Motorcade migration management system and method

Abstract

The invention provides a fleet migration management system and method. A fleet management system is disclosed. A system may include a transceiver configured to receive vehicle information from each vehicle of a vehicle fleet and receive environmental condition information associated with each vehicle. The fleet management system may also include a processor configured to: obtain the vehicle information and the environmental condition information; and predicting future vehicle efficiency and future vehicle health for each vehicle. Based on the prediction, the processor may calculate a first resource required to operate the vehicle fleet according to a current fleet allocation. The processor may determine an updated fleet allocation for a fleet of vehicles and calculate a second resource required to operate the fleet of vehicles according to the updated fleet allocation. When the second resource may be less than the first resource, the processor may transmit instructions to at least one vehicle of the vehicle fleet to replace based on the updated fleet allocation.

Description

Technical Field

[0001] The present disclosure relates to fleet migration management systems and methods, and more particularly, to systems and methods for facilitating fleet migration management based on environmental conditions and vehicle information associated with each vehicle of a fleet (or sub-fleet) of vehicles. Background Art

[0002] Many commercial fleet companies have fleets of vehicles distributed in remote geographic locations. Some vehicles in the fleet of vehicles may be exposed to extreme weather conditions, such as extremely cold or hot temperatures. Similarly, some vehicles may be exposed to unpaved or poorly maintained roads (e.g., in remote rural areas). Such exposure may cause some vehicles to age prematurely, which may affect expected residual values, lease costs, and other fleet company metrics. Therefore, commercial fleet companies proactively monitor and manage their fleet vehicles to minimize operating expenses. Summary of the invention

[0003] The present disclosure describes a vehicle fleet management system and method. The system may be configured to monitor vehicle performance and vehicle health of each vehicle in a vehicle fleet and environmental conditions in each geographic location / region in which the vehicle may be distributed, and perform reallocation of one or more vehicles to optimize overall fleet operations. For example, the system may obtain a current fleet allocation (e.g., an allocated geographic location for each vehicle), calculate future resources required to operate the vehicle fleet based on the current fleet allocation, and may determine an updated fleet allocation to reduce future resources required to operate the vehicle fleet.

[0004] In some aspects, the system may obtain vehicle information (e.g., vehicle efficiency and vehicle health) and environmental condition information at a first timestamp (e.g., at the end of a first weather season). The system may predict future vehicle efficiency and future vehicle health based on the vehicle information and the environmental condition information according to the current fleet assignment. In other words, the system may predict future vehicle efficiency and future vehicle health at a second timestamp (e.g., in a second weather season at the same geographic location to which the vehicle may currently be assigned). The system may predict future resources required to operate the fleet of vehicles according to the current fleet assignment based on the future vehicle efficiency and the future vehicle health. The system may also determine an updated fleet assignment to reduce future resources. In response to determining the updated fleet assignment, the system may transmit instructions to one or more vehicles to relocate (e.g., autonomously) based on the updated fleet assignment.

[0005] In some aspects, instead of or in addition to relocating a vehicle from one location to another, the system may provide recommendations for replacing vehicle components. For example, if the resources required to relocate the vehicle exceed a threshold, the system may provide recommendations for replacing vehicle tires or vehicle batteries (when the battery / tire health may be poor).

[0006] The present disclosure discloses a fleet management system that optimizes the resources or costs required to operate a fleet of vehicles. The system can predict the vehicle performance and health of each vehicle based on the environmental conditions (e.g., temperature, road conditions, etc.) of the geographic location where the vehicle may be located, and relocate the vehicle to minimize the impact of the environmental conditions on the specific vehicle. The system can also transmit instructions and automatically relocate the vehicle from one location to another, thereby minimizing the manual workload in the relocation of the vehicle.

[0007] These and other advantages of the present disclosure are provided in detail herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Detailed description is set forth with reference to the accompanying drawings. The use of the same reference numerals may indicate similar or identical items. Various embodiments may utilize elements and / or components other than those shown in the accompanying drawings, and some elements and / or components may not be present in various embodiments. The elements and / or components in the accompanying drawings are not necessarily drawn to scale. Throughout this disclosure, singular and plural terms may be used interchangeably, depending on the context.

[0009] Figure 1 Depicted are example systems in which techniques and structures for providing the systems and methods disclosed herein may be implemented.

[0010] Figure 2 An example fleet management system according to the present disclosure is depicted.

[0011] Figure 3 An exemplary embodiment for managing vehicle assignments according to the present disclosure is depicted.

[0012] Figure 4 A flow chart of an example fleet management method according to the present disclosure is depicted. DETAILED DESCRIPTION

[0013] The present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the disclosure are shown and which are not intended to be limiting.

[0014] Figure 1An example system 100 is depicted in which techniques and structures for providing the systems and methods disclosed herein may be implemented. The system 100 may include a plurality of vehicles 102a, 102b (collectively, a plurality of vehicles 102) and a fleet management system 104 (or system 104) that may be connected to each other via a network 106.

[0015] The plurality of vehicles 102 may take the form of any commercial vehicle, such as, for example, an SUV, a car, a crossover vehicle, a van, a minivan, a bus, a truck, etc. In addition, the plurality of vehicles 102 may include any powertrain, such as, for example, a gasoline engine, one or more electrically actuated motors, a hybrid powertrain, etc. Furthermore, the plurality of vehicles 102 may be configured and / or programmed to operate in a fully autonomous (e.g., driverless) mode (e.g., Level 5 autonomy) or in one or more partially autonomous modes that may include driver assistance technologies.

[0016] The network 106 illustrates an example communication infrastructure in which the connected devices discussed in various embodiments of the present disclosure may communicate. The network 106 may be and / or may include the Internet, a private network, a public network, or other configurations operating using any one or more known communication protocols, such as, for example, Transmission Control Protocol / Internet Protocol (TCP / IP), Wi-Fi, UWB, and cellular technologies based on the Institute of Electrical and Electronics Engineers (IEEE) standard 802.11, such as Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), High Speed ​​Packet Access (HSPDA), Long Term Evolution (LTE), Global System for Mobile Communications (GSM), and Fifth Generation (5G), to name a few examples.

[0017] In some aspects, the system 104 may be associated with a vehicle fleet company / operator that may manage / operate a plurality of vehicles 102. The plurality of vehicles 102 may be distributed or positioned over a plurality of geographic locations or regions according to a fleet allocation distribution plan (or "current fleet allocation") that may be planned by the vehicle fleet company / operator and may be pre-stored in the system memory. In some aspects, such geographic locations may be spread over a large geographic area (e.g., spread over different cities or states in a country) and may have different environmental conditions and road conditions. For example, vehicle 102a may be located or assigned to location "A" where the average temperature in the winter may be 20 degrees Fahrenheit, and vehicle 102b may be assigned to location "B" where the average temperature in the winter may be 60 degrees Fahrenheit.

[0018] Because vehicle 102a and vehicle 102b may be operating under different environmental conditions and on different roads, vehicle 102a and vehicle 102b may have different associated operating efficiencies and life expectancies. For example, vehicle 102a assigned to location "A" may provide a smaller range than vehicle 102b assigned to location "B" (e.g., when vehicles 102a, 102b may be electric vehicles) because vehicle 102a may need to operate in colder weather conditions. As another example, if vehicle 102a is assigned to a city (with maintained roads) and vehicle 102b is assigned to a rural area (with poorly maintained roads), the life expectancy of vehicle 102b (especially the vehicle tires) may be less than the life expectancy of vehicle 102a.

[0019] The system 104 may be configured to analyze the performance / health of each vehicle and the corresponding environmental conditions of the geographic area / region in which the vehicle 102 may be located, and may relocate one or more or all vehicles in the vehicle fleet from one location to another to optimize the overall vehicle fleet operation. For example, the system 104 may swap the location / assignment of the vehicle 102a and the vehicle 102b to optimize the vehicle fleet operation, such as Figure 1 shown.

[0020] Specifically, the system 104 may be configured to obtain vehicle information associated with each vehicle 102 at a predefined frequency or after a specific duration. For example, the system 104 may obtain vehicle information associated with each vehicle 102 at the end of a first weather season (e.g., winter). The vehicle information may include information associated with vehicle efficiency (e.g., cost per mile), vehicle component health (e.g., battery health, tire health, etc.), vehicle type, etc. The system 104 may also obtain environmental condition information for the corresponding geographic region in which each vehicle 102 may be located. The environmental condition information may include information associated with climate temperature and road conditions. In some aspects, the environmental condition information may additionally include expected future environmental condition information (e.g., expected future weather seasons or second weather seasons).

[0021] The system 104 may be configured to predict the future vehicle efficiency and future vehicle health of each vehicle 102 based on the obtained vehicle information and corresponding environmental condition information (e.g., based on expected future environmental condition information) according to the current fleet allocation. For example, the system 104 may analyze the current vehicle efficiency and current vehicle component health of the vehicle 102a in location "A", and may predict or estimate the future vehicle efficiency and future vehicle health under expected future environmental conditions in location "A".

[0022] The system 104 may also be configured to calculate or estimate a first resource (or a first future resource or a total future cost) required to operate the vehicle fleet in an expected future weather season (e.g., in the second season or any future duration) based on the current fleet allocation. The system 104 may calculate the first resource based on a prediction of the future vehicle efficiency and future vehicle health of each vehicle 102 under expected future environmental conditions. In response to calculating the first resource, the system 104 may identify an updated fleet allocation for the vehicle fleet, and calculate or estimate a second resource (or a second future resource or an updated total cost) required to operate the vehicle fleet in the second season based on the updated fleet allocation. The system 104 may calculate the second resource based on a prediction of the future vehicle efficiency and future vehicle health of each vehicle 102. In some aspects, the second resource may include a third resource required to relocate some or all vehicles in the vehicle fleet from the current fleet allocation to the updated fleet allocation. For example, if in the updated fleet allocation, the vehicle 102a is relocated from location "A" to location "B", the third resource may indicate the resource required to move the vehicle 102a from location "A" to location "B".

[0023] When the second resource may be less than the first resource, the system 104 may transmit instructions to some or all vehicles in the fleet of vehicles to relocate based on the updated fleet allocation. Vehicles (e.g., autonomous vehicles) that may be instructed to relocate may receive the instructions and move to another location based on the updated fleet allocation.

[0024] In additional aspects, the system 104 may be configured to provide recommendations for replacing vehicle components based on the predicted future vehicle health and the third resource required to relocate the vehicle 102. For example, when the system 104 determines that the third resource (cost) for exchanging vehicle 102a (assigned to a city) and vehicle 102b (assigned to a rural area with poorly maintained roads) exceeds a threshold, the system 104 may provide recommendations for replacing the tires of vehicle 102b (e.g., when the third resource may be greater than the resource required to replace the tires of vehicle 102b).

[0025] The vehicle 102 , vehicle operator, and system 104 may implement and / or perform operations as described herein in the present disclosure in accordance with the vehicle owner's manual and safety guidelines.

[0026] Combined with the following Figure 2 Further details of system 104 are described.

[0027] Figure 2 An example fleet management system 200 is depicted in accordance with the present disclosure. Fleet management system 200 can be the same as fleet management system 104. As described herein, fleet management system 200 can be implemented in hardware, software (eg, firmware), or a combination thereof.

[0028] The fleet management system 200 can communicate with a plurality of vehicles 202 (in Figure 2 The fleet management system 200 may be connected to the fleet computing device 204 (shown as a single vehicle 202 in the figure). The plurality of vehicles 202 and the fleet computing device 204 may be further connected to each other via a network 206. The network 206 may be the same as the network 106. In some aspects, the fleet management system 200 may be part of the fleet computing device 204. In other aspects, the fleet management system 200 may be separate from the fleet computing device 204 (e.g., Figure 2 shown).

[0029] The plurality of vehicles 202 may be the same as the plurality of vehicles 102 and may form a fleet of vehicles. In some aspects, the movement, storage, use, etc. of each vehicle 202 may be controlled and / or managed by an individual vehicle operator (not shown) and / or a fleet operator (not shown) using the fleet management system 200. In some aspects, the fleet operator may use the fleet computing device 204 to store the combined Figure 1 Describes vehicle information and environmental conditions.

[0030] In some aspects, the vehicle 202 can be programmed to operate in a fully autonomous (e.g., driverless) mode (e.g., Level 5 autonomy) or in one or more partially autonomous modes that may include driver assistance technologies. Examples of partially autonomous (or driver assistance) modes are widely understood in the art as Level 1 to Level 4 autonomy.

[0031] The vehicle 202 may include multiple units, including but not limited to a vehicle control unit (VCU) 208, one or more vehicle processors 210 (or vehicle processors 210), and a vehicle memory 212 (which may be part of an onboard vehicle computer (not shown)) that are communicatively connected to each other. The VCU 208 may include multiple units, including but not limited to a driver assistance technology (DAT) controller 214, a vehicle transceiver 216, multiple electronic control units (ECUs) 218, etc. In some aspects, the vehicle transceiver 216 may be external to the VCU 208. The VCU 208 may be configured and / or programmed to coordinate vehicle units, connected computing devices (e.g., fleet computing device 204), other vehicles operating as part of a vehicle fleet (e.g., a fleet of vehicles), and / or other vehicles. Figure 2 ) and data within the fleet management system 200.

[0032] The DAT controller 214 may provide Level 1 to Level 4 autonomous driving and driver assistance functions to the vehicle 202 operator. One or more ECUs 218 may be configured to determine vehicle operating states (or vehicle information), including but not limited to vehicle battery health, vehicle tire health, vehicle efficiency (e.g., cost per mile), vehicle range, etc. In some aspects, the ECU 218 may be further configured to determine environmental condition information, such as average climate temperature / season or road conditions associated with the location in which the vehicle 202 may be operating and expected future climate temperature / season at the location. For example, the ECU 218 may determine the upcoming season (winter or summer or expected temperature) in which the vehicle 202 may operate based on the current season of the location in which the vehicle 202 may be located.

[0033] The battery state of health may include information associated with the current vehicle battery discharge rate, battery degradation over time, etc. Similarly, the vehicle tire state of health may include information associated with tire degradation over time.

[0034] The vehicle processor 210 may be configured to communicate with one or more memory devices (e.g., vehicle memory 212 and / or Figure 2 The vehicle processor 210 may utilize the vehicle memory 212 to store programs in code form and / or store data to perform various vehicle 202 operations according to the present disclosure. The vehicle memory 212 may be a non-transitory computer-readable memory. The vehicle memory 212 may include any one or combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), etc.), and may include any one or more non-volatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electronically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.).

[0035] In some aspects, the vehicle memory 212 can store vehicle information determined by the ECU 218. In some aspects, the VCU 210 / ECU 218 can send vehicle information to the vehicle memory 212 at a predefined frequency or at the end of each weather season for storage purposes. In other aspects, the vehicle memory 212 can store environmental condition information (including expected future environmental conditions) determined by the ECU 218.

[0036] In additional aspects, the vehicle memory 212 may store information associated with vehicle type, vehicle model, vehicle manufacturing year, vehicle battery type (and other vehicle component types), vehicle battery manufacturing year, etc. as part of the vehicle information.

[0037] The vehicle transceiver 216 may be configured to obtain vehicle information and / or environmental condition information from the vehicle memory 212 and transmit the information to one or more external systems or servers. For example, the vehicle transceiver 216 may transmit the information to the fleet computing device 204 and / or the fleet management system 200 via the network 206 at a predefined frequency or at the end of each weather season.

[0038] It can be understood by those skilled in the art that Figure 2 The vehicle architecture shown may omit certain vehicle units and / or vehicle computing modules. Figure 2 The vehicle 202 depicted in FIG. 2 is an example of possible implementations according to the present disclosure and, therefore, should not be considered limiting or exclusive.

[0039] In addition, although the above description describes that the vehicle transceiver 216 can obtain vehicle information and environmental condition information from the vehicle memory 212 and transmit the information to one or more external systems (e.g., the fleet management system 200), it can be appreciated by those skilled in the art that some portions of the vehicle information and environmental condition information can be obtained from other systems other than the vehicle 202. For example, the fleet management system 200 can obtain environmental condition information from the fleet computing device 204 and / or an external server (not shown).

[0040] The fleet management system 200 may receive vehicle information and / or environmental condition information from the vehicle transceiver 216 and / or the fleet computing device 204. The fleet management system 200 may include a plurality of units, including, but not limited to, a system transceiver 220, one or more system processors 222 (or system processors 222), and a system memory 224. The system transceiver 220 may be configured to transmit and receive information, data, notifications, instructions, etc. to and from the vehicle 202, the fleet computing device 204, and other external devices via the network 206. For example, the system transceiver 220 may be configured to receive vehicle information from the vehicle transceiver 216 and / or the fleet computing device 204 at the end of a first weather season (e.g., winter) associated with a first geographic region, in which the vehicle 202 may be located according to the current fleet assignment. The system transceiver 220 may also be configured to receive environmental condition information associated with the first geographic region from the vehicle transceiver 216 and / or the fleet computing device 204 at the end of the first weather season. As described above, the environmental condition information may include expected future environmental conditions. For example, the system transceiver 220 may receive information associated with a second weather season (e.g., summer, which may be a future season following winter) from the vehicle transceiver 216 and / or the fleet computing device 204. The information associated with the second weather season may include, for example, an expected future average temperature in the summer for a first geographic region in which the vehicle 202 may be located.

[0041] Although the above description describes aspects in which the system transceiver 220 receives vehicle information and environmental condition information from the vehicle 202 , one of ordinary skill in the art will appreciate that the system transceiver 220 receives such associated vehicle information and environmental condition information from each vehicle in the fleet of vehicles.

[0042] The system processor 222 may be configured to communicate with one or more memory devices (eg, the system memory 224 and / or one or more external databases ( Figure 2 The system processor 222 may utilize the system memory 224 to store programs in code and / or store data to perform various system operations according to the present disclosure. The system memory 224 may be a non-transitory computer-readable memory that stores fleet management program code. The system memory 224 may include any one or combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), etc.), and may include any one or more non-volatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electronically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.).

[0043] In some aspects, the system memory 224 may include multiple modules and databases, including but not limited to a vehicle information database 226, an environmental condition information database 228, and a fleet migration management module 230. The fleet migration management module 230 as described herein may be stored in the form of computer executable instructions, and the system processor 222 may be configured and / or programmed to execute the stored computer executable instructions for performing the fleet management system functions according to the present disclosure.

[0044] The vehicle information database 226 can store vehicle information that the system transceiver 220 can receive from each vehicle (e.g., from the vehicle transceiver 216). Specifically, in response to receiving the vehicle information, the system transceiver 220 can send the vehicle information to the vehicle information database 226 for storage purposes. Similarly, the environmental condition information database 228 can store environmental condition information associated with each vehicle 202 that the system transceiver 220 can receive from the vehicle transceiver 216 and / or the fleet computing device 204.

[0045] In operation, the system processor 222 may be configured to obtain vehicle information from the vehicle information database 226 and / or from the system transceiver 220 for each vehicle 202 in the vehicle fleet. The system processor 222 may obtain the vehicle information at the end of the first weather season (or any other specific duration). The system processor 222 may obtain environmental condition information associated with the respective geographic region in which each vehicle 202 may be assigned from the environmental condition information database 228 and / or the system transceiver 220. In some aspects, the system processor 222 may estimate the expected future environmental condition information based on the current environmental condition information stored in the environmental condition information database 228, and may not receive / obtain the expected future environmental condition information from the vehicle transceiver 216. The system processor 222 may store the expected future environmental condition information in the environmental condition information database 228 as part of the environmental condition information.

[0046] In response to obtaining the vehicle information and environmental condition information, the system processor 222 may be configured to predict the future vehicle efficiency and future vehicle health status of each vehicle 202 in the second weather season or the upcoming weather season (or any future duration) based on the current fleet allocation. Specifically, the system processor 222 may analyze the vehicle information (i.e., the current vehicle efficiency / health status), and then may estimate the future vehicle efficiency / health status, for example, in the second weather season. For example, the system processor 222 may analyze the vehicle efficiency (e.g., historical efficiency over the past 6 months / one season) and vehicle health information (e.g., battery and tire degradation information) of the vehicle 202 in location "A", and estimate the future vehicle efficiency and future health status at location "A".

[0047] As an example, for an electric vehicle (e.g., vehicle 202), the system processor 222 may predict future vehicle efficiency (or cost per mile) based on a function of battery kilowatt-hours, cost kilowatt-hours, and vehicle range (based on expected future environmental conditions, such as climate temperature and road conditions), as shown in the expression below.

[0048]

[0049] Additionally, the system processor 222 may predict the battery health status based on expected environmental conditions (eg, climate temperature) and current battery throughput, as shown in the following expression.

[0050] Battery wear = f(temperature, current throughput)

[0051] Similarly, the system processor 222 may predict tire health based on expected environmental conditions (e.g., road conditions and climate temperature), as shown in the accompanying expression below. For example, if the vehicle 202 is operating in a rural area where the vehicle 202 may need to traverse poorly maintained roads, the system processor 222 may predict that the vehicle's tires may have a shorter life expectancy.

[0052] Tire wear = f(temperature, road)

[0053] In further aspects, the system processor 222 can be configured to calculate the resources (e.g., "first resources") required to operate the fleet of vehicles (i.e., the plurality of vehicles 202) according to the current fleet allocation. In this case, the system processor 222 can calculate the total cost required to operate the fleet of vehicles according to the current fleet allocation. In some aspects, the system processor 222 can calculate the first resources based on predictions of future vehicle efficiency and future vehicle health, as described above.

[0054] In some aspects, to calculate the first resource, the system processor 222 may calculate individual vehicle operating expenses based on future vehicle efficiency and future vehicle health of a vehicle (e.g., vehicle 202) according to the current fleet allocation. In other words, the system processor 222 may calculate the operating expenses of each vehicle 202 in the vehicle fleet. The system processor 222 may aggregate the individual vehicle operating expenses to calculate the first resource.

[0055] In an additional aspect, the system processor 222 can calculate the cost of operating a regional fleet (or "sub-fleet") based on the current fleet allocation. Specifically, the system processor 222 can calculate the operating cost of operating a first regional fleet, a second regional fleet, etc. The system processor 222 can calculate the first resource based on the operating cost required to operate the first regional fleet, the second regional fleet, etc., as shown in the following expression.

[0056] Cost = f(F1, F2, ... Fm)

[0057] In the expressions shown above, F1, F2, Fm are regional fleets, and m is a count of regions of a geographic area where a fleet of vehicles may have associated fleet vehicles located.

[0058] In some aspects, the system processor 222 can calculate the individual vehicle operating expenses by calculating the corresponding vehicle scores based on the prediction of the corresponding future vehicle efficiency and future vehicle health (e.g., at the end of the first weather season) according to the current fleet allocation. The system processor 222 can calculate the individual vehicle scores based on predefined rules or metrics defined by the fleet operator, which can be pre-stored in the system memory 224. The system processor 222 can also be configured to correlate the individual vehicle scores with a cost matrix (which can be pre-stored in the system memory 224). The cost matrix can include a mapping of vehicle scores to vehicle operating expenses. The system 200 can generate the mapping based on historical fleet information or corresponding vehicle historical information. In some aspects, the system 200 can obtain the mapping from an external server. Based on the correlation, the system processor 222 can determine the operating expenses of operating each vehicle 202 according to the current fleet allocation, for example, in the second weather season.

[0059] As described above, the system processor 222 may be configured to aggregate the individual vehicle costs to calculate the first resource. For example, when the system processor 222 determines that vehicle "A" may have a vehicle score of 7 out of 10 and vehicle "B" may have a vehicle score of 3 out of 10, the system processor 222 may determine the operating costs of operating vehicle "A" and vehicle "B" by using vehicle scores of 3 and 7 and correlating the vehicle scores with the cost matrix stored in the system memory 224. In an exemplary scenario, the operating costs associated with a vehicle having a higher vehicle score (e.g., vehicle "A") may be lower than the operating costs associated with a vehicle having a lower vehicle score (e.g., vehicle "B").

[0060] In a manner similar to that described above, the system processor 222 may calculate the operating expenses of operating a regional fleet by calculating corresponding regional fleet scores for different regions in which a fleet of vehicles (or fleet vehicles) may be located. Specifically, the system processor 222 may calculate scores for individual regions, such as a first regional score, a second regional score, and the like, by aggregating individual vehicle scores of vehicles that may be operating in the corresponding region. For example, the first regional score may include an aggregation of vehicle scores associated with vehicles located in a first region, the second regional score may include an aggregation of vehicle scores associated with vehicles located in a second region, and the like. In a manner similar to that described above, the system processor 222 may calculate the operating expenses of operating a regional fleet using a cost matrix. The system processor 222 may be configured to aggregate individual regional operating expenses to calculate a first resource.

[0061] In some aspects, in response to calculating the first resource, the system processor 222 may determine an updated fleet allocation for the vehicle fleet (i.e., the plurality of vehicles 202) to minimize the total operating expense (the first resource) of operating the vehicle fleet. The system processor 222 may use machine learning or artificial intelligence (AI) techniques to determine the updated fleet allocation. In an exemplary aspect, the system processor 222 may apply a neural network model (not shown) to determine the updated fleet allocation. The neural network model may be stored in the system memory 224.

[0062] In one or more aspects, the neural network model may include electronic data that may be implemented, for example, as a software component and may rely on a code database, library, script, or other logic or instructions for the system processor 222 to execute the neural network algorithm. The neural network model may be implemented as code and routines configured to enable a computing device (such as the fleet management system 200) to perform one or more operations. In some aspects, the neural network model may be implemented using hardware including a processor, a microprocessor, a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In other aspects, the neural network model may be implemented using a combination of hardware and software.

[0063] Examples of neural network models may include, but are not limited to, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), CNN-recurrent neural networks (CNN-RNNs), R-CNNs, fast R-CNNs, faster R-CNNs, artificial neural networks (ANNs), RNNs based on long short-term memory (LSTM) networks, CNN+ANNs, LSTM+ANNs, RNNs based on gated recurrent units (GRUs), fully connected neural networks, deep Bayesian neural networks, generative adversarial networks (GANs), and / or combinations of such networks. In some aspects, the neural network model may include numerical computation techniques using data flow graphs. In one or more aspects, the neural network model may be based on a hybrid architecture of multiple deep neural networks (DNNs).

[0064] In an exemplary aspect, the system processor 222 may use an LSTM neural network to determine an updated fleet allocation and minimize the first resource. In another aspect, the system processor 222 may use a batch gradient descent algorithm to determine and / or select an updated fleet allocation. The details of this determination may be understood as follows.

[0065] The system processor 222 may obtain the current fleet allocation from the system memory 224 and / or the fleet computing device 204. The current fleet allocation may have geographic region / area allocations for different types of vehicles operating in the vehicle fleet. For example, the current fleet allocation may be described as the expression:

[0066] Fleeti (t) = n 11,t V1+n 12,t V2+…+n 1n,t V n

[0067] in:

[0068] n is the vehicle category,

[0069] t is the duration (e.g., the duration of a season),

[0070] V is the vehicle,

[0071] i is the regional or national fleet identifier or count.

[0072] The system processor 222 may then use a batch gradient descent algorithm to update the fleet assignment or determine an updated fleet assignment. For example, the system processor 222 may change the fleet as described in the following expression:

[0073] F1(+1),...,Fm(+1)

[0074] where F is the regional fleet, and m is the number of regions.

[0075] In an exemplary aspect, in the updated fleet allocation, one or more or all vehicles in the vehicle fleet may be relocated to different locations to optimize the overall vehicle fleet operating expenses (i.e., resources). In response to determining the updated fleet allocation, the system processor 222 may then calculate the second resource (e.g., second expense) required to operate the vehicle fleet according to the updated fleet allocation. The system processor 222 may calculate the second resource in the same manner as the first resource, for example based on vehicle information and environmental condition information. Specifically, the system processor 222 may predict the future vehicle efficiency and future vehicle health in the "reallocated area" based on the vehicle information in the "reallocated area" and the expected future environmental conditions. Based on the future vehicle efficiency and future vehicle health in the "reallocated area", the system processor 222 may calculate the second resource. In some aspects, the system processor 222 may be additionally configured to calculate a third resource (third expense) required to relocate the vehicle according to the updated fleet allocation. The third resource may be a portion of the second resource.

[0076] The system processor 222 may be configured to compare the first resource (required to operate the vehicle fleet according to the current fleet allocation) with the second resource (required to operate the vehicle fleet and relocate the vehicle according to the updated fleet allocation). The system processor 222 may be configured to determine, based on the comparison, that the second resource is likely to be less than the first resource. The system processor 222 may be configured to transmit, via the system transceiver 220, an instruction to relocate to at least one vehicle from the vehicle fleet when the system processor 222 determines that the second resource is likely to be less than the first resource.

[0077] In some aspects, the system processor 222 may iteratively update the fleet assignment using a batch gradient descent algorithm until the system processor 222 determines that the updated fleet assignment may have an associated second resource required to operate (and relocate) the fleet of vehicles that may be less than the first resource.

[0078] The system processor 222 may update the vehicle fleet assignments to reduce variability in the vehicle scores in the vehicle fleet. For example, when the system processor 222 determines that vehicle "A" may have a score of 7 out of 10 (because vehicle "A" may not be assigned to an extreme weather condition) and vehicle "B" may have a score of 3 out of 10 (because vehicle "B" may be assigned to an extreme weather condition), the system processor 222 may swap the assignments of vehicle "A" and vehicle "B".

[0079] In some aspects, the system processor 222 may update the fleet allocation for a particular vehicle or region (such as for a first region and a second region) based on the respective individual / regional scores, and may not update the fleet allocation for the entire fleet. In other words, the system processor 222 may select a group of vehicles in the vehicle fleet for reallocation based on the individual vehicle scores or the group vehicle scores (e.g., the region scores). In other aspects, the system processor 222 may update the fleet allocation for a particular vehicle or region based on the respective fees.

[0080] Figure 3 An exemplary embodiment for managing vehicle assignments according to the present disclosure is depicted. Figure 3, when vehicle 202 may be assigned to location “A” where the expected future climate temperature may be 20 degrees Fahrenheit (in the winter, as shown in view 302), and vehicle 202 may be an electric vehicle with a four-year-old battery, system processor 222 may predict that if vehicle 202 continues to operate at location “A” during the winter, the vehicle score may decrease. For example, system processor 222 may determine that vehicle efficiency / range may be significantly reduced (because vehicle 202 is an electric vehicle) and the vehicle battery may not be able to support winter conditions. Therefore, the operating expenses for operating vehicle 202 at location “A” may increase (and may exceed a threshold). In response to such a determination, system processor 222 may transmit instructions to vehicle 202 to automatically relocate to another location “B” where the average temperature may be 60 degrees Fahrenheit, as shown in view 304.

[0081] In other aspects, when the vehicle battery may be four years old and may not be able to support the vehicle 202 to continue operating in location "A", the system processor 222 may transmit to the vehicle 202 a recommendation to replace the vehicle battery rather than move the vehicle 202 to another location, as shown in view 306. In some aspects, the system processor 222 may recommend replacing the vehicle battery (or other vehicle component) rather than moving the vehicle 202 to another location based on the cost of relocating the vehicle 202 to another location.

[0082] Similarly, system processor 222 may analyze the road conditions and vehicle tire conditions at location “A” and may recommend replacing the vehicle tires or transmit instructions to relocate vehicle 202 to another location with better road conditions based on the cost of relocating vehicle 202 to another location.

[0083] Figure 4 A flow chart of an example fleet management method 400 according to the present disclosure is depicted. Figure 4 The following process is exemplary and is not limited to the steps described below. Furthermore, alternative embodiments may include more or fewer steps shown or described herein, and may include the steps in an order different from that described in the example embodiments below.

[0084] refer to Figure 4 At step 402, the method 400 may begin. At step 404, the method 400 may include obtaining, by the system processor 222, vehicle information and environmental condition information associated with each vehicle in the vehicle fleet. Details of the vehicle information and environmental condition information are described above in conjunction with Figure 1 and Figure 2 In some aspects, the system processor 222 may obtain such information at the end of the first season (or any duration).

[0085] At step 406 , the method 400 may include predicting, by the system processor 222 , the vehicle future efficiency and the vehicle future health based on the obtained vehicle information and environmental condition information (eg, based on the current fleet allocation in the second / upcoming season).

[0086] At step 408, the method 400 may include the system processor 222 calculating a first resource (cost) required to operate the fleet of vehicles according to the current fleet allocation. The system processor 222 may calculate the first resource based on the prediction described in step 406. In some aspects, the system processor 222 may calculate individual vehicle scores based on the prediction, and the system processor 222 may calculate the cost required to operate each vehicle according to the current allocation. The system processor 222 may aggregate the costs associated with each vehicle to calculate the first resource.

[0087] At step 410, the method 400 may include determining, by the system processor 222, an updated fleet assignment, as described above in conjunction with Figure 2 At step 412, the method 400 may include calculating, by the system processor 222, a second resource required to operate the vehicle fleet based on the updated fleet allocation. Specifically, the system processor 222 may predict future vehicle health conditions and future vehicle efficiency in the "reallocated area" based on vehicle information in the "reallocated area" and expected future climate conditions, and calculate the second resource based on the predicted future vehicle health conditions and future vehicle efficiency in the "reallocated area". The second resource may include a third resource required to relocate the vehicle fleet according to the updated fleet allocation.

[0088] At step 414, method 400 may include determining whether the second resource is less than the first resource. In response to determining that the second resource may be equal to or greater than the first resource, method 400 may move back to step 410, where system processor 222 may again determine an updated fleet allocation. In this manner, the step of determining an updated fleet allocation may be an iterative process and may be performed until system processor 222 determines an updated fleet allocation that reduces the first resource.

[0089] On the other hand, in response to determining at step 414 that the second resource may be less than the first resource, method 400 may move to step 416. At step 416, method 400 may include transmitting, by system processor 222, instructions to one or more vehicles to relocate according to the updated fleet assignment.

[0090] At step 418 , the method 400 may stop.

[0091] In the above disclosure, reference has been made to the accompanying drawings that form a part of the above disclosure, which illustrate specific embodiments in which the present disclosure may be practiced. It should be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure. References in this specification to "one embodiment," "embodiment," "example embodiment," etc. indicate that the described embodiment may include specific features, structures, or characteristics, but each embodiment may not necessarily include the specific features, structures, or characteristics. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when features, structures, or characteristics are described in conjunction with an embodiment, whether or not explicitly described, those skilled in the art will recognize such features, structures, or characteristics in conjunction with other embodiments.

[0092] In addition, where appropriate, the functions described herein may be performed in one or more of the following: hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and programs described herein. Certain terms are used throughout the specification and claims to refer to specific system components. As will be appreciated by those skilled in the art, components may be referred to by different names. This document is not intended to distinguish between components that have different names but the same function.

[0093] It should also be understood that the word "example" as used herein is intended to be non-exclusive and non-limiting in nature. More specifically, the word "example" used herein indicates one of several examples, and it should be understood that no undue emphasis or preference is placed on the specific example described.

[0094] Computer-readable media (also referred to as processor-readable media) include any non-transitory (e.g., tangible) media that participate in providing data (e.g., instructions) that can be read by a computer (e.g., by a processor of a computer). Such media can take many forms, including but not limited to non-volatile media and volatile media. A computing device may include computer-executable instructions, where the instructions are executable by one or more computing devices (such as those listed above) and stored on a computer-readable medium.

[0095] With respect to the processes, systems, methods, heuristics, etc. described herein, it should be understood that although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes may be practiced by performing the described steps in an order different from the order described herein. It should also be understood that certain steps may be performed simultaneously, other steps may be added, or certain steps described herein may be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating various embodiments and should in no way be construed as limiting the claims.

[0096] Therefore, it should be understood that the above description is intended to be illustrative rather than restrictive. Upon reading the above description, many embodiments and applications other than the examples provided will be apparent. The scope should not be determined with reference to the above description, but should be determined with reference to the entire scope of the appended claims and equivalents to which such claims are entitled. It is anticipated and expected that the technology discussed herein will develop in the future, and the disclosed systems and methods will be incorporated into such future embodiments. In short, it should be understood that the present application is capable of modification and change.

[0097] Unless explicitly indicated to the contrary in this article, all terms used in the claims are intended to be given their ordinary meanings as understood by the skilled person described herein. Specifically, unless the claims state an explicit limitation to the contrary, the use of singular articles such as "one", "the", "said" and the like should be interpreted as describing one or more of the indicated elements. Unless otherwise specifically stated or understood in other ways within the context when used, conditional language such as, in particular, "can", "may", "can" or "may" is generally intended to express that certain embodiments may include certain features, elements and / or steps, while other embodiments may not include certain features, elements and / or steps. Therefore, such conditional language is generally not intended to imply that one or more embodiments require each feature, element and / or step in any way.

[0098] In one aspect of the present invention, the method includes: comparing the first future resource with the second future resource; determining that the second future resource is less than the first future resource; and transmitting the instruction to the at least one vehicle in response to determining that the second future resource is less than the first future resource.

[0099] In one aspect of the invention, the method includes providing a recommendation to replace a vehicle component of the at least one vehicle based on the future vehicle health and the third future resource required to relocate the at least one vehicle.

[0100] In one aspect of the present invention, the method includes: calculating a vehicle score for each vehicle according to the current fleet allocation based on the future vehicle efficiency and the future vehicle health at the end of a first weather season; correlating the vehicle score with a cost matrix; calculating a first cost for operating each vehicle according to the current fleet allocation in a second weather season based on the correlation; and calculating the first future resource required to operate the vehicle fleet based on the first cost for each vehicle.

[0101] In one aspect of the present invention, the method includes: calculating a regional score for each geographic area based on the corresponding vehicle score at the end of the first weather season; correlating the regional score with the cost matrix at the end of the first weather season; and calculating a second cost for operating each regional fleet according to the current fleet allocation in the second weather season based on the correlation; and calculating the first future resources required to operate the vehicle fleet based on the second cost of each regional fleet.

[0102] In one aspect of the present invention, a non-transitory computer-readable medium is provided, which stores computer-executable instructions, which when executed by one or more processors result in the execution of operations, and the operations include: obtaining vehicle information and environmental condition information associated with each vehicle in a vehicle fleet; predicting a future vehicle efficiency and a future vehicle health status of each vehicle based on the corresponding environmental condition information and the vehicle information; calculating a first resource required to operate the vehicle fleet according to a current fleet allocation based on the future vehicle efficiency and the future vehicle health status; determining an updated fleet allocation for the vehicle fleet; calculating a second resource required to operate the vehicle fleet according to the updated fleet allocation based on the environmental condition information and the vehicle information; and when the second resource is less than the first resource, transmitting instructions for relocation based on the updated fleet allocation to at least a vehicle in the vehicle fleet.

Claims

1. A fleet management system, comprising: A transceiver, the transceiver being configured to: receiving vehicle information from each vehicle in the fleet of vehicles; as well as receiving environmental condition information of a corresponding geographic area in which each vehicle is located; and a processor communicatively coupled to the transceiver; wherein the processor is configured to: obtaining the vehicle information and the environmental condition information associated with each vehicle from the transceiver; predicting a future vehicle efficiency and a future vehicle health status of each vehicle based on the corresponding environmental condition information and the vehicle information; calculating a first future resource required to operate the fleet of vehicles according to a current fleet allocation based on the future vehicle efficiency and the future vehicle health; determining an updated fleet assignment for the fleet of vehicles; calculating a second future resource required to operate the fleet of vehicles according to the updated fleet allocation based on the environmental condition information and the vehicle information; as well as When the second future resource is less than the first future resource, an instruction is transmitted to at least one vehicle of the fleet of vehicles to relocate based on the updated fleet allocation.

2. The fleet management system of claim 1, wherein the vehicle information includes information associated with vehicle efficiency, vehicle component health, and vehicle type.

3. The fleet management system of claim 1, wherein the environmental condition information includes information associated with climate temperature and road conditions.

4. The fleet management system of claim 1, wherein the processor is further configured to: calculating a third future resource required to reposition the at least one vehicle based on the updated fleet allocation; and The second future resource is calculated based on the third future resource.

5. The fleet management system of claim 4, wherein the processor is further configured to: comparing the first future resource to the second future resource; determining that the second future resource is less than the first future resource; and In response to a determination that the second future resource is less than the first future resource, the instruction is transmitted to the at least one vehicle.

6. The fleet management system of claim 4, wherein the processor is further configured to provide a recommendation to replace a vehicle component of the at least one vehicle based on the future vehicle health status and the third future resource required to relocate the at least one vehicle. 7 . The fleet management system of claim 1 , wherein the transceiver is configured to receive the vehicle information and the environmental condition information at the end of a first weather season.

8. The fleet management system of claim 7, wherein the processor is further configured to: calculating a vehicle score assigned to each vehicle according to the current fleet based on the future vehicle efficiency and the future vehicle health at the end of the first weather season; correlating the vehicle score to a cost matrix; calculating a first cost to operate each vehicle according to the current fleet allocation during a second weather season based on the correlation; as well as The first future resource required to operate the fleet of vehicles is calculated based on the first cost per vehicle.

9. The fleet management system of claim 8, wherein the processor is further configured to: calculating a region score for each geographic region based on the corresponding vehicle score at the end of the first weather season; correlating the region scores with the cost matrix; as well as calculating a second cost for operating each regional fleet according to the current fleet allocation during the second weather season based on the correlation; as well as The first future resources required to operate the vehicle fleet are calculated based on the second cost for each regional fleet.

10. The fleet management system of claim 1, wherein the environmental condition information includes current environmental condition information and expected future environmental condition information.

11. The fleet management system of claim 1 , wherein each vehicle is an autonomous vehicle.

12. A method for fleet management, the method comprising: obtaining, by a processor, the vehicle information and the environmental condition information associated with each vehicle in a fleet of vehicles; predicting, by the processor, a future vehicle efficiency and a future vehicle health status of each vehicle based on corresponding environmental condition information and the vehicle information; calculating, by the processor, a first future resource required to operate the fleet of vehicles according to a current fleet allocation based on the prediction of the future vehicle efficiency and the future vehicle health; determining, by the processor, an updated fleet assignment for the fleet of vehicles; calculating, by the processor, second future resources required to operate the fleet of vehicles according to the updated fleet allocation based on the environmental condition information and the vehicle information; as well as When the second future resource is less than the first future resource, the processor transmits an instruction to at least one vehicle of the fleet of vehicles to relocate based on the updated fleet allocation.

13. The method of claim 12, wherein the vehicle information includes information associated with vehicle efficiency, vehicle component health, and vehicle type.

14. The method of claim 12, wherein the environmental condition information includes information associated with climate temperature and road conditions.

15. The method of claim 12, further comprising: calculating a third future resource required to reposition the at least one vehicle based on the updated fleet allocation; as well as The second future resource is calculated based on the third future resource.

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