SYSTEM FOR MANAGING A FLEET OF ELECTRIC VEHICLES

Abstract

A system (10) for managing a fleet (12) of electric vehicles (14) and the respective fleet drivers (18), wherein the system (10) comprises: an instruction unit (16) comprising a processor (P) and a tangible, non-transferable memory (M) in which instructions are recorded, wherein the instruction unit (16) is capable of: Receiving input variables, including the respective fleet tasks and a priority status of the respective fleet tasks; Obtaining route data for the respective fleet tasks; Obtained from an objective function defined by a multitude of influencing factors with corresponding weights; and Obtaining optimal charging plans for the electric vehicles (14) and assigning the respective fleet tasks to the electric vehicles (14) and the respective fleet drivers (18), among other things based on the objective function, the input variables and the route data, wherein the input variables include the energy requirements of the respective fleet tasks without propulsion, including the energy to operate one or more electrical devices to perform the respective fleet tasks, the system comprises an allocation matching module (208), an output module (210), an allocation evaluation module (230), a load allocation module (240) and a fleet task module (250), wherein the command unit (16) is trained to take into account the availability of excess battery energy of the electric vehicles (14) when selecting the appropriate electric vehicle (14) and driver (18) for a task, in order to meet the non-propulsion energy requirements of the fleet tasks, where the target function is stored in the target module (204) and entered into the assignment matching module (208), the assignment matching module (208) takes into account the task requirements, the drivers' abilities with regard to energy consumption, and the vehicle characteristics, wherein the output module (210) receives the results of the optimal coordination between the electric vehicles (14), the drivers (18) and the tasks, as well as the charging plan based on the coordination results, wherein the assignment evaluation module (230) determines the optimal selection of driver (18) and electric vehicle (14), wherein the charge assignment module (240) determines the charging plans of the electric vehicles (14) in a charging infrastructure (20), wherein the fleet task module (250) stores the details of which drivers (18) and electric vehicles (14) have been assigned to the tasks and their respective charging plans, wherein the charge allocation module 240 is designed to assign the electric vehicles (14) to a charging process as required, wherein an electric vehicle (14) is automatically sent to a charging process if it is not selected for a task.

Description

[0001] This disclosure relates generally to a system and method for managing fleet vehicles powered by electricity. Fleet vehicles are groups of vehicles used and / or owned by an entity such as an organization, company, or public authority. Fleet vehicles are becoming increasingly common. Examples of fleet vehicles include those used by car rental companies, taxis, public buses, and police departments. Many online retailers also purchase or lease fleet vehicles to deliver products or packages to customers or to enable sales representatives to travel to customers. Electric fleet vehicles present particular challenges compared to fleets with internal combustion engines, for example, regarding battery charging and available driving range.

[0002] German patent application DE 10 2017 119 709 A1 discloses a system for optimizing the selection of battery electric vehicles (BEVs) for delivery orders. Within a group of BEVs, a specific BEV is selected based on its battery charge status to carry out a delivery order. The BEV can be selected based on one or more of the following: proximity to a requested pickup location, battery state of charge (SOC), proximity of a charging station to a requested delivery location, and connection availability at the charging station (e.g., waiting time until access to a charging point). The BEV selection can be optimized so that a BEV arrives at a charging station with an optimal remaining SOC.

[0003] German patent application DE 10 2021 125 322 A1 discloses a system for optimizing vehicle deployment. It uses a software application to obtain information about a route. This information is used to evaluate the energy depletion characteristic of a battery used to power a battery-powered vehicle and to determine whether the battery-powered vehicle can be used on the route. One of the factors that can influence the battery's energy depletion characteristic is the ambient temperature, as battery performance can be adversely affected by extreme temperatures. Consequently, the operating range of the battery-powered vehicle may be compromised if the route involves extreme ambient temperatures.If the assessment indicates that the battery's energy storage discharge characteristic is not suitable for the route, another battery-powered vehicle with a better battery can be used on the route.

[0004] German patent DE 10 2022 102 926 A1 discloses a system and method for assigning routes to vehicles. The method can include evaluating a first vehicle for use on a first route and a second vehicle for a second route. Evaluating the first vehicle can include determining a first probability that the first vehicle will require an initial energy replenishment during its use on the first route and determining initial operating costs for the first vehicle. These initial operating costs can include initial energy replenishment costs based on the initial probability. Evaluating the second vehicle can include determining second operating costs for the second vehicle, where these second operating costs include second energy replenishment costs.The first vehicle is assigned to the first route and the second vehicle to the second route if the first deployment costs are lower than the second deployment costs.

[0005] DE 10 2021 109 015 A1 discloses a method comprising: initiating, by a means of transport, a request to transfer a first part of stored energy to a charging station; determining, by the charging station, an actual amount of energy required by the means of transport, wherein the determination is based on a first destination of the means of transport and on data received by the charging station on the basis of a route linked to the first destination, wherein the actual amount of energy is not the same amount as the first part of stored energy; and depositing, by the means of transport, the actual amount of energy in the charging station.

[0006] DE 10 2020 131 877 A1 discloses a method and an associated system for selecting a charging station for a vehicle, which includes determining the state of charge of a vehicle's own DC power source, arranged to supply electrical energy to the vehicle's drive system. A route to a destination is determined, and the locations of a multitude of charging stations near the route are identified. Desired states and corresponding weighting factors for several user-selectable parameters are determined, and a sorting routine is executed to rank the multiple charging stations near the route based on the desired states, the corresponding weighting factors for the user-selectable parameters, and the state of charge of the vehicle's own DC power source.One of the charging stations is selected based on the ranking, and a charging reservation is scheduled. It can be seen as a task to overcome challenges related to managing a fleet of electric vehicles due to issues such as variable charging times and available range, with the aim of optimizing energy costs for a fleet, thereby making the acquisition of electric vehicles more affordable for a fleet.

[0007] This problem is solved by a system according to claims 1 to 10.

[0008] This paper presents a system for managing a fleet of electric vehicles and their respective drivers. The system comprises a command unit with a processor and tangible, non-transferable memory in which instructions are stored. The command unit is designed to receive input variables, including the respective fleet tasks and their priority status. It determines route data for each fleet task. The command unit is designed to receive an objective function defined by a variety of influencing factors with corresponding weights. The command unit is designed to generate optimal charging schedules for the electric vehicles and assign the respective fleet tasks to the electric vehicles and their drivers, partly based on the objective function, the input variables, and the route data.

[0009] The input variables can include the driving style and alertness index of the respective fleet drivers. In some embodiments, the input variables include available range, charging capacity, charging profile, and drive energy consumption rate, each associated with the electric vehicles.

[0010] Input variables can include data on charging infrastructure, including the types of available chargers, the locations of the chargers, the availability of charging sessions, and the respective costs of charging sessions.

[0011] In some embodiments, the input variables include data on the respective fleet tasks, including a trip start point, a trip end point, and corresponding time ranges between the start and end points. The input variables can include the non-propulsion energy requirements of the respective fleet tasks, including the energy required to operate one or more electrical devices to perform the respective tasks. The command unit can be adapted to assign the respective fleet tasks to the electric vehicles, partly based on the availability of excess battery energy in the electric vehicles to meet the non-propulsion energy requirements. The multitude of influencing factors can include optimizing energy costs, ensuring on-time task completion, and minimizing range anxiety.The respective weighting of the several influencing factors is determined by a fleet manager.

[0012] Optimal charging plans include an assigned charging location, charging costs, a target battery state of charge, and a charging time, each assigned to the electric vehicles. The command unit can be customized to determine the amount of excess battery energy available for transfer to a public grid from at least one of the electric vehicles, as well as a time for transferring the excess battery energy. The command unit can be configured to dispatch the remaining electric vehicles to their respective charging locations after the respective fleet tasks have been assigned.

[0013] The command unit can be adapted to determine a proposed new charging infrastructure by comparing the results of a charging infrastructure simulation with historical data and various combinations of chargers. In some embodiments, the command unit is stored in a cloud computing service, and the electric vehicles have a corresponding telematics control unit to establish two-way communication with the command unit.

[0014] This document describes a method for managing a fleet of electric vehicles and their respective drivers. The method involves setting up an instruction unit with a processor and tangible, non-transferable memory in which instructions are recorded. Input variables, including the fleet tasks and their priority status, are received via the instruction unit. The method also includes obtaining route data for each fleet task and a target function defined by a variety of influencing factors with corresponding weights. Finally, the method includes obtaining optimal charging schedules for each electric vehicle and adapting the fleet tasks to the specific electric vehicles and drivers, partly based on the target function, input variables, and route data, via the instruction unit. Fig. Figure 1 is a schematic fragment of a system for managing electrically powered fleet vehicles; Fig. 2 is a flowchart for a procedure for operating the system of Fig. 1; Fig. Figure 3 is a schematic diagram of an exemplary modular architecture derived from the system of Fig. 1 can be used; Fig. Figure 4 is a schematic diagram of an example of an assignment selection process implemented by the system of Fig. 1 can be used; and Fig. Figure 5 is a schematic diagram of an example of an allocation evaluation process implemented by the system of Fig. 1 is applicable.

[0015] Referring to the figures in which identical reference numbers refer to identical components, shows Fig. Figure 1 schematically depicts a system 10 for managing a fleet 12 of electric vehicles 14, e.g., a first electric vehicle 14A, a second electric vehicle 14B, and a third electric vehicle 14C. The electric vehicles 14 can be purely electric or partially electric / hybrid. The electric vehicles 14 can be, among other things, a passenger vehicle, a sport utility vehicle, a light truck, a heavy-duty vehicle, a minivan, a bus, a transit vehicle, a bicycle, a self-driving robot, agricultural equipment (e.g., a tractor), sports equipment (e.g., a golf cart), a train, or another mobile platform. It is understood that the electric vehicles 14 can take many different forms and have additional components.

[0016] Referring to Fig. 1 The system 10 comprises an instruction unit 16 with an integrated controller C having at least one processor P and at least one memory M (or a non-transferable, tangible, computer-readable storage medium) in which instructions for a procedure 100 (below in Fig. (described in section 2) for managing the fleet 12 are recorded. Memory M can store control-executable instruction sets, and processor P can execute the control-executable instruction sets stored in memory M. System 10 uses a data-driven recommendation for an optimized charging infrastructure.

[0017] It is a demanding task for fleet managers to assign the tasks of the fleet to the appropriate electric vehicles 14 and drivers 18 (e.g., those in Fig. The system 10 assigns tasks to the drivers shown (18A, 18B, 18C) and optimizes their available range and energy consumption, as well as planning charging times and locations for the fleet (12). The system uses as input information about the characteristics of the electric vehicles (14), the drivers' (18) capabilities in terms of energy consumption, task requirements and priorities, and the charging infrastructure (20), which includes the fleet chargers (22) and public chargers (24). The system 10 is adapted to take into account the constraints to ensure that the fleet's (12) tasks can be completed on time, with a focus on the highest-priority tasks.

[0018] As in Fig. As shown in Figure 1, the electric vehicles 14 each contain a vehicle control unit 30 that collects information from a network of sensors 32 inside the electric vehicles 14. Referring to Fig. 1. The electric vehicles 14 can each include a telematics control unit 34 for establishing two-way communication with the control unit 16, including the recording and transmission of vehicle data. For example, the respective telematics control unit 34 can collect telemetry data from the electric vehicles 14, such as location, speed, motor data, maintenance requirements, and maintenance, by providing an interface to various internal subsystems. The respective telematics control unit 34 can enable vehicle-to-vehicle (V2V) communication and / or vehicle-to-everything (V2X) communication.

[0019] With reference to Fig. 1. The electric vehicles 14 can include a corresponding mobile application 36 for communication with the control unit 16. The mobile application 36 can be embedded in a smart device (e.g., a smartphone) belonging to a user of the electric vehicles 14, which can be connected to or otherwise linked to the electric vehicles 14. The respective mobile application 36 can be physically connected (e.g., wired) to the electric vehicles 14 as part of the vehicle infotainment unit. The circuits and components of a mobile application (“apps”) available to a person skilled in the art can be used.

[0020] As in Fig. As shown in Figure 1, the command unit 16 can be stored in an "external" or remote cloud computing service 40. The cloud computing service 40 can comprise one or more remote servers hosted on the internet to store, manage, and process data. The cloud computing service 40 can be managed, at least partially, by personnel at different locations. The cloud computing service 40 can be a private or public information source maintained by an organization, such as a research institute, a company, a university, and / or a hospital.

[0021] System 10 can establish a wireless network 42 for communication between the electric vehicles 14 and the in Fig. Use the command unit 16 shown in Figure 1. The wireless network 42 can be a short-range network or a long-range network. The wireless network 42 can be a communication bus, which may take the form of a serial Controller Area Network (CAN bus). The wireless network 42 can be a serial communication bus in the form of a local area network. The local area network can include, among other things, a Controller Area Network (CAN), a Controller Area Network with Flexible Data Rate (CAN-FD), Ethernet, Bluetooth™, Wi-Fi, and other forms of data. The wireless network 42 can be a wireless local area network (LAN) that connects multiple devices via a wireless distribution method, a metropolitan area wireless network (MAN) that connects multiple wireless LANs, or a wide area wireless network (WAN) that covers large areas such as neighboring cities.Other types of network technologies or communication protocols available to the professional may be used.

[0022] In Fig. Figure 2 shows a flowchart of a procedure 100 for operating the system 10 (for managing the fleet 12 of electric vehicles 14). In some embodiments, the procedure 100 can be embodied as computer-readable code or stored instructions and can be executed, at least partially, by the command unit 16. The procedure 100 need not be applied in the order shown here. Furthermore, some blocks can be omitted.

[0023] In block 102 of Fig. 2. Procedure 100 includes the procurement of input variables, including a list of orders or fleet tasks and their respective priority order. The fleet tasks may include the start and end points of a trip, the type of freight or cargo required for the fleet task, the respective time intervals to the start and end points of the trip, and other factors. The input variables may be stored in a database or data module 202 (see Fig. 1) Input variables also include driver and vehicle data. Driver data can include driving style, energy-efficient driving for each type of trip (city, highway), trailer handling, alertness index, and other factors. In one example, the alertness index is defined as being inversely proportional to the number of hours driven by the respective driver in a previous period, e.g., the last 24 hours.

[0024] The vehicle data can include data specific to each of the electric vehicles 14, such as the drive energy consumption rate, available range, energy consumption per trip type, and charging profile. The vehicle data can also include the battery thermal preconditioning requirements, load / charge capability, trailer performance for each of the electric vehicles 14, and other factors. Input variables also include charging infrastructure data, such as the type of chargers (AC or DC), the geographical location of the chargers within the charging infrastructure 20, and the respective times of available charging.

[0025] The input variables can also include the non-propulsion-related energy requirements of fleet tasks. In other words, the command unit 16 can be adapted to take into account cases in which the task requires the use of energy from the electric vehicles 14 to operate one or more electrical devices 50 (see Fig. 1) required to perform a task. This is referred to here as energy transfer from vehicle to load. For example, the electrical device 50 could be an electric chainsaw connected to the battery of an electric vehicle 14 to perform a task or assignment. The command unit 16 (via the assignment matching module 208 described below) can be adapted to take into account the availability of excess battery energy from the electric vehicles 14 when selecting the appropriate electric vehicle 14 and driver 18 for a task, in order to meet the fleet task requirements for non-propulsion energy.

[0026] Continue to Block 104 of Fig. 2: Procedure 100 involves determining an objective function. The objective function defines the optimization goals and encompasses maximizing or minimizing a real-world function. The objective function is defined by a variety of influencing factors, such as optimizing energy costs, completing the task on time (taking priority into account), and minimizing range anxiety (e.g., maintaining a minimum battery level above 10%). Each influencing factor is assigned an appropriate weight or percentage. In some implementations, fleet managers define the influencing factors and assign them appropriate weights. As in Fig. As shown in Figure 3, the objective function can be stored in a target module 204 and entered into the assignment matching module 208, which is described below. For example, if the fleet manager believes that optimizing energy costs has a relatively higher priority than completing tasks on time, then system 10 can be adjusted to delay tasks in order to charge the electric vehicle 14 during off-peak hours, which is cheaper.

[0027] In block 106 of Fig. Procedure 100 includes the procurement of route data for each of the orders or fleet tasks. The route data can be obtained via a route planner 206 (see Fig. 1) will be obtained, which calculates the routes between the start and end points of the respective journeys in the orders.

[0028] In block 108 of Fig. Procedure 100 includes the coordination of drivers 18 and electric vehicles 14 with fleet tasks and the determination of optimal charging schedules. An example of a modular architecture for system 10 is shown in Fig. 3 shown. Referring to Fig. 3. The respective data from the data module 202, the target module 204 and the route calculator 206 are entered into an assignment matching module 208.

[0029] As in Fig. As shown, the allocation matching module 208 takes into account task requirements, driver skills with regard to energy consumption, and vehicle characteristics. Drivers 18 and electric vehicles 14 are matched with fleet tasks / assignments based on a weighted multitude of influencing factors. The output module 210 (see Fig. 3) receives the results of the optimal coordination between the electric vehicles 14, the drivers 18 and the tasks, as well as the charging plan based on the coordination results.

[0030] The search for the optimal allocation policy can be carried out in various ways available to the professional. For example, Bellman equations can be used to maximize the total reward for all tasks / assignments in the fleet. In another example, a fast-forward planning system with heuristic estimation can be used. The goal is to find a sequence of actions that maximizes rewards (discounted or not, depending on the value of Gamma). The allocation matching module 208 can be updated at regular intervals or triggered by specific events. Triggers can include a change in the status of at least one of the electric vehicles 14 (e.g., change in availability and battery status), a change in the status of at least one driver 18 (e.g., on duty, assigned / unassigned), and a change in the status of a task (e.g.,Changes to the place of work or deadlines are included.

[0031] An example of an assignment selection procedure that can be applied by System 10 is in Fig. Figure 4 illustrates this. It is understood that other types of methods available to a person skilled in the art may be used. Fig. Figure 4 shows an assignment evaluation module 230, a charge assignment module 240, and a fleet task module 250. For each task, the assignment evaluation module 230 determines the optimal selection of driver 18 and electric vehicle 14. The charge assignment module 240 determines the charging schedules (e.g., time and location) of the electric vehicles 14 in the charging infrastructure 20. The fleet task module 250 stores the details of which drivers 18 and electric vehicles 14 have been assigned to the tasks and their respective charging schedules.

[0032] The process is iterative and handles each task in order of priority, with the highest priority task being assigned first. As shown at the starting point 220, the assignment evaluation module 230 receives a state input 222 (representing the current state S), a reward input 224 (representing an expected benefit or reward), and a task input 226 (representing the current task T). Here, S represents the state of the fleet 12, which changes after each assignment of a driver 18 and an electric vehicle 14 to a task.

[0033] The process begins with the first fleet task (T1), which has the highest priority. In state S1, the allocation evaluation module 230 assigns a driver (D) to the fleet task (T1). t1 ) and a vehicle (V t1 ) to. An example implementation of the allocation evaluation module 230 is in Fig. 5 is shown. This information is sent to the load allocation module 240, which generates the load plan for the assigned vehicle (V). t1 ) determined. The Fleet Task Module 250 will then be updated.

[0034] The situation changes because the driver (D t1 ) and the vehicle (V t1 ) are already assigned and their attributes may have changed. These attributes can include, for example, the expected location, the expected availability time (completion), changes to future journey times and routes (due to changes in traffic), driver fatigue, and available range.

[0035] In the second cycle, the second fleet task (T2) with the second-highest priority is entered into the assignment evaluation module 230 via task input 226, which also receives updated values ​​for the status input 222 and the reward input 224. For status S2, the assignment evaluation module 230 assigns a driver (D t2 ) and a vehicle (V t2 ) for the second fleet task (T2). This can be the same driver or vehicle assigned to the first fleet task, especially if the second fleet task is on the route or near the location of the first fleet task. This information is sent to the load allocation module 240, which determines the load plan for the assigned vehicle (Vt2). The fleet task module 250 is updated. The transition occurs state by state until each of the fleet tasks (T) n) was assigned to a driver (18) and an electric vehicle (14). Once all tasks are assigned, the remaining unassigned vehicles (as indicated in line 252) are sent to the load allocation module (240).

[0036] In Fig. Figure 5 shows an example implementation of the assignment evaluation module 230. As mentioned earlier, the assignment evaluation module 230 selects or optimally adapts a driver 18 and an electric vehicle 14 for a specific task (Tn). Fig. Figure 5 shows a first set 310 of driver nodes (e.g., D1, D2, and D3), a second set 320 of vehicle nodes (e.g., V1, V2, and V3), and a task node 330 (for task Tn). Each node in the first group 310 is connected to other nodes in the second group 320 and to the task node via corresponding edges E. For example, driver node D1 is connected to vehicle nodes V1, V2, and V3 via the first DV edge 312, the second DV edge 314, and the third DV edge 316, respectively. Driver node D1 is connected to task node 330 via the first DT edge 318. Vehicle nodes V1, V2, and V3 are connected to task node 330 via the first VT edge 322, the second VT edge 324, and the third VT edge 326, respectively.

[0037] Each respective node in Fig. A score of 5 is assigned based on maximizing the objective function. The driver score can be a weighted combination of average driving speed, fatigue, energy-efficient driving, and other related characteristics. The vehicle score can focus on energy efficiency and available range.

[0038] Each of the respective edges E is assigned a score that reflects the interactions or fit between the nodes. The score can reflect the driver's expertise, the distance of the driver 18 to the electric vehicle 14 and to the location of the task, and the type of vehicle or machine required for the task in question. For example, the third DV edge 316 between the driver node D1 and the vehicle node V3 (in this example, a minivan) can receive a high weight if the driver D1 has the best energy consumption performance when driving a minivan (V3). The third VT edge 326 between the task node 330 and the vehicle node V3 can receive a high weight if the task Tn requires a minivan. The weights can be calibrated or predefined and learned over time using gradient descent methods and other methods available to a person skilled in the art.After each assignment has been executed, the performance of the assignment matching module 208 can be used to update the weights.

[0039] As in Fig. As shown in Figure 4, the charge allocation module 240 is responsible for assigning the electric vehicles 14 to a charging session as needed. If an electric vehicle 14 is not selected for a task, it can be automatically sent to a charging session. Factors considered by the charge allocation module 240 include whether the battery state is below a certain threshold and the proximity of the electric vehicle 14 to a charger in the charging infrastructure 20. Other factors considered are whether an electric vehicle 14 has been selected for a task but requires additional energy to complete that task, and whether the trip completion time is close to a time when a charger is available.

[0040] If a conflict arises because more electric vehicles need charging than there are charging points available in the charging infrastructure, charging can be prioritized according to criteria such as energy consumption ranking or specific tasks that cannot be performed by other vehicles. Electric vehicles with higher priority tasks can be sent to the faster chargers first. For cost reasons, charging at fleet charging stations can be preferred over public charging stations.

[0041] Continue to Block 110 of Fig. 2. Procedure 100 can include the determination of auxiliary factors such as the availability of vehicle-to-grid energy transfers. The command unit 16 can be adapted to take into account the sale and transfer of available energy from the electric vehicles 14 to a public or private grid, referred to here as vehicle-to-grid energy transfer. This leads to a reduction in costs for the fleet 12. The command unit 16 can receive real-time updates on energy costs from the grid and adjust charging times accordingly to minimize energy costs.

[0042] Auxiliary factors may include determining the availability of charging points in the fleet chargers 22 for public use. The results of the allocation matching module 208 (see Fig. 3) can be used to sell the use of the Fleet 22 charging stations to the public during unused time slots. As mentioned previously, the output module 210 receives a schedule of available charging slots in the Fleet 22 charging stations. These time slots can be offered to the public at a specific price, with the public being informed about charging time limits and possible fines for exceeding these limits.

[0043] In block 112 of Fig.2. The command unit 16 can be adapted to determine a proposed new charging infrastructure by comparing the respective results of a charging infrastructure simulation with historical data and various combinations of chargers. First, the allocation matching module 208 is executed several times over a period of time (e.g., at times t1, t2, t3, tn) with different input variables from historical data. A charging infrastructure simulation with different combinations of chargers is then performed using these results. The results of the charging infrastructure simulations are compared with previous, matching data reflecting energy costs, range anxiety, punctuality, and fulfillment rate over the same period (e.g., at times t1, t2, t3, tn). Based on a comparison of the advantages of different combinations of chargers, optimal parameters for the charging infrastructure 20 can be determined.

[0044] In summary, a comprehensive system 10 for managing a fleet 12 is presented. This system 10 overcomes the challenges of managing a fleet 12 of electric vehicles due to issues such as variable charging times and available range. Energy costs for a fleet 12 are optimized, thereby making the acquisition of electric vehicles 14 more affordable for a fleet 12.

Claims

[1] A system (10) for managing a fleet (12) of electric vehicles (14) and the respective fleet drivers (18), wherein the system (10) comprises: an instruction unit (16) comprising a processor (P) and a tangible, non-transferable memory (M) in which instructions are recorded, wherein the instruction unit (16) is capable of: Receiving input variables, including the respective fleet tasks and a priority status of the respective fleet tasks; Obtaining route data for the respective fleet tasks; Obtained from an objective function defined by a multitude of influencing factors with corresponding weights; and Obtaining optimal charging plans for the electric vehicles (14) and assigning the respective fleet tasks to the electric vehicles (14) and the respective fleet drivers (18), among other things based on the objective function, the input variables and the route data, wherein the input variables include the energy requirements of the respective fleet tasks without propulsion, including the energy to operate one or more electrical devices to perform the respective fleet tasks, the system comprises an allocation matching module (208), an output module (210), an allocation evaluation module (230), a load allocation module (240) and a fleet task module (250), wherein the command unit (16) is trained to take into account the availability of excess battery energy of the electric vehicles (14) when selecting the appropriate electric vehicle (14) and driver (18) for a task, in order to meet the non-propulsion energy requirements of the fleet tasks, where the target function is stored in the target module (204) and entered into the assignment matching module (208), the assignment matching module (208) takes into account the task requirements, the drivers' abilities with regard to energy consumption, and the vehicle characteristics, wherein the output module (210) receives the results of the optimal coordination between the electric vehicles (14), the drivers (18) and the tasks, as well as the charging plan based on the coordination results, wherein the assignment evaluation module (230) determines the optimal selection of driver (18) and electric vehicle (14), wherein the charge assignment module (240) determines the charging plans of the electric vehicles (14) in a charging infrastructure (20), wherein the fleet task module (250) stores the details of which drivers (18) and electric vehicles (14) have been assigned to the tasks and their respective charging plans, wherein the charge allocation module 240 is designed to assign the electric vehicles (14) to a charging process as required, wherein an electric vehicle (14) is automatically sent to a charging process if it is not selected for a task. [2] System (10) according to claim 1, wherein the input variables include the driving style and the alertness index of the respective fleet drivers (18). [3] System (10) according to claim 1, wherein the input variables comprise an available range, a charging capacity, a charging profile and a drive energy consumption rate, each associated with the electric vehicles (14). [4] System (10) according to claim 1, wherein the input variables contain data about the charging infrastructure (20), including the types of available chargers, the locations of the chargers, the respective availability of charging operations and the respective costs of the charging operations. [5] System (10) according to claim 1, wherein the input variables contain data relating to the respective fleet tasks, including a journey start point, a journey end point and respective time ranges between the journey start point and the journey end point. [6] System (10) according to claim 1, wherein: the input variables include the non-propulsion energy requirements of the respective fleet tasks, including the energy for operating one or more electrical devices (50) to perform the respective fleet tasks; and the command unit (16) is adapted to assign the respective fleet tasks to the electric vehicles (14), partly based on the availability of excess battery energy from the electric vehicles (14) to meet the non-propulsion energy requirements. [7] System (10) according to claim 1, wherein the majority of the influencing factors include the optimization of energy costs, the punctuality of task completion and the minimization of range anxiety. [8] System (10) according to claim 7, wherein the respective weights of the multiple influencing factors are determined by a fleet manager. [9] System (10) according to claim 1, wherein the command unit (16) is configured to determine an amount of excess battery energy available from at least one of the electric vehicles (14) for transmission to a public network and a time for the transmission of the excess battery energy. [10] System (10) according to claim 1, wherein the command unit (16) is designed to determine a proposed new charging infrastructure by comparing the respective results of a charging infrastructure simulation using historical data and different combinations of chargers.

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