The one or more processors 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuits, and/or any devices that manipulate data based on operating instructions. Among other capabilities, the processor(s) 202 are configured to fetch and execute computer-readable instructions stored in a memory 204 of controller 106. Memory 204 may store one or more computer-readable instructions or routines that may be retrieved and executed to create or share the data units over a network service. The memory 204 may include any non-transitory storage device, including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.

According to one embodiment, the controller 106 may also include one or more interfaces 206. The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the monitoring device 206 with various devices coupled to the controller 106. The interface(s) 206 may also provide a communication path for one or more components of the controller 106. Examples of such components include, but are not limited to, the processing engine(s) 208 and the database 210.

According to one embodiment, the processing engine(s) 208 may be implemented as a combination of hardware and programming (e.g., programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In the examples described herein, such combinations of hardware and programming may be implemented in a variety of ways. For example, the programming for the processing engine(s) 208 may consist of executable processor instructions stored on a non-transitory machine-readable storage medium, and the hardware for the processing engine(s) 208 may include a processing resource (e.g., one or more processors) to execute such instructions.

In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 208. In such examples, the controller 106 may include the machine-readable storage medium storing the instructions and the processing resource for executing the instructions, or the machine-readable storage medium may be separate but accessible to the system 100 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by an electronic circuit. The database 210 may contain data that is either stored or generated as a result of functionality implemented by one of the components of the processing engine(s) 208. According to one embodiment, the processing engine(s) 208 may include an anomaly detection unit 212, a torque calculation unit 214, a command unit 216, and other units 218. Furthermore, the other unit(s) 218 may implement functionalities that complement applications/functions executed by the controller 106.

According to one embodiment, the anomaly detection unit 212 may detect various anomalies on the route, including uneven surface conditions such as broken road, dirt road, speed bumps, potholes, crowd, and the like. According to one embodiment, the anomaly detection unit 212 may detect, via the monitoring unit 102, the presence of one or more anomalies on a road on which the ego vehicle is traveling. In another embodiment, the anomaly detection unit 212 may receive the detected one or more anomalies from the monitoring unit 102.

According to another embodiment, the torque calculation unit 214 may further determine real-time parameters of the ego vehicle, wherein the determined parameters may include, among others, the type of vehicle, the speed, the acceleration, the deceleration of the vehicle, and the distance of the vehicle from the detected one or more anomalies. Furthermore, the torque calculation unit 214 may calculate a required target torque for one or more wheels of the ego vehicle to overcome the detected one or more anomalies, wherein the required target torque may be calculated based on the determined real-time parameters.

According to yet another embodiment, the control unit 216 may control operation of one or more motors associated with the one or more wheels of the ego vehicle based on the calculated desired torque. In one embodiment, the command unit 216 may deactivate at least one of the one or more motors associated with the one or more wheels of the ego vehicle that approach the detected one or more anomalies, and may correspondingly increase the torque of the remaining motors by an appropriate amount to enable the ego vehicle to efficiently traverse the detected one or more anomalies.

In one embodiment, the system 100 may not be able to detect the one or more anomalies due to poor visibility conditions caused by heavy rain, water congested roads, during the night, in foggy situation and the like. This may result in the ego vehicle getting stuck in the anomalies, e.g. a pothole, on the road. In the scenario, the control unit 216 may trigger a set of signals corresponding to a predefined change in the steering angle with respect to a reference angle, such that the predefined change in the steering angle results in pulses of positive and negative torque across the wheels stuck in the anomaly to enable the vehicle to overcome the anomaly.

In another embodiment, in the scenario, the learning engine 104 may provide data relating to a controlled change in steering angle and a controlled application of the accelerator pedal of the ego vehicle based on a training and test data set associated with the learning engine 104. The training and test data set may include data relating to previous such scenarios in which the ego vehicle got stuck in the anomaly and the manner in which it got out of the anomaly. In an exemplary embodiment, the data relating to previous scenarios may provide a change in steering angle, amount of acceleration/acceleration, and brake application required in the previous scenarios.

In yet another embodiment, when the system 100 is unable to detect the one or more anomalies due to poor visibility conditions, the monitoring unit 102 may be configured to track the motion and parameters of an object vehicle traveling in front of the ego vehicle. According to one embodiment, the system 100 may detect an anomaly based on the tracked motion of the object vehicle. The system 100 may also process the tracked parameters of the object vehicle as it crosses/overcomes the anomaly. Further, the system 100 may map the processed parameters to the parameters of the ego vehicle, for example, the system 100 may compare the type of the object vehicle with that of the ego vehicle and accordingly process and convert the speed and acceleration of the object vehicle for the ego vehicle. Furthermore, the system 100 may trigger the set of signals related to the change in steering angle and the operation of the accelerator pedal to allow the ego vehicle to cross/overcome the anomaly, wherein the change in steering angle and the operation of the accelerator pedal are calculated taking into account the mapping of the processed parameters with the parameters of the ego vehicle.

In one embodiment, the command unit 216 may trigger an alarm when the anomaly is detected, wherein the warning may be in the form of an alarm, an audible signal that may be displayed on a display/display screen 108 of the ego vehicle, or an alert text/audio-visual message or email that may be transmitted to a mobile computing device, such as a smartphone or laptop of a user connected to the vehicle.

According to one embodiment, the system 100 may also be configured to calculate a translational torque and suggest an acceleration and a change in steering angle accordingly, and may also provide a suggestive pass of torque split across different axles coupled to the one or more wheels required to cross/overcome the anomaly. In an exemplary embodiment, the suggestion could be displayed on the ego vehicle's display screen 108 or transmitted as a text/audio-visual message or email to the user's mobile computing device.

Referring to Figure 3A, the operation of the proposed system 100 can be observed in different scenarios. At block 302, brake pedal, accelerator pedal (also referred to herein as accpedal), accelerometer, and speed data is extracted by the system 100. At block 304, the system 100 checks whether the accelerometer value exceeds a threshold or whether the brake pedal (also referred to herein as brake pedal) is frequently pressed. In one embodiment, if the accelerometer value does not exceed the threshold or the brake pedal is not frequently pressed, then the system 100 does not process the data and discards it at block 306.

In another embodiment, if it is determined that the accelerometer reading exceeds the threshold or the brake pedal is frequently pressed, the system 100 may classify the vehicle type by processing the data at block 308. Further, at block 310, the system 100 may filter the data using a classifier and send it for further processing, where the filtered data may be used to match vehicle/vehicle type at block 312. In addition, the system 100 may determine acceleration from the filtered data. Extract speedometer data in block 314, brake pedal data in block 316, and acceleration data in block 318.

In one embodiment, the accelerometer data, the brake pedal data, and the acceleration data may be simultaneously transmitted to block 320, where the classifier is used to perform statistical techniques/methods, including random forest, support vector, and the like, and road anomalies may be detected accordingly, where the detected road anomalies may be potholes 322, speed breakers 324, and jaywalkers 326.

According to one embodiment, at block 328, if the detected road anomaly is classified as potholes 322, the system 100 again checks whether the accelerometer reading exceeds a threshold or the brake pedal is frequently pressed. If so, then it goes to step 1. Otherwise, it goes to step 2 (step 1 and step 2 are detailed in FIG. 3B).

In another embodiment, if the detected road anomaly is classified as a speed breaker 324, the system 100 checks at block 330 whether the ego vehicle has crossed the speed breaker bump. If so, then it goes to step 2. Otherwise, it goes to step 1.

In another embodiment, if the detected road anomaly is classified as a jaywalker 326, the system 100 checks whether the ego vehicle is approaching jaywalkers at block 332. If so, then it goes to step 2. Otherwise, it goes to step 3 (detailed in FIG. 3B).

Referring to Figure 3B, in step 1, the system 100 may first calculate the target torque (also referred to herein as trq) at the wheels of the ego vehicle at block 336. Further, at block 338, it is confirmed whether the override request is accepted. If accepted, then identification of the affected axle and wheel is performed at block 340, and the corresponding torque split is performed at block 342. However, in the event that the override request is not accepted, torque conversion and suggestive dynamics are provided at block 344. In another embodiment, based on the target torque calculated at block 336, drive profile and NVH (Noise, Vibration, and Harshness) data is recorded at block 346, which is further stored in a central storage, such as a cloud app, at block 348. In an exemplary embodiment, data of the ego vehicle, including vehicle speed (also referred to as Veh Spd), state of charge (SoC), and NVH may be stored on the central storage. In addition, it corresponds to the drive profile and NVH data recorded at block 346. The system 100 may switch operation to all-electric or activate only one motor (if multiple motors are present) and may also propose to be mounted on the dashboard of the ego vehicle at block 352 and may also be recorded at block 346.

In one embodiment, in step 2 at block 350, the system 100 may check if any road anomaly is approaching the ego vehicle. If so, then the system 100 may switch operation to all-electric or activate only one motor and may also suggest being mounted on the dashboard of the ego vehicle at block 352. Otherwise, at block 354, the system 100 may retrieve inputs from the cloud to provide information (intelligence) about the next anomaly, if any.

In another embodiment, the system 100 may retrieve inputs directly from the cloud in step 3 to provide information about the next anomaly at block 354.

Referring to FIG. 4, the proposed torque management method 400 (also referred to herein as method 400) may be implemented in an ego vehicle. The method 400 may include the step 402 of detecting, at a controller, the presence of one or more anomalies on a road on which the ego vehicle is traveling.

In one embodiment, the method 400 may include the step 404 of determining, by the controller, real-time parameters of the ego vehicle. The method 400 may further include the step 406 of calculating the required target torque for one or more wheels of the ego vehicle at the controller to overcome the detected one or more anomalies based on the real-time parameters determined at the step 404.

In another embodiment, the method 400 may include the step 408 of controlling, by the controller, operation of one or more motors associated with the one or more wheels of the ego vehicle; wherein at least one of the one or more motors associated with the one or more wheels of the ego vehicle approaching the detected one or more anomalies lies is deactivated and the torque of the remaining engines is increased by an appropriate value to enable the ego vehicle to efficiently cross the detected one or more anomalies.

In another embodiment, in the event that the vehicle becomes stuck in the anomaly, the method 400 may include the step of triggering, by the controller, a set of signals corresponding to a predefined change in steering angle with respect to a reference angle, such that the predefined change in steering angle results in pulses of positive and negative torque across the wheels stuck in the anomaly to enable the vehicle to overcome the anomaly.

According to one embodiment, the method 400 may include providing data related to a controlled change in the steering angle and the controlled actuation of the accelerator pedal of the ego vehicle, wherein the data is provided based on a training and test data set associated with the learning machine.

In another embodiment, the method 400 may include tracking movements and parameters of an object vehicle traveling in front of the ego vehicle. Further, the method 400 may include detecting the anomaly based on the tracked movement of the object vehicle; and processing the tracked parameters of the object vehicle as it crosses/overcomes the anomaly. Furthermore, the method 400 may include triggering the set of signals related to the change in steering angle and the operation of the accelerator pedal by mapping the processed parameters with the parameters of the ego vehicle to enable the ego vehicle to cross/overcome the anomaly.

In another embodiment, the method 400 may also include triggering an alarm upon detection of the anomaly. Furthermore, the method 400 may include calculating the translational torque and accordingly suggesting an acceleration, a change in steering angle and also providing a suggestive override of the torque distributed over different axes coupled to the one or more wheels required to cross/overcome said anomaly.

According to one embodiment, the proposed system and method are equipped with a torque control algorithm that delivers a small torque pulse to the wheel of the vehicle stuck in the anomaly for every small steering angle change from the reference steering angle. It disables the motor connected to an axle approaching the anomaly and transfers the torque of the disabled motor to the remaining motors. It also extends the battery life of the vehicle and optimizes the efficiency of the vehicle. Therefore, the present disclosure provides an intelligent and smart system and method that facilitates torque management in the vehicle along with energy conservation.

Referring to Figure 5, block diagram 500 illustrates a computer system including an external storage device 510, a bus 520, a main memory 530, a read-only memory 540, a mass storage device 550, a communications port 560, and a processor 570. One of ordinary skill in the art will recognize that a computer system may include more than one processor and communications ports. Examples of processor 570 include, but are not limited to, one or more Intel® ltanium® or Itanium-2 processors, or AMD® Opteron® or Athlon MP processors®, Motorola processors@, FortiSOC system-on-a-chip processors™, or other future processors. Processor 570 may include various modules associated with embodiments of the present invention. The communications port 560 may be any RS-232 port for use with a modem-based dial-up connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber optic, a serial port, a parallel port, or other existing or future ports. The communications port 560 may be selected depending on a network, such as a local area network (LAN), a wide area network (WAN), or any network to which a computer system is connected.

In one embodiment, memory 530 may be random access memory (RAM) or other dynamic storage device well known in the art. Read-only memory 540 may be any static storage device, such as, but not limited to, a programmable read-only memory (PROM) chip for storing static information, such as boot or BIOS instructions for processor 570. Mass storage 560 may be any current or future mass storage solution that can be used to store information and/or instructions. Example storage solutions include Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g. with Universal Serial Bus (USB) and/or Firewire interfaces), such as from Seagate (e.g. the Seagate Barracuda 7102 family) or Hitachi (e.g. the Hitachi Deskstar 7K1000), one or more optical disks, Redundant Array of Independent Disks (RAID) storage, such as an array of hard disks (e.g. SATA arrays), available from various vendors, including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc., and Enhance Technology, Inc.

In one embodiment, bus 520 communicatively couples processor(s) 570 to the other memory, storage, and communication blocks. Bus 520 may be, for example, a peripheral component interconnect (PCI/PCI-X) bus, a small computer system interface (SCSI), USB, or the like for connecting expansion cards, drives, and other subsystems, as well as other buses, such as a front-side bus (FSB), that connects processor 570 to the software system.

In another embodiment, operator and management interfaces, such as a display, keyboard, and cursor control device, may also be coupled to bus 520 to support direct operator interaction with the computer system. Other operator and management interfaces may be provided via network connections connected via communications port 560. External storage device 510 may be any type of external hard drives, floppy disk drives, IOMEGA Zip Drives®, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Scribeable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM). The components described above are merely illustrative of various possibilities. In no way should the exemplary computer system mentioned above limit the scope of the present disclosure.

While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the following claims. The invention is not limited to the described embodiments, versions, or examples, which are included to enable a person of ordinary skill in the art to make and use the invention when combined with information and knowledge available to one of ordinary skill in the art.

The present disclosure provides an efficient and reliable system and method that facilitates torque management in the vehicle.

The present disclosure provides a system and method equipped with a torque control algorithm that applies a small torque pulse to the wheel of the vehicle stuck in the anomaly for each small steering angle change from the reference steering angle.

The present disclosure provides a system and method that disables the motor associated with an axle that is about to approach the anomaly.

The present disclosure provides a system and method that transfers torque from the deactivated motor to the remaining motors.

The present disclosure provides a system and method that improves vehicle battery life.

The present disclosure provides a system and method that optimizes vehicle efficiency.

The present disclosure provides an intelligent and intelligent system for optimizing vehicle efficiency.

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions.

Cited patent literature

• US 10821968 B2 [0004]
• US 7152707 B2 [0005]

Claims (10)

A torque management system (100) implemented in an ego vehicle, the system (100) comprising: a controller (106) comprising a processor (202) coupled to a memory (204), the memory (204) storing one or more instructions executable by the processor to: drive the ego vehicle; determine real-time parameters of the ego vehicle; calculate, based on the determined real-time parameters, the required target torque for one or more wheels of the ego vehicle to overcome the detected one or more anomalies; and control operation of one or more motors associated with one or more wheels of the ego vehicle; wherein at least one of the one or more motors associated with the one or more wheels of the ego vehicle approaching the detected one or more anomalies is deactivated and the torque of the remaining motors is increased by a corresponding amount to enable the ego vehicle to efficiently traverse the detected one or more anomalies. Torque management system (100) according to Claim 1 wherein, in the event that the vehicle becomes stuck in an anomaly, the controller (106) initiates a set of signals corresponding to a predefined change in steering angle with respect to a reference angle, the predefined change in steering angle resulting in pulses of positive and negative torque across the wheels stuck in the anomaly to enable the vehicle to overcome the anomaly. Torque management system (100) according to Claim 2 , wherein the controller (106) is operatively coupled to a learning machine (104), the learning machine (104) providing data relating to a controlled change in steering angle and a controlled actuation of the accelerator pedal of the ego vehicle based on a training and test data set associated with the learning machine (104). Torque management system (100) according to Claim 3 , characterized in that the system (100) is configured to: track the movement and parameters of an object vehicle running in front of the ego vehicle; detect the anomaly based on the tracked movement of the object vehicle; process the tracked parameters of the object vehicle as it crosses/overcomes the anomaly; and trigger, by associating the processed parameters with the parameters of the ego vehicle, the set of signals related to the change in steering angle and the operation of the accelerator pedal to enable the ego vehicle to cross/overcome the anomaly. Torque management system (100) according to Claim 2 wherein the system (100) triggers an alarm upon detection of the anomaly, and wherein the system (100) calculates a translational torque and accordingly provides an acceleration, a change in steering angle and also a suggestive crossing of a torque distributed over different axles coupled to the one or more wheels required to cross/overcome said anomaly. A torque management method (400) implemented in an ego vehicle, the method (400) comprising: detecting (402) at a controller the presence of one or more anomalies on a road on which the ego vehicle is traveling; determining (404) at the controller real-time parameters of the ego vehicle; calculating (406) at the controller the required target torque for one or more wheels of the ego vehicle to overcome the detected one or more anomalies based on the determined real-time parameters; and controlling (408) by the controller the operation of one or more motors connected to the one or more wheels of the ego vehicle; wherein at least one of the one or more motors associated with the one or more wheels of the ego vehicle approaching the detected one or more anomalies is deactivated and the torque of the remaining motors is increased by a corresponding amount to enable the ego vehicle to efficiently traverse the detected one or more anomalies. Torque management procedure (400) according to Claim 6 , wherein, if the vehicle becomes stuck in the anomaly, the method (400) comprises: triggering a set of signals corresponding to a predefined change in the steering angle with respect to a reference angle, the predefined change in the steering angle resulting in pulses of positive and negative torque across the wheels stuck in the anomaly to to enable the vehicle to overcome the anomaly. Torque management procedure (400) according to Claim 7 wherein the method (400) comprises providing data relating to a controlled change in the steering angle and the controlled actuation of the accelerator pedal of the ego vehicle, the data being provided based on a training and test data set associated with the learning machine. Torque management procedure (400) according to Claim 8 , the method (400) comprising: tracking the movement and parameters of an object vehicle running in front of the ego vehicle; detecting the anomaly based on the tracked movement of the object vehicle; processing the tracked parameters of the object vehicle while crossing/overcoming the anomaly; and triggering the signals related to the change in steering angle and the operation of the accelerator pedal by associating the processed parameters with the parameters of the ego vehicle to enable the ego vehicle to cross/overcome the anomaly. Torque management procedure (400) according to Claim 7 wherein the method (400) comprises triggering an alarm upon detection of the anomaly, and wherein the method (400) also comprises calculating the translational torque and, accordingly, an acceleration, a change in the steering angle and also providing a suggestive overrun of the torque distributed over different axes coupled to the one or more wheels required to cross/overcome the anomaly.

Images