Battery powered work machine downtime analysis

Abstract

In some implementations, a computing device may receive machine information and operating parameters associated with a battery-powered work machine. The computing device may estimate utilization of the battery-powered work machine based at least in part on a workday simulation using the machine information and the operating parameters. The computing device may determine a disturbance score indicative of a predicted impact of replacing the diesel-powered work machine with the battery-powered work machine, where the disturbance score is based at least in part on a predicted charge downtime of the battery-powered work machine, as described above. The computing device may display, using a user interface of the computing device, a recommended suggestion associated with replacing the diesel powered work machine with the battery powered work machine, wherein the recommended suggestion is based at least in part on the interference score as described above.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to analyzing machines at a worksite, for example, to analyzing downtime associated with using one or more battery-powered work machines at a worksite. BACKGROUND

[0002] Work machines at construction sites are often powered by diesel. Replacing diesel-powered work machines with battery-powered work machines can provide several advantages. For example, battery-powered work machines can reduce environmental impact. Additionally, operating costs for battery-powered work machines can be lower, particularly when the cost of charging batteries is lower than the price of diesel. Furthermore, operating costs for battery-powered work machines can be more predictable than operating costs for diesel-powered work machines because fluctuations in the cost of electricity are not as large as fluctuations in the cost of diesel.

[0003] Despite the advantages of battery-powered work machines over diesel-powered work machines, not all construction sites can accommodate battery-powered work machines. Additionally, construction site managers can not know how to use battery-powered work machines to plan construction projects. Thus, even though battery-powered work machines can reduce costs and speed completion of construction projects, construction site managers can delay replacing one or more diesel-powered work machines with suitable battery-powered work machines.

[0004] Computing devices and methods of the present disclosure address one or more of the problems described above and / or other problems in the art. SUMMARY

[0005] A method can include receiving, by a computing device, machine information and operating parameters associated with a battery-powered work machine; estimating, by the computing device, utilization of the battery-powered work machine based at least in part on a workday simulation using the machine information and the operating parameters; determining, by the computing device, a disruption score indicating a predicted impact of replacing a diesel-powered work machine with the battery-powered work machine, wherein the disruption score is based at least in part on a predicted charge downtime of the battery-powered work machine; and displaying, using a user interface of the computing device, a recommended suggestion associated with replacing the diesel-powered work machine with the battery-powered work machine, wherein the recommended suggestion is based at least in part on the disruption score.

[0006] A computing device can include a user interface having a display screen, one or more memories, and one or more processors communicatively coupled with the one or more memories and configured to receive machine information and operational parameters associated with a battery-powered work machine, estimate utilization of the battery-powered work machine based at least in part on a workday simulation using the machine information and operational parameters, determine a disruption score indicative of a predicted impact of replacing a diesel-powered work machine with the battery-powered work machine, wherein the disruption score is based at least in part on a predicted charge downtime of the battery-powered work machine, and output to the display screen a recommended suggestion associated with replacing the diesel-powered work machine with the battery-powered work machine, wherein the recommended suggestion is based at least in part on the disruption score.

[0007] A computing device can include a user interface having a display screen, one or more memories, and one or more processors communicatively coupled with the one or more memories and configured to receive machine information and operational parameters associated with one or more battery-powered work machines, estimate utilization of the one or more battery-powered work machines based at least in part on a workday simulation using the machine information and operational parameters, determine a charger load based at least in part on the workday simulation and the utilization of the one or more battery-powered work machines, wherein the charger load is based at least in part on a predicted charge time of each of the one or more battery-powered work machines, and output to the display screen a recommended suggestion associated with the one or more battery-powered work machines, wherein the recommended suggestion is based at least in part on the charger load. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 is a schematic diagram of an example computing device associated with analyzing downtime associated with battery-powered work machines.

[0009] Figure 2 and Figure 3 is a schematic diagram of an example chart associated with estimated utilization of battery-powered work machines.

[0010] Figure 4 is a flowchart of an example process associated with battery-powered work machine downtime analysis. DETAILED DESCRIPTION

[0011] The present disclosure relates to analyzing disruption associated with replacing one or more diesel-powered work machines with one or more battery-powered work machines at a construction site.

[0012] Figure 1is a schematic diagram of an example computing device 100 associated with analyzing downtime associated with battery-powered work machines. The computing device can be a laptop, a tablet, a smartphone, or the like. For example, the computing device can be used to analyze downtime associated with replacing one or more diesel-powered work machines with one or more battery-powered work machines. Examples of diesel-powered work machines that can be replaced by battery-powered work machines can include excavators, bulldozers, dump trucks, cranes, backhoes, tractors, forklifts, skid loaders, generators, forestry equipment, and / or combinations thereof, or the like.

[0013] The computing device can perform the analysis for a particular construction project or construction site. The construction site can be a location where a construction project is being performed. For a construction project that uses battery-powered work machines, the construction site can include one or more charging locations. Each charging location can include a power source and a charger for charging the batteries of the battery-powered work machines. As shown, the example computing device 100 includes a user interface 105, a memory 110, and a processor 115. Figure 1

[0014] The user interface 105 can include one or more electronic components that allow a user to interact with the computing device 100. The user interface 105 can include one or more input devices 120 and / or one or more output devices 125. Examples of input devices 120 can include a keyboard, a mouse, a touch-sensitive surface, a microphone, and / or combinations thereof, or the like. Examples of output devices 125 can include one or more of a display, a speaker, and / or combinations thereof, or the like. The user interface 105 can further include software components that can interpret user input and generate corresponding output. The software components can include graphical user interface elements, command-line interface elements, or any other type of interface components that facilitate communication between the user and the computing device 100. The user interface 105 can also include a processing unit that can be configured to execute instructions associated with the user interface 105 and manage the exchange of data between the input devices 120, the output devices 125, and the software components.

[0015] ​The memory 110 can include one or more physical memory devices configured to store data, instructions, or other information. The memory 110 can be volatile or non-volatile. The memory 110 can include random access memory, read only memory, flash memory, or any other type of memory usable by the computing device 100. The memory 110 can be configured to store executable instructions that are retrievable and executable by the processor 115 of the computing device 100. The memory 110 can further be configured to store data usable by one or more applications, processes, or functions of the computing device 100. The memory 110 can also include one or more memory modules or storage devices that can be arranged in a particular configuration or architecture, such as a single inline memory module, a dual inline memory module, and / or combinations thereof. The memory 110 can be accessed by the processor 115 or other components of the computing device 100 through one or more memory interfaces, buses, or controllers. As discussed in greater detail below, the information that can be stored in the memory 110 can include machine information 130 (e.g., information about one or more battery-powered work machines), parameters associated with the operation of one or more battery-powered work machines, one or more lookup tables, and / or combinations thereof.

[0016] The processor 115 can include circuitry configured to execute instructions to perform operations on data. The processor 115 can include one or more processing units, such as a central processing unit, a graphics processing unit, a digital signal processor, an application-specific integrated circuit, or a field programmable gate array. Each processing unit can include one or more cores, and each core can be configured to independently and in parallel execute instructions. The processor 115 can further include one or more memory controllers, cache memory, or communication interfaces that can be configured to facilitate data access and transfer between the processor 115 and other components of the computing device 100. The processor 115 can be configured to interact with various types of memory (e.g., the memory 110) through one or more buses or other communication channels, including volatile memory, non-volatile memory, or external memory. The processor 115 can be implemented as a single integrated circuit or a combination of multiple integrated circuits within the computing device 100.

[0017] The processor 115 can be configured to access machine information 130 and operational parameters 135 associated with a battery-powered work machine. The machine information 130 and operational parameters 135 can be stored in the memory 110, and the processor 115 can receive the machine information 130 and operational parameters 135 by accessing the memory 110. The machine information 130 can include one or more of ground speed, fuel rate, or location information. The operational parameters 135 can include one or more of battery capacity, battery state of health, charger information, charging threshold, utilization metric, or fuel consumption rate.

[0018] The processor 115 can be configured to estimate utilization of the battery- powered work machine based at least in part on a workday simulation using the machine information 130 and the operational parameters 135. The workday simulation can be a process performed to replicate or simulate operating conditions, tasks, or activities associated with using the battery-powered work machine in a workday (e.g., a period of time within a single calendar day). The workday simulation can include one or more software modules that generate scenarios that can reflect various work environments, workloads, or machine operations. The workday simulation can include input data representing parameters such as machine settings, environmental conditions, task sequences, or operator actions. The workday simulation can further include a computational model for processing the input data to generate simulation output that can represent performance, task completion, or operational efficiency of the battery-powered work machine over a work period. The workday simulation can be based on actual usage of the work machine, including examples such as the battery-powered work machine, a diesel-powered work machine, and / or a combination thereof.

[0019] Estimating utilization of the battery-powered work machine can include determining a battery discharge rate having at least a first battery state of charge (SoC) and a second battery state of charge (SoC), comparing the first battery state of charge (SoC) to a first threshold, and comparing the second battery state of charge (SoC) to a second threshold. The battery discharge rate can be a rate of consumption of the battery of the battery-powered work machine during operation of the battery-powered work machine. The battery discharge rate can be based at least in part on the machine information 130, the operational parameters 135, settings of the workday simulation, and / or a combination thereof. The first threshold can be a state of charge (SoC) value that indicates the battery of the battery-powered work machine needs to be charged (e.g., 10% state of charge (SoC), 15% state of charge (SoC), 20% state of charge (SoC), etc.). The first threshold can include a buffer, for example, to ensure the battery-powered machine has sufficient power to travel from a work site to a charging location. The second threshold can be a state of charge (SoC) value that indicates the battery has been charged to a sufficient level for the battery-powered work machine to return to the work site and resume operation for the remainder of the workday (e.g., 40% state of charge (SoC), 60% state of charge (SoC), 80% state of charge (SoC), 100% state of charge (SoC), etc.).

[0020] The processor 115 can be configured to estimate the utilization of the battery- powered work machine by estimating a work period from the battery discharge rate. The work period can be a period of time that the battery-powered work machine can operate. A faster battery discharge rate can indicate a shorter work period. A slower battery discharge rate can indicate a longer work period. The battery discharge rate can be based at least in part on one or more charging opportunities. Accordingly, the processor 115 can be configured to identify one or more charging opportunities. To identify the one or more charging opportunities, the processor 115 can be configured to estimate the occurrence of an idle period, which can be a length of time that the battery-powered work machine is not expected to operate. The processor 115 can be configured to compare the length of the idle period to an opportunity charging threshold and identify the one or more charging opportunities if the length of the idle period is greater than the opportunity charging threshold. The opportunity charging threshold can be a value (e.g., a length of time) associated with traveling the battery-powered work machine to a charging location, charging the battery of the battery-powered work machine to a sufficient level (e.g., the second threshold), and returning the battery-powered work machine to the work site. If the idle period is greater than the amount of time required to charge the battery of the battery-powered work machine and return the battery-powered work machine to the work site (e.g., the amount of time required for a charging opportunity), the processor 115 can determine that the idle period is a charging opportunity. If the idle period is shorter than the opportunity charging threshold, the processor 115 can be configured to determine that the idle period is not a charging opportunity.

[0021] The processor 115 can be configured to determine an interference score that indicates a predicted impact of replacing a diesel-powered work machine with the battery-powered work machine on a particular workday. A first interference score can indicate that the battery-powered vehicle will not need to charge during the workday. Accordingly, the first interference score can be assigned to a workday simulation in which the battery-powered work machine is able to replace the diesel-powered work machine without any negative impact on the construction project. A second interference score can indicate that, according to the workday simulation, the battery-powered work machine will need to charge during one or more idle periods, resulting in a minimal impact on the construction project. This impact can be considered “minimal” because the time to charge the battery of the battery-powered work machine can not extend the construction project. A third interference score can indicate that the battery-powered work machine will be forced to undergo a mandatory charge, which is a period of time in which the battery-powered work machine must stop operating to charge. A mandatory charge can have a significant impact on the construction project because the construction project and / or the workday can be delayed if the battery-powered work machine is used in place of the diesel-powered work machine. A fourth interference score can indicate that the battery-powered work machine will need more than 24 hours to complete the work of the diesel-powered work machine, which can have a significant impact on the construction project if the battery-powered work machine is used in place of the diesel-powered work machine.

[0022] The interference score can be based at least in part on a predicted charge downtime of the battery-powered work machine. Similar to the opportunity charge threshold, the predicted charge downtime can be a duration of time associated with navigating the battery-powered work machine to a charging location, charging the battery of the battery-powered work machine to a sufficient level (e.g., a second threshold), and returning the battery-powered work machine to the work site. The interference score can be based on a travel time overhead, an energy usage overhead, and / or a charge event overhead. The travel time overhead can be a time value associated with navigating the battery-powered work machine between the charging location and the work site. Thus, the travel time overhead can be based at least in part on a machine speed and a charger distance (e.g., a distance between the battery-powered work machine and the charging location), among other examples. The energy usage overhead can be a SoC value associated with an amount of battery power used to navigate the battery-powered work machine between the charging location and the work site. Thus, the energy usage overhead can be based at least in part on a travel time (e.g., a time for the battery-powered work machine to navigate to the charging location) and an average power consumption (e.g., an average rate at which the battery-powered work machine consumes battery power). The charge event overhead can be a time value associated with initiating and ending charging of the battery of the battery-powered work machine. The charge event overhead can include an amount of time for an operator of the battery-powered work machine to exit the battery-powered work machine, plug in a plug to a charging port, unplug the plug from the charging port, and re-enter the battery-powered work machine, among other examples. Thus, the processor 115 can be configured to estimate the travel time overhead from the machine speed and the charger distance, and determine the interference score from the travel time overhead. The processor 115 can be configured to estimate the energy usage overhead from the travel time and the average power consumption, and determine the interference score from the energy usage overhead. The processor 115 can be configured to estimate the charge event overhead from an amount of time associated with initiating and ending charging of the battery-powered work machine, and determine the interference score from the charge event overhead.

[0023] The processor 115 can be configured to determine a charger load (e.g., a value associated with simultaneous use of a charger by one or more battery-powered work machines). The processor 115 can be configured to determine the charger load from the workday simulation and a utilization of the one or more battery-powered work machines. The processor 115 can be configured to determine the charger load based at least in part on a predicted charge time for each of the one or more battery-powered work machines. The processor 115 can be configured to determine the interference score, identify the one or more charging opportunities, determine the opportunity charge threshold, determine the charge event overhead, and / or a combination thereof, among other examples, from the charger load. For example, if the processor 115 determines that an idle period of a first battery-powered work machine is a sufficient duration of time to charge, but the charger load indicates that a second battery-powered work machine will use the charger during the idle period, the processor 115 can determine that the idle period of the first battery-powered work machine is not a charging opportunity.

[0024] The processor 115 can be configured to control the user interface 105 to display a recommended suggestion associated with replacing a diesel-powered work machine with a battery-powered work machine. The recommended suggestion can be based at least in part on the interference score, the charger load, and / or combinations thereof, among other examples. The processor 115 can determine the recommended suggestion by querying the lookup table 140 stored in the memory 110. For example, the processor 115 can determine the recommended suggestion by querying the lookup table as a result of the interference score indicating that replacing the diesel-powered work machine with the battery-powered work machine will not significantly delay completion of the construction project. The recommended suggestion can include identifying a battery-powered work machine in the lookup table that is capable of replacing the diesel-powered work machine without increasing the interference score. If no suitable battery-powered work machine exists (e.g., the workday simulation using the battery-powered work machine is assigned a third interference score or a fourth interference score), the recommended suggestion can identify a different battery-powered work machine with a lower interference score. Alternatively, the recommended suggestion can indicate that multiple battery-powered work machines are available to replace a single diesel-powered work machine. If the interference score is negatively affected by the charger load, the recommended suggestion can indicate altering the schedule (e.g., altering the occurrence time of the idle period and / or the work period of one or more battery-powered work machines) to allow multiple battery-powered work machines to use the charger at different times. Alternatively, the recommended suggestion can include a suggestion to increase the number of charging locations at the work site.

[0025] As described above, Figure 1 are provided by way of example. Other examples can differ from those described. Figure 1 without departing from the spirit and scope of the disclosure. Figure 1 The number and arrangement of devices shown in Figure 1 may be greater, fewer, different, or arranged differently without departing from the spirit and scope of the disclosure. Figure 1 Two or more devices shown in Figure 1 may be implemented within a single device, or Figure 1 a single device shown in Figure 1 may be implemented as multiple, distributed devices. Additionally or alternatively, A set of devices (e.g., one or more devices) shown in

[0026] may perform one or more functions described as being performed by another set of devices. Figure 2is an illustration of an example plot 200 associated with an estimated utilization of a battery-powered work machine. The example plot 200 includes an X-axis 205 and a Y-axis 210. The X-axis can represent time, and the Y-axis can represent battery state of charge. The example plot 200 illustrates how the battery state of charge of a battery-powered work machine changes over time according to a workday simulation. Thus, the example plot 200 illustrates an example battery discharge curve 215 during a workday 220 according to a workday simulation.

[0027] The workday includes idle periods 225 and work periods 230. During the idle periods 225, the battery-powered work machine is not in use. During some idle periods 225, the battery-powered work machine can be turned off. During some idle periods 225, the battery-powered work machine can be in an idle state (e.g., running but not performing work). During the work periods 230, the battery-powered work machine is in use. As shown in the example plot 200, the battery discharge curve 215 can be based at least in part on the battery discharge rates discussed above and the occurrence of idle periods 225 and / or work periods 230 in the workday. The battery discharge rate during idle periods 225 can be different than the battery discharge rate during work periods 230. For example, the battery discharge rate during work periods 230 can be greater than the battery discharge rate during idle periods 225. Figure 1

[0028] In the example plot 200, the battery discharge curve 215 indicates that the battery-powered work machine will consume more battery available power than is available without charging. For example, as shown in the example plot 200, the battery state of charge will reach 0% shortly before noon in the workday. Furthermore, as shown in the example plot 200, the battery-powered work machine is estimated to have a state of charge of -50% to -100% at the end of the workday. Because in a real battery (e.g., a battery in a battery-powered machine in the real world), the state of charge cannot be negative, the example plot 200 indicates that the battery-powered work machine needs to be charged during the workday. Alternatively, the example plot 200 can indicate that the battery-powered work machine is not suitable to replace a diesel-powered work machine, especially if the battery-powered work machine cannot be sufficiently charged throughout the workday.

[0029] ​If the computing device 100 determines that the battery-powered work machine is not suitable to replace the diesel-powered work machine, the computing device 100 can assign an interference score (e.g., the third interference score or the fourth interference score discussed above) to the workday simulation that indicates that the battery-powered work machine will be highly disruptive to the construction project. If the computing device 100 determines that the battery-powered work machine can be suitable to replace the diesel-powered work machine, the computing device 100 can assign an interference score (e.g., the first interference score or the second interference score discussed above) to the workday simulation that indicates that the battery-powered work machine will not be highly disruptive to the construction project.

[0030] As discussed above, Figure 2 are provided by way of example. Other examples can differ from what is described Figure 2 without departing from the spirit of the disclosure.

[0031] Figure 3 is an illustration of an example graph 300 associated with an estimated utilization of a battery-powered work machine. The example graph 300 includes an X-axis 305 and a Y-axis 310. The X-axis can represent time, while the Y-axis can represent battery state of charge. As discussed above Figure 2 with respect to the example graph 200 of the battery state of charge of the battery-powered work machine over time according to the workday simulation. In addition, the example graph 300 illustrates how the battery state of charge of the battery-powered work machine changes due to the idle periods 325 having battery charging opportunities being considered. Thus, the example graph 300 illustrates an example battery discharge curve 315 during the workday according to the workday simulation.

[0032] Figure 3 The workday of the example graph 300 includes idle periods 325 and work periods 330. During the idle periods 325, the battery-powered work machine is not being used. During the work periods 330, the battery-powered work machine is being used. In Figure 3 In the example graph 300, the length of the one or more idle periods 325 can be sufficient to charge the battery of the battery-powered work machine. For example, in Figure 3 In the workday of the example graph 300, an idle period can occur from 12:30 PM to 2:00 PM, which the computing device 100 can identify as a charging opportunity 335 if, for example, the 90 minutes is greater than the opportunity charging threshold.

[0033] As illustrated by the example graph 300, the battery discharge curve can be based at least in part on the above discussion with respect to Figure 1The battery discharge rate is discussed along with occurrences of idle periods 325 and / or work periods 330 during the workday. The battery discharge rate during idle periods 325 can be different than the battery discharge rate during work periods 330. For example, the battery discharge rate during work periods 330 can be greater than the battery discharge rate during idle periods 325.

[0034] In the example graph 300, the battery discharge curve indicates that the state of charge of the battery will fall below the first threshold at 11 :00 AM on the workday. The computing device 100 can identify a forced charging period 340, which can be a period in which the battery of the battery-powered work machine must be charged to a state of charge equal to or greater than the second threshold. In this example, the forced charging period 340 is identified as a 30-minute period that occurs during the workday. Figure 3 In the example graph 300, the second threshold can be based on the time at which the charging opportunity 335 is to occur.

[0035] Thus, Figure 3 The example graph 300 indicates that two hours of charging time will be required using the battery-powered work machine (e.g., a 30-minute forced charging period 340 and a 90-minute charging period that occurs during an idle period identified as a charging opportunity 335). Thus, replacing the diesel-powered work machine with the battery-powered work machine can result in an additional 30 minutes of downtime because only the forced charging period 340 interrupts a work period, while the 90-minute charging period occurs during an idle period. The disruption score for the workday simulation represented by the example graph 300 can reflect a moderate degree of disruption. Thus, for example, the computing device 100 can assign a second disruption score to the workday simulation.

[0036] As described above, Figure 3 are provided by way of example. Other examples can differ from what is described with respect to at least the following Figure 3 described with respect to at least the following

[0037] Figure 4 is a flow diagram of an example process 400 associated with battery-powered work machine downtime analysis. Figure 4 One or more of the processing blocks of Figure 4 may be performed by another device or set of devices separate from or including the computing device, such as another device or component internal or external to the computing device.

[0038] As Figure 4As shown, process 400 can include receiving machine information and operational parameters associated with the battery-powered work machine (block 410). For example, the computing device can receive machine information and operational parameters associated with the battery-powered work machine, as described above. The operational parameters can include one or more of a battery capacity, a battery state of health, charger information, a charge threshold, a utilization metric, or a fuel consumption rate. The machine information can include one or more of a ground speed, a fuel rate, or location information.

[0039] As shown, process 400 can include receiving machine information and operational parameters associated with the battery-powered work machine (block 410). For example, the computing device can receive machine information and operational parameters associated with the battery-powered work machine, as described above. The operational parameters can include one or more of a battery capacity, a battery state of health, charger information, a charge threshold, a utilization metric, or a fuel consumption rate. The machine information can include one or more of a ground speed, a fuel rate, or location information. Figure 4 Further shown, process 400 can include estimating a utilization of the battery-powered work machine based at least in part on a workday simulation using the machine information and the operational parameters (block 420). For example, the computing device can estimate a utilization of the battery-powered work machine based at least in part on a workday simulation using the machine information and the operational parameters, as described above. Estimating the utilization of the battery-powered work machine can include determining a battery discharge rate having at least a first battery state of charge and a second battery state of charge, comparing the first battery state of charge to a first threshold, and comparing the second battery state of charge to a second threshold. Estimating the utilization of the battery-powered work machine can include estimating a work period from the battery discharge rate. Estimating the utilization of the battery-powered work machine can include identifying one or more charging opportunities and determining the battery discharge rate from the one or more charging opportunities. Identifying the one or more charging opportunities can include estimating a length of an idle period, comparing the length of the idle period to an opportunity charge threshold, and identifying the one or more charging opportunities as a result of the length of the idle period being greater than the opportunity charge threshold.

[0040] As shown, process 400 can include receiving machine information and operational parameters associated with the battery-powered work machine (block 410). For example, the computing device can receive machine information and operational parameters associated with the battery-powered work machine, as described above. The operational parameters can include one or more of a battery capacity, a battery state of health, charger information, a charge threshold, a utilization metric, or a fuel consumption rate. The machine information can include one or more of a ground speed, a fuel rate, or location information. Figure 4 Further shown, process 400 can include determining a disturbance score indicative of a predicted impact of replacing a diesel-powered work machine with the battery-powered work machine, wherein the disturbance score is based at least in part on a predicted charge downtime of the battery-powered work machine (block 430). For example, the computing device can determine a disturbance score indicative of a predicted impact of replacing a diesel-powered work machine with the battery-powered work machine, wherein the disturbance score is based at least in part on a predicted charge downtime of the battery-powered work machine, as described above. In some implementations, the disturbance score is based at least in part on a predicted charge downtime of the battery-powered work machine. Determining the disturbance score can include estimating a travel time overhead from a machine speed and a charger distance, and determining the disturbance score from the travel time overhead. Determining the disturbance score can include estimating an energy usage overhead from a travel time and an average power consumption, and determining the disturbance score from the energy usage overhead. Determining the disturbance score can include estimating a charge event overhead from an amount of time associated with a start and end charge of the battery-powered work machine, and determining the disturbance score from the charge event overhead.

[0041] As shown, process 400 can include receiving machine information and operational parameters associated with the battery-powered work machine (block 410). For example, the computing device can receive machine information and operational parameters associated with the battery-powered work machine, as described above. The operational parameters can include one or more of a battery capacity, a battery state of health, charger information, a charge threshold, a utilization metric, or a fuel consumption rate. The machine information can include one or more of a ground speed, a fuel rate, or location information. Figure 4Further, process 400 can include displaying, using a user interface of the computing device, a recommended suggestion associated with replacing the diesel-powered work machine with the battery-powered work machine, where the recommended suggestion is based at least in part on the interference score (block 440). For example, the computing device can display, using a user interface of the computing device, a recommended suggestion associated with replacing the diesel-powered work machine with the battery-powered work machine, where the recommended suggestion is based at least in part on the interference score as described above.

[0042] Although Figure 4 Example blocks of process 400 are shown, but in some implementations, process 400 can include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. 4. Additionally, or alternatively, two or more of the blocks of process 400 can be performed concurrently. Figure 4 Example blocks of process 400 are shown, but in some implementations, process 400 can include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. 4. Additionally, or alternatively, two or more of the blocks of process 400 can be performed concurrently.

[0043] Industrial applicability The computing device described herein can be used to analyze a workday of a construction project. Using a workday simulation, the computing device can indicate how replacing a diesel-powered work machine with a battery-powered work machine would affect the workday. By assigning an interference score to the workday simulation, the computing device can provide one or more recommended suggestions that an operator can use to reduce downtime and / or minimize the negative impact of replacing one or more diesel-powered work machines with one or more battery-powered work machines. The computing device can determine the interference score based on a utilization rate of the one or more battery-powered work machines. The utilization rate can be estimated using a workday simulation that takes into account machine information and operating parameters associated with each battery-powered work machine used at the construction site on the workday being analyzed.

[0044] Because the workday simulation models a real-world scenario, the computing device can accurately represent how replacing one or more diesel-powered work machines with one or more battery-powered work machines at a construction site would affect the workday. Furthermore, by providing recommended suggestions, the computing device can help a construction site manager schedule a construction project to minimize downtime and complete the construction project on time.

[0045] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementations to the precise form disclosed. Modifications and variations can be made in light of the above disclosure or can be acquired from practice of the implementations. Additionally, any of the implementations described herein can be combined with one another, unless expressly provided otherwise. While specific combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various implementations. Although each dependent claim listed below can only stand directly in the dependency relationship with one claim, the disclosure of various implementations includes each dependent claim in combination with every other dependent claim.

[0046] When a “processor” or “one or more processors” (or another device or component, such as a “controller” or “one or more controllers”) is described or claimed as performing operations or configured to perform operations (within a single claim or in multiple claims), this statement is intended to encompass both a single processor performing the operations and / or a set of processors performing the operations collectively, unless the context explicitly dictates otherwise (e.g., using “first processor” and “second processor” or other language that distinguishes between the processors). For example, when a claim has the form “one or more processors configured to: perform X; perform Y; and perform Z,” the claim should be interpreted as meaning “one or more processors configured to perform X; one or more (possibly different) processors configured to perform Y; and one or more (possibly also different) processors configured to perform Z.”

[0047] As used herein, “a,” “an,” and “the” are intended to include one or more items, and can be used interchangeably with “one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items, and the term “set of” is intended to include one or more groups of one or more items. Furthermore, as used herein, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to mean “and / or” unless explicitly stated otherwise (e.g., in an if “A or B” statement, the “or” is intended to mean “and / or” unless explicitly stated otherwise).

Claims

1. A method comprising: The computing device receives mechanical information and operating parameters associated with the battery-powered machinery. The utilization rate of the battery-powered operating machinery is estimated by the computing device based at least in part on a workday simulation using the machinery information and the operating parameters; The computing device determines an interference score, which indicates the predicted impact of replacing diesel-powered machinery with the battery-powered machinery. The interference score is based, at least in part, on the predicted charging downtime of the battery-powered machinery. as well as The user interface of the computing device displays recommendations associated with replacing the diesel-powered work machinery with the battery-powered work machinery, wherein the recommendations are based at least in part on the interference score.

2. The method of claim 1, wherein estimating the utilization rate of the battery-powered operating machinery comprises: Determine the battery discharge rate, wherein the battery discharge rate has at least a first battery state of charge and a second battery state of charge; Compare the state of charge of the first battery with a first threshold; as well as The second battery state of charge is compared with a second threshold.

3. The method according to any one of claims 1-2, wherein estimating the utilization rate of the battery-powered operating machinery includes estimating the operating period based on the battery discharge rate.

4. The method according to any one of claims 1-2, wherein estimating the utilization rate of the battery-powered operating machinery comprises: Identify one or more charging opportunities; as well as The battery discharge rate is determined based on one or more of the charging opportunities.

5. The method of claim 4, wherein identifying the one or more charging opportunities comprises: Estimate the length of the idle period; Compare the length of the idle period with the opportunity charging threshold; as well as The one or more charging opportunities are identified because the length of the idle period is greater than the opportunity charging threshold.

6. A computing device, comprising: A user interface with a display screen; One or more memory units; as well as One or more processors, communicatively coupled to the one or more memories, are configured to: Receive mechanical information and operating parameters associated with battery-powered machinery; The utilization rate of the battery-powered operating machinery is estimated, at least in part, based on a workday simulation using the aforementioned machinery information and operating parameters. An interference score is determined, which indicates the predicted impact of replacing diesel-powered work machinery with the battery-powered work machinery. The interference score is based, at least in part, on the predicted charging downtime of the battery-powered machinery. as well as The display screen outputs recommendations related to replacing the diesel-powered work machinery with the battery-powered work machinery, wherein the recommendations are based at least in part on the interference score.

7. The computing device of claim 6, wherein the one or more processors are configured to determine the interference score in the following manner: Estimate travel time cost based on mechanical speed and charger distance; and The interference score is determined based on the travel time cost.

8. The computing device according to claim 6, wherein, The one or more processors are configured to determine the interference score in the following manner: Energy usage costs are estimated based on driving time and average power consumption; and The interference score is determined based on the energy usage cost.

9. The computing device according to claim 6, wherein, The one or more processors are configured to determine the interference score in the following manner: The charging event overhead is estimated based on the amount of time associated with starting and stopping the charging of the battery-powered operating machinery; and The interference score is determined based on the charging event overhead.

10. The computing device according to any one of claims 6-9, wherein the operating parameters include one or more of battery capacity, battery health status, charger information, charging threshold, utilization rate index, or fuel consumption rate; and The mechanical information mentioned therein includes one or more of ground speed, fuel rate, or location information.

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