Generated power control method of extended-range mine truck and related device

Abstract

The invention discloses a generated power control method for an extended range mine truck and a related device, and the method comprises the steps: obtaining the operation information of an ith working condition if a target vehicle is at a working condition starting point of the (i + 1) th working condition, and determining the segmented average driving power of N time periods in the (i + 1) th working condition based on the average vehicle speed and gradient information of the N time periods in the ith working condition. And determining a target charge state sequence corresponding to the tail ends of the N time periods in the (i + 1)-th working condition according to the segmented average driving power of the N time periods in the (i + 1)-th working condition, the current charge state of the target vehicle and the target charge state of the (i + 1)-th working condition. And determining the generated power of the target vehicle according to the target charge state sequence and the actual running state of the target vehicle, so that the charge state of the target vehicle converges to the target charge state sequence. Therefore, by introducing the segmented operation information of the previous complete working condition, segmented prediction is performed on the charge state of the next working condition, and the accuracy of the generated power is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method and related apparatus for controlling the power generation of a range-extended mining truck. Background Technology

[0002] Range-extended mining trucks are engineering machines suitable for open-pit mines. They use a range extender system and a power battery as their power source, supplying electrical energy to the drive motor to propel the truck. Typical operating conditions for range-extended mining trucks include loading, full-load uphill driving, unloading, and unloaded downhill driving. Due to limitations in the capacity of the installed power battery and the power output of the range extender system, the power output needs to be rationally determined based on the actual operating conditions of the truck. This avoids situations where the total power supply (range extender system + power battery) is insufficient during full-load uphill driving, leading to insufficient power, or during downhill driving, the power battery's state of charge (SOC) is too high, preventing braking and regeneration.

[0003] In related technologies, the power generation capacity of range-extended mining trucks is typically determined based on the vehicle's current operating conditions to reduce fuel consumption. However, the accuracy of the power generation capacity determined by this method is relatively low. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method and related apparatus for controlling the power generation of extended-range mining trucks, thereby improving the accuracy of the power generation of extended-range mining trucks.

[0005] Based on this, the following technical solution is disclosed in this application: In a first aspect, embodiments of this application provide a method for controlling the power generation of a range-extended mining truck, the method comprising: If the target vehicle is at the starting point of the (i+1)th working condition, obtain the operating information of the i-th working condition. The operating information includes the average vehicle speed over N time periods in the i-th working condition. The i-th working condition includes N time periods, where i and N are positive integers. Based on the average vehicle speed and gradient information of N time periods in the i-th working condition, determine the segmented average driving power of N time periods in the (i+1)-th working condition; Based on the segmented average driving power of N time periods in the (i+1)th operating condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th operating condition, determine the target state of charge sequence corresponding to the end of the N time periods in the (i+1)th operating condition. Based on the target state of charge sequence and the actual operating state of the target vehicle, the power generation capacity of the target vehicle is determined so that the state of charge of the target vehicle converges to the target state of charge sequence.

[0006] Secondly, embodiments of this application provide a power generation control device for a range-extended mining truck, the device comprising: The acquisition unit is used to acquire the operating information of the i-th working condition if the target vehicle is at the starting point of the i+1-th working condition. The operating information includes the average vehicle speed over N time periods in the i-th working condition. The i-th working condition includes N time periods, where i and N are positive integers. The determining unit is used to determine the segmented average driving power of the N time periods in the (i+1)th working condition based on the average vehicle speed and gradient information of the N time periods in the i-th working condition. The determining unit is further configured to determine the target charge state sequence corresponding to the end of the N time periods in the (i+1)th working condition based on the segmented average driving power of the N time periods in the (i+1)th working condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th working condition. The control unit is configured to determine the power generation capacity of the target vehicle based on the target state of charge sequence and the actual operating state of the target vehicle, so that the state of charge of the target vehicle converges to the target state of charge sequence.

[0007] Thirdly, embodiments of this application provide a vehicle, the vehicle including a processor and a memory: The memory is used to store computer programs and to transfer the computer programs to the processor; The processor is configured to execute the method described in the first aspect above according to the computer program.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium for storing a computer program for performing the method described in the first aspect above.

[0009] Fifthly, embodiments of this application provide a computer program product including a computer program, which, when run on a computer device, causes the computer device to perform the method described in the first aspect above.

[0010] As can be seen from the above technical solutions, this application has at least the following beneficial effects: If the target vehicle is at the starting point of the (i+1)th operating condition, obtain the operating information for the i-th operating condition. This information includes the average vehicle speed over N time periods within the i-th operating condition, where i and N are positive integers. Based on the average vehicle speed and gradient information over the N time periods in the i-th operating condition, determine the segmented average driving power over the N time periods in the (i+1)-th operating condition. Based on the segmented average driving power over the N time periods in the (i+1)-th operating condition, the target vehicle's current state of charge (SOC), and the target SOC for the (i+1)-th operating condition, determine the target SOC sequence corresponding to the end of the N time periods in the (i+1)-th operating condition. Based on the target SOC sequence and the actual operating state of the target vehicle, determine the target vehicle's power generation capacity to ensure that the target vehicle's SOC converges to the target SOC sequence. Therefore, compared to related technologies that determine power generation based solely on current operating conditions or short time periods, this application introduces segmented operating information from the previous complete operating condition to predict the state of charge (SOC) of the next operating condition in segments. It performs a global analysis of the target vehicle's complete operating conditions, thus ensuring that power generation is no longer limited to local time periods but is planned based on the energy demand of the complete operating conditions across time periods, thereby improving the accuracy of power generation. Furthermore, since the average driving power and the target SOC sequence are determined based on segmented average vehicle speeds, storage resources are greatly saved, and the efficiency of SOC determination is improved, thus balancing accuracy and efficiency. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic flowchart illustrating a power generation control method for a range-extended mining truck provided in this application embodiment; Figure 2 A flowchart illustrating an application scenario embodiment of power generation control for an extended-range mining truck provided in this application; Figure 3 This is a structural schematic diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0013] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0014] In related technologies, power prediction and control typically rely on state information within a local time period. In other words, the time span on which the prediction is based is relatively short, failing to cover the changes in multiple stages of the complete operating conditions of extended-range mining trucks. Due to the significant differences in operating conditions of mining trucks within the complete operating conditions (such as the correlation between high power demand uphill and energy recovery stages downhill), the energy demand between different stages has a clear correlation.

[0015] When relying solely on short-term forecasts, the determined power generation often deviates from the global optimum. For example, during high-load periods, insufficient power generation allocation in the early stages may lead to over-discharge of the battery. Conversely, during low-load or energy recovery phases, improper power generation control may result in excessively high battery state of charge (SOC), limiting energy recovery efficiency.

[0016] Based on this, the embodiments of this application provide a power generation control method and related device for range-extended mining trucks. By introducing segmented operation information of the previous complete operating condition, the state of charge of the next operating condition is predicted in segments, and the complete operating condition of the target vehicle is analyzed globally. Thus, the determination of power generation is no longer limited to a local time period, but is based on the energy demand of the complete operating condition across time periods, thereby improving the accuracy of power generation.

[0017] The power generation control method for extended-range mining trucks provided in this application can be applied to computer equipment with control capabilities, such as terminal devices, servers, and vehicle controllers. Specifically, terminal devices can be desktop computers, laptops, mobile phones, and tablets; servers can be independent physical servers, server clusters composed of multiple physical servers, or distributed systems. Terminal devices and servers can be directly or indirectly connected via wired or wireless communication, and this application does not impose any restrictions on this connection.

[0018] See Figure 1 This figure is a schematic flowchart of the power generation control method for an extended-range mining truck provided in an embodiment of this application. For ease of description, the following embodiment uses a server as the executing entity of the power generation control method for the extended-range mining truck. Figure 1 As shown, the power generation control method of this range-extended mining truck includes S101-S104.

[0019] S101: If the target vehicle is at the starting point of the (i+1)th working condition, obtain the operating information of the i-th working condition.

[0020] The target vehicle is a range-extended mining truck. This application will subsequently use the target vehicle as an example to illustrate the power generation control method for range-extended mining trucks.

[0021] Each working condition can be regarded as a complete operation of the target vehicle. For example, the entire process of the target vehicle starting from the parking and loading point, experiencing full-load uphill transportation, arriving at the unloading point for unloading, and then returning uphill to the loading point in an empty state can be regarded as a complete working condition.

[0022] In this embodiment, the (i+1)th working condition is considered the current working condition, and the ith working condition is considered the previous working condition, where i is a positive integer. It should be noted that the operational information for each working condition needs to be statistically analyzed.

[0023] The operational information includes the average vehicle speed over N time periods in the i-th operating condition, where the i-th operating condition includes N time periods, and N is a positive integer.

[0024] As one possible implementation, the operational information includes the total operating time of the working condition, that is, the time it takes for the target vehicle to complete the corresponding working condition. The total operating time is used to divide the next working condition into N time periods.

[0025] The starting point of a working condition is the moment when each working condition begins, used to distinguish adjacent working conditions.

[0026] The embodiments of this application do not specifically limit the location of the starting point of the working condition. In one possible implementation, the starting point of the working condition is the moment when the target vehicle jumps from parking and loading to transporting materials, or from parking and unloading to returning to the loading point.

[0027] Since the load state of the target vehicle changes significantly at the aforementioned moments, using these moments as dividing points to differentiate between different operating conditions ensures structural consistency across all conditions. This allows the segmented average vehicle speed obtained based on the i-th operating condition to more accurately characterize the operational features of each stage. Furthermore, predicting the next operating condition based on structurally consistent historical operating conditions improves the accuracy of segmented drive power calculation and makes the planning of subsequent state-of-charge sequences more aligned with actual energy demand changes, thereby enhancing the overall precision of power generation control.

[0028] Furthermore, it can be determined whether the target vehicle is at the starting point of the operating condition in the following ways, as detailed in A1-A2: A1: Obtain the operating status information of the target vehicle.

[0029] The operating condition information indicates the target vehicle's status during operation, including at least gradient, altitude, load status, speed, and location information. Gradient information reflects the road's inclination, altitude information reflects the road's elevation, load status reflects the target vehicle's load, and location information reflects the target vehicle's position.

[0030] A2: If the difference between the operating condition status information and the target operating condition status information is less than the first threshold, then the target vehicle is determined to have reached the operating condition starting point.

[0031] The target operating condition status information is a type of operating condition status information used to reflect the state that the target vehicle should be in at the starting point of the operating condition. The target operating condition status information can be pre-calibrated or obtained by fitting historical operating condition status information and the actual starting point of the operating condition. The first threshold is used to determine the magnitude of the difference between the current operating condition status information and the target operating condition status information.

[0032] If the difference between the operating condition status information and the target operating condition status information is less than the first threshold, it indicates that the operating condition status information and the target operating condition status information are relatively consistent, and the vehicle is at the starting point of the operating condition.

[0033] Therefore, by acquiring working condition status information including slope information, altitude information, load status, vehicle speed and location information, and matching the working condition status information with the target working condition status information, when the difference between the two is less than a first threshold, it is determined that the target vehicle has reached the working condition starting point. This makes the determination of the working condition starting point based on the matching and judgment of multi-dimensional status information, making the working condition division more accurate, thereby improving the accuracy of the operation information of the previous complete working condition (such as the i-th working condition).

[0034] In one possible implementation, during the (i+1)th operating condition, the operating information of the (i+1)th operating condition can be continuously collected based on the N time periods to provide data support for determining the power generation of the (i+2)th operating condition.

[0035] In the process of collecting operational information, as a possible approach, if the last time period has been completed and the target vehicle has not yet reached the starting point of the operating condition, then the last time period needs to be extended until the moment the mining truck returns to the starting point of the operating condition; if the last time period has not been completed and the target vehicle has already reached the starting point of the operating condition, then the last time period needs to be shortened to the end of the current moment, and the collection of data such as average vehicle speed and total operating time should be terminated.

[0036] S102: Based on the average vehicle speed and gradient information of N time periods in the i-th working condition, determine the segmented average driving power of N time periods in the (i+1)-th working condition.

[0037] Slope information is used to reflect the degree of inclination of a road; for example, slope information can be determined through map information.

[0038] Based on the gradient information and the average vehicle speed over N time periods, the required driving power for the target vehicle in each time period can be calculated, thereby predicting the segmented average driving power for the N time periods in the (i+1)th driving condition. The segmented average driving power is the average value of the driving power required by the target vehicle for the corresponding time period.

[0039] One possible approach is to use vehicle dynamics equations to determine the segmented average driving power for the N time periods in the (i+1)th operating condition, based on the average vehicle speed and gradient information for the N time periods in the i-th operating condition.

[0040] This application does not specifically limit how to determine the segmented average driving power of N time periods in the (i+1)th working condition based on the average vehicle speed and gradient information of N time periods in the i-th working condition. The following are two exemplary processing methods: First, the two processing methods are selected based on the available controller resources of the target vehicle. Available controller resources refer to the resources that the vehicle's controllers can use to execute computational tasks in the current operating state. Available controller resources can be storage resources or computing resources. Before selecting a processing method, the available controller resources of the target vehicle are obtained. If the available controller resources of the target vehicle are less than or equal to a resource threshold, it means that the target vehicle has few available controller resources, and processing method one is selected. If the available controller resources of the target vehicle are greater than the resource threshold, it means that the target vehicle has many available controller resources, and processing method two is selected. The two processing methods are described below.

[0041] Method 1: Determine the average gradient over N time periods based on the gradient information. Then, based on the average vehicle speed and average gradient over N time periods in the i-th operating condition, determine the segmented average drive power over N time periods in the (i+1)-th operating condition.

[0042] In the first processing method, the average slope of each time period is first determined, and then the average driving power of each segment is calculated by combining the average vehicle speed of each segment. That is, the processing is carried out with time period as the calculation granularity.

[0043] Method 2: Based on the average vehicle speed and gradient information over N time periods in the i-th operating condition, determine the driving power at multiple moments in the (i+1)-th operating condition. Based on the driving power at multiple moments in the (i+1)-th operating condition, determine the segmented average driving power over N time periods in the (i+1)-th operating condition.

[0044] In processing method two, the driving power at multiple moments within the (i+1)th driving condition is predicted directly based on the gradient information and the average vehicle speed of each segment. For example, the determined power at multiple moments can be the driving power curve of the (i+1)th driving condition, reflecting the change of driving power over time at each moment. Then, based on the driving power at multiple moments within the (i+1)th driving condition, the segmented average driving power for N time periods within the (i+1)th driving condition is determined. That is, processing is performed at the moment-to-moment level. It should be noted that in processing method two, the time interval between adjacent moments is less than the length of a single time period.

[0045] Therefore, in processing method one, by averaging the gradient information in segments and combining it with the average vehicle speed of each segment, the segmented average drive power is determined with time intervals as the granularity of calculation. This reduces computational complexity and data processing volume, thereby improving execution efficiency under limited controller resources. In processing method two, the drive power at multiple moments is calculated using gradient information, and the segmented average drive power is further obtained. This allows the drive power to more accurately reflect the energy demand that changes over time during actual operation, improving the calculation accuracy of segmented drive power and the ability to characterize changes in operating conditions. Furthermore, by adaptively selecting between the two processing methods based on the available controller resources of the target vehicle, a low-complexity method (processing method one) can be used to ensure algorithm operability when controller resources are limited, while a high-precision method (processing method two) can be used to improve calculation accuracy when controller resources are sufficient. This achieves a dynamic balance between computational efficiency and accuracy under different hardware conditions, improving the overall accuracy and stability of power generation control.

[0046] S103: Based on the segmented average driving power of N time periods in the (i+1)th working condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th working condition, determine the target state of charge sequence corresponding to the end of the N time periods in the (i+1)th working condition.

[0047] The current state of charge (SOC) is the SOC of the power battery at the start of the operating condition, and the target SOC is the desired SOC that the power battery will achieve at the end of the complete operating condition. The target SOC sequence includes the SOC at the end of each of the N time periods.

[0048] In one possible implementation, this application provides a specific implementation of S103, as detailed in B1-B3: B1: Based on the segmented average driving power of N time periods in the (i+1)th operating condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th operating condition, multiple power generation sequences are obtained.

[0049] The power generation sequence includes power generation over N time periods. In other words, multiple power generation sequences are candidate target power generation sequences. Multiple power generation sequences can be obtained by searching for various power generation sequences that satisfy the requirements of piecewise average drive power, current state of charge, and target state of charge.

[0050] In one possible implementation, the charging and discharging power constraints of the target vehicle's power battery can be obtained. While satisfying these constraints, multiple power generation sequences are searched based on the segmented average drive power over N time periods during the (i+1)th operating condition.

[0051] The charging and discharging power constraints of a power battery refer to the maximum charging power and maximum discharging power range allowed by the power battery during actual operation. These constraints are used to limit the battery's energy input and output capabilities in different time periods. For example, in a certain time period, when the SOC is 30%, the battery is allowed a larger discharge power, such as a maximum discharge power of 300kW; when the SOC is 80%, the battery's charging capacity is limited, for example, the maximum charging power is only 100kW.

[0052] When fully loaded and going uphill, the driving power demand is high. If the required power exceeds the battery's maximum discharge power, it needs to be supplemented by generating electricity through the range extender. When going downhill, the vehicle has an energy recovery demand, but if the current SOC is high and exceeds the maximum charging power limit, not all energy can be recovered.

[0053] Therefore, by introducing power battery charging and discharging power constraints when searching for the power generation sequence, the resulting power generation sequence not only meets the requirements of segmented average drive power but also conforms to the actual charging and discharging capacity limitations of the power battery in each time period, thereby avoiding unreasonable situations such as battery overcharging or over-discharging. This makes the power generation sequence more closely resemble actual operating characteristics, improves the feasibility of the target state of charge sequence, and further enhances the reliability of power generation control and the stability of vehicle operation.

[0054] B2: The power generation sequence with the lowest cumulative fuel consumption is determined as the target power generation sequence for the (i+1)th operating condition.

[0055] The cumulative fuel consumption corresponding to each power generation sequence can be calculated, and the power generation sequence with the lowest cumulative fuel consumption can be determined as the target power generation sequence for the (i+1)th operating condition.

[0056] One possible implementation is to execute B1-B2 using a dynamic programming (DP) algorithm. Specifically, the (i+1)th operating condition can be divided into N time periods, with each time period representing a stage. The state of charge (SBC) of the target vehicle at the end of each time period is used as the state variable. Within each time period, the power generation is used as the decision variable, establishing a state transition relationship from the current time period to the next. During the state transition, the change in the SBC of the power battery is determined based on the segmented average driving power and the power generation. Under the target SBC condition, the power generation of each time period is searched stage by stage, resulting in multiple power generation sequences. For each power generation sequence, the cumulative fuel consumption can be calculated based on its corresponding energy consumption. Using the cumulative fuel consumption as the optimization objective, the power generation sequence with the lowest cumulative fuel consumption is selected from the multiple power generation sequences as the target power generation sequence for the (i+1)th operating condition.

[0057] B3: Based on the target power generation sequence of the (i+1)th operating condition, determine the target state of charge sequence corresponding to the end of N time periods in the (i+1)th operating condition.

[0058] Based on the target power generation sequence of the (i+1)th operating condition and the corresponding segmented average drive power, the change in the state of charge of the power battery can be calculated segment by segment according to the order of each time period, so as to obtain the target state of charge at the end of each time period and thus form a target state of charge sequence.

[0059] In one possible implementation, during the (i+1)th operating condition, as the target vehicle continues to travel, the operating information for the remaining time period can be dynamically updated by combining the previously generated operating information. Specifically, taking the i-th time period as an example, it can be determined that the target vehicle is currently in the i-th time period of the (i+1)th operating condition, and the average speed for the remaining time period is updated based on the historical vehicle speed already generated within that time period and the average speed for the remaining historical time. Furthermore, the driving power sequence for the remaining time period is updated by incorporating gradient information.

[0060] Based on the updated driving power sequence for the remaining future time, the target power generation sequence can be corrected or reprogrammed to make the target state of charge sequence calculated based on the power generation sequence more closely match the actual operating conditions of the target vehicle.

[0061] Compared to related technologies that determine power generation based solely on the current moment or a single time period, the above method searches for power generation across multiple time periods within a complete operating condition. This allows for coordinated energy distribution across different time periods, thereby avoiding the problem of unreasonable overall energy distribution caused by local optima and improving the global optimality of power generation allocation.

[0062] Furthermore, by using cumulative fuel consumption as the optimization target, the optimal power generation sequence is selected from multiple power generation sequences, thereby improving the energy utilization efficiency of the vehicle while meeting the target state of charge, thus reducing energy consumption during vehicle operation and improving economy.

[0063] Furthermore, by back-calculating the target state of charge sequence based on the target power generation sequence, the state of charge evolves according to the optimization results throughout the entire operating process, thereby providing a clear tracking target for subsequent power generation control. This transforms power generation control from unconstrained regulation to convergent control based on the target trajectory, further improving the accuracy and stability of power generation control.

[0064] S104: Determine the power generation capacity of the target vehicle based on the target state of charge sequence and the actual operating state of the target vehicle, so that the state of charge of the target vehicle converges to the target state of charge sequence.

[0065] Actual operating status reflects the actual operating status of the target vehicle, which may include the target and vehicle's current SOC, vehicle speed, gradient, load status, actual drive power, etc., and can be used to reflect the real-time energy demand of the target vehicle.

[0066] In other words, the segmented average drive power is used to determine the average drive power for each time period in order to predict the target state of charge sequence. The target state of charge sequence is used to determine the state of charge that the target vehicle should reach at the end of each time period. By determining the power generation of the target vehicle and applying it to the target vehicle, the target vehicle can operate according to the state of charge indicated by the target state of charge sequence, thereby reducing cumulative fuel consumption.

[0067] As one possible implementation, taking a time period in the (i+1)th operating condition as an example, based on the driving power sequence (an actual operating state) and the target state of charge sequence for the remaining future time, the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) algorithm can be used. Under the condition of satisfying the power battery charging and discharging power constraints, the equivalence factor of the A-ECMS algorithm can be adjusted by the bisection method or PID algorithm so that the actual SOC at the end of the time period converges into the target state of charge sequence, thereby obtaining the target power generation sequence of the range extender, and taking the first element of the target power generation sequence as the final target power generation of the range extender.

[0068] In one possible implementation, if the operating information for the i-th condition also includes the total operating time of the target vehicle in the i-th condition, then C1-C2 can be executed: C1: If the target vehicle does not return to the starting point of the operating condition after the total running time of the target vehicle in the (i+1)th operating condition, then obtain the power generation table.

[0069] The power generation meter includes multiple power generation capacities.

[0070] If the target vehicle does not return to the starting point of the operating condition after the total running time of the i-th operating condition in the (i+1)-th operating condition, it indicates that the operating data of the i-th operating condition is difficult to use to predict the power generation of subsequent road segments. In this case, a power generation table can be obtained, which can be used to indicate the power generation with lower cumulative fuel consumption under different operating conditions.

[0071] C2: Based on the power generation table, determine the state of charge sequence corresponding to the end of the remaining time period in the (i+1)th operating condition.

[0072] The operating status of the target vehicle can be mapped by the power generation table to determine the charge state sequence corresponding to the end of the remaining time period in the (i+1)th working condition, so that the vehicle can be executed in an orderly manner according to the preset power generation in subsequent road sections where there is insufficient data support.

[0073] Therefore, by introducing a power generation meter, even when historical operating data is insufficient to support subsequent road segment predictions, energy allocation can still be reasonably controlled based on a preset strategy. This improves the continuity and stability of power generation in the remaining time period, thereby reducing the probability of unreasonable energy allocation caused by drastic fluctuations in power generation.

[0074] In one possible implementation, during the (i+1)th working condition, the target vehicle repeatedly executes S101-S104 for the total running time of the i-th working condition.

[0075] In one possible implementation, a preset power generation sequence is obtained. Based on the target state of charge sequence and the actual operating state of the target vehicle, the power generation of the target vehicle is determined from the preset power generation sequence.

[0076] The preset power generation sequence is obtained based on the universal characteristic diagram of the target vehicle and the noise, vibration and acoustic roughness (NVH) calibration. The preset power generation sequence includes multiple preset power generation values. The universal characteristic diagram is the performance distribution diagram of the range extender at different speeds and different torques. That is, the preset power generation sequence includes multiple discrete power generation points, which are used to characterize the power generation values ​​that can meet the optimal balance of speed performance, torque performance and NVH under different operating conditions.

[0077] In other words, the power generation capacity of the target vehicle is not arbitrarily determined, but selected from a preset power generation sequence within a preset power generation sequence.

[0078] Therefore, by determining the power generation of the target vehicle from the preset power generation sequence, the power generation value falls within the efficient operating range, while taking into account noise and vibration performance. This avoids the efficiency reduction or NVH performance deterioration caused by arbitrary power generation output, thereby improving the economy, stability and comfort of the vehicle operation while ensuring the accuracy of power generation control.

[0079] As can be seen from the above technical solution, if the target vehicle is at the starting point of the (i+1)th operating condition, the operating information of the (i)th operating condition is obtained. This operating information includes the average vehicle speed over N time periods within the (i)th operating condition, where the (i)th operating condition comprises N time periods, and i and N are positive integers. Based on the average vehicle speed and gradient information over the N time periods within the (i)th operating condition, the segmented average driving power over the N time periods in the (i+1)th operating condition is determined. Based on the segmented average driving power over the N time periods in the (i+1)th operating condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th operating condition, the target state of charge sequence corresponding to the end of the N time periods in the (i+1)th operating condition is determined. Based on the target state of charge sequence and the actual operating state of the target vehicle, the power generation capacity of the target vehicle is determined, so that the state of charge of the target vehicle converges to the target state of charge sequence. Therefore, compared to related technologies that determine power generation based solely on current operating conditions or short time periods, this application introduces segmented operating information from the previous complete operating condition to predict the state of charge (SOC) of the next operating condition in segments. It performs a global analysis of the target vehicle's complete operating conditions, thus ensuring that power generation is no longer limited to local time periods but is planned based on the energy demand of the complete operating conditions across time periods, thereby improving the accuracy of power generation. Furthermore, since the average driving power and the target SOC sequence are determined based on segmented average vehicle speeds, storage resources are greatly saved, and the efficiency of SOC determination is improved, thus balancing accuracy and efficiency.

[0080] See Figure 2 , Figure 2 A power generation control device for a range-extended mining truck provided in this application embodiment, device 200 includes: The acquisition unit 201 is used to acquire the operating information of the i-th working condition if the target vehicle is at the starting point of the i+1-th working condition. The operating information includes the average vehicle speed over N time periods in the i-th working condition. The i-th working condition includes N time periods, where i and N are positive integers. The determining unit 202 is used to determine the segmented average driving power of the N time periods in the (i+1)th working condition based on the average vehicle speed and gradient information of the N time periods in the i-th working condition. The determining unit 202 is further configured to determine the target charge state sequence corresponding to the end of the N time periods in the (i+1)th working condition based on the segmented average driving power of the N time periods in the (i+1)th working condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th working condition. Control unit 203 is used to determine the power generation of the target vehicle based on the target state of charge sequence and the actual operating state of the target vehicle, so that the state of charge of the target vehicle converges to the target state of charge sequence.

[0081] As one possible implementation, the starting point of the working condition is the moment when the target vehicle jumps from parking and loading to transporting materials, or from parking and unloading to returning to the loading point.

[0082] As one possible implementation, the device 200 further includes a working condition start point determination unit, used for: Obtain the operating status information of the target vehicle, including slope information, altitude information, load status, vehicle speed and location information; If the difference between the operating condition status information and the target operating condition status information is less than a first threshold, then the target vehicle is determined to have reached the operating condition starting point.

[0083] As one possible implementation, the device 200 further includes a preset power generation sequence acquisition unit: A preset power generation sequence is obtained, which is based on the universal characteristic diagram of the target vehicle and the noise, vibration and acoustic roughness (NVH) calibration. The preset power generation sequence includes multiple preset power generation, and the universal characteristic diagram is a performance distribution diagram of the range extender at different speeds and different torques. The control unit 203 is specifically used for: The power generation of the target vehicle is determined from the preset power generation sequence based on the target state of charge sequence and the actual operating state of the target vehicle.

[0084] As one possible implementation, the determining unit 202 is specifically used for: Obtain the available controller resources of the target vehicle; If the available controller resources of the target vehicle are less than or equal to the resource threshold, then the average slope of the N time periods is determined based on the slope information. Based on the average vehicle speed and the average gradient of the N time periods in the i-th working condition, determine the segmented average driving power of the N time periods in the (i+1)-th working condition. If the available controller resources of the target vehicle are greater than the resource threshold, then the driving power at multiple moments in the (i+1)th working condition is determined based on the average vehicle speed and gradient information of N time periods in the i-th working condition. Based on the driving power at multiple moments in the (i+1)th operating condition, determine the segmented average driving power for N time periods in the (i+1)th operating condition.

[0085] As one possible implementation, the determining unit 202 is specifically used for: Based on the segmented average driving power of N time periods in the (i+1)th operating condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th operating condition, multiple power generation sequences are obtained, and the power generation sequence includes the power generation power of N time periods. The power generation sequence with the lowest cumulative fuel consumption is determined as the target power generation sequence for the (i+1)th operating condition. Based on the target power generation sequence of the (i+1)th operating condition, determine the target state of charge sequence corresponding to the end of N time periods in the (i+1)th operating condition.

[0086] As one possible implementation, the determining unit 202 is specifically used for: Obtain the power battery charging and discharging power constraints of the target vehicle; Under the condition that the power battery charging and discharging power constraints are met, multiple power generation sequences are obtained by searching based on the segmented average driving power of N time periods in the (i+1)th operating condition.

[0087] As one possible implementation, if the operating information for the i-th operating condition also includes the total operating time of the target vehicle in the i-th operating condition, then the device 200 further includes a remaining road segment control unit, used for: If the target vehicle does not return to the starting point of the working condition after the total running time of the i+1th working condition, then the power generation table is obtained, and the power generation table includes multiple power generation values. Based on the power generation table, determine the state of charge sequence corresponding to the end of the remaining time period in the (i+1)th operating condition.

[0088] See Figure 3 This application also provides a vehicle, which includes a memory 301 and a processor 302: The memory is used to store computer programs and to transfer the computer programs to the processor; The processor is used to execute the method of the above method embodiment according to the computer program.

[0089] This application also provides a computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, the computer program being used to execute the method of the above-described method embodiments.

[0090] This application also provides a computer program product including a computer program, which, when run on a computer device, causes the computer device to perform the method described in the above method embodiments.

[0091] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0092] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0093] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0094] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0096] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the power generation of a range-extended mining truck, characterized in that, The method includes: If the target vehicle is at the starting point of the (i+1)th working condition, obtain the operating information of the i-th working condition. The operating information includes the average vehicle speed over N time periods in the i-th working condition. The i-th working condition includes N time periods, where i and N are positive integers. Based on the average vehicle speed and gradient information of N time periods in the i-th working condition, determine the segmented average driving power of N time periods in the (i+1)-th working condition; Based on the segmented average driving power of N time periods in the (i+1)th operating condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th operating condition, determine the target state of charge sequence corresponding to the end of the N time periods in the (i+1)th operating condition. Based on the target state of charge sequence and the actual operating state of the target vehicle, the power generation capacity of the target vehicle is determined so that the state of charge of the target vehicle converges to the target state of charge sequence.

2. The method according to claim 1, characterized in that, The starting point of the operating condition is the moment when the target vehicle jumps from parking and loading to transporting materials, or from parking and unloading to returning to the loading point.

3. The method according to claim 2, characterized in that, The method further includes: Obtain the operating status information of the target vehicle, including slope information, altitude information, load status, vehicle speed and location information; If the difference between the operating condition status information and the target operating condition status information is less than a first threshold, then the target vehicle is determined to have reached the operating condition starting point.

4. The method according to claim 1, characterized in that, The method further includes: A preset power generation sequence is obtained, which is based on the universal characteristic diagram of the target vehicle and the noise, vibration and acoustic roughness (NVH) calibration. The preset power generation sequence includes multiple preset power generation, and the universal characteristic diagram is a performance distribution diagram of the range extender at different speeds and different torques. The step of determining the power generation capacity of the target vehicle based on the target state of charge sequence and the actual operating state of the target vehicle includes: The power generation of the target vehicle is determined from the preset power generation sequence based on the target state of charge sequence and the actual operating state of the target vehicle.

5. The method according to claim 1, characterized in that, The step of determining the segmented average drive power for the N time periods in the (i+1)th operating condition based on the average vehicle speed and gradient information over the N time periods in the i-th operating condition includes: Obtain the available controller resources of the target vehicle; If the available controller resources of the target vehicle are less than or equal to the resource threshold, then the average slope of the N time periods is determined based on the slope information. Based on the average vehicle speed and the average gradient of the N time periods in the i-th working condition, determine the segmented average driving power of the N time periods in the (i+1)-th working condition. If the available controller resources of the target vehicle are greater than the resource threshold, then the driving power at multiple moments in the (i+1)th working condition is determined based on the average vehicle speed and gradient information of N time periods in the i-th working condition. Based on the driving power at multiple moments in the (i+1)th operating condition, determine the segmented average driving power for N time periods in the (i+1)th operating condition.

6. The method according to claim 1, characterized in that, The step of determining the target state of charge sequence corresponding to the end of the N time periods in the (i+1)th operating condition based on the segmented average driving power of N time periods in the (i+1)th operating condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th operating condition includes: Based on the segmented average driving power of N time periods in the (i+1)th operating condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th operating condition, multiple power generation sequences are obtained, and the power generation sequence includes the power generation power of N time periods. The power generation sequence with the lowest cumulative fuel consumption is determined as the target power generation sequence for the (i+1)th operating condition. Based on the target power generation sequence of the (i+1)th operating condition, determine the target state of charge sequence corresponding to the end of N time periods in the (i+1)th operating condition.

7. The method according to claim 6, characterized in that, The method involves searching for multiple power generation sequences based on the segmented average drive power over N time periods in the (i+1)th operating condition, including: Obtain the power battery charging and discharging power constraints of the target vehicle; Under the condition that the power battery charging and discharging power constraints are met, multiple power generation sequences are obtained by searching based on the segmented average driving power of N time periods in the (i+1)th operating condition.

8. The method according to claim 1, characterized in that, If the operating information for the i-th operating condition also includes the total operating time of the target vehicle in the i-th operating condition, then the method further includes: If the target vehicle does not return to the starting point of the working condition after the total running time of the i+1th working condition, then the power generation table is obtained, and the power generation table includes multiple power generation values. Based on the power generation table, determine the state of charge sequence corresponding to the end of the remaining time period in the (i+1)th operating condition.

9. A power generation control device for a range-extended mining truck, characterized in that, The device includes: The acquisition unit is used to acquire the operating information of the i-th working condition if the target vehicle is at the starting point of the i+1-th working condition. The operating information includes the average vehicle speed over N time periods in the i-th working condition. The i-th working condition includes N time periods, where i and N are positive integers. The determining unit is used to determine the segmented average driving power of the N time periods in the (i+1)th working condition based on the average vehicle speed and gradient information of the N time periods in the i-th working condition. The determining unit is further configured to determine the target charge state sequence corresponding to the end of the N time periods in the (i+1)th working condition based on the segmented average driving power of the N time periods in the (i+1)th working condition, the current state of charge of the target vehicle, and the target state of charge of the (i+1)th working condition. The control unit is configured to determine the power generation capacity of the target vehicle based on the target state of charge sequence and the actual operating state of the target vehicle, so that the state of charge of the target vehicle converges to the target state of charge sequence.

10. A vehicle, characterized in that, The vehicle includes a processor and a memory: The memory is used to store computer programs and to transfer the computer programs to the processor; The processor is configured to perform the method according to any one of claims 1-8 according to the computer program.

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