DE-102022126525-B4 - SYSTEM FOR MANAGING A FLEET OF ELECTRIC VEHICLES

DE102022126525B4DE 102022126525 B4DE102022126525 B4DE 102022126525B4DE-102022126525-B4

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, where 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.

Inventors

ARIEL TELPAZ
Refael Blanca
Nadav Baron
Ravid Erez
Daniel Urieli
Ron Hecht
Barak Hershkovitz

Assignees

GM Global Technology Operations LLC

Dates

Publication Date: 20260513
Application Date: 20221012
Priority Date: 20220324

Claims (10)

A system (10) for managing a fleet (12) of electric vehicles (14) and their respective fleet drivers (18), wherein the system (10) comprises: an instruction unit (16) with 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; receiving route data for the respective fleet tasks; receiving an objective function defined by a plurality 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, whereby the input variables include the energy requirements of the respective non-propulsion fleet tasks, including the energy to operate one or more electrical devices to perform the respective fleet tasks, whereby the system comprises an allocation matching module (208), an output module (210), an allocation evaluation module (230), a charge allocation module (240) and a fleet task module (250), whereby the command unit (16) is configured 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, whereby the objective function in the The target module (204) is stored and entered into the assignment matching module (208), where the assignment matching module (208) takes into account the task requirements, the drivers' energy consumption capabilities, and the vehicle characteristics, where the output module (210) receives the results of the optimal matching between the electric vehicles (14), the drivers (18), and the tasks, as well as the charging plan based on the matching results, where the assignment evaluation module (230) determines the optimal selection of driver (18) and electric vehicle (14), where the charge allocation module (240) determines the charging plans of the electric vehicles (14) in a charging infrastructure (20), where 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, where the charge allocation module (240) is designed to assign the electric vehicles (14) according to The requirement is to assign a charging process, whereby an electric vehicle (14) is automatically sent to a charging process if it is not selected for a task.
System (10) according Claim 1 , where the input variables include the driving style and alertness index of the respective fleet drivers (18).
System (10) according Claim 1 , wherein the input variables include an available range, a charging capacity, a charging profile and a drive energy consumption rate, each associated with the electric vehicles (14).
System (10) according Claim 1 , wherein the input variables include data on the charging infrastructure (20), including the types of chargers available, the locations of the chargers, the respective availability of charging operations and the respective costs of charging operations.
System (10) according Claim 1 , where the input variables contain data on 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.
System (10) according 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.
System (10) according Claim 1 , with the majority of influencing factors including the optimization of energy costs, the punctuality of task completion and the minimization of range anxiety.
System (10) according Claim 7 , whereby the respective weights of the several influencing factors are determined by a fleet manager.
System (10) according 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 grid and a time for the transmission of the excess battery energy.
System (10) according 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 various combinations of chargers.

Description

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. The DE 10 2017 119 709 A1 This reveals 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 fulfill 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 charging station availability (e.g., waiting time to access a charging point). The BEV selection can be optimized so that a BEV arrives at a charging station with an optimal remaining SOC. The DE 10 2021 125 322 A1 This document 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 discharge characteristic of a battery used to power a battery-powered vehicle and to determine whether the battery-powered vehicle is suitable for the route. One factor that can influence the battery's energy discharge characteristic is the ambient temperature, as extreme temperatures can adversely affect battery performance. Consequently, the operating range of the battery-powered vehicle may be compromised if the route involves extreme ambient temperatures. If the evaluation indicates that the battery's energy discharge characteristic is unsuitable for the route, a different battery-powered vehicle with a better battery can be used for the route. The DE 10 2022 102 926 A1 This document discloses a system and procedure for assigning routes to vehicles. The procedure may include evaluating a first vehicle for use on a first route and a second vehicle for use on a second route. Evaluating the first vehicle may include determining a first probability that the first vehicle will require a first energy replenishment while on the first route and determining the first deployment cost for the first vehicle. The first deployment cost may include the first energy replenishment cost based on the first probability. Evaluating the second vehicle may include determining the second deployment cost for the second vehicle, where the second deployment cost includes the second energy replenishment cost. The first vehicle is assigned to the first route and the second vehicle to the second route if the first deployment cost is lower than the second deployment cost. The DE 10 2021 109 015 A1 The method discloses a process comprising: initiating, by a means of transport, a request to transfer an initial portion 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 an initial destination of the means of transport and on data received by the charging station on the basis of a route associated with the initial destination, wherein the actual amount of energy is not the same amount as the initial portion of stored energy; and depositing, by the means of transport, the actual amount of energy in the charging station. The DE 10 2020 131 877 A1 Disclosed is 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 a drive system for the vehicle. A route to a destination is determined, and the locations of a plurality 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 select 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. to establish a ranking. One of the charging stations is selected based on this ranking, and a charging reservation is scheduled. It can be seen as a task to overcome challenges related to managing an electric vehicle fleet 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 m