Energy consumption processing method and system of unmanned vehicle

Abstract

The invention relates to an energy consumption processing method and system of an unmanned vehicle, and relates to the technical field of vehicles, and the method comprises the steps: determining an absolute direction for the unmanned vehicle; the driving state of the unmanned vehicle is monitored in real time, and the actual physical driving direction of the driving state relative to the absolute direction is determined; collecting energy consumption data of a plurality of driving motors of the unmanned vehicle through a plurality of driving motor controllers configured on the unmanned vehicle; determining a label for the collected energy consumption data in combination with the actual physical driving direction, analyzing and processing the energy consumption data according to the label, and determining energy consumption values of the unmanned vehicle in multiple dimensions; and storing the energy consumption value of the unmanned vehicle. The data reliability can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and more specifically, to an energy consumption management method and system for autonomous vehicles. Background Technology

[0002] The energy consumption statistics method for four-wheel drive vehicles cannot be matched with that of autonomous vehicles traveling in both directions, mainly in the following aspects: The energy statistics model is based on a fixed driving direction. When moving forward, the motor consumes energy, and when braking, energy is fed back. In the process of bidirectional driving, forward and backward are relative concepts, and the direction is ambiguous. In related technologies, due to the unclear definition of direction, the energy statistics report and data analysis do not match the actual physical state of the vehicle, reducing the reliability and usability of the data. Summary of the Invention

[0003] The purpose of this disclosure is to provide an energy consumption processing method and system for autonomous vehicles, thereby overcoming, to at least some extent, the problems of data not matching the actual physical state and low reliability caused by the limitations and defects of related technologies.

[0004] According to one aspect of this disclosure, an energy consumption processing method for an autonomous vehicle is provided, comprising: determining an absolute direction for the autonomous vehicle; monitoring the driving state of the autonomous vehicle in real time and determining the actual physical driving direction of the driving state relative to the absolute direction; collecting energy consumption data of multiple drive motors of the autonomous vehicle through multiple drive motor controllers configured on the autonomous vehicle; determining labels for the collected energy consumption data in conjunction with the actual physical driving direction, and analyzing and processing the energy consumption data according to the labels to determine energy consumption values ​​of the autonomous vehicle in multiple dimensions; and storing the energy consumption values ​​of the autonomous vehicle.

[0005] In one exemplary embodiment of this disclosure, the driverless vehicle is a bidirectional driverless vehicle; the real-time monitoring of the driving state of the driverless vehicle and the determination of the actual physical driving direction of the driving state relative to the absolute direction includes: monitoring the heading angle and wheel speed of the driverless vehicle; comparing the heading angle with the absolute direction to determine a preliminary direction; and correcting the preliminary direction based on the wheel speed to determine the actual physical driving direction.

[0006] In one exemplary embodiment of this disclosure, determining a label for the collected energy consumption data in conjunction with the actual physical driving direction includes: classifying the collected energy consumption data according to the actual physical driving direction to determine a direction label.

[0007] In one exemplary embodiment of this disclosure, the label is a direction label; the step of analyzing and processing the energy consumption data according to the label to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions includes: integrating energy consumption data with the direction label as a first direction to determine a first total energy consumption in the first direction, integrating energy consumption data with the direction label as a second direction to determine a second total energy consumption in the second direction, and determining the overall total energy consumption based on the first total energy consumption and the second total energy consumption; determining the local energy consumption data of each drive motor in the first direction, and determining the local energy consumption data of each drive motor in the second direction.

[0008] In one exemplary embodiment of this disclosure, the method further includes: storing the energy consumption values ​​of the autonomous vehicle in multiple dimensions in both the cloud and local storage.

[0009] In one exemplary embodiment of this disclosure, the method further includes: for the next road that the autonomous vehicle will travel on, obtaining the predicted physical driving direction of the autonomous vehicle, the road conditions of the next road, and the load on the next road; retrieving historical vehicle driving states that match the predicted physical driving direction, the road conditions of the next road, and the load on the next road; and using the historical energy consumption value corresponding to the retrieved historical vehicle driving state as the predicted energy consumption value of the autonomous vehicle for the next road.

[0010] In one exemplary embodiment of this disclosure, the method further includes: determining the average energy consumption value of the autonomous vehicle in multiple dimensions within a preset time period, and obtaining the real-time energy consumption value of the autonomous vehicle; and locating the fault of the autonomous vehicle based on the difference between the real-time energy consumption value and the average energy consumption value.

[0011] According to one aspect of this disclosure, an energy consumption processing system for an autonomous vehicle is provided, comprising: a direction reference definition module for determining the absolute direction of the autonomous vehicle; a driving direction detection module for real-time monitoring of the driving state of the autonomous vehicle and determining the actual physical driving direction of the driving state relative to the absolute direction; a four-wheel drive motor energy consumption acquisition module for acquiring energy consumption data of multiple drive motors of the autonomous vehicle through multiple drive motor controllers configured on the autonomous vehicle; a data processing and statistics module for determining labels for the acquired energy consumption data in conjunction with the actual physical driving direction, and analyzing and processing the energy consumption data according to the labels to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions; and a data storage and output module for storing the energy consumption values ​​of the autonomous vehicle.

[0012] The technical solution provided in this disclosure, on the one hand, avoids the problem of directional ambiguity and the impact of driving direction on energy consumption during bidirectional driving of the autonomous vehicle, since the absolute direction of the autonomous vehicle is determined. On the other hand, by determining the absolute direction of the autonomous vehicle, the problem of inconsistent energy consumption data statistics and analysis with the actual physical state of the autonomous vehicle caused by the confusion in direction definition is avoided. Energy consumption statistics can be performed based on the absolute physical state of the autonomous vehicle, improving the credibility and usability of energy consumption data and enhancing reliability.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0015] Figure 1 The flowchart illustrating an energy consumption management method for an autonomous vehicle according to an embodiment of the present disclosure is shown in the schematic diagram.

[0016] Figure 2 A schematic diagram illustrating the absolute direction in an embodiment of this disclosure is shown.

[0017] Figure 3 The schematic diagram illustrates the specific process flow of energy consumption processing in the embodiments of this disclosure.

[0018] Figure 4 The diagram illustrates a block diagram of an energy consumption management system for an autonomous vehicle according to an embodiment of the present disclosure.

[0019] Figure 5 The diagram illustrates the deployment of various modules in the energy consumption management system of an autonomous vehicle according to an embodiment of the present disclosure. Detailed Implementation

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0021] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0022] Current energy consumption statistics technology for four-wheel drive vehicles suffers from several key shortcomings in addressing bidirectional driving: 1. Ambiguity in direction: Traditional energy statistics models are based on a fixed driving direction. The motor consumes energy when moving forward and regenerates energy when braking. In bidirectional driving, forward and backward movement are relative concepts. 2. Inappropriate energy distribution: In four-wheel drive vehicles, the energy consumption ratio of the four drive motors dynamically changes due to factors such as driving direction, road conditions, and load distribution. Traditional methods struggle to accurately and rationally distribute total energy consumption to each drive unit in scenarios with dynamic direction switching, hindering energy efficiency analysis and fault diagnosis. 3. Inconsistent data: Due to the unclear definition of direction, energy consumption statistics reports and data analysis do not match the actual physical state of the vehicle, reducing the reliability and usability of the data.

[0023] To address the aforementioned technical problems, this disclosure provides an energy consumption processing method for autonomous vehicles, enabling accurate and traceable energy consumption statistics unaffected by logical driving direction. (Reference) Figure 1 As shown, the energy consumption management method for this autonomous vehicle mainly includes the following steps: In step S110, the absolute direction is determined for the autonomous vehicle; In step S120, the driving status of the unmanned vehicle is monitored in real time, and the actual physical driving direction of the driving status relative to the absolute direction is determined. In step S130, energy consumption data of multiple drive motors of the autonomous vehicle are collected through multiple drive motor controllers configured on the autonomous vehicle. In step S140, a label is determined for the collected energy consumption data based on the actual physical driving direction, and the energy consumption data is analyzed and processed according to the label to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions. In step S150, the energy consumption value of the autonomous vehicle is stored.

[0024] The technical solution provided in this disclosure, on the one hand, avoids the problem of directional ambiguity and the impact of driving direction on energy consumption during bidirectional driving of the autonomous vehicle by determining its absolute direction. On the other hand, by determining the absolute direction of the autonomous vehicle, it avoids the problem of energy consumption data statistics and analysis not matching the actual physical state of the autonomous vehicle caused by confusing direction definitions. Energy consumption statistics can be performed based on the absolute physical state of the autonomous vehicle, improving the credibility and usability of energy consumption data and enhancing reliability.

[0025] Next, refer to Figure 1 The diagram illustrates the energy consumption handling method for unmanned vehicles in this embodiment.

[0026] In step S110, the absolute direction is determined for the driverless vehicle.

[0027] In this embodiment, the driverless vehicle can be a mining truck, specifically a bidirectional pure electric four-wheel drive driverless mining truck. Bidirectional driving means that the front and rear of the driverless vehicle can be interchanged. The bidirectional pure electric four-wheel drive driverless mining truck can use its rear as an "effective front" by switching control logic without steering, significantly improving efficiency in confined spaces. The driverless vehicle can travel on any type of road and can be applied in any suitable scenario.

[0028] First, an absolute orientation reference can be established based on the orientation reference definition module. For example, a fixed physical orientation reference can be designed for the autonomous vehicle, defining the two ends of the vehicle as end A and end B. Specifically, an absolute orientation can be determined according to the vehicle structure, for example, the loading port as direction A and the unloading port as direction B. See [reference needed] for details. Figure 2 As shown in the image.

[0029] In step S120, the driving status of the unmanned vehicle is monitored in real time, and the actual physical driving direction of the driving status relative to the absolute direction is determined.

[0030] In this embodiment, the heading angle and wheel speed of the autonomous vehicle can be monitored using a driving direction detection module as the driving state, and the actual physical driving direction of the autonomous vehicle can be determined based on the driving state. For example, the heading angle can be compared with a reference heading angle corresponding to the absolute direction to determine the difference between the two. If the difference is less than a threshold, the absolute direction can be determined as the preliminary direction. The threshold can be determined according to actual needs, for example, it can be 5 degrees. For example, if the difference between the heading angle and the reference heading angle in direction A is less than 5 degrees, the preliminary direction can be considered to be direction A.

[0031] Next, the initial direction can be corrected based on wheel speed to determine the actual physical direction of the autonomous vehicle. Specifically, when the vehicle speed is greater than 0, if the wheel speeds of all four wheels of the autonomous vehicle are positive, the initial direction is determined as the actual physical direction. A positive value indicates consistency with the initial direction. The forward direction can be set to the direction of wheel rotation, consistent with the reference direction of direction A or B. For example, assuming the wheel speeds of all four wheels of the autonomous vehicle are positive, and the initial direction is determined to be direction A based on the heading angle, then the actual physical direction is confirmed to be direction A. If the wheel speed is negative, or if the initial direction determined by the heading angle contradicts the wheel speed direction, a direction verification warning is triggered, and the actual physical direction is further confirmed by combining the vehicle's gear position signal. Specifically, when the initial direction is direction A, but the wheel speed is negative, it can be further determined through the vehicle's gear position signal. The vehicle gear position information can be, for example, forward or reverse. For example, if the gear is forward and the initial direction is direction A, then the actual physical direction is direction A. If the gear is reverse and the initial direction is A, then the actual physical driving direction is B.

[0032] When the actual physical driving direction is consistent multiple times consecutively, it can be taken as the actual physical driving direction of the unmanned vehicle relative to its absolute direction. This actual physical driving direction can be determined based on the actual physical driving direction signal. The actual physical driving direction signal is transmitted to the driving direction detection module via the CAN bus, and simultaneously synchronized to the data processing and statistics module.

[0033] In step S130, energy consumption data of multiple drive motors of the autonomous vehicle are collected through multiple drive motor controllers configured on the autonomous vehicle.

[0034] In this embodiment of the disclosure, the energy consumption data of multiple drive motors of an autonomous vehicle can be collected through a four-wheel drive motor energy consumption acquisition module.

[0035] For example, energy consumption data of four drive motors can be collected in real time through four drive motor controllers. The four drive motors can be motor FL, motor FR, motor RL, and motor RR. Each drive motor controller can correspond to one drive motor. The four drive motors can be set at the edge of the wheel or inside the wheel; similarly, the four drive motor controllers can be set at the edge of the wheel or inside the wheel, depending on the actual needs.

[0036] Energy consumption data can be instantaneous, such as current, voltage, and power. Here, we will use power as an example. When the energy consumption data is power, the instantaneous power of the four drive motors can be expressed as P_FL, P_FR, P_RL, and P_RR.

[0037] The collected energy consumption data can include data from various road conditions and under different loads. Road conditions can be determined based on road surface type, slope grade, and congestion level. Load refers to the weight of the cargo carried by the autonomous vehicle. Energy consumption data can also include data from different directions.

[0038] In step S140, a label is assigned to the collected energy consumption data based on the actual physical driving direction, and the energy consumption data is analyzed and processed according to the label to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions.

[0039] In this embodiment, labels can be determined for the collected energy consumption data based on the actual physical driving direction of the autonomous vehicle. The label can be a single-dimensional label or a multi-dimensional label, without specific limitations. For example, the label can be a direction label, a road condition label, and a load label. Exemplarily, the direction label for the energy consumption data can be determined solely based on the actual physical driving direction; alternatively, a road condition label can be determined based on the road conditions of the road where the autonomous vehicle is located, and a load label can be determined based on the load of the autonomous vehicle. The label for the energy consumption data can be a single label or a combination of labels. For example, the direction label can be used alone as the label for the energy consumption data; or, at least two of the direction label, road condition label, and load label can be combined to obtain a combined label for the energy consumption data. For example, the label can be a combination of the direction label, road condition label, and load label. After obtaining the labels, the energy consumption data corresponding to each label can be accumulated into the corresponding energy consumption statistics pool.

[0040] After obtaining energy consumption data from multiple drive motors, this data can be categorized according to tags to enable analysis and processing, thereby obtaining multi-dimensional energy consumption values ​​for the autonomous vehicle. This tag-based categorization can be based on a single tag or a combination of tags, depending on the specific needs.

[0041] For example, when the label is a direction label, multiple dimensions can include a single drive motor in a single direction dimension, a single direction dimension, and an overall dimension. When the label only contains a direction label, the local energy consumption value, the overall total energy consumption value, the first total energy consumption in the first direction, and the second total energy consumption in the second direction of a single drive motor can be obtained. Here, the first direction can be direction A, and the second direction can be direction B. The overall total energy consumption value can be represented as E_A + E_B, the first total energy consumption in the first direction can be represented as E_A, and the second total energy consumption in the second direction can be represented as E_B.

[0042] In some embodiments, the first total energy consumption in the first direction can be calculated using the following formula: E_A=(P_FL+P_FR+P_RL+P_RR) Formula (1) for Δt The second total energy consumption in the second direction can be calculated using the following formula: E_B=(P_FL+P_FR+P_RL+P_RR) Formula (2) for Δt Where E_A and E_B are the total energy consumption in directions A and B, respectively, and Δt is the sampling period.

[0043] The local energy consumption values ​​of a single drive motor in the first and second directions can include the energy consumption of a single drive motor in the A and B directions collected by each drive motor controller.

[0044] When the direction is A, the energy consumption of motor FL is E_MotorFL_A = P_MotorFL Δt; The energy consumption of motor FR is expressed as E_MotorFR_A=P_MotorFR The energy consumption of motor RL is expressed as E_MotorRL_A = P_MotorRL. Δt; The energy consumption of motor RR can be expressed as E_MotorRR_A=P_MotorRR Δt.

[0045] When the direction is B, the energy consumption of motor FL is E_MotorFL_B = P_MotorFL Δt; The energy consumption of motor FR is expressed as E_MotorFR_B=P_MotorFR The energy consumption of motor RL is expressed as E_MotorRL_B = P_MotorRL. Δt; The energy consumption of motor RR can be expressed as E_MotorRR_B=P_MotorRR Δt.

[0046] When the label includes at least two of the following: direction label, road condition label, and load label, the multiple dimensions can include a single drive motor or multiple drive motors in at least two dimensions of a single direction, road condition, and load. For example, the energy consumption values ​​of the multiple dimensions can include the local energy consumption value of a single drive motor under each road condition in the first direction, the local energy consumption value of a single drive motor under each load in the first direction, the local energy consumption value of a single drive motor controller under each road condition and each load in the first direction; the local energy consumption value of a single drive motor controller under each road condition in the second direction, the local energy consumption value of a single drive motor controller under each load in the second direction, the local energy consumption value of a single drive motor controller under each road condition and each load in the second direction; the first total energy consumption in the first direction and the second total energy consumption in the second direction; the total energy consumption under each road condition; the total energy consumption under each load; and the overall total energy consumption value, without specific limitations here. For multiple drive motor controllers, the energy consumption data of each drive motor can be collected in real time.

[0047] In step S150, the energy consumption value of the autonomous vehicle is stored.

[0048] In this embodiment of the disclosure, after determining the energy consumption values ​​of the autonomous vehicle across multiple dimensions, these energy consumption values ​​can be stored. For example, the energy consumption values ​​of the autonomous vehicle across multiple dimensions can be stored in the cloud and locally. Specifically, energy consumption values ​​within a first preset time period can be stored in local storage to meet the autonomous vehicle's need for rapid local querying. The first preset time period can be, for example, 24 hours. Cloud storage can be divided into a hot data area and a cold data area. The hot data area stores energy consumption values ​​within a second preset time period; the second preset time period can be 3 months. The cold data area stores energy consumption values ​​exceeding the second preset time period, i.e., energy consumption values ​​exceeding 3 months. The cold data area can employ compression storage technology to reduce storage costs. After storing the energy consumption values ​​of the autonomous vehicle, a formatted report can also be output to the upper-level control system or cloud platform.

[0049] After storing the energy consumption values ​​of autonomous vehicles across multiple dimensions in both the cloud and local storage, these values ​​can be queried periodically or based on user requests. By combining these multi-dimensional energy consumption values, the differences in energy consumption in different directions and the operating status of each drive motor can be determined. This allows for optimization of the selection of high-efficiency points in motor control, thereby saving energy.

[0050] In some embodiments, after determining energy consumption values ​​across multiple dimensions, the average energy consumption value on the target road within a preset time period can be determined. The preset time period could be within a week, a month, etc. When the real-time energy consumption value of the autonomous vehicle on the target road is found to be inconsistent with the average energy consumption value, or when the difference between the real-time energy consumption value of any drive motor and other drive motors exceeds a difference threshold determined based on the average energy consumption value, an automatic warning signal can be issued, and the location and type of the autonomous vehicle's fault can be located based on the real-time energy consumption value. Specifically, the location and type of the fault can be determined based on the mapping relationship between the degree of anomaly in the real-time energy consumption value and the fault. For example, when the real-time energy consumption value of motor RL is consistently more than 30% higher than that of other motors, combined with data such as motor temperature and speed, it can be determined whether there are problems such as motor stalling or controller failure, and a fault location report can be generated, clearly identifying the faulty motor number and possible causes of the fault, thus shortening the maintenance and troubleshooting time.

[0051] In this embodiment, the energy consumption values ​​of the autonomous vehicle across multiple dimensions can be stored in cloud storage and local storage. For the next road the autonomous vehicle will travel on, the predicted physical driving direction, road conditions of the next road, and load on the next road can be obtained. Furthermore, historical vehicle driving states that match the predicted physical driving direction, road conditions of the next road, and load on the next road can be retrieved from the cloud storage and local storage, and the historical energy consumption value corresponding to the historical vehicle driving state can be used as the predicted energy consumption value for the next road. For example, when the predicted physical driving direction is the same as the physical driving direction in the historical vehicle driving state, the road conditions are similar to the road conditions in the historical vehicle driving state, and the load difference between the predicted and historical vehicle driving states is less than a load difference threshold, a matching historical vehicle driving state can be considered to exist, and the historical energy consumption value corresponding to the historical vehicle driving state can be determined as the predicted energy consumption value for the next road. Similar road conditions refer to the same road surface type, a gradient difference less than a gradient threshold, and similar congestion levels. Of course, other methods can also be used to determine the predicted energy consumption value of the autonomous vehicle on the next road; no specific limitation is made here. Based on this, the remaining energy consumption of the autonomous vehicle can be compared with the predicted energy consumption of the next road to determine whether the autonomous vehicle can pass through the next road and thus determine the predicted traffic status. If the remaining energy consumption of the autonomous vehicle is less than the predicted energy consumption of the next road, the predicted traffic status is that the autonomous vehicle cannot pass through the next road, and the charging status can then be determined. The charging status can be determined by whether charging is needed and when charging should be done. When the predicted traffic status is that the autonomous vehicle cannot pass through the next road, it is determined that charging is needed. Based on this, the timing of charging can be determined, allowing for timely charging of the autonomous vehicle and avoiding problems caused by insufficient energy consumption.

[0052] In some embodiments, vehicle-mounted sensors such as GPS, gyroscopes, steering angle sensors, and vehicle speed sensors can also collect driving data in real time. This driving data includes driving direction, road conditions, load distribution, and drive motor status data to determine the current scenario and operating parameters. The current scenario can be straight driving, turning, climbing, lateral movement, etc. Operating parameters can include vehicle speed, load demand, lateral force, and gradient.

[0053] Based on a pre-defined 3D mapping model representing the scenario, load, and efficient operating range, the initial allocation weights of the four drive motors are obtained. The total torque is allocated according to the initial weights, and the total energy consumption of the drive motors is summarized by combining the efficient operating range of each drive motor. If the total energy consumption is higher than the optimal threshold, the allocation ratio is readjusted, for example, by switching the load-bearing entity, prioritizing the use of efficient drive motors, etc., until the total energy consumption is optimal, so as to optimize the efficient point of motor control, save energy, ensure that each drive motor operates in the efficient operating range, and at the same time ensure that the total torque remains unchanged.

[0054] Because it can accurately collect energy consumption data of multiple drive motors in autonomous vehicles through multiple drive motor controllers, it can accurately and reasonably distribute the total energy consumption to each drive motor in scenarios with dynamic direction switching, thereby improving the rationality of energy consumption distribution and facilitating energy efficiency analysis and fault diagnosis.

[0055] Figure 3 The diagram illustrates the specific process flow for energy consumption management. (See reference) Figure 3 As shown, the main steps include: Step S301: Establish absolute direction.

[0056] Step S302: Real-time determination of the actual physical driving direction and data collection.

[0057] Step S303: Data labeling and classification.

[0058] Step S304: The energy consumption data of the drive motor is subdivided to obtain energy consumption values ​​in multiple dimensions.

[0059] Step S305: Output and storage of energy consumption values ​​across multiple dimensions.

[0060] In this embodiment, by determining the absolute direction of the autonomous vehicle, the problem of directional ambiguity is avoided during bidirectional driving. Because the energy consumption data of multiple drive motors of the autonomous vehicle can be accurately collected through multiple drive motor controllers, in scenarios with dynamic direction switching, the total energy consumption can be accurately and rationally distributed to each drive motor, improving the rationality of energy consumption allocation and facilitating energy efficiency analysis and fault diagnosis. Since the absolute direction of the autonomous vehicle is determined, the problem of inconsistent energy consumption data statistics and analysis with the actual physical state of the autonomous vehicle caused by confusing direction definitions is avoided, improving the reliability and usability of energy consumption data. Energy consumption statistics can be performed based on the absolute physical state of the autonomous vehicle, freeing it from the constraints of "forward / backward" logic. The energy statistics system and method for bidirectional pure electric four-wheel drive autonomous vehicles in this embodiment can achieve accurate and traceable energy consumption statistics unaffected by logical driving direction.

[0061] This disclosure also provides an energy consumption management system for autonomous vehicles, with reference to... Figure 4 As shown, the energy consumption management system 400 of the autonomous vehicle mainly includes the following modules: The orientation reference definition module 401 is used to determine the absolute orientation for autonomous vehicles. The driving direction detection module 402 is used to monitor the driving status of the unmanned vehicle in real time and determine the actual physical driving direction of the driving status relative to the absolute direction. The four-drive motor energy consumption acquisition module 403 is used to collect energy consumption data of multiple drive motors of the autonomous vehicle through multiple drive motor controllers configured on the autonomous vehicle. The data processing and statistics module 404 is used to determine labels for the collected energy consumption data in combination with the actual physical driving direction, and to analyze and process the energy consumption data according to the labels to determine the energy consumption values ​​of the unmanned vehicle in multiple dimensions. The data storage and output module 405 is used to store the energy consumption value of the unmanned vehicle.

[0062] In some embodiments, the aforementioned direction reference definition module, driving direction detection module, four-wheel drive motor energy consumption acquisition module, data processing and statistics module, and data storage module can be integrated into the autonomous vehicle, deployed with the vehicle control unit (VCU) as the hub. (Reference) Figure 5 As shown, the direction reference definition module, driving direction detection module, four-wheel drive motor energy consumption acquisition module, data processing and statistics module, and data storage and output module can be deployed in different locations on the autonomous vehicle, with the vehicle controller (VCU) as the hub.

[0063] For example, the direction reference definition module can establish an absolute direction reference, designing a fixed physical direction reference for the autonomous vehicle, defining the two ends of the autonomous vehicle as end A and end B. The driving direction detection module monitors the autonomous vehicle's heading angle, wheel speed, and other signals in real time to determine the autonomous vehicle's driving state. Based on the autonomous vehicle's driving state, it determines whether the autonomous vehicle is driving towards end A or towards end B, thus determining the actual physical driving direction of the autonomous vehicle relative to the absolute direction. The four-wheel drive motor energy consumption acquisition module uses multiple drive motor controllers to synchronously and in real time acquire the instantaneous power P_FL, P_FR, P_RL, and P_RR of the four drive motors. The data processing and statistics module is used to synchronously and individually statistically analyze the energy consumption data of each drive motor. It acquires the energy consumption data collected by the four drive motor controllers in real time through the CAN (Controller Area Network) bus. The energy consumption data includes current, voltage, power, and the real-time torque and speed of the four drive motors. It can also calculate the energy flow transfer efficiency from direction A to direction B, thereby obtaining the energy consumption values ​​of the autonomous vehicle in multiple dimensions.

[0064] The data storage and output module can store the energy consumption values ​​of the autonomous vehicle. Based on this, it can periodically or upon user request output energy consumption data for each direction, namely the first total energy consumption E_A in the first direction and the second total energy consumption E_B in the second direction; the overall total energy consumption E_A+E_B, and the local energy consumption data of each drive motor in direction A or direction B, can also determine the efficiency of each drive motor. Combining the energy consumption differences of the autonomous vehicle in different directions and the operating conditions of each drive motor, the selection of the high-efficiency point of drive motor control can be optimized to save energy.

[0065] For example, the data storage and output module is used to store the energy consumption values ​​of the autonomous vehicle in both the cloud and local storage. Cloud storage can be accessed and read, or displayed through the display module. Local storage can be achieved using a memory card, which prevents network interference with cloud data. In addition, local backup can be implemented, or the energy consumption values ​​of the autonomous vehicle can be directly exported by accessing the storage address via a network cable. To reduce memory usage, the energy consumption values ​​can be periodically updated. For example, the storage time of the energy consumption values ​​can be checked cyclically; if the storage time exceeds a time threshold, previously stored energy consumption values ​​can be overwritten. The time thresholds for cloud storage and local storage can be different; for example, the time threshold for cloud storage could be 2 months, and the time threshold for local storage could be 1 month. This is not specifically limited here. Based on this, the timeliness of information callbacks can always be maintained.

[0066] In other embodiments, the energy consumption status under a given state can be traced back according to a time period. For example, the energy consumption value for that time period can be obtained from cloud storage or local storage based on the time period.

[0067] In one exemplary embodiment of this disclosure, the driverless vehicle is a bidirectional driverless vehicle; the real-time monitoring of the driving state of the driverless vehicle and the determination of the actual physical driving direction of the driving state relative to the absolute direction includes: monitoring the heading angle and wheel speed of the driverless vehicle; comparing the heading angle with the absolute direction to determine a preliminary direction; and correcting the preliminary direction based on the wheel speed to determine the actual physical driving direction.

[0068] In one exemplary embodiment of this disclosure, determining a label for the collected energy consumption data in conjunction with the actual physical driving direction includes: classifying the collected energy consumption data according to the actual physical driving direction to determine a direction label.

[0069] In one exemplary embodiment of this disclosure, the label is a direction label; the step of analyzing and processing the energy consumption data according to the label to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions includes: integrating energy consumption data with the direction label as a first direction to determine a first total energy consumption in the first direction, integrating energy consumption data with the direction label as a second direction to determine a second total energy consumption in the second direction, and determining the overall total energy consumption based on the first total energy consumption and the second total energy consumption; determining the local energy consumption data of each drive motor in the first direction, and determining the local energy consumption data of each drive motor in the second direction.

[0070] In one exemplary embodiment of this disclosure, the system is configured to perform: cloud storage and local storage of energy consumption values ​​for multiple dimensions of the autonomous vehicle.

[0071] In one exemplary embodiment of this disclosure, the system is configured to perform: for the next road that the autonomous vehicle will travel on, obtain the predicted physical driving direction of the autonomous vehicle, the road conditions of the next road, and the load on the next road; retrieve historical vehicle driving states that match the predicted physical driving direction, the road conditions of the next road, and the load on the next road; and use the historical energy consumption value corresponding to the retrieved historical vehicle driving state as the predicted energy consumption value of the autonomous vehicle for the next road.

[0072] In one exemplary embodiment of this disclosure, the system is configured to perform: determining the average energy consumption value of the autonomous vehicle in multiple dimensions over a preset time period, and obtaining the real-time energy consumption value of the autonomous vehicle; and locating the fault of the autonomous vehicle based on the difference between the real-time energy consumption value and the average energy consumption value.

[0073] In one exemplary embodiment of this disclosure, the system further includes a display module. The display module can display one or more of the following via a display interface: energy consumption values ​​across multiple dimensions of the autonomous vehicle, fault status of the autonomous vehicle, predicted traffic status, and charging status. Specifically, it can display local energy consumption data for each drive motor in a first direction, local energy consumption data for each drive motor in a second direction, a first total energy consumption in the first direction, a second total energy consumption in the second direction, and the overall total energy consumption. In addition, the display module can also display the fault status of each drive motor in the autonomous vehicle, such as whether each starter motor is faulty, its fault level and type, and the cause of the fault.

[0074] The display module can also show the autonomous vehicle's predicted road conditions for the next route, indicating whether the next trip can be completed. The charging status can include a charging reminder icon indicating whether charging is needed and when to start charging. The display module facilitates timely access to necessary information, improving the convenience of information retrieval.

[0075] It should be noted that the specific details of each module in the energy consumption treatment system of the above-mentioned autonomous vehicle have been described in detail in the corresponding energy consumption treatment method of autonomous vehicle, so they will not be repeated here.

[0076] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0077] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0078] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0079] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for managing the energy consumption of an unmanned vehicle, characterized in that, include: Determine the absolute direction for driverless vehicles; The driving status of the unmanned vehicle is monitored in real time, and the actual physical driving direction of the driving status relative to the absolute direction is determined. Energy consumption data of multiple drive motors in an autonomous vehicle are collected by using multiple drive motor controllers configured on the vehicle. Based on the actual physical driving direction, labels are assigned to the collected energy consumption data, and the energy consumption data is analyzed and processed according to the labels to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions. The energy consumption value of the autonomous vehicle is stored.

2. The energy consumption management method for unmanned vehicles according to claim 1, characterized in that, The driverless vehicle is a two-way driverless vehicle; The real-time monitoring of the autonomous vehicle's driving status and the determination of the actual physical driving direction relative to the absolute direction include: Monitor the heading angle and wheel speed of the unmanned vehicle; The heading angle is compared with the absolute direction to determine the preliminary direction; The initial direction is corrected based on the wheel speed to determine the actual physical driving direction.

3. The energy consumption management method for unmanned vehicles according to claim 1, characterized in that, The step of assigning labels to the collected energy consumption data based on the actual physical driving direction includes: The collected energy consumption data is classified according to the actual physical driving direction to determine the direction label.

4. The energy consumption management method for unmanned vehicles according to claim 1, characterized in that, The label is a directional label; the step of analyzing and processing the energy consumption data based on the label to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions includes: The energy consumption data labeled as the first direction is integrated to determine the first total energy consumption in the first direction, the energy consumption data labeled as the second direction is integrated to determine the second total energy consumption in the second direction, and the overall total energy consumption is determined based on the first total energy consumption and the second total energy consumption. Determine the local energy consumption data of each drive motor in the first direction, and determine the local energy consumption data of each drive motor in the second direction.

5. The energy consumption management method for unmanned vehicles according to claim 1, characterized in that, The method further includes: The energy consumption values ​​of the autonomous vehicle in multiple dimensions are stored in the cloud and locally.

6. The energy consumption management method for unmanned vehicles according to claim 1, characterized in that, The method further includes: For the next road that the autonomous vehicle will travel on, obtain the predicted physical driving direction of the autonomous vehicle, the road conditions of the next road, and the load on the next road. The system retrieves and predicts historical vehicle driving states that match the physical driving direction, road conditions of the next road, and load on the next road. The historical energy consumption value corresponding to the retrieved historical vehicle driving state is used as the predicted energy consumption value for the next road of the autonomous vehicle.

7. The energy consumption management method for unmanned vehicles according to claim 1, characterized in that, The method further includes: Determine the average energy consumption of the autonomous vehicle in multiple dimensions within a preset time period, and obtain the real-time energy consumption of the autonomous vehicle; The fault of the autonomous vehicle is located based on the difference between the real-time energy consumption value and the average energy consumption value.

8. An energy consumption management system for an unmanned vehicle, characterized in that, include: The orientation reference definition module is used to determine the absolute orientation of autonomous vehicles; The driving direction detection module is used to monitor the driving status of the unmanned vehicle in real time and determine the actual physical driving direction of the driving status relative to the absolute direction; The four-wheel drive motor energy consumption acquisition module is used to collect energy consumption data of multiple drive motors of the autonomous vehicle through multiple drive motor controllers configured on the autonomous vehicle. The data processing and statistics module is used to determine labels for the collected energy consumption data based on the actual physical driving direction, and to analyze and process the energy consumption data according to the labels to determine the energy consumption values ​​of the autonomous vehicle in multiple dimensions. The data storage and output module is used to store the energy consumption value of the autonomous vehicle.

9. The energy consumption management system for unmanned vehicles according to claim 8, characterized in that, The data storage and output module is used to store the energy consumption value of the autonomous vehicle in the cloud and locally.

10. The energy consumption management system for unmanned vehicles according to claim 8, characterized in that, The system also includes: The display module is used to display one or more of the following: energy consumption values ​​of the autonomous vehicle in multiple dimensions, fault status of the autonomous vehicle, predicted traffic status, and charging status.

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