VEHICLE AND METHOD FOR CONTROLLING THE SAME USING ESTIMATED WEIGHT

Abstract

A method for estimating and controlling a vehicle's weight may include determining initial estimated vehicle weight information based on a first weight estimation applied to vehicle data. This first weight estimation may be based on recursive least squares (RLS) combined with a first forgetting factor. The method may further include determining second estimated vehicle weight information based on a second weight estimation applied to the vehicle data. This second weight estimation may also be based on RLS combined with a second forgetting factor.The procedure may further include: determining a third forgetting factor for RLS of a third weight estimate, based on the first and second estimated weight information; determining a third estimated vehicle weight information, based on the third weight estimate applied to the vehicle data; and controlling the vehicle based on the third estimated weight information.

Description

STATE OF THE ART

[0001] The present disclosure relates to a method and a vehicle for estimating a weight based on multiple RLS with a forgetting factor, and in particular a weight estimation method and a vehicle that enable a weight estimation in the vehicle to converge rapidly and obtain an accurate estimated weight, thereby maximizing the stability of the vehicle control. BACKGROUND

[0002] Weight estimation involves calculating complex factors during driving, such as vehicle speed, engine torque feedback, powertrain efficiency, and driving resistance. Powertrain efficiency and other factors are difficult to model accurately. In such situations, a recursive least squares (RLS) forgetting factor close to 1 is used for a stable weight estimate. Therefore, accuracy needs to be improved to allow for the use of many measurements for weight estimation.

[0003] However, with a large commercial vehicle where the difference between the empty and loaded weight varies considerably, e.g., from 14 tonnes to 36 tonnes, which is more than double, it can take a long time for an initially assumed value, usually based on an average such as 25 tonnes, to converge with the actual weight. For example, it can take more than 10 minutes for the initially assumed value of 25 tonnes to converge to an actual weight of 14 tonnes. Consequently, when applying weight-adapted torque and regenerative braking control, a shift in vehicle control may occur during this initial convergence period, which degrades the vehicle's performance.

[0004] Furthermore, an observed value from an accelerometer is used as the standard input for weight estimation, but if a sensor or controller is replaced as a single element, no offset correction can be performed. In such a situation, where the RLS weight estimation deviates, an anomaly in the vehicle control system caused by an error in the weight estimation must be prevented. SUMMARY OF THE INVENTION

[0005] The present disclosure technically aims to provide a method and a vehicle for estimating a weight based on multiple RLS with a forgetting factor, enabling a weight estimation in the vehicle to converge rapidly and obtain an accurate estimated weight, thereby maximizing the stability of the vehicle control.

[0006] The technical problems solved by the present disclosure are not limited to the technical problems mentioned above, and other technical problems not described here will be clearly understood by a person with ordinary expertise in the technical field to which the present disclosure belongs from the following description.

[0007] According to one or more embodiments of the present disclosure, a method performed by a device of a vehicle may comprise: determining, based on a first weight estimation applied to vehicle data, first estimated vehicle weight information. The first weight estimation may be based on recursive least squares (RLS) associated with a first forgetting factor. The method may further comprise determining second estimated vehicle weight information based on a second weight estimation applied to the vehicle data. The second weight estimation may be based on RLS associated with a second forgetting factor.The procedure may further include: determining a third forgetting factor for RLS of a third weight estimate based on the first estimated weight information and the second estimated weight information; determining third estimated vehicle weight information based on the third weight estimate applied to the vehicle data; and controlling the vehicle based on the third estimated weight information.

[0008] The third forgetting factor can be determined differently based on the difference between the first and second estimated weight information. The second forgetting factor may exhibit a lower degree of forgetting than the first.

[0009] The procedure may further include: determining whether an aggressive update request for the third weight estimate occurs, based either on a vehicle operating condition or a weight estimation error state related to a deviation state of the first weight estimate, or on a difference between the first estimated weight information and the second estimated weight information; and, based on the aggressive update request, applying an aggressive forgetting factor to the third forgetting factor. The aggressive forgetting factor may specify a forgetting characteristic associated with an increased forgetting rate. Determining the third estimated weight information may involve determining the third estimated weight information based on the vehicle data by aggressively updating the third weight estimate.

[0010] The procedure may further include: determining whether an aggressive update request for the third weight estimate occurs, based on at least either a vehicle operating condition or a weight estimation error condition related to a deviation state of the first weight estimate, or on a difference between the first estimated weight information and the second estimated weight information; and determining the third forgetting factor, which may include determining the third forgetting factor based on the finding that the aggressive update request does not occur. A conservative forgetting factor may be applied to the third forgetting factor. The conservative forgetting factor may specify a forgetting characteristic associated with a reduced forgetting rate.

[0011] The aggressive update of the third weight estimate can be performed a certain number of times. Determining the third estimated weight information can include determining the third estimated weight information after the aggressive update of the third weight estimate.

[0012] The vehicle's operating state can include a transition from an OFF state to an ON state.

[0013] The weight estimation error state can include the initial weight estimate as a deviation state. One standard deviation of the initial estimated weight information can be greater than a deviation reference value based on an acceleration offset caused by a slope on which the vehicle is traveling or a replaced vehicle component.

[0014] The procedure can further include correcting the acceleration offset before determining the third estimated weight information by aggressively updating the third weight estimate. The aggressive update of the third weight estimate can be performed after correcting the acceleration offset.

[0015] Determining whether an aggressive update request occurs can involve activating the aggressive update request based on a successive difference between the first estimated weight information and the second estimated weight information that is greater than a threshold difference. The successive difference can be determined over time.

[0016] The procedure may further include performing an update of the first weight estimate and the second weight estimate while the aggressive update of the third weight estimate is carried out.

[0017] According to one or more embodiments of the present disclosure, a vehicle may comprise: a memory that stores at least one instruction for controlling the vehicle; and a processor configured to execute the at least one instruction stored in the memory. The at least one instruction may be configured, when executed by the processor, to cause the vehicle to determine initial estimated vehicle weight information based on a first weight estimation applied to vehicle data. The first weight estimation may be based on recursive least squares (RLS) associated with a first forgetting factor. The at least one instruction may further be configured, when executed by the processor, to cause the vehicle to determine second estimated vehicle weight information based on a second weight estimation applied to the vehicle data.The second weight estimate can be based on RLS, which is associated with a second forgetting factor. The at least one instruction can further be configured, when executed by the processor, to cause the vehicle, based on the first estimated weight information and the second estimated weight information, to determine a third forgetting factor for RLS of a third weight estimate; based on the third weight estimate applied to the vehicle data, to determine third estimated vehicle weight information; and, based on the third estimated weight information, to control the vehicle.

[0018] The third forgetting factor can be determined differently based on the difference between the first and second estimated weight information. The second forgetting factor may exhibit a lower degree of forgetting than the first.

[0019] The at least one instruction may be configured, when executed by the processor, to cause the vehicle to: determine whether an aggressive update request for the third weight estimate occurs, based on at least either a vehicle operating state or a weight estimation error state with respect to a deviation state of the first weight estimate or a difference between the first estimated weight information and the second estimated weight information, and, based on the aggressive update request, apply an aggressive forgetting factor to the third forgetting factor.The aggressive forgetting factor can indicate a forgetting characteristic associated with an increased forgetting rate and determine the third estimated weight information by aggressively updating the third weight estimate to determine the third estimated weight information based on the vehicle data.

[0020] The at least one instruction can be configured, when executed by the processor, to cause the vehicle to: determine whether an aggressive update request for the third weight estimate occurs, based on at least either a vehicle operating state or a weight estimation error state related to a deviation state of the first weight estimate, or on a difference between the first estimated weight information and the second estimated weight information; and determine the third forgetting factor by calculating the third forgetting factor based on a finding that no aggressive update request occurs. A conservative forgetting factor can be applied to the third forgetting factor. The conservative forgetting factor can specify a forgetting characteristic associated with a reduced forgetting rate.

[0021] The aggressive update of the third weight estimate can be performed a specified number of times. The at least one instruction may be configured to cause the vehicle to determine the third estimated weight information after the aggressive update of the third weight estimation.

[0022] The vehicle's operating state can include a transition from an OFF state to an ON state.

[0023] The weight estimation error state can include the initial weight estimate as a deviation state. One standard deviation of the initial estimated weight information can be greater than a deviation reference value based on an acceleration offset caused by a slope on which the vehicle is traveling or a replaced vehicle component.

[0024] The at least one instruction can be configured, when executed by the processor, to cause the vehicle to correct the acceleration offset before determining the third estimated weight information by aggressively updating the third weight estimate. The aggressive update of the third weight estimate can be performed after the acceleration offset correction.

[0025] The at least one instruction can be configured, when executed by the processor, to cause the vehicle to activate the aggressive update request based on a successive difference between the first estimated weight information and the second estimated weight information that exceeds a threshold difference. The successive difference can be determined over time.

[0026] The at least one instruction can be configured, when executed by the processor, to cause the vehicle to perform an update of the first weight estimate and the second weight estimate, while the aggressive update of the third weight estimate is performed.

[0027] The vehicle can be configured to perform one or more of the operations and / or procedures described here.

[0028] The features of the present revelation summarized here are only examples of aspects of the features of the present revelation and the subsequent detailed description of the revelation and are not intended to limit the scope of the present revelation.

[0029] The technical problems solved by this disclosure are not limited to those mentioned above. Other technical problems solved by this disclosure, which are not described here, should be more clearly understood by a person with ordinary knowledge in the technical field to which this disclosure belongs, based on the following description.

[0030] According to the present disclosure, it is possible to provide a method and a vehicle for estimating a weight based on multiple RLS with a forgetting factor, whereby a weight estimation in the vehicle can converge quickly and an accurate estimated weight can be obtained, thus maximizing the stability of the vehicle control.

[0031] The effects obtainable through this disclosure are not limited to the effects mentioned above, and other effects not mentioned here will be clearly understood by those skilled in the art based on the following descriptions. BRIEF DESCRIPTION OF THE FIGURES Fig. Figure 1 illustrates an example of a vehicle communicating with another device to transmit and receive data. Fig. Figure 2 illustrates an example of individual modules of a vehicle according to an example in the present disclosure. Fig. Figure 3 is a block diagram of a system that performs weight estimation in a vehicle according to an embodiment of the present disclosure. Fig. Figure 4 illustrates an example of functional modules of a weight estimator. Fig. Figure 5 illustrates an example of RLS logic applied to a weight estimator. Fig. Figure 6 is a flowchart of a method for estimating a weight according to another embodiment of the present disclosure. Fig. Figure 7 illustrates an example of logic processed in a multi-RLS monitoring system. Fig. Figure 8 is a flowchart of a process that is carried out in a vehicle state determination logic. Fig. Figure 9 is a flowchart of a process for determining the weight estimation error, which is carried out in a logic for determining the state of convergence. Fig. Figure 10 is a flowchart of a process related to an aggressive update request of the convergence state determination logic. Fig. Figure 11 is a flowchart of a process carried out in the forgetting factor determination logic. Fig. Figure 12 illustrates an example of data from a result to which a weight estimation method according to the present disclosure is applied. Fig. Figure 13 illustrates an example of other data of a result to which a weight estimation method according to the present disclosure is applied. DETAILED DESCRIPTION OF THE INVENTION

[0032] One embodiment of the present disclosure is described below in such detail with reference to the accompanying drawings that someone with ordinary technical skills can easily implement the present disclosure. However, other examples of the present disclosure can be carried out in different ways, so that the present disclosure is not limited to the examples described therein.

[0033] In describing examples of the present disclosure, known functions or constructions were not described in detail, as a comprehensive description would have unnecessarily obscured the core of the present disclosure. Identical components are identified in the drawings with the same reference symbols, and repeated or duplicate descriptions of the same elements have been omitted.

[0034] When, in the present disclosure, an element is simply described as "connected with," "coupled with," or "linked with" another element, this may mean that an element is "directly connected with," "directly coupled with," or "directly linked with" another element, or it may mean that an element is connected, coupled, or linked with another element, with another element interposed. Furthermore, an element that "includes" or "has" another element means that an element may include another element without excluding another component, unless expressly stated otherwise.

[0035] In this disclosure, the terms “first”, “second”, etc., are used only to distinguish one element from another and do not restrict the order or degree of importance between the elements unless expressly stated otherwise. Accordingly, a first element in one example may be called a second element in another example, and similarly, a second element in one example may be called a first element in another example, without this departing from the scope of this disclosure.

[0036] In this disclosure, the elements are distinguished from one another in order to clearly describe their individual features; however, this does not necessarily mean that the elements are separate. For example, a multitude of elements may be integrated into a single hardware or software unit, or an element may be distributed and constituted in a multitude of hardware or software units. Therefore, such integrated or distributed examples fall within the scope of this disclosure, even if not explicitly stated otherwise.

[0037] In the present revelation, the elements described in the various examples do not necessarily represent essential elements, and some of them may be optional. Therefore, an example consisting of a subset of the elements described in an example also falls within the scope of the present revelation. Furthermore, examples that contain additional elements beyond those described in the various examples also fall within the scope of the present revelation.

[0038] The advantages and features of this disclosure, and the means of achieving them, should become clear to the person skilled in the art with reference to examples of this disclosure, which are described in detail here in connection with the accompanying drawings. However, the examples of this disclosure can take many different forms and should not be considered limited to those presented here. Rather, the examples described here serve to supplement this disclosure and to convey its scope to those who possess normal technical knowledge in the field to which this disclosure relates.

[0039] In the present disclosure, each of the formulations such as “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, “at least one of A, B or C” and “at least one of A, B, C or a combination thereof” may include any or all possible combinations of the elements listed together in the corresponding formulation.

[0040] In this description, terms such as "above," "below," "left," and "right" are used for ease of explanation, and if the drawings presented in this description are reversed, the spatial relationships described in the description may be understood in reverse. Where a component, device, element, or the like is described within the scope of this disclosure as a component, device, or element that fulfills a purpose or performs an operation, function, or the like, the component, device, or element should here be considered "configured" to fulfill that purpose or perform that operation or function.

[0041] The following, referring to the Fig. 1 and Fig. 2, a vehicle is described which performs a driving control with adaptive regenerative braking according to an embodiment of the present disclosure. Fig. Figure 1 is an example view of a vehicle communicating with another device to transmit and receive data.

[0042] The automation level of an autonomous vehicle can be classified as follows, according to the American Society of Automotive Engineers (SAE). Level 0 autonomous driving can correspond to the SAE classification standard of "no automation," where an autonomous driving system temporarily intervenes in emergency situations (e.g., automatic emergency braking) and / or only issues warnings (e.g., blind spot warning, lane keeping assist, etc.), and a driver is expected to operate the vehicle. Level 1 autonomous driving can correspond to the SAE classification standard of "driver assistance," where the system performs some driving functions (e.g., steering, accelerating, braking, lane centering, adaptive cruise control, etc.).), while the driver controls the vehicle during normal operation, and the driver is expected to determine the system's operating state and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. For Level 2 autonomous driving, the SAE classification standard may correspond to "partial automation," where the system steers, accelerates, and / or brakes under the driver's supervision, and the driver is expected to determine the system's operating state and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. For Level 3 autonomous driving, the SAE classification standard may correspond to "conditional automation," where the system controls the vehicle under certain conditions (e.g.,Driving functions such as steering, accelerating, and / or braking are performed automatically. However, the system transfers control of the vehicle to the driver if the necessary conditions are not met. The driver is expected to determine the system's operating state and / or timing and to take control in emergency situations, but otherwise does not control the vehicle (e.g., steer, accelerate, and / or brake). In Level 4 autonomous driving, the SAE classification standard may correspond to "high automation," where the system performs all driving functions and the driver only needs to take control of the vehicle in emergency situations. In Level 5 autonomous driving, the SAE classification may correspond to "full automation," where the system performs all driving functions without any assistance from the driver, even in emergency situations, and the driver is not expected to perform any driving functions other than determining the system's operating state.Although the present disclosure may apply the SAE classification standard for the classification of autonomous driving, other classification methods and / or algorithms may be used in one or more of the configurations described herein. One or more features associated with autonomous driving control may be enabled based on one or more settings configured for autonomous driving control (e.g., based on at least one of the following features: an autonomous driving classification, a selection of an autonomous driving level for a vehicle, etc.).

[0043] Based on one or more of the features described here (e.g., weight estimator), the operation of the vehicle can be controlled. Vehicle control can include various operational controls associated with the vehicle (e.g., autonomous driving control, sensor control, brake control, braking time control, acceleration control, acceleration rate change control, alarm time control, forward collision warning time control, etc.).

[0044] One or more auxiliary devices (e.g., engine brake, exhaust brake, hydraulic retarder, electric retarder, regenerative braking, etc.) can also be controlled, for example, based on one or more of the features described here (e.g., weight estimators). One or more communication devices (e.g., a modem, a network adapter, a radio transmitter / receiver, an antenna, etc., capable of communicating via one or more wired or wireless communication protocols, such as Ethernet, Wi-Fi, Near Field Communication (NFC), Bluetooth, Long-Term Evolution (LTE), 5G New Radio (NR), Vehicle-to-Everything (V2X), etc.) can also be controlled, for example, based on one or more of the features described here (e.g., weight estimation features).

[0045] The minimum risk maneuver (MRM) process(s) can also be controlled based on one or more of the features described here (e.g., weight estimators). A minimum risk maneuver (e.g., a minimum risk maneuver) can be a maneuver performed by a vehicle to minimize (e.g., reduce) the risk of a collision with surrounding vehicles in order to achieve a lowered (e.g., minimal) risk state. A minimum risk maneuver can be an operation that can be activated during autonomous driving if a driver is unable to respond to a request to intervene. During the minimum risk maneuver, one or more of the vehicle's processors can control the vehicle's driving operations for a specific period of time.

[0046] Preset driving maneuvers can also be controlled based on one or more of the features described here (e.g., weight estimators). A driving control device can perform a biased driving maneuver. To execute a preset drive, the driving control device can steer the vehicle to travel in a lane by maintaining a lateral distance between the vehicle's center position and the center of the lane. For example, the driving control device can steer the vehicle to remain in the lane, but not in the center of the lane.

[0047] The vehicle control device can determine a preset target lateral distance for the preset vehicle control. For example, a preset target lateral distance can be an intentionally set lateral distance that a vehicle is to maintain from a reference point, such as the center of a lane or another vehicle, during maneuvers like lane changes. This setting can be made to improve the vehicle's stability, safety, and / or handling under various driving conditions, etc. For example, during a lane change, the vehicle control system can adjust the lateral distance to maintain a safer distance from adjacent vehicles, taking into account factors such as the vehicle's speed, road conditions, and / or the presence of obstacles, etc.

[0048] One or more sensors (e.g., IMU sensors, camera, LIDAR, RADAR, blind spot monitoring sensor, lane departure warning sensor, parking sensor, light sensor, rain sensor, traction control sensor, anti-lock braking system sensor, tire pressure monitoring sensor, seat belt sensor, airbag sensor, fuel level sensor, emission sensor, throttle position sensor, inverter, converter, engine control unit, power distribution unit, high-voltage cables and connectors, auxiliary power modules, charging interface, etc.) can also be controlled, for example, based on one or more of the features described here (e.g., weight estimation features).

[0049] An operational control system for autonomous driving of the vehicle can include various driving controls of the vehicle by the vehicle control device (e.g. acceleration, deceleration, steering, gear shifting, braking system, traction control, stability control, cruise control, lane keeping assist, collision avoidance system, emergency braking assist, traffic sign recognition, adaptive headlight control, etc.).

[0050] Referring to Fig. 1. A vehicle 100 can be powered by electrical energy. In the case of electrical energy, the vehicle 100 can, for example, be a purely battery-powered vehicle driven solely by a high-voltage battery, or it can utilize a gas-powered fuel cell as its energy source. In the case of a fuel cell, the vehicle 100 can charge a high-voltage battery by generating electricity from the fuel cell and perform various functions required / used by the vehicle 100's modules using the power output of the high-voltage battery. Furthermore, the fuel cell can utilize various types of gases capable of generating electrical energy, such as hydrogen. However, various gases are applicable, though not limited to hydrogen.

[0051] For the sake of simplicity, the present disclosure describes an example in which a vehicle powered by electricity is the fuel cell-based vehicle 100. However, the present disclosure is applicable to a vehicle in which a high-voltage battery and a cell are of different types and which uses a method for charging the high-voltage battery by generating energy from the cell to supply energy for starting and driving the vehicle 100 and a load device 120. Furthermore, the present disclosure can also be applied to a vehicle powered solely by an electric battery.

[0052] Vehicle 100 can refer to a device capable of movement. Vehicle 100 is a vehicle in the sense of a ground-based vehicle that travels on the ground and can be a regular passenger car or a commercial vehicle, a mobile office, or a mobile hotel. Vehicle 100 can be a four-wheeled vehicle, such as a sedan, a sport utility vehicle (SUV), and a pickup truck, but also a vehicle with five or more wheels, such as a bus, a truck, a container vehicle, and a heavy-duty vehicle. Vehicle 100 can be operated manually and / or autonomously (either semi-autonomously or fully autonomously).

[0053] Meanwhile, under the control of a communication control unit (CTU) installed in the vehicle 100, the vehicle 100 can communicate with another device 200 or another vehicle. For example, another device could include a server 200 to support various control, condition management, and driving functions of the vehicle 100, an ITS device to receive information from an intelligent transportation system (ITS), and various types of user devices.

[0054] The vehicle 100 can communicate with another vehicle or device based on cellular communication, in-vehicle wireless access (WAVE), dedicated short range communication (DSRC), short range communication, or another communication scheme.

[0055] For example, the vehicle 100 can use LTE as a cellular network, a communication network such as 5G, a WiFi communication network, a WAVE communication network, and the like to communicate with the server 200 and another vehicle. As another example, DSRC can be used in the vehicle 100 for vehicle-to-vehicle communication. A communication scheme between the vehicle 100, the server 200, another vehicle, and a user device is not limited to the embodiment described above.

[0056] To support autonomous driving and various services for the vehicle 100, the server 200 can transmit various types of information and software modules used to control the vehicle 100 to the vehicle 100 in response to a request and data transmitted by the vehicle 100 and a user device.

[0057] Fig. Figure 2 is a view illustrating the individual modules of a vehicle according to an example in the present disclosure.

[0058] The vehicle 100 can include a sensor unit 102, a transceiver 104, a display 106 and a charging device 108.

[0059] The sensor unit 102 can be equipped with a detector to record various conditions, processes, and situations occurring in the external environment and inside the vehicle 100. Furthermore, the sensor unit 102 can be equipped with various types of detectors that determine the location information of the vehicle 100.

[0060] Specifically, the sensor unit 102 can include a position sensor 102a for obtaining location information of the vehicle 100, a tilt sensor 102b, a brake request level sensor 102c for detecting a brake request level (brake position) and an acceleration request level requested by a user or the processor 122, and a wheel speed sensor 102d for measuring the speed of the vehicle 100.

[0061] The position sensor 102a can generate two-dimensional location data by measuring the latitude and longitude of the vehicle 100, or by measuring an altitude in addition to the two-dimensional location data.

[0062] For example, the tilt sensor 102b can use a gyroscope, an inertial sensor, or similar device to measure the position and tilt angle of the vehicle 100. The processor 122 can calculate the inclination of a road on which the vehicle 100 is traveling, or the tilt of the vehicle 100 itself, based on a value measured by the tilt sensor 102b.

[0063] The brake request level sensor 102c can detect a request level contained in a user's brake request and, for example, capture the amount of pedal actuation by a user via a brake pedal or the user's request level via an accelerator interface. The brake request level sensor 102d, which can measure the user's pedal actuation, can be a brake point sensor (BPS).

[0064] The present disclosure mainly describes sensors of the sensor unit 102, which are described by an embodiment mentioned herein, but may also include a sensor for detecting various situations not listed here, such as a camera, a lidar sensor and a radar sensor.

[0065] The transceiver 106 can support mutual communication with the server 200, the adjacent vehicle 300, and the like. Within the scope of this disclosure, the transceiver 106 can transmit data generated or stored during travel to the server 200 and receive data and a software module transmitted from the server 200. In this disclosure, the vehicle 100 can send and receive data used in a process according to this disclosure via the transceiver 106. In this disclosure, the transceiver 106 can receive map information and situational information and forward it to the memory 120 and the processor 122.

[0066] The display 108 can serve as an interface for the user. Through the processor 122, the display 108 can show the operating and control status of the vehicle 100, route information according to the navigation system, traffic information around the vehicle 100, information about the remaining energy level, content requested by the driver, and the like. The display 108 can be configured to recognize driver input and receive a driver request displayed to the processor 122.

[0067] The charging device 108 can be an accessory attached to the vehicle 100 that consumes energy supplied by the battery 110 when used by an occupant or user. In the present disclosure, the charging device 108 can be an electrical device for non-propulsion purposes that does not constitute a drive system such as the motor unit 110 for wheel propulsion. For example, the charging device 108 can be an air conditioning system, a lighting system, a seating system, and various other devices installed in the vehicle 100.

[0068] The vehicle 100 can comprise the battery 110, a fuel cell 112, a motor unit 114, a switching unit 118, a retarder 124, and a wheel unit 116. In the present disclosure, the motor unit 114, the switching unit 118, the retarder 124, and the wheel unit 116 can form a powertrain component 126.

[0069] Vehicle 100 is a multi-wheeled mobility vehicle, and all wheels can be driven, for example, by connecting them to the motor unit 114. For the sake of simplicity, in the present disclosure, the motor unit 114, the switching unit 118, and the wheel unit 116 are referred to as Fig. 2 is represented as a single unit. Corresponding to the number of wheels of the vehicle 100, the motor unit 114, the switching unit 118, and the wheel unit 116 increase, but since the motor unit 114, the switching unit 118, and the wheel unit 116, which drive each wheel, essentially have the same function, these elements can be understood as modules that drive each wheel. Another example is that only some of the wheels can be coupled to the motor unit 114, and that wheels not coupled to the motor unit 114 can be driven by a wheel driven by a motor.

[0070] The battery 110 and the fuel cell 112 can both supply power continuously, or the battery 110 and the fuel cell 112 can be used as the main power source or as an auxiliary power source.

[0071] Battery 110 can be a purely electric battery configured to be charged as a secondary cell by fuel cell 112. During regenerative braking, battery 110 can be charged by a counter-rotating electromechanical force from motor unit 114.

[0072] Fuel cell 112 can have a lower output voltage than battery 110, but can be configured to have a high energy density or a high charging capacity. For example, fuel cell 112 can be configured as a hydrogen-based fuel cell that generates electrical energy through the reaction between hydrogen gas, which is supplied externally to a tank (not illustrated), and oxygen, which comes from a supplier (not illustrated).

[0073] The battery 110 can be charged by receiving a voltage output from a converter (not illustrated) that converts a voltage from the fuel cell 112. Furthermore, the converter 106 can supply a motor of the motor unit 114 and the charging device 108, which operate in a high-voltage range, with a voltage converted from the fuel cell 112.

[0074] The motor unit 114 can generate a driving force by receiving electrical current from the battery 110. The motor unit 114 can transmit this driving force to the wheel unit 116, causing a wheel to rotate. For example, the motor unit 114 can be equipped with a motor to transmit driving force to the wheel unit 116 and a motor control module to control the motor torque, direction of rotation, and braking. The motor unit 114 can be powered by receiving electrical energy from the battery 110 via an inverter (not illustrated). An inverter can convert one form of electrical power from the battery 110, such as alternating current (AC), into another form, such as direct current (DC), and reduce the voltage.The wheel unit 116 can include a wheel that receives a drive force from the motor unit 114, a main brake module for braking the wheel drive, and a steering module for realizing the horizontal control of the wheel.

[0075] The switching unit 118 can be equipped with a mechanical component that transmits a drive force delivered by the motor unit 114 to the wheel unit 116. This mechanical component can, for example, be a combination of a shaft and a gear. Alternatively, the switching unit 118 can displace a drive force and transmit this displaced drive force, along with a drive force transmission module, to the wheel unit 116. In large vehicles, the switching unit 118 can be coupled with a brake assist module, such as the retarder 124. The retarder 124 can, for example, be equipped with a hydraulic or electromagnetic device for suppressing the rotation of a shaft connecting the wheel unit 116 and the motor unit 114. At the user's request, the retarder can brake the vehicle 100 when traveling downhill, independently of regeneration and main braking.Depending on the vehicle specification 100, the brake assist equipment and transmission described above can be omitted.

[0076] In the present disclosure, the motor unit 114, the wheel unit 116, the switching unit 118 and the retarder 124 can form the drive train component 126.

[0077] Furthermore, the vehicle can include 100 memory units and 122 processor units.

[0078] The memory 120 can store an application for controlling the vehicle 100 and various data, and, upon request from the processor 122, load the application or read and record data. In the present disclosure, the memory 120 can have an application that determines an adaptive third forgetting factor for an RLS-based third weight estimate based on estimated weight information generated (e.g., determined) in a first weight estimate and a second weight estimate using the Recursive Least Squares (RLS) method based on different forgetting factors, and generates (e.g., determined) the final estimated weight information (e.g., third estimated weight information) from the third weight estimate according to the determined third forgetting factor.The application managed in memory 120 can be implemented to determine whether or not there is an aggressive update request for the third weight estimate based on a predetermined situation, and to generate ultimate weight information in response to the presence of the request (e.g., to determine) by using an aggressive update of the third weight estimate that applies an aggressive forgetting factor to the third forgetting factor. Memory 120 can store vehicle data, a hyperparameter, and various types of data used for weight estimation based on multiple RLS according to the present disclosure.

[0079] Memory 120 can contain and manage map information for identifying the location of vehicle 100. Map information can be used to create a driving route in vehicle 100 at the request of a user or processor 122, or to identify the location of vehicle 100, such as latitude, longitude, and altitude. Furthermore, map information can be used to obtain information about the driving situation on a given route. Additionally, map information can be used for autonomous driving and may include a low-resolution or high-definition map. Map information can be provided to incorporate various information and data into the object and its environment as described above.

[0080] Processor 122 can take over the overall control of vehicle 100. Processor 122 can be configured to execute an application and an instruction stored in memory 120.

[0081] In connection with the present disclosure, the processor 122 can perform a weight estimation based on several RLS according to the present disclosure by using an application, an instruction and data stored in memory 120.

[0082] Specifically, the processor 124 can perform processing to generate first estimated weight information and second estimated weight information based on vehicle data. This processing utilizes a first and second weight estimate, each based on a first and second forgetting factor with different forgetting characteristics, to determine an adaptive third forgetting factor for a third weight estimate constructed by an RLS based on the first and second estimated weight information. Finally, it generates final estimated weight information based on vehicle data using the third weight estimate to which the determined third forgetting factor is applied. The third forgetting factor can be determined in response to the absence of an aggressive update request.The processor 122 can perform processing to determine, based on a predetermined situation, whether or not there is an aggressive update request for the third weight estimate, and in response to the request to generate the final weight information based on vehicle data by aggressively updating the third weight estimate, which applies an aggressive forgetting factor to the third forgetting factor.

[0083] In the present disclosure, the processor 122 can be implemented as a single processing module that performs the processing described above in connection with weight estimation and the processing of various vehicle operations. Alternatively, the processor 122 can perform the processing described above as distributed processing across a multitude of processing modules. For example, the processor 122 can include a vehicle control unit (VCU), an engine control unit (MCU), an electric braking system (EBS), and a transmission control unit (TCU), and execute the process described above in a distributed manner according to each corresponding module.

[0084] For the sake of simplicity, in the present disclosure, although the processor 124 with a multitude of processing modules performs the processes described above, the one in Fig. The processor 124 shown is described in order to refer generally to the multitude of processing modules.

[0085] Fig. Figure 3 is a block diagram of a system that implements weight estimation in a vehicle according to an embodiment of the present disclosure.

[0086] Fig. Figure 3 mainly shows the individual modules for weight estimation in Fig. 2 and the detailed functional modules of the processor 122. The system can include the powertrain component 126, an electronic braking system (EBS) 128, the position sensor 102a, an inclination calculator 130, an acceleration offset corrector 132, and a weight estimator 134. The inclination calculator 130, the acceleration offset corrector 132, and the weight estimator 134 can be designed to be implemented in the processor 122.

[0087] The powertrain component 126 can transmit 100 vehicle data points from a specific module during vehicle operation, as described in Fig. 2 shown, output, e.g., data used in a longitudinal dynamics model. For example, the powertrain component 126 can output vehicle data such as engine torque, engine rotation data, shaft rotation data, a transmission state, a retarder state, and the like. The transmission state can include whether a transmission is present or not, a gear ratio, and the like, and the retarder state can include whether the retarder 124, which corresponds to an auxiliary braking device, is in operation or not. Although in Fig. Not shown in Figure 3, the weight estimator 134 is provided with a vehicle operating state, which is a type of vehicle data. This vehicle operating state can be a state in which the start (e.g., the ignition) of the vehicle 100 is switched from OFF to ON (e.g., the vehicle 100 transitions from an OFF state to an ON state). The EBS 128 can output a value detected by the brake request level sensor 102c, which acts as a brake pedal sensor, and a vehicle speed as vehicle data. Based on a value obtained from the tilt sensor 102b, the tilt calculator 130 can provide a tilt of the vehicle 100 or of a road as vehicle data. The acceleration offset corrector 132 can be used to correct a distortion and error that may occur in the tilt calculator 130 by comparing the position sensor 102a and the tilt sensor 102b, which are installed in the vehicle 100.Furthermore, the acceleration offset corrector 132 can provide an offset-corrected gradient and also generate and output the number of acceleration offset correction estimates. The weight estimator 134 can generate weight information based on estimated vehicle data. The estimated weight information can include, for example, an estimated weight value, a weight estimation variance, a weight estimation state, a weight estimation standard deviation, and various types of weight-related data.

[0088] Fig. Figure 4 illustrates the functional modules of a weight estimator. Fig. Figure 4 illustrates detailed functional modules of the weight estimator 134. Fig. 3.

[0089] The weight estimator 134 can include a preprocessor 136, a first weight estimator 138, a second weight estimator 140, a third weight estimator 142 and a multi-RLS monitoring system 144.

[0090] The preprocessor 136 can handle the tasks described in Fig. Convert the vehicle data listed in section 2 into a predefined form of vehicle condition information, such as a parameter value of a longitudinal dynamics model.

[0091] Each of the first weight estimators 138 and the second weight estimator 140 can include an RLS estimator based on a first forgetting factor and a second forgetting factor, a post-processor to generate estimated weight information based on a value output by the RLS estimator, and a filter to reduce the variability of the values ​​of the estimated weight information output sequentially. The first weight estimator 138 can use the first forgetting factor, which has a high forgetting feature, for an aggressive update of the weight estimate. An aggressive forgetting factor can specify a forgetting feature associated with an increased forgetting rate (e.g., compared to a non-aggressive forgetting factor). Conversely, a conservative forgetting factor can specify a forgetting feature associated with a reduced forgetting rate (e.g.,(compared to a non-conservative forgetting factor). The first weight estimator 138 can perform an initial weight estimation, generating initial weight estimation information based on vehicle condition information. This initial weight estimation information can include an initial estimated weight, an initial estimated weight variance, an initial weight estimation standard deviation, and various types of weight-related data. For a conservative update of the weight estimation, the second weight estimator 140 can use the second forgetting factor with a lower forgetting characteristic than the first forgetting factor. The second weight estimator 140 can perform a second weight estimation, generating second weight estimation information based on vehicle condition information.The second weight estimation information can include a second estimated weight, a second estimated weight variance, a second weight estimation standard deviation, and various types of weight-related data. The first and second forgetting factors can be specified as fixed values.

[0092] In practice, the third weight estimator 142 can contain an RLS estimator, a post-processor, and a filter in the same way as the first and second weight estimators 138 and 140. The third forgetting factor used in the third weight estimator 142 differs from the first and second forgetting factors and, in particular, can be adaptively determined depending on the first and second weight estimation information or a predefined vehicle state. By using the adaptive third forgetting factor, the third weight estimator 142 can perform a third weight estimation, which produces final estimated weight information. The final estimated weight information can be used in Fig. The data described in section 2 is included.

[0093] Based on the first and second estimated weight information, the number of acceleration offset estimates, and vehicle condition information, the Multi-RLS Monitoring System 144 can check whether an aggressive update request for the third weight estimator 142 is present and determine and provide a third forgetting factor accordingly. Based on the information described above, the Multi-RLS Monitoring System 144 can request updates to the weight estimates for the first through third weight estimators 138, 140, and 142.

[0094] The longitudinal dynamics model for generating vehicle state information based on vehicle data, as described above, is below described by Equation 1. In this disclosure, the longitudinal dynamics model may be referred to as the longitudinal dynamics equation. v˙Lon+g(μ cos(θ)+sin(θ))=(ggbxgrearrtire(∈(τ,ωin,ωout)τ−Jω˙)−12CdρAv2)1M

[0095] In equation 1, the parameters have the following meaning.

[0096] v̇ Lonis a rate of change of longitudinal velocity and can be estimated using a Kalman filter based on a vehicle speed. θ can be an offset-corrected inclination. In a specific situation, such as a vehicle breakdown, an inclination including an offset θ can be entered for a certain time. If, in this case, the number of corrections (or estimates) of the acceleration offset corrector 132 increases, it can be observed that the correction is being performed normally. g is the acceleration due to gravity of 9.8 m / s². 2 , and µ can be a rolling resistance constant such as 0.008. In the case of a vehicle with a transmission, g gbx a gear ratio of a current gear stage, and in the case of a vehicle with a deceleration device, it can be a deceleration ratio. g rearThis is a rear axle ratio and can be defined as 1 if there is no separate rear axle. r tire is the dynamic radius of the tire, which can be, for example, 0.5 m, and ∈ (τ, ω in , ω out ) the efficiency of the drive train (0-1), which can be determined experimentally. τ can be a motor torque Nm, w in can an input shaft speed and w out an output shaft speed. J can be an effective moment of inertia (kg · m). 2 ) of the powertrain, which can be determined experimentally. ω is an angular acceleration (rad / s²). 2 ) and can be calculated from an engine speed.

[0097] C dρ is an aerodynamic coefficient and can be determined experimentally. ρ can be determined from the atmospheric density, altitude, and ambient temperature. A is the effective frontal area of ​​a vehicle, v is the relative speed of the vehicle with respect to the surrounding air and depends on the influence of wind speed, although wind speed can be assumed to be 0 for weight estimation. M can be the mass (kg) of a vehicle.

[0098] The first to third weight estimators 138, 140, and 142 may contain a recursive least squares (RLS) estimator that uses RLS with a forgetting factor. The RLS with the forgetting factor is described below. y=Φx

[0099] RLS can be expressed in linear parametric form as Equation 2, using a given observed value y, an estimated value y, and a function Φ. In this case, an RLS estimate can be computed by a series of equations, namely, x̂(k) = x̂(k - 1) + L(k)(y(k) - Φ). T (k)x̂(k - 1)), L(k) = P(k - 1)Φ(k)(λ k + Φ T (k)P(k - 1)Φ(k)) -1 ‘ P(x)=1λk(1−L(k)ΦT(k))P(k−1). In the equations, k can represent the number of updates to the weight estimate performed in a weight estimator, or a stage index of the weight estimate. The forgetting factor λ can also be represented. kDetermine the degree of forgetting of information from past observations. When λ = 1, an RLS algorithm can be implemented such that previous observational information or previous estimation results are not forgotten. As λ approaches 0, RLS can aggressively forget the past estimation result and obtain observational information that is as close to the present as possible. As above, in the present disclosure, a first forgetting factor and a second forgetting factor are not time-variable, and only a third forgetting factor can be time-variable. Finally, in the present disclosure, the weight of a vehicle is to be estimated, where the variable x to be estimated can be defined as the inverse of the scaled mass, as illustrated in Equation 3. x=M0M

[0100] M0 is the reference mass and can be selected appropriately, taking into account the numerical error of an algorithm. The equation for the longitudinal dynamics according to Equation 1 can be found in =v˙Lon+g(μ cos(θ)+sin(θ))and Φ=1M0(ggbxgrearrtire(∈(τ,ωin,ωout)τ−Jω˙)−12CdρAv2) be replaced by equation 3

[0101] Applying RLS and the equation of longitudinal dynamics allows for an estimate x̂ of the weight-related variable x. As illustrated in Equation 4 below, the weight estimator M̂ can be obtained from x̂. M^=M0x^

[0102] Referring to Fig. Section 5 describes the structure of an RLS estimator used in every weight estimator. Fig. Figure 5 illustrates the RLS logic applied to a weight estimator.

[0103] The RLS estimator can generate a current estimate based on an operating mode parameter, current vehicle state information, and a past covariance of estimates and their covariance.

[0104] The operating mode parameters r transmitted by the Multi-RLS monitoring system 144 k and d k Each can specify a reset state related to whether RLS is reset or not and whether an update is permitted or not. The Multi-RLS Monitoring System 144 can r kas 1 in a vehicle operating state such as the first start and the initialization of the RLS. For example, the Multi-RLS Monitoring System 144 can determine the appropriateness of the equation for longitudinal dynamics based on the detection of an anomaly in a current sensor state, the determination of whether a retarder is in operation or not, and other vehicle data used for weight estimation, and d k output as 1. Meanwhile, the Multi-RLS monitoring system 144 can only use a forgetting factor λ in the case of a third weight estimator. k Vary the time-dependent values ​​and use a predefined constant for a different forgetting factor.

[0105] Meanwhile, it is not necessary to convert x into a vehicle weight, as an estimated value x is calculated by RLS. In particular, according to the equation described above, M^k=M0xk, x can be converted into a vehicle weight M̂ k The variance of the weight estimator can be determined by P(M^k)=P(xk)(dMdx)2, and the standard deviation of the weight estimator can be determined by STD(M^k)=|dMdx|P(xk)=M^kxP(xk) to be determined.

[0106] Meanwhile, if the forgetting factor λ is small and an observed input value y k or a noise level of the function Φ kIf an effect level not included in the model is high, a chatter problem may occur in the RLS output result. In the present disclosure, the chatter problem must be prevented by using an RLS result where λ is set small, so that the convergence rate can be improved and failover can be achieved for a situation in which weight estimation is impossible. A simple low-pass filter is capable of preventing the chatter problem, but its use may not be suitable if a sudden change in the filter output is physically acceptable, such as during the initial weight estimation or a sudden change in the vehicle weight due to a dropped object. In the present disclosure, a filter used in a weight estimator may be an adaptive rate-limited filter.In addition to preventing chatter problems, the filter can have a filter output that rapidly approximates a filter input in a given situation. The adaptive rate limiting filter can be a filter that prevents an output from deviating excessively from a previous output at each operating cycle time of the individual logic. For an estimated weight M̂. k , M̂ o = M̅0; f u,k ≥ M̅ k - M̅ k-1 ≥ f l,k , f u,k ≥ 0, f l,k ≤ 0 between the output of an adaptive rate limiting filter M̅ k-1 a previous step and the current filter output are applied M̅ k . In detail, a filter output can be determined by the following procedure. if M^k>M¯k−1 [if M^k>M¯k−1+fu,k,M¯k=M¯k−1+fu,k, otherwise M^k=M¯k−1] otherwise [if M^k>M¯k−1−fl,k,M¯k=M¯k−1−fl,k,otherwise M^k=M¯k−1]

[0107] Meanwhile, the upper and lower rate limits of a filter can be adaptively determined by an input / output deviation of the filter. That is, if there is a large deviation between the filter input and the filter output, the filter output can quickly follow a result of the weight estimation by adjusting f u,k and f l,k can be increased, and in the case of a small deviation, an unnecessary fluctuation in the filter output can be suppressed by f u,k and f l,k can be reduced. This can be achieved by ΔM k = |M̂ k - M̅ k |,f u,k = f u (ΔM k-1 ), f l,x = f l (ΔM k-1 ) can be printed. The functions f u,k and f l,k can be determined based on experience. For example, the functions described above can be f u,k and f l,kIt can be defined as a piecewise linear function. In a specific application, adjustments may be necessary due to the noise level of individual sensors or the characteristics of a vehicle.

[0108] In summary, any weight estimator can perform an RLS estimation based on the hyperparameters P0, x0 and M0, the vehicle state information and the operating mode parameters r k , d k and λ k based on a result of the weight estimation. A post-processor can calculate an estimated weight and a standard deviation based on an output value of RLS, and a filtered estimated weight M̅. k can be computed using an adaptive rate-limiting filter to avoid the chatter problem. The final output of each weight estimator can be derived from the weight estimate values ​​M̂ k and M̅ kand a standard deviation STD(M̂ k ) of the current estimate.

[0109] The weight estimation method of this embodiment is described below in accordance with the Fig. 3 and Fig. 4 based on the Fig. 6 to Fig. 11 described in detail. The weight estimation method can be mainly carried out by the processor 122 of the vehicle 100, and the processor 122 and the vehicle 100 can be described interchangeably. Fig. Figure 6 is a flowchart of a method for estimating a weight according to another embodiment of the present disclosure.

[0110] Initially, the processor 122 of the vehicle 100 can be operated by a user and receive vehicle data (S105).

[0111] For example, the user might turn the vehicle's start button from "off" to "on" and the vehicle is ready to drive. The vehicle data can be stored in Fig. 3 described data, i.e., data used in an equation for longitudinal dynamics.

[0112] Next, the vehicle's processor 122 can generate first estimated weight information and second estimated weight information based on the vehicle data through the first weight estimator 138 and the second weight estimator 140, which use RLS based on a first forgetting factor and a second forgetting factor respectively (S110).

[0113] The second forgetting factor may have a lower forgetting characteristic than the first forgetting factor, but a higher value than the first forgetting factor. Accordingly, the first weight estimator 138 may perform an aggressive weight estimation as its first weight estimation, and the second weight estimator 140 may perform a conservative weight estimation as its second weight estimation compared to the first weight estimator 138. The generation of the first and second estimated weight information is essentially the same as in the Fig. 3 to Fig. 5 described.

[0114] Next, based on a vehicle operating state, a weight estimation error state related to a deviation state of the first weight estimation, or a difference between the first estimated weight information and the second weight estimation (S115), the processor 122 can determine whether or not there is an update request for the third weight estimator 142.

[0115] The vehicle's operating state can include a state in which the vehicle 100 is switched from the OFF to the ON state (e.g., the vehicle 100 transitions from an OFF state to an ON state). If the start process is not running, a reset state is entered. kFor example, the reset status is specified as 1, and if the startup process is running, the reset status can be specified as 0. An aggressive update request can only occur if the reset status changes to a vehicle operating state where the startup process switches from OFF to ON, i.e., when the reset status changes from 1 to 0. Furthermore, the vehicle operating state can be the operating state of a module of the vehicle 100 involved in weight estimation. For example, a module that outputs vehicle data for weight estimation could be a powertrain component 126 or a sensor for vehicle speed or braking requirement (e.g., EBS 128, wheel speed sensor 102d). If the operating state of the modules is detected as faulty, the reset status becomes 1, and a reset command can be generated.The content of an aggressive update request relating to the operating state of a vehicle is described in detail below.

[0116] Depending on whether the weight estimation error state e kWhether or not an update request exists, the weight estimation error state can be triggered. The weight estimation error state can have a different value depending on whether the first estimated weight information from the first weight estimator 138 deviates from a predefined criterion. If the first estimated weight information deviates from the predefined criterion, the weight estimation error state can be designated as 1, and if the first estimated weight information does not deviate, the weight estimation error state can be represented as 0. For example, the deviation for which the weight estimation error state is represented as 1 can occur because the correction of the acceleration offset corrector 132 is neither temporary nor normal.For example, if the correction of an acceleration offset due to a change in inclination during the journey or a replaced component of the vehicle 100 is not carried out temporarily or normally, unstable variability of the vehicle data input into the first weight estimator 138 may cause the deviation of the first weight estimate.

[0117] Furthermore, apart from the event described above, an aggressive update request may be triggered by a discrepancy between the initial and second estimated weight information caused by a sudden change in the weight of vehicle 100 while it is in motion. This sudden change could be caused, for example, by an object falling from the loaded vehicle 100.

[0118] The decision as to whether an aggressive update is occurring or not is made by the Multi-RLS monitoring system 144, which refers to Fig. 7 to Fig. 10 is described in detail. Fig. Figure 7 illustrates the logic processed in a multi-RLS monitoring system.

[0119] The Multi-RLS Monitoring System 144 can determine whether an aggressive update is occurring and also generate an adaptive third forgetting factor, which is passed to the third weight estimator 142. To perform the above, the Multi-RLS Monitoring System 144 can functionally include and process vehicle state determination logic, convergence state determination logic, and forgetting factor determination logic.

[0120] The vehicle condition determination logic is based on Fig. 8 described. Fig. Figure 8 is a flowchart of a process that is carried out in a vehicle state determination logic.

[0121] The processor 122 of the vehicle 100 can check whether the start state is on or off (S205), and if the start state is on, the processor 122 can detect whether a module of the vehicle 100 that outputs vehicle data for weight estimation is failing or not (S210). For example, the module of the vehicle 100 could be the powertrain component 126 or the EBS 128. If the module is not failing, the processor 122 can determine a reset status r as the operating state of the vehicle. kDetermine a value from 0, which corresponds to dissatisfaction, and pass this to the convergence state determination logic (S215). Here, the subscription k is a discretized time index of the weight estimation performed sequentially in a weight estimator, and in the present disclosure it may be called a stage index.

[0122] Processor 122 can determine whether an operating state of the vehicle module 100 is applicable to the longitudinal dynamics equation (S220) described above for outputting vehicle data. For example, releasing the lock of a torque converter, actuating a hydrodynamic retarder, and applying a service brake may be operating states that are difficult to model using the dynamics equation.

[0123] In the event that no operating condition occurs for which modeling is difficult, processor 122 can determine whether the model data Φ in y = Φx according to equation 2 is greater than a constant c1, which is a model threshold (S225). As Φ approaches 0, x becomes increasingly indeterminate due to an observed value y, and since the influence of observational noise on the error of x increases, step S225 can be performed. For example, c1 can be set to 0.1.

[0124] If the model data Φ is greater than the model threshold c1, processor 122 can determine whether a condition for permitting the inclination calculation for the acceleration offset corrector 132 is met (S230). Since the inclination calculation is used as an input factor for the weight estimation, processor 122 can check whether a condition prohibiting the inclination update exists. If the inclination calculation permit condition is met, processor 122 can send a signal to request updates of the first and second weight estimates to the first and second weight estimators 138 and 140 (S235). The request signal can be generated to include a first update request and a second update request. dk2 to mark as 1.

[0125] In steps S205 and S210, if the start state is switched off or a failure of the vehicle module 100 is detected, the processor 122 can reset the vehicle's operating state to the reset status r. k Determine as 1, which corresponds to a satisfaction, supply it to the convergence state determination logic, and transmit a signal to the first to third weight estimator 142 to request that the update of the first to third weight estimators not be performed (S245). The non-performance signal can be generated to suppress the first update request. dk1, the first update request dk3 and the third update request dk3 Mark as 0.

[0126] Even if the reset status does not occur in step S215, if the operating state of the vehicle module 100 for outputting vehicle data in step S220 is not applicable to the longitudinal dynamics equation, the signal to request not to update the first to third weight estimators can be transmitted to the first to third weight estimator 142 (S245). Furthermore, if in step S225 the model data Φ is less than the constant c1, which is the model threshold, or if in step S230 the condition for approving the inclination calculation is not met, the processor 122 can send the signal to the first to third weight estimator 142 (S245) requesting not to update the first to third weight estimators.

[0127] The following describes the logic for determining the state of convergence with reference to Fig. 9 described. Fig. Figure 9 is a flowchart of a procedure for determining the weight estimation error state, which is carried out in the convergence state determination logic.

[0128] The processor 122 can have a stage index k, a weight estimation error state e k and the number of acceleration offset estimates N k Initialize to 0 (S305). The logic for determining the weight estimation error state can initialize the weight estimation error state e k output as a non-zero value only if the start state is enabled, and a weight estimation error state can be determined using the method of Fig. 9 can be determined when the vehicle operating state is switched on. The number of acceleration offset estimates N k This can be the number of acceleration offset estimates received when the k-th stage index was executed. If there is no new acceleration estimate, Nk = N k-1 .

[0129] While the stage index is increasing (S310), processor 122, when the startup state is enabled (Y of S315), can determine the number of acceleration offset estimates and the first estimated weight information from the first weight estimator 138 (S320). The first weight estimator 138 can generate the first estimated weight information through a first update request, and the identified first estimated weight information can provide a standard deviation. STD(M^k1) of the first estimated weight for the first estimated weight. Meanwhile, in step S315, if the start state is off (N of S315), it can be determined that there is no weight estimation error state for the current step.

[0130] Next, processor 122 can determine whether a weight estimation error condition from a previous step (k-1) exists (S325). In particular, processor 122 can check whether e k-1 is equal to 1 or not. If there is no weight estimation error state from the previous step, processor 122 can determine whether the standard deviation is STD(M^k1) The first estimated weight is greater than a deviation reference value c2 (S330) or not. The deviation reference value can be 30,000 kg. If the standard deviation is greater than the deviation reference value, processor 122 can determine that a weight estimation error condition of the current step (e) exists. k = 1) is present (S335).

[0131] Meanwhile, processor 122 can, if in step S325 a weight estimation error state of the previous step (e k-1If = 1), the acceleration offset corrector 132 determines whether a new estimate of the acceleration offset is available or not (S340). If no new estimate is available (N k = N k-1 ), processor 122 can maintain or determine the weight estimation error state of the current step (e k = 0). However, if a new estimate is available (N k ≠ N k-1 ), the processor 122 can proceed to step S330 and determine, based on a current standard deviation of the first weight estimate, whether a current weight estimation error state exists or not.

[0132] After it has been determined in steps S335 and S350 whether or not there is a weight estimation error condition of the current step, if the vehicle 100 has not been switched off (N of S350), steps S310 to S350 can be repeated to check whether or not there is a weight estimation error condition of a next step.

[0133] The logic relating to the weight estimation error state can generate a signal relating to a weight estimation error state and prevent the estimates from exhibiting a chatter problem due to the signal when a normal weight estimation is impossible due to an abnormal correction of the acceleration offset corrector 132, which is due to abnormal behavior of an accelerometer.

[0134] The following section describes the logic for determining the forgetting factor, with reference to Fig. 10 described. Fig. Figure 10 is a flowchart of a process relating to an aggressive update request of the convergence state determination logic.

[0135] The processor 122 of the vehicle 100 can initialize a stage index (S405) to follow a stage index of steps S305 and S310, similar to those steps, and increment the stage index (S410).

[0136] Next, processor 122 can identify the state and information of a current step, which it has received from a vehicle state determination logic, a convergence state determination logic, and the first and second weight estimators 138 and 140, i.e., a reset state r. k , a weight estimation error state e k and first and second estimated weight information M^k1,M^k1,M^k2,M^k2 (S415).

[0137] Next, the processor 122 can generate a difference between the first and second estimated weight information, a difference level variable, a set of difference variables, and a state variable (S420).

[0138] In step S420, data can be generated to determine, based on the difference between the first and second estimated weight information, whether an aggressive update request exists or not.

[0139] With regard to the generated data, as with the difference between the first and second estimated weight information, a variable representing a difference between the estimated weight information output by the first and second weight estimators 138 and 140 can be called δ k for each stage index k. As illustrated in equation 5, δ can k as a function of the first and second weight estimates M^k1 M̅ k and M^k2 and the filtered first and second weight estimates M¯k1 M̅ k and M¯k2 defined, which are calculated by an adaptive rate limiting filter. δk=g(M^k1,M¯k1,M^k2,M¯k2)

[0140] The equation below can be described, for example, as equation 6. δk=|M¯k1−M^k2|

[0141] The equation above can be formulated taking into account the fact that an unnecessarily aggressive update request may occur according to a filter setting parameter if the behavior of the second weight estimator 140 changes conservatively. As illustrated in Equation 7, an aggressive update request can be determined based on a reset condition, the determination of an estimated weight estimation error state, and a difference between the first and second weight estimation information. ak=a(δ[.],r[.],e[.])

[0142] The indices are omitted in the equation above because it is assumed that a k determined by using multiple data points from the past, if necessary.

[0143] An aggressive update request based on a difference between the first and second estimated weight information can be determined as follows. First, a difference level variable, i.e., a logic variable D, can be used. k , determined by the following equation 8 (or logic equation). Dk={0,k≤0δk>c3,k>0

[0144] If the above logical equation is true, the logical variable can be 1, and if the above logical equation is not true, the logical variable can be 0. Here, c3 is a constant representing the difference between the first and second estimated weight information, and if, for example, the maximum weight of a large truck is approximately 35 tons, c3 can be set to 40,000 kg. Furthermore, a difference variable set S can be used. k S k = (D k-Nv+1 , D k-Nv+2 , ..., D k} be defined. S kcan therefore be a set of D [.] be as large as the previous windows Nv. A state variable ξ k can be considered ξ k = min(S k ) can be defined. For example, if k > 100, N v If the time is set to 100 and 10 seconds, the value of ξ can be k only be 1 if the logical equation D k is true 100 times in a row. That means, ξ k is set to 1 when signal D [.] during the last N v Counts are continuously true, and ξ k is set to 0 when the signal D [.] during the last N v The counts are incorrect one or more times.

[0145] Next, the processor 122 can determine whether or not there is an aggressive update request for the third weight estimator 142, based on a reset status as a vehicle operating state, a weight estimation error state, or a difference between the first estimated weight information and the second estimated weight information (S425).

[0146] A logic variable β k To determine whether the query exists or not, the equation shown in equation 9 below can be used. In the following equation, '||' is an OR operator. βk={0,k≤0[rk==1‖ek==1‖(ξk>ξk−1)],k>0

[0147] As can be seen from the equation above, a precondition that depends on the presence of an aggressive update requirement can be a k refers to being considered fulfilled when an event occurs in which a reset station r kequal to 0, a weight estimation error state e k occurs, or a difference in the state variables between previous and current steps occurs based on a difference between the first and second weight information. If the reset status r k or the fault condition e k If the above condition is true, the third weight estimator 142 is prohibited from updating its weight estimate. On the other hand, as described below, a logic for maintaining a predetermined number of update requests prohibits updating the weight estimators at the moment the reset status r k or the fault condition e k This is true, but an aggressive update can be performed a predetermined number of times from the time the prohibition condition is lifted.

[0148] Processor 122 maintains a state corresponding to an event for a predetermined duration. During this maintained state, if Processor 122 detects that the startup process is enabled, a normal acceleration offset is being corrected, or a difference in state variables is being maintained, Processor 122 can generate an aggressive update request after the predetermined duration has elapsed. In summary, if an aggressive update request is triggered by a weight estimation error state and a reset state following a normal weight estimation performed due to the startup process being enabled or an acceleration offset correction, the third weight estimator 142 will issue an aggressive update request, thus generating the final weight information according to the aggressive update.An aggressive update request based on the difference can be activated if a continuous difference between the first and second estimated weight information is greater than a threshold difference.

[0149] A set of conditions T k who have had so many Ns in the past w accumulated, and the aggressive update request a k can each be considered T k = {β k-Nw+1 , β k - Nw+2 , ..., β k} and a k = max(T k ) are defined.

[0150] If β [.] If at any time the value is recognized as 1, the processor 122 can request the third weight estimator 142 to perform an aggressive update with a predetermined number of update times N. w to perform. N wFor example, it could be 1,200 times, or 120 seconds. If the reset status or weight estimation error state in the equations above is 0, if a previous difference between the first and second estimated weights was equal to or less than cc3, but a difference between estimated weights continuously exceeds c3 during a predetermined number of counts, i.e., for a predetermined duration or longer, it is estimated that the physical condition of the vehicle has changed due to a dropped load, and the equations above may mean that a weight estimation for time N w (or number of times) is aggressively updated. Meanwhile, steps S410 to S425 can be repeated until the vehicle is 100% stationary (S430).

[0151] Referring to Fig. 6. When an aggressive update request for the third weight estimator 142 occurs in step S115, the processor 122 of the vehicle 100 can determine an adaptive third forgetting factor as a preset aggressive forgetting factor by means of a forgetting factor determination logic and transmit the aggressive forgetting factor and a third update request to the third weight estimator 142 (S120).

[0152] Step S120 is described with reference to Fig. 11 described. Fig. Figure 11 is a flowchart of a process carried out in the forgetting factor determination logic.

[0153] The processor 122 can, according to a stage index k, provide initial values ​​for the first and second forgetting factors. λ01 and λ02 and the third forgetting factor λ03 as well as an aggressive forgetting factor λf3 (S505) determine. Next, processor 122 can initialize the stage index and the third forgetting factor (S510) and increment the stage index (S515).

[0154] Next, if an aggressive update request occurs for the third weight estimator 142 (Y of S520, a k = 1), the third forgetting factor lk3 Use the preset aggressive forgetting factor Al (S525). The aggressive forgetting factor must not be determined based on the first and second estimated weight information, but can be set to a preset value. The aggressive forgetting factor can be set according to a design specification so that as much past data as possible is forgotten and data from the near present is preferably used.

[0155] Subsequently, the processor 122 can generate ultimate weight information based on vehicle data by aggressively updating the third weight estimator 142 with the aggressive forgetting factor for a number of times or a time period required by the aggressive update request (S125).

[0156] The third forgetting factor of the third weight estimator 142, which performs the aggressive update, is not affected by estimated weight information caused by the updates of the first and second weight estimators 138 and 140. Even if a preset value is used, an update of the weight estimate by the first and second weight estimators 138 and 140 can be performed during the aggressive update. This is to ensure that the divergence and difference of the first and second estimated information approximate the actual weight information while the aggressive update is being performed, and that the third forgetting factor is determined based on the converging information. After a third weight estimate has been performed by the aggressive update, processor 122 can proceed to step S115.

[0157] Meanwhile, if there is no aggressive update request for the third weight estimator 142 in step S115, the processor 122 can determine an adaptive third forgetting factor based on the first and second estimated weight information through the forgetting factor determination logic and deliver the determined third forgetting factor and a third update request to the third weight estimator 142 (S130).

[0158] Regarding step S130, which relates to Fig. 11 refers to the processor 122 if an aggressive update request for the third weight estimator 142 does not occur (Y of S520, a k= 0), determine the third forgetting factor based on a difference between the first and second estimated weight information according to the update of the weight estimate in the first and second weight estimators 138 and 140 (S330). Steps S515 to S530 can be repeated until the vehicle comes to a standstill.

[0159] As illustrated in Equation 10 below, an adaptive third forgetting factor can lk3 Based on the weight estimation information from the first and second weight estimators 138 and 140, the values ​​vary. λk3=h(M^k1,M¯k1,M^k2,M¯k2)

[0160] For example, the third forgetting factor lk3 Depending on the section, the difference between the first and second estimated weight information may be determined differently, as illustrated in Equation 11. λk3=h(M^k1,M¯k1,M^k2,M¯k2)={λmin3, δk<c5 λmax3−λmin3c6−c5(δk−c5)+λmin3,c5≤0≤c6 λmax3,δk> c6

[0161] As described above, the first and second forgetting factors may lk1 and lk2 They must not vary over time, but must remain constant. In the equation above, the parameters can, for example, be the first forgetting factor of λ01=0.95, second forgetting factor of λ02=0.999,λmin3=0.99,λmax3=0.999, c5 = 2,000 kg and c6 = 4,000 kg. The forgetting factors listed above can be determined empirically using a sensor measurement and a disturbance level, which are used as inputs to a longitudinal dynamics model.

[0162] Next, processor 122 can generate estimated weight information based on vehicle data by means of the third weight estimator 142, to which the determined third forgetting factor is applied (S135). Meanwhile, if no aggressive update request occurs after the aggressive update of the third weight estimator 142, processor 122 can generate the final estimated weight information using the third weight estimator 142 through steps S130 and S135.

[0163] Processor 122 can then check whether the startup process has finished after generating the final estimated weight information. If Processor 122 confirms that the startup process has not finished, it repeats the process after step S110.

[0164] Fig. Figure 12 is a view illustrating data of a result to which a weight estimation procedure according to the present disclosure is applied.

[0165] Fig. Figure 12 illustrates a fail-safe weight estimation and a procedure for reverting to a normal weight estimation when only real driving data of a vehicle weighing 14 tonnes with an inaccurate acceleration offset are available. Initially, an acceleration offset converted to gradients of 0.017 rad is incorrectly assumed, but when the actual acceleration offset converted to gradients is -0.033 rad, a weight estimation algorithm incorrectly recognizes that the vehicle is traveling uphill at a gradient of approximately 5%, when in fact it is traveling on a level surface. Accordingly, as shown in (a) of Fig. Figure 12 illustrates that the first weight estimator does not show good convergence, and the second weight estimator illustrates a miscalculation that the vehicle has a higher weight than its actual weight.

[0166] As in (b) of Fig. Figure 12 shows that the first weight estimator and a third weight estimator, following a normal acceleration-offset estimation, converge to the actual weight of 14 tonnes in approximately 600 seconds. The diagrams in (c) illustrate this. Fig. 12 show a k , lk3, e k from above. Until approximately 1,800 seconds have elapsed, a k The value is retained as 1 because the estimated weights of the first and second weight estimators differ. After an error in weight estimation is detected within approximately 60 seconds, the weight estimation error e is set to 1. kheld at 1, and the update of the weight estimate of the third weight estimator is prohibited until the offset correction is processed again.

[0167] Fig. Figure 13 is a view illustrating other data of a result to which a weight estimation procedure is applied in accordance with the present disclosure.

[0168] Fig. Figure 13 illustrates real driving data of a vehicle weighing 14 tonnes and a procedure for estimating weight when an acceleration offset is normally applied. As in (a) of Fig. Figure 13 illustrates that a first weight estimator converges relatively quickly from an initial assumption of 25 tons to the actual weight of 14 tons, but initially demonstrates unstable behavior with a shortfall of 10 tons or less. On the other hand, a second weight estimator converges steadily to 14 tons. However, when a reference point of 16 tons is input with a relative error rate of 16%, this takes approximately 320 seconds. Meanwhile, for a third weight estimator, it takes approximately 170 seconds to converge to 16 tons. Furthermore, the third weight estimator exhibits a small fluctuation but does not demonstrate as significant a shortfall as the first weight estimator.

[0169] (am Fig. Figure 13 is the result of an estimation of the acceleration offset by inclination conversion and illustrates that the result varies. The diagrams of (c) in Fig. 13 show ak , lk3, e k from above. a k The value = 1 is maintained because 120 seconds pass after the initial reset, and a difference then arises between the first and second weight estimates. Once both the first and second weight estimators have approximated the actual weight, RLS3 is operated stably by maintaining a high lk3 will be maintained.

[0170] While the processes of this disclosure are presented as a series of operations for the sake of clarity, the order in which the steps are carried out is not intended to be restricted. The steps may be carried out simultaneously or in different orders if necessary. Furthermore, to carry out the process according to this disclosure, the described steps may include other or additional steps, may include the remaining steps except for some of the steps, or may include other additional steps except for some of the steps.

[0171] The various examples in this revelation are not a list of all possible combinations, but serve to describe representative aspects of this revelation. The aspects or characteristics described in the various examples can be applied independently of one another or in combination with two or more.

[0172] Furthermore, various embodiments of the present disclosure can be implemented in hardware, firmware, software, or a combination thereof. In the case of hardware implementation of the present disclosure, it can be carried out using application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, etc.

[0173] A method for weight estimation based on multiple Recursive Least Squares (RLS) with a forgetting factor can be performed by a device of a vehicle.The weight estimation procedure may include: generating first estimated weight information and second estimated weight information based on vehicle data by means of a first weight estimation and a second weight estimation that use an RLS based on a first forgetting factor and an RLS based on a second forgetting factor with a lower forgetting characteristic than the first forgetting factor; determining an adaptive third forgetting factor of a third weight estimation constructed by an RLS based on the first estimated weight information and the second estimated weight information; and generating final estimated weight information based on the vehicle data by means of the third weight estimation to which the determined third forgetting factor is applied.

[0174] The third forgetting factor can be determined differently depending on the section of the difference between the first estimated weight information and the second estimated weight information.

[0175] The procedure may further include: determining whether or not there is an aggressive update request for the third weight estimate, based on at least one of a vehicle's operating conditions, a weight estimate error condition relating to a deviation condition of the first weight estimate, or the difference between the first estimated weight information and the second estimated weight information; and applying, in response to the aggressive update request, an aggressive forgetting factor to the third forgetting factor and generating, by aggressively updating the third weight estimate, the final weight information based on the vehicle data.

[0176] Determining the third forgetting factor based on the first estimated weight information and the second estimated weight information can be performed in response to the absence of an aggressive update request.

[0177] The aggressive update of the third weight estimate can be performed a predetermined number of times and further includes generating, after the aggressive update of the third weight estimate, the final weight information based on the vehicle data by the third weight estimate with the third forgetting factor, which is determined based on the first estimated weight information and the second estimated weight information.

[0178] The vehicle's operating state includes a state in which the vehicle's start can be switched from off to on.

[0179] The weight estimation error state can consider the initial weight estimate as a deviation state if a standard deviation of the initial estimated weight information is greater than a deviation reference value due to an acceleration deviation caused by a tilt or a replaced component of the vehicle.

[0180] The procedure may further include correcting the acceleration offset prior to generating the final weight information by aggressively updating the third weight estimate, wherein the aggressive update of the third weight estimate is performed after the acceleration offset has been corrected.

[0181] Determining whether or not the aggressive update request can occur based on the first estimated weight information and the second estimated weight information may involve enabling the aggressive update request if a successive difference between the first estimated weight information and the second estimated weight information generated in the time sequence is greater than a threshold difference.

[0182] The procedure may further include performing an update of the first weight estimate and the second weight estimate, while the aggressive update of the third weight estimate is carried out.

[0183] Vehicle that performs a weight estimation based on multiple recursive least squares (RLS) with a forgetting factor, wherein the vehicle may include: a memory configured to store at least one instruction for controlling the vehicle;and a processor configured to execute the at least one instruction stored in memory, wherein the processor is configured to: generate first estimated weight information and second estimated weight information based on vehicle data by means of a first weight estimator and a second weight estimator using an RLS based on a first forgetting factor and an RLS based on a second forgetting factor with a lower forgetting feature than the first forgetting factor; determine an adaptive third forgetting factor of a third weight estimator constructed by an RLS based on the first estimated weight information and the second estimated weight information; and generate final estimated weight information based on the vehicle data by means of the third weight estimator to which the determined third forgetting factor is applied.

[0184] The scope of the disclosure includes software or machine-executable instructions (e.g., an operating system, an application, firmware, a program, etc.) that enable operations to be carried out on a device or computer according to the procedures of various examples, as well as a non-volatile, computer-readable medium on which such software or instructions are stored and which can be executed on the device or computer.

Claims

[1] Method carried out by a device of a vehicle, the method comprising: Determine, based on an initial weight estimate applied to vehicle data, first estimated vehicle weight information, wherein the initial weight estimate is based on Recursive Least Squares (RLS) associated with an initial forgetting factor; Determine, based on a second weight estimate applied to the vehicle data, second estimated vehicle weight information, wherein the second weight estimate is based on RLS associated with a second forgetting factor; Determine, based on the first estimated weight information and the second estimated weight information, a third forgetting factor for RLS of a third weight estimate; Determine, based on the third weight estimate applied to the vehicle data, a third estimated vehicle weight information, and Steering the vehicle based on the third estimated weight information. [2] Method according to claim 1, wherein the third forgetting factor is determined differently based on a difference between the first estimated weight information and the second estimated weight information, and wherein the second forgetting factor has a lower forgetting feature than the first forgetting factor. [3] Method according to claim 1, further comprising: Determine whether an aggressive update request for the third weight estimate occurs, based on at least either a vehicle operating condition or a weight estimation error condition related to a deviation state of the first weight estimate, or on a difference between the first estimated weight information and the second estimated weight information; and Applying, based on the aggressive update requirement, an aggressive forgetting factor to the third forgetting factor, where the aggressive forgetting factor specifies a forgetting characteristic associated with an increased forgetting rate, and where determining the third estimated weight information includes determining the third estimated weight information based on the vehicle data by aggressively updating the third weight estimate. [4] Method according to claim 1, further comprising: Determine whether an aggressive update request for the third weight estimate occurs, based on at least either a vehicle operating condition or a weight estimation error condition related to a deviation state of the first weight estimate, or on a difference between the first weight estimate and the second weight estimate. where determining the third forgetting factor includes determining the third forgetting factor based on a determination that no aggressive update request occurs, and wherein a conservative forgetting factor is applied to the third forgetting factor, and wherein the conservative forgetting factor specifies a forgetting feature that is associated with a reduced forgetting rate. [5] Method according to claim 3, wherein the aggressive update of the third weight estimate is performed a predetermined number of times, and wherein determining the third estimated weight information comprises determining the third estimated weight information after the aggressive update of the third weight estimate. [6] Method according to claim 3, wherein the operating state of the vehicle comprises a transition state from an OFF state to an ON state. [7] Method according to claim 3, wherein the weight estimation error state comprises the first weight estimation which is a deviation state, wherein a standard deviation of first estimated weight information is greater than a deviation reference value based on an acceleration offset caused by an incline on which the vehicle is traveling or a replaced component of the vehicle. [8] Method according to claim 7, further comprising correcting the acceleration offset prior to determining the third estimated weight information by aggressively updating the third weight estimate, wherein the aggressive update of the third weight estimate is performed after the acceleration offset has been corrected. [9] Method according to claim 3, wherein determining whether the aggressive update request occurs comprises activating the aggressive update request based on a successive difference between the first estimated weight information and the second estimated weight information that is greater than a threshold difference, and wherein the successive difference is determined over time. [10] The method of claim 3, further comprising performing an update of the first weight estimate and the second weight estimate while the aggressive update of the third weight estimate is performed. [11] Vehicle encompassing: a memory that stores at least one instruction for controlling the vehicle; and a processor configured to execute at least one instruction stored in memory, where at least one instruction is set up to cause the vehicle to: when executed by the processor. Determine, based on an initial weight estimate applied to vehicle data, first estimated vehicle weight information, wherein the initial weight estimate is based on Recursive Least Squares (RLS) associated with an initial forgetting factor; Determine, based on a second weight estimate applied to the vehicle data, second estimated vehicle weight information, wherein the second weight estimate is based on RLS associated with a second forgetting factor; Determine, based on the first estimated weight information and the second estimated weight information, a third forgetting factor for RLS of a third weight estimate; Determine, based on the third weight estimate applied to the vehicle data, a third estimated vehicle weight information; and Steering the vehicle based on the third estimated weight information. [12] Vehicle according to claim 11, wherein the third forgetting factor is determined differently based on a difference between the first estimated weight information and the second estimated weight information, and wherein the second forgetting factor has a lower forgetting feature than the first forgetting factor. [13] Vehicle according to claim 11, wherein the at least one instruction is configured to cause the vehicle to: when executed by the processor. Determine whether an aggressive update request for the third weight estimate occurs, based on at least either a vehicle operating condition or a weight estimation error condition related to a deviation state of the first weight estimate, or a difference between the first estimated weight information and the second estimated weight information, and Applying, based on the aggressive update requirement, an aggressive forgetting factor to the third forgetting factor, where the aggressive forgetting factor specifies a forgetting characteristic associated with an increased forgetting rate, and Determining the third estimated weight information by aggressively updating the third weight estimate to determine the third estimated weight information based on the vehicle data. [14] Vehicle according to claim 11, wherein the at least one instruction is configured to cause the vehicle to: when executed by the processor. Determine whether an aggressive update request for the third weight estimate occurs, based either on a vehicle operating condition or a weight estimation error state related to a deviation state of the first weight estimate, or on a difference between the first estimated weight information and the second estimated weight information; and Determining the third forgetting factor by determining the third forgetting factor based on the finding that the aggressive update request does not occur, and wherein a conservative forgetting factor is applied to the third forgetting factor, and wherein the conservative forgetting factor specifies a forgetting feature that is associated with a reduced forgetting rate. [15] Vehicle according to claim 13, wherein the aggressive update of the third weight estimate is performed a predetermined number of times, and wherein the at least one instruction is provided to cause the vehicle, when executed by the processor, to determine the third estimated weight information by determining the third estimated weight information after the aggressive update of the third weight estimate. [16] Vehicle according to claim 13, wherein the operating state of the vehicle comprises a state of transition from an OFF state to an ON state. [17] Vehicle according to claim 13, wherein the weight estimation error state comprises the first weight estimation which is a deviation state, wherein a standard deviation of first estimated weight information is greater than a deviation reference value based on an acceleration offset caused by an incline on which the vehicle is traveling or a replaced component of the vehicle. [18] Vehicle according to claim 17, wherein the at least one instruction is configured to cause the vehicle, when executed by the processor, to correct the acceleration offset before determining the third estimated weight information by aggressively updating the third weight estimate, and wherein the aggressive update of the third weight estimate is performed after the acceleration offset has been corrected. [19] Vehicle according to claim 13, wherein the at least one instruction is configured to cause the vehicle, when executed by the processor, to activate the aggressive update request based on a successive difference between the first estimated weight information and the second estimated weight information that is greater than a threshold difference, and wherein the successive difference is determined in the time sequence. [20] Vehicle according to claim 13, wherein the at least one instruction is configured to cause the vehicle, when executed by the processor, to perform an update of the first weight estimate and the second weight estimate while the aggressive update of the third weight estimate is performed.

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