Providing charging plans for electric vehicles

Abstract

A computer-implemented method for providing an optimized future charging plan (70) for charging batteries (91) of electric vehicles (90) of a fleet of electric vehicles (90) using electric vehicle chargers (81), the method including: obtaining a future trip plan (10) for each of the electric vehicles (90); obtaining battery data (20) for the batteries (91) of the electric vehicles (90), the battery data (20) including, for each electric vehicle (90), a battery state of charge (701, 702, 703) and a battery state of health; and performing an optimization process to determine a future charging plan (70) for charging the electric vehicles (90) using the electric vehicle charger (81), the optimization process having one or more constraints including a first constraint requiring the future charging plan (70) to increase the state of charge of the batteries (91) such that the electric vehicles (90) can execute their respective future trip plans (10). Subject to one or more constraints, the optimization process aims to minimize the rate of deterioration of the battery's (91) state of health.

Description

[Technical Field]

[0001] This application claims priority from EP23159132.2, filed February 28, 2023, the contents and elements of which are incorporated herein by reference for all purposes.

[0002] The present invention relates to a method for providing a future charging plan for charging batteries of electric vehicles in an electric vehicle fleet and a system configured to do the same. [Background technology]

[0003] Mass transit networks (e.g., buses, trains, trams, light rail vehicles, etc.) are increasingly being electrified to reduce pollution associated with the vehicles operating on these networks and to enable them to operate efficiently with electricity generated from renewable energy sources. One form of electrification is to equip these mass transit vehicles with batteries to store the energy needed to propel the vehicle and enable it to execute a trip plan.

[0004] Compared to mass transit vehicles powered by internal combustion engines (ICEs), mass transit battery electric vehicles (EVs) typically have a shorter range before needing to be refueled / charged, and the recharging process for an EV takes longer than refueling an ICE mass transit vehicle. Therefore, in addition to developing a trip plan for each mass transit vehicle in the network, when mass transit battery EVs are used in a mass transit network, an associated charging plan can be developed to dictate when, and potentially where, the EVs will be charged so that their batteries are sufficiently charged to enable the EVs to execute the trip plan. When developing charging plans for mass transit electric vehicles (EVs) in conjunction with their trip plans, the traditional focus has understandably been on preventing the EVs from running out of power during times when the vehicles are available to charge and on ensuring that the EVs take advantage of available charging facilities.

[0005] Because mass transit vehicles typically have longer lifespans and higher lifetime mileages than private vehicles, batteries in mass transit battery-powered electric vehicles (EVs) are expected to undergo more charge-discharge cycles over their lifetime than private vehicles. However, batteries are known to degrade as the number of charge-discharge cycles increases, and this degradation typically manifests as a reduction in energy storage capacity. Therefore, batteries in mass transit EVs may have a shorter lifespan (i.e., the number of years a battery can last before its storage capacity degrades to the point where the EV can no longer perform its planned journey) than other components of the mass transit EV's drivetrain, such as the motor and inverter, and may also have a shorter lifespan than the ICE in conventional vehicles. In other words, the lifespan of an EV may be substantially determined by the lifespan of the EV's battery. Therefore, public transit electric vehicles (EVs) may be configured to allow the battery to be swapped out for a replacement battery when the energy storage capacity of the existing battery degrades to the point where it is no longer sufficient to perform the planned journey.

[0006] Similar concerns arise in other forms of electric vehicle fleets, such as electric micromobility type vehicles such as electric scooters, electric bicycles, etc. The present invention has been devised with these considerations in mind. Summary of the Invention

[0007] It is desired to provide a method and system that can provide a future charging plan for charging electric vehicle batteries in a fleet of electric vehicles using electric vehicle chargers, the future charging plan being optimized to facilitate execution of the electric vehicle trip plan while reducing the rate of battery health degradation.

[0008] Thus, in a first aspect, a computer-implemented method for providing optimized future charging plans for charging batteries of electric vehicles in a fleet of electric vehicles using an electric vehicle charger is provided. The method includes the following steps: acquiring future trip plans for each of the electric vehicles; acquiring battery data for the batteries of the electric vehicles, the battery data including, for each electric vehicle, a battery state of charge (SOC) and a battery state of health (SOH); and performing an optimization process to determine future charging plans for charging the electric vehicles using the electric vehicle charger, the optimization process having one or more constraints, including a first constraint requiring the future charging plans to increase the battery SOC so that the electric vehicles can execute their respective future trip plans; wherein, within the one or more constraints, the optimization process seeks to minimize a degradation rate of the battery SOH. Thus, it is possible to provide future charging plans that reduce the rate at which the SOH of the batteries of the electric vehicles in the fleet degrades and extend the life of the batteries.

[0009] The SOH of an electric vehicle battery at a given time may be defined as the energy capacity of the battery at that time divided by the original energy capacity of the battery or the rated energy capacity of the battery. The energy capacity of the battery may be expressed in terms of the battery's power (i.e., watt-hours, Wh) or the battery's current (i.e., ampere-hours, Ah).

[0010] The SOC of an electric vehicle battery at a given time may be defined as the remaining energy capacity (e.g., defined in Wh or Ah) stored in the battery at that time divided by the energy capacity of the battery when fully charged at that time (also defined in Wh or Ah, for example).

[0011] Performing the optimization process may include determining one or more constraints based on the obtained data, for example, constraints based on future trip plans and battery data.

[0012] The step of acquiring future driving plans for each of the electric vehicles and the step of acquiring battery data for the batteries of the electric vehicles may be performed simultaneously or sequentially in any order.

[0013] If the electric vehicle charger is powered by a power grid, the method may further include obtaining future charging capacity data of the power grid, and the optimization process may have a second constraint requiring the future charging plan to fit within the future charging capacity of the power grid. Advantageously, this prevents the future charging plan created by the method from overloading the power grid. The charging capacity of the power grid may change over time, and therefore the second constraint may be a time-dependent function rather than a fixed value.

[0014] The method may further include obtaining charging capability data, the charging capability data including a maximum charging rate for each electric vehicle (and possibly each electric vehicle charger), and if such data is obtained, the optimization process may include a further constraint requiring future charging plans to fall within the maximum charging rate of the electric vehicle battery (and possibly the maximum charging rate of the electric vehicle charger), thereby reducing damage to the electric vehicle battery or the electric vehicle charger due to excessive current during charging.

[0015] If the price of electricity from the power grid varies over time, the charging capability data may further include a maximum electricity price for each electric vehicle, each electric vehicle charger, and selected one or more items of the fleet of electric vehicles, wherein the vehicle and / or charger can only be charged if the electricity price is at or below the maximum electricity price.

[0016] The optimization process may operate on an objective function that includes an expression for the battery SOH degradation rate, which may be the SOH degradation rate of a single battery, may incorporate the SOH degradation rates of multiple batteries, or may even incorporate the battery SOH degradation rate of all batteries. The battery SOH degradation rate may be defined as the change in the battery SOH per unit time, the change in the battery SOH per charge cycle, or the change in the battery SOH per unit distance traveled by the associated electric vehicle.

[0017] The optimization process may attempt to determine future charging plans that cause the batteries' SOHs to converge with one another so that the batteries all reach a predetermined cutoff SOH simultaneously. Typically, the predetermined cutoff SOH is the SOH at which a given battery is too low for the associated EVs to execute the trip plan under any charging plan. Once the cutoff SOH is reached, the batteries of most or all EVs in the fleet can be replaced simultaneously, reducing maintenance costs associated with battery replacement and increasing the efficiency of maintenance associated with battery replacement.

[0018] If the SOH of the batteries is to converge with one another so that they all reach a predetermined cutoff SOH at the same time, the optimization process may include determining future charging plans for the batteries sequentially in ascending order of their respective SOH, starting with the battery with the lowest SOH, and after a future charging plan has been determined for each battery in the ascending order, updating one or more constraints for the remaining batteries in the ascending order to take into account the determined future charging plans before determining a future charging plan for the next battery in the ascending order. Advantageously, this computationally simplifies the optimization process because fewer optimization variables are considered simultaneously compared to other possible implementations of the optimization process.

[0019] However, according to another option, if the SOH of the batteries is to converge with each other so that they all reach a predetermined cutoff SOH at the same time, the optimization process can include generating an expression for an overall deterioration rate of the battery SOH that is a weighted linear combination of the deterioration rates of the batteries, with the weights set according to the SOH of the batteries, and running the optimization process by including the expression in the objective function. Advantageously, this improves the computational efficiency of the optimization process by reducing the risk of having to restart the optimization process because a solution could not be reached. If the objective function of the optimization is such that it is directly proportional to the deterioration rate of the SOH of the batteries, the weights may be negatively correlated with the SOH of the batteries, e.g., the weights may decrease in proportion to the state of health of the batteries. If the objective function of the optimization is such that it is inversely proportional to the deterioration rate of the SOH of the batteries, the weights may be positively correlated with the SOH of the batteries.

[0020] The future trip plan for each electric vehicle may include an indication of one or more time periods during which the vehicle is in use, an indication of one or more time periods during which the vehicle is available for charging, and an estimated energy use and / or power use profile for the one or more time periods during which the electric vehicle is in use. The future charging plan may include one or more future charging plan variables, and an optimization process may be performed by varying the one or more future charging plan variables as control variables for the optimization process. The future charging plan variables may be selected from the group consisting of a battery charge rate, a state of charge at the end of a charging period, an average state of charge of the battery during the period during which the vehicle is available for charging, a charging frequency, an average state of charge of the battery during execution of the future trip plan, a start time of a charging period, and an end time of a charging period.

[0021] The optimization process may include accessing a database containing a plurality of charging profiles, the charging profiles being lines on a plot of battery SOC versus time, associated with a given battery SOH, and associated battery SOH degradation rates, and building a future charging plan by assigning one or more charging profiles from the database whose associated battery SOH matches the battery SOH of the electric vehicle to one or more time periods during which the electric vehicle is available for charging. This may speed up the optimization process by providing predefined charging profiles whose associated battery SOH degradation rates have already been determined.

[0022] The database of charging profiles may be filtered so that only those charging profiles that match the battery SOH of the electric vehicle for which a future charging plan is being determined are accessible, and may be further filtered based on starting SOC, ending SOC, and chargeable time period within the future charging plan to reduce the number of charging profiles in the database.

[0023] The battery SOH degradation rate associated with each charging profile may be based on a computer simulation of the battery charging under that charging profile, which is advantageously generally more efficient than the alternative of performing physical experiments on the battery.

[0024] If a database of charging profiles is used in the optimization process, the battery SOH degradation rate associated with a particular charging profile is generally a function of the battery's SOH, and the optimization process can further include providing the SOH information to the database, so that the battery SOH degradation rate associated with the charging profile can be better tailored to the battery targeted by the future charging plan.

[0025] The battery data may further include the electrochemical characteristics of the battery. The battery SOH degradation rate associated with each charging profile in the database may be a function of the electrochemical characteristics of the battery for which it is intended. Thus, by providing the electrochemical characteristics of the battery for which future charging plans are being developed, the SOH degradation rate of the provided battery associated with the charging profile may be better tailored to that battery.

[0026] The computer-implemented method for providing an optimized future charging plan may further include communicating the future charging plan to a corresponding electric vehicle and / or an electric vehicle charger and automatically implementing the future charging plan. The future charging plan may be communicated to these entities directly or indirectly.

[0027] In a second aspect, there is provided a method for charging batteries of electric vehicles of a fleet of electric vehicles using an electric vehicle charger, the method comprising: performing a method according to the first aspect; and charging batteries of the electric vehicles using the electric vehicle charger by implementing a future charging plan determined by the method.

[0028] In a third aspect, there is provided a computer-implemented operations management system configured to perform a method according to the first aspect.

[0029] For example, in a third aspect, a system may include a constraint determination unit configured to: acquire a future trip plan for each of the electric vehicles; and acquire battery data for the batteries of the electric vehicles, the battery data including, for each electric vehicle, a state of charge and a state of health of the battery. The system may further include a future charging plan determination unit configured to execute an optimization process to determine a future charging plan for charging the electric vehicles using the electric vehicle charger, the optimization process having one or more constraints including a first constraint requiring that the future charging plan increase the state of charge of the battery to enable the electric vehicles to execute each future trip plan. Thus, a system is provided that can determine a future charging plan that reduces a rate at which the health of the batteries of the electric vehicles in a fleet of electric vehicles deteriorates and extends the life of the batteries. The constraint determination unit may include a power management unit configured to acquire the battery data and a charging time management unit configured to acquire the future trip plans.

[0030] If the system utilizes charge capacity data, the power management unit may be configured to obtain the charge capacity data.

[0031] If the system utilizes charging capacity data, the charging time management unit may be configured to obtain the charging capacity data.

[0032] The future charging plan determination unit may be in communication with a database containing a plurality of charging profiles and associated battery SOH degradation rates, each charging profile being associated with a given battery SOH and a line on a plot of the battery's state of charge versus time. The database may reside within the operational management system or may be external to the operational management system.

[0033] In a fourth aspect, there is provided a computer program arranged to perform the method of the first aspect when run on a computer.

[0034] In a fifth aspect, there is provided a computer readable storage medium carrying a computer program according to the fourth aspect.

[0035] A fleet of electric vehicles for which the first, second, third, fourth, and fifth aspects of the present disclosure provide or are configured to provide an optimized charging profile can form part of a mass transit system. Such vehicles in a mass transit system may be, for example, one or more of buses, trains, trams, and light rail vehicles. According to another option, the fleet of vehicles may be electric taxis or electric micromobility vehicles (e.g., electric scooters, electric bicycles, etc.). The fleet of vehicles may also be composed of a mixture of any two or more vehicles selected from the aforementioned vehicles.

[0036] The present invention includes combinations of the described embodiments and preferred features except where such combinations are expressly not permitted or explicitly avoided. [Brief explanation of the drawings]

[0037] Embodiments illustrating the principles of the present disclosure will now be described with reference to the accompanying drawings. FIG. 1 illustrates the interconnection of a computer-implemented operations management system configured to provide an optimized future charging plan for charging batteries of electric vehicles in an electric vehicle fleet. FIG. 2 is an information flow diagram of the computer-implemented traffic control system of FIG. FIG. 3 is a flowchart of a method for providing an optimized future charging plan for charging batteries of a fleet of EVs. Figure 4 shows a schematic diagram of the future travel plans of three EVs, along with a graph of the available charge power from the power grid. 5A-5F show schematically various possible charging profiles for an EV battery; Figure 6 shows a schematic representation of the future travel and charging plans for three EVs, along with a graph of the available charging power from the power grid and the power consumption of the electric vehicle charger. Figure 7 shows a schematic diagram of the future travel plans and future charging plans for the three EVs, along with a graph of the charging status of the three EVs. FIG. 8 is a flow chart of the implementation of the optimization process. 9A and 9B are schematic plots of the battery health over time for three EVs with and without the implementation of an optimized future charging plan. DETAILED DESCRIPTION OF THE INVENTION

[0038] Aspects and embodiments of the present invention will now be described with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art.

[0039] FIG. 1 illustrates the interconnection of a computer-implemented operations management system 1 configured to provide an optimized future charging plan for charging the batteries of a fleet of electric vehicles (EVs) 90 using electric vehicle chargers 81 .

[0040] Each EV 90 is equipped with a battery 91 and a communication unit 92 that receives data from the battery 91. The communication unit 92 of each EV 90 is in network communication with the fleet management system 1, and can transfer data from the EV 90 to the system 1 and vice versa. The battery 91 of each EV 90 shown in FIG. 1 is connected to an electric vehicle charger 81 configured to increase the state of charge (SOC) of the battery 91. The SOC of the battery 91 of the EV 90 at a given point in time can be defined as the electrochemical energy stored in the battery 90 at that point in time divided by the energy capacity of the battery 90 at the same point in time.

[0041]

number

[0042] The electric vehicle chargers 81 in FIG. 1 belong to the same charging station 80. The charging station 80 is provided with a power source 82 that provides the power necessary to charge the batteries 91 of the EVs 90 connected to the charging station 80. Typically, the power source 82 is a power grid, but the power source 82 can take other forms, such as a generator, a solar cell, or a wind turbine, or the power source 82 can be a combination of one or more of these forms. The charging stations 80 are in network communication with the driving management system 1 so that data can be transferred from the charging stations 80 to the system 1 and vice versa. While FIG. 1 illustrates only a single charging station 80 and its associated charger 81, the system 1 may be in network communication with multiple charging stations 80, and each charging station 80 may be composed of one or more chargers 81 for the EVs 90.

[0043] By connecting the operation management system 1 to the EV 90 and the charging station 80, the system 1 obtains input data from these sources, uses it to generate an optimized future charging plan, and outputs the optimized future charging plan so that it can be executed when charging the EV 90 at the charging station 80.

[0044] The traffic management system 1 includes a constraint determination unit 50 configured to obtain data from the EVs 90 and charging stations 80 and define one or more constraints for the optimization process. A first constraint for the optimization process requires that future charging plans increase the battery's SOC to a level that allows the EVs 90 to execute their respective future trip plans. The one or more constraints are transmitted from the constraint determination unit 50 to a future charging plan determination unit 60 that performs optimization to determine a future charging plan for the battery 91 of the EVs 80. Among the one or more constraints, the optimization process seeks to minimize the degradation rate of the battery's SOH so as to extend the battery's lifespan while allowing the EVs 90 to execute their future trip plans.

[0045] The future charging plan determination unit 60 is also connected to a database 100 that contains a plurality of charging profiles and associated battery state of health (SOH) degradation rates. The database 100 and its role in optimizing the future charging plan for the battery of the EV 90 will be further described in relation to the charging profiles shown in Figure 5. In Figure 1, the database 100 is external to the driving management system 1 and is networked with the system 1 so that data can be transferred from the database 100 to the system 1 and vice versa.

[0046] FIG. 2 is an information flow diagram relating to the computer-implemented fleet management system 1 of FIG. 1 and its inputs and outputs. Features in FIG. 2 that correspond to features in FIG. 1 are similarly numbered. Details of the information flow shown in FIG. 2 are described below with reference to FIGS. 3 through 7. FIG. 3 is a flowchart of a method for providing an optimized future charging plan 70 for charging batteries of a fleet of EVs. FIG. 4 is a schematic diagram of a future trip plan for three EVs, along with a graph of available charging power from the power grid. FIGS. 5A through 5F schematically illustrate different charging profiles for EV batteries. FIG. 6 is a schematic diagram of a future trip plan for three EVs, along with a graph of available charging power from the power grid. FIG. 7 is a schematic diagram of a trip plan and a charging plan for three EVs, along with a graph of SOC information for the three EVs.

[0047] 2 shows four types of input data to the system 1: future trip plans 10 for each of the EVs 90 for which the system 1 optimizes the future charging plan 70; battery data 20 related to the batteries of the EVs 90; future charging capacity data 30; and charging capacity data 40. To provide the optimized future charging plan 70, a constraint determination unit 50 in the system 1 acquires at least the future trip plans 10 and the battery data 20, and typically also acquires the future charging capacity data 30 and the charging capacity data 40. The constraint determination unit 50 in FIG. 2 includes a charging time management unit 51 configured to acquire the future trip plans 10 and the charging capacity data 40, and a power management unit 52 configured to acquire the battery data 20 and the charging capacity data 30.

[0048] The future trip plan 10 indicates, for each EV 90 for which the system 1 is configured to provide an optimized future charging plan 70, the amount of energy usage required to enable the EV 90 to perform the planned trip. The future trip plan 10 for the EV 90 may simply include the route the EV is planned to travel and the timing of the planned trip, from which estimated energy usage can be calculated based on, for example, one or more of the route's distance and elevation change, the EV's mass, traffic conditions, and the required average speed, and this can be provided along with the future trip plan 10. Alternatively, the future trip plan 10 may include one or more times within the future trip plan when the EV 90 is in use, a pre-calculated estimated energy usage profile, and / or a pre-calculated estimated power usage profile.

[0049] Typically, in a fleet of EVs 90, each having a future trip plan 10, each future trip plan 10 may include one or more trips that the EVs 90 plan to travel and the time at which the EVs 90 plan to travel. Furthermore, the future trip plan 10 is typically repeated periodically. For example, FIG. 4 provides a 24-hour future trip plan 10 that is repeated every 24 hours for three EVs 90: EV1, EV2, and EV3. The EVs 90 in FIG. 4 are part of a fleet of buses. For each EV 90, the future trip plan 10 indicates periods during which the vehicle is traveling and therefore cannot be charged at a charging station 80. The time between trips is defined as the time during which the EVs 90 can be charged. In the example of FIG. 4, each EV 90 has multiple time periods during the day during which it plans to travel (i.e., each EV's future trip plan includes multiple trips), and the length of these time periods varies for each EV 90. This means that even if the EVs' energy consumption rates are constant, the minimum SOC required for the EVs' batteries to complete each trip within the plan 10 without running out of charge varies. FIG. 4 further illustrates that during certain times of the day, multiple EVs 90 may be chargeable, or all EVs 90 may be chargeable, or during certain times of the day, no EVs 90 may be chargeable, or even during certain times of the day, only one EV 90 may be chargeable.

[0050] Battery data 20 is data specific to each EV 90 and includes at least the SOC and SOH of the battery of that EV 90. The SOH of the battery of an EV 90 at a given point in time can be defined as the energy capacity of the battery at that point in time divided by either the original energy capacity of the battery or the rated energy capacity of the battery.

[0051]

number

[0052] The battery data 20 is stored in a constraint determination unit 50, which calculates the estimated energy consumption amount as a minimum state of charge (SOC) that the battery of the EV 90 should have in order to execute a future travel plan.min )

[0053]

number

[0054] The battery's original energy capacity may be provided with battery data 20 or may already be known by system 1. If future trip plan 10 includes multiple trip periods (such as those shown in FIG. 4), the minimum SOC can be calculated at the beginning of each trip period. Thus, the minimum SOC effectively acts as a first constraint on the optimization process for determining future charging plans 70 for EV 90; future charging plans 70 must increase the battery's SOC so that EV 90 can execute each future trip plan 10 (i.e., the battery's minimum SOC value must be achieved at the relevant time point).

[0055] 1 and 2 , the power source 82 of the charging station 80 is the power grid, and the future charging capacity data 30 is obtained by the power management unit 52. The future charging capacity data 30 indicates the charging power available from the power grid, essentially indicating an upper limit on the amount of power that can be drawn from the grid supply to recharge the fleet of EVs 90 at a given time. FIG. 4 includes a diagram that provides an illustration of the available charging power from the power grid 400 over the 24-hour period of the future trip plan of FIG. 4 . If the charging station 80 is powered by the power grid and has a power upper limit, the available charging power from the power grid for the charging station 80 becomes a second constraint on the optimization of the future charging plan 70 by the system 1; that is, the total power consumption based on the future charging plan 70 of the multiple EVs 90 at a given time should remain within the charging capacity of the power grid 400 at that time.

[0056] In the embodiment shown in FIGS. 1 and 2 , charging capability data 40 is acquired by the charging time management unit 51. The charging capability data 40 includes, for each EV battery 91, at least the maximum charging rate at which the battery 91 is rated. It can be appreciated that the charging rate (i.e., the rate at which the battery's SOC increases) can affect the battery's SOH degradation rate d(SOH) / dt. The charging capability data 40 can also be acquired from the charging station 80 so that the maximum charging rate of the charger 81 can be obtained. The battery 91's response to a given charging rate depends on the electrochemical characteristics of the battery 91, but generally, the faster the charging rate, the faster the battery SOH degradation rate. The maximum charging rate of the battery 91 and charger 81 pair may be set according to the maximum desired degradation rate of the battery SOH, the safety rating of the battery 91 or the charger 81, or a combination thereof. The maximum charging rate may be a further constraint on the future charging plan 70, i.e., the future charging plan 70 should be such that the planned charging rate of the battery 91 falls within the maximum charging rate of the battery 91 and charger 81.

[0057] The constraint determination unit 50 is configured to determine one or more constraints for the optimization process based on the data obtained by the charging time management unit 51 and the power management unit 52. As described above, a first constraint for the optimization process requires that the future charging plans 70 increase the battery's SOC so that the EV 90 can execute each future driving plan 10. A second constraint that may be imposed on the optimization process requires that the future charging plans 70 stay within the charging capacity of the power grid. A further constraint that may be imposed on the optimization process is a constraint that requires that the future charging plans 70 stay within the maximum charging rate of either the EV battery 91 or the electric vehicle charger 81.

[0058] The one or more constraints defined by the constraint determiner 50 are provided to a future charging plan determination unit 60. The future charging plan determination unit 60 is configured to execute an optimization process using the constraints to determine a future charging plan 70 for the EV 90. The optimization process may take the form of nonlinear programming.

[0059] Within one or more constraints, the optimization process seeks to minimize the battery SOH degradation rate. In other words, the future charging plan 70 determined by the future charging plan determination unit 60 is determined by determining a future charging plan 70 that reduces the battery SOH degradation rate compared to a base case. This is typically done by performing the optimization process based on an objective function that includes the battery SOH degradation rate expression d(SOH) / dt. Such objective functions are described in more detail with respect to FIG. 8. The future charging plan determination unit 60 is connected to the database 100 and can retrieve charging profiles and associated battery SOH degradation rates from the database 100 for use in determining the optimized future trip plan 70. This is described in more detail with respect to the charging profiles shown in FIG. 5.

[0060] After determining the future charging plan 70, the future charging plan determination unit 60 is configured to output the future charging plan 70 to the EVs 90 and the charging stations 80 that charge the EVs 90, so that the future charging plan 70 determined by the system 1 can be automatically implemented by the EVs 90 and the chargers 81.

[0061] The flowchart of Figure 3 includes steps S110 of acquiring a future trip plan for each of the electric vehicles, S120 of acquiring battery data for the electric vehicle's batteries, S130 of acquiring charging capacity data, and S140 of acquiring charging capability data. After acquiring these data, the system executes an optimization process S200 to determine a future charging plan for the electric vehicles according to one or more constraints based on the acquired data. Step S110 of acquiring a future trip plan for each of the electric vehicles and step S120 of acquiring battery data for the electric vehicle's batteries may be performed simultaneously with each other or sequentially in any order. Similarly, optional steps S130 and S140, if performed, may be performed in either order, in parallel or sequentially with each other, or in parallel or sequentially with steps S110 and S120.

[0062] If the future driving plan 10 for the EV 90 includes multiple trips and multiple time periods during which the EV 90 can be charged, the future charging plan 70 for the EV 90 will consist of multiple charging profiles, each corresponding to one of the time periods during which the EV 90 can be charged, associated with a given battery SOH, and resulting in a line on a plot of battery SOC versus time. When performing an optimization process to determine the future charging plan 70 for minimizing the battery SOH degradation rate (step S200), one or more control variables of the EV 90's charging profile can be set within the future charging plan 70 to adjust the impact of charge / discharge cycles on the battery SOH. Figures 5A-5F provide illustrations of different possible charging profiles, and Table 1 below lists numerical data associated with the charging profiles in Figures 5A-5F. Each of Figures 5A-5F also includes an estimated energy usage profile for the time periods during which the electric vehicle is in use. Figure 5A relates to a "base case," while Figures 5B-5F relate to variations from that base case, numbered 1-5, respectively.

[0063] Each charging profile is specific to the SOC change required over a period of time. That is, the starting and final SOC of the charging profile are fixed, and the charging profile then defines how the SOC increases from the starting SOC to the final SOC over the duration of the charging profile. Each charging profile has an associated battery SOH degradation rate (i.e., how much the battery's SOH will degrade as a result of executing the charging profile as part of a future charging plan). This associated battery SOH degradation rate is a function of the battery's current SOH; in other words, the battery's current SOH affects the rate at which the battery's SOH will degrade when a given charging profile is applied to that battery. Thus, each charging profile associated with a battery SOH degradation rate is relevant to a given battery SOH.

[0064] [Table 1]

[0065] The base-case profile in Figure 5A shows an EV that is charged at the maximum charge rate as soon as it becomes available, charged to 100% SOC, and then stationary at 100% SOC for a period of time before being driven and discharged. Figure 5A shows two such charge-discharge cycles. The EV performs two trips as part of a future trip plan (the energy consumption profile of these trips is indicated in Figure 5A by the nonlinear portion of the SOC line where the SOC decreases). The first trip begins with a full charge, the EV is fully charged during the first period between the first and second trips, the second trip is performed, and the EV is fully recharged during the second period at the end of the second trip. Table 1 lists the EV's battery SOC at 50% at the beginning of the charging period and the end of the discharging period.

[0066] Next, Figures 5B-5F provide illustrations of different charging profiles in which one or more variables related to the base charging profile are changed to provide a new charging profile that has a reduced rate of battery SOC degradation compared to the base charging profile of Figure 5A.

[0067] Charging Profile No. 1 in Figure 5B differs from the base case in Figure 5A in that it reduces the average battery SOC during the execution of the future trip plan (i.e., during the period when the vehicle is traveling). Alternatively, the modification in Figure 5B can be understood as reducing the average battery SOC during periods when the vehicle is available for charging (i.e., during periods when the vehicle is not traveling). That is, instead of charging and discharging the battery so that the SOC value always ranges between 50% and 100% (see Table 1), Charging Profile No. 1 in Figure 5B varies the SOC between 0% and 50%. Starting each trip in the EV's future trip plan with an SOC of 50% is sufficient for the EV to complete each trip and return to a charging station to recharge before the next trip in the future trip plan. Reducing the average battery SOC and avoiding charging to high SOC values ​​reduces the battery's SOH degradation rate compared to the base case shown in Figure 5A. Charging Plan No. 1 is rated "High" in Table 1 for its effectiveness in reducing the battery SOH degradation rate.

[0068] Charging Profile No. 2 in Figure 5C differs from the base case in Figure 5A in that the battery charging rate is reduced. Alternatively, the change in Figure 5C can be understood as reducing the average battery SOC during periods when the vehicle is chargeable (i.e., periods when the vehicle is not traveling). Table 1 lists the base case charging current as 100 amperes, while the charging rate in Charging Profile No. 2 is 50 amperes. This change in charging profile results in the battery reaching 100% SOC in Figure 5B only immediately before the EV departs for the next trip in the future trip plan. This shortens the time the battery SOC is at or near 100%, reducing the rate of increase in the battery's stored energy and thus reducing the battery SOH degradation rate. Charging Profile No. 2 is rated "low" in Table 1 for its SOH degradation rate reduction effect. Reducing the charging rate, as shown in Figure 5C, is not as effective in reducing the battery SOH degradation rate as reducing the average battery SOC during the future trip plan execution, as shown in Figure 5B.

[0069] Charging Profile No. 3 in FIG. 5D differs from the base case in FIG. 5A by charging the battery less frequently, charging the battery only after every other EV trip, rather than before every trip. Alternatively, the modification in FIG. 5D can be understood as reducing the average battery SOC during periods when the vehicle is available for charging (i.e., periods when the vehicle is not moving) or during execution of a future trip plan (i.e., periods when the vehicle is moving). The battery SOC is 50% after the first trip, and after commencing the trip at 100% SOC, the EV completes further trips and arrives at a charging station at 0% SOC. The battery is then recharged at the maximum charge rate from 0% to 100% SOC, ensuring the EV is fully charged in time for the next trip in the upcoming trip schedule.

[0070] Therefore, although the EV has time to charge between trips, it is not charged at all and the SOC remains constant at 50%. This reduces the time the battery's SOC is at or near 100%, resulting in a wider range of battery capacity (0% to 100%). Charging Profile No. 3 is rated "medium" in Table 1 for its effectiveness in suppressing battery SOH degradation. While reducing the charging frequency as shown in Figure 5D is not as effective in suppressing battery SOH degradation as reducing the average battery SOC as shown in Figure 5B, because the average battery SOC in Charging Profile No. 3 is lower than that in Charging Profile No. 2, it is more effective in suppressing battery SOH degradation than reducing the charging rate as shown in Figure 5C.

[0071] Charging Profile No. 4 in Figure 5E differs from the Base Case in Figure 5A in that the charging start and end times are shifted to fall within the period during which the EV is available for charging. Alternatively, the changes in Figure 5E can be understood as reducing the average battery SOC during periods during which the vehicle is available for charging (i.e., periods during which the vehicle is not moving). Rather than waiting until the EV's SOC is 100% at the end of charging before being moved, as in the Base Case scenario, the charging start and end times are set to move the EV as soon as its SOC reaches 100%, shortening the period during which the EV remains at 100% SOC. Charging Profile No. 4 is rated "High" in Table 1 for its effectiveness in reducing the battery's SOH degradation rate.

[0072] Charging Profile No. 5 in Figure 5F differs from the Base Case in Figure 5A in that each charging period in the Base Case is divided into two charging periods separated by a period when no charging or discharging occurs. This results in a lower average battery SOC during periods when the EV is chargeable (i.e., periods when the EV is not in motion) compared to the Base Case. That is, rather than charging and discharging the battery so that the SOC value increases linearly from 50% to 100% during charging (see Table 1) and then remains at 100% for a certain period until the EV is dispatched, Charging Profile No. 5 in Figure 5F first increases the SOC from 25% to 50%, holds it at 50% for a certain period, and then increases it from 50% to 75% just before the EV is dispatched. Therefore, Charging Profile No. 5 allows the EV battery to spend more time near 50% SOC, thereby reducing the rate of battery SOH degradation compared to periods when the SOC is near 0% or 100%. Charging profile No. 5 was rated "high" in Table 1 for its effectiveness in suppressing the battery's SOH degradation rate.

[0073] Based on the above discussion regarding the charging profiles of FIGS. 5A-5F , it can be understood that control variables regarding the charging profile of an EV (i.e., future charging plan variables) that can be set within a future charging plan to affect the effect of charge / discharge cycles on the battery's SOH include one or more variables selected from the group consisting of: Battery charging rate, Charge state at the end of the charging period, Charging frequency, the average state of charge of the battery during the period the vehicle is available for charging; The average state of charge of the battery during the execution of the future trip plan, The start time of the charging period, and The end time of the charging period.

[0074] 5A-5F are merely examples of a wide range of charging profiles that can be generated based on controlling one or more of the variables described above. A future charging plan for an EV can be thought of as consisting of one or more charging profiles, each of which defines whether and how the EV's SOC should be increased during the time period during which the EV is chargeable. To determine the impact of a future charging plan on the degradation rate of the battery's SOH, the impact of one or more charging profiles that make up the future charging plan on the degradation rate of the battery's SOH can be determined and combined.

[0075] If not available from other sources, the degradation rate of the battery's SOH under a given charging profile can be determined using either a physical experiment in which repeated charging cycles are performed according to that charging profile to determine the effect of that charging profile on the degradation rate of the battery's SOH, or a computer simulation of the battery charging under that charging profile.

[0076] Typically, when implementing the optimization process according to the present disclosure, the system is connected to a database containing multiple charging profiles similar to those illustrated in FIGS. 5A-5F and their associated battery SOH degradation rates, each associated with a given battery SOH. The charging profiles provide information about whether / how to charge the battery during time periods when the vehicle is available for charging (lines on plots of battery SOC versus time). In executing the optimization process, the future charging plan determination unit accesses the database containing the multiple charging profiles and associated battery SOH degradation rates and can assign one or more charging profiles to one or more respective time periods in the future charging plan during which the EV is available for charging. Specifically, first, the optimization process uses one or more constraints to filter the multiple charging profiles in the database to eliminate those that do not satisfy the constraints, and second, the optimization process selects from the filtered charging profiles a charging profile with the lowest associated battery SOH degradation rate, thereby selecting from the database a charging profile whose associated battery SOH matches that of the EV and assigning it to a time period in the future charging plan. The battery SOH degradation rate associated with a given charging profile is a function of the battery's SOH and the battery's electrochemical characteristics. Therefore, the battery's SOH (state of health) and electrochemical characteristics are provided in a database such that the battery SOH degradation rate associated with a given charging profile is specific to the battery associated with the future charging plan.

[0077] FIG. 6 is similar to FIG. 4 but further includes details of the future charging plan developed by system 1 along with the future trip plan. FIG. 6 includes an indication of the period each EV spends charging (solid black boxes) within the period in which it is available to charge (outline boxes). FIG. 6 also includes a diagram showing the power demand for charging each EV at different times (demands 601, 602, and 603 corresponding to EV1, EV2, and EV3, respectively) compared to the power available from the power grid 600, and the total power demand 604 for executing the plan. Most notably in FIG. 6, due to the second constraint of staying within the charging capacity of the power grid 600, there is no time in the future charging plan where multiple EVs are charging because there is no time when sufficient power is available from the power grid 600 to allow multiple EVs to charge simultaneously.

[0078] As is clear from the information about the future charging plan shown in Figure 6, the future charging plan is optimized by selecting a charging profile that controls at least the charging start and end times, compared to the base case charging profile in which EVs are charged at the maximum charging rate as soon as they arrive at the charging station. In Figure 6, EV1 can be charged from 22:00 to 06:00, but is only charged from 03:00 to 06:00 and is dispatched immediately thereafter for the next trip in the future trip plan. However, due to a second constraint in the power grid 600, which does not allow multiple EVs to be charged simultaneously, EV2 and EV3 are charged earlier in the available charging time slots. EV2 and EV3 can be charged between 21:00 and 04:00 and between 22:00 and 06:30, respectively, but are charged early within these time slots, and then fully charged to carry out the next trip in the future trip plan, with a period of no further charging and stationary (e.g., EV3 from 06:30 to 10:00). As discussed above in connection with Figure 5E, a charging profile that sets the charging start and end times so that the EV begins driving as soon as it reaches its target SOC and minimizes the time it spends at high SOC is desirable from a battery SOH perspective. However, for EV2 and EV3, this is not the case as it is for EV1 in Figure 6. Due to other constraints in the optimization process, the charging start and end times for EV2 cannot be pushed as far toward the end of EV2's available charging window. For EV3, constraints also prevent the charging start and end times from being pushed further toward the end of EV3's available charging window than would be possible with a base-case charging profile that would begin charging as soon as the EV becomes available.

[0079] Figure 6 further demonstrates that the method and system of the present invention can provide an optimized future charging profile that converges the SOH of the batteries. In Figure 6, EV1 has the lowest SOH, followed by EV2, and then EV3 has the highest SOH. Therefore, in the future charging plan determined by the optimization process, maintaining EV1's SOH is prioritized over EV2's SOH, and maintaining EV2's SOH is prioritized over EV3's SOH. This is evident from the fact that the charging time slot for EV1, which has the lowest SOH (03:00-06:00), is shifted the most toward the end of the time slot (22:00-06:00) when all three EVs are available for charging. Of the three charging profiles for EV1, EV2, and EV3 from 21:00 to 06:00 in Figure 6, the charging profile assigned to EV1's future charging plan is the charging profile with the lowest battery SOH degradation rate, followed by the charging profile for this part of EV2's future charging plan, and then the charging profile for this part of EV3's future charging plan is the charging profile that has the worst battery SOH degradation rate of the three charging profiles.

[0080] FIG. 7 provides further details of the future charging plan developed by System 1 in conjunction with the future driving plan. The future charging plan for EV1, EV2, and EV3 in FIG. 7 is the same as that shown in FIG. 6, but FIG. 7 also includes plots 701, 702, and 703 showing the SOC of each of EV1, EV2, and EV3's batteries over time. In the plots, the slopes of the lines indicate the rate at which the batteries are charged and discharged according to the plan. In the future charging plan, the batteries will be exposed to a variety of different charge rates, both for a given battery and between different batteries.

[0081] Similar to Figure 6, the information about future charging plans shown in Figure 7 illustrates another way in which future charging plans can be optimized. Figure 7 shows how the battery charge rate is controlled compared to a base-case charging profile in which EVs charge at the maximum charge rate as soon as they arrive at the charging station. As mentioned above, the slope of the plot in Figure 7 represents the battery charge rate. We can see that EV1's charge rate from 03:00 to 06:00 is lower than EV2's charge rate from 00:00 to 03:00. Similarly, EV2's charge rate from 15:00 to 16:30 is lower than EV3's charge rate from 17:00 to 18:00. As discussed in relation to Figure 5C, reducing the charge rate as much as possible can reduce the degradation rate of the battery's SOH caused by future charging plans.

[0082] FIG. 7 further illustrates how the present method and system can provide optimized future charging profiles that converge the battery SOHs. Similar to FIG. 6, EV1 has the lowest SOH, followed by EV2 and EV3. Comparing the charging rates of EV1, EV2, and EV3 in FIG. 7 for each charging period from 21:00 to 06:00, we can see that EV1's SOH takes priority over EV2 and EV3's SOH, and EV2's SOH takes priority over EV3's SOH. EV1 has the lowest charging profile during this period, while EV2's charging profile has a higher charging rate than EV1, and EV3's charging profile has a higher charging rate than both EV1 and EV2.

[0083] As described above in connection with FIG. 2, the optimization process may operate based on an objective function that includes the expression for the deterioration rate of the battery SOH, d(SOH) / dt, such that the optimization process seeks to minimize the deterioration rate of the battery SOH.

[0084] Additionally, the optimization process desirably converges the batteries' SOHs to determine future charging plans that will cause the batteries to all reach a predetermined health cutoff state at the same time. This means that the SOH of each battery will be similar or the same when it reaches a point where the battery's SOH is too low for the associated EV to perform the trip plan with any charging plan (i.e., the battery's SOH is too low to store enough energy to perform one trip for that trip). This is desirable because, since these batteries are all degrading similarly, it is possible to replace the batteries of several or all EVs in the fleet at the same time. This reduces the maintenance costs associated with battery replacement.

[0085] One way to provide an optimization process with an objective function that achieves the above objectives is to generate an expression for the overall battery SOH degradation rate as a weighted linear combination of the battery SOH degradation rates of each battery in the EV.

[0086]

number

[0087] where N is the number of EVs in the fleet that provide the optimized charging plan, SOH i is EV i is the SOH of the battery, and d(SOH) / dt| T is the overall degradation rate of the battery's SOH, x i is EV i This equation can be included in the objective function.

[0088] The optimization process determines the degradation rate of the battery's state of health (SOH TTo determine a future charging plan that converges the battery SOH to minimize dt, the weights x_i in the above function are set to have a negative correlation with the battery SOH, i.e., the lower the battery SOH, the larger the corresponding weight. i and x i (e.g. x i =1-SOH i ) can be a simple linear relationship between the SOH and the degradation rate of the battery with the lowest SOH. Since the battery with the lowest SOH is weighted the most, minimizing this equation is the degradation rate d(SOH i ) / dt, and the weighting ensures that the terms in the above objective function related to these batteries are T ) / dt.

[0089] Alternatively, a second method of implementing the optimization process to include the degradation rate of the battery SOH, d(SOH) / dt, in the objective function to provide a future charging plan that causes the battery SOH to converge towards a non-zero convergence value is shown in the flowchart of FIG. 8.

[0090] First, in step S210, the N batteries of the EV for which an optimized future driving plan is to be determined are ranked based on their SOH, with the battery with the lowest SOH ranked first, the battery with the second lowest SOH ranked second, and so on. In step S220, an iterative loop begins, and the first battery in the ranking is selected (iteration counter, i=1). Then, in step S230, a future charging plan for the first-ranked battery is determined by optimization that attempts to minimize the SOH degradation rate of the first-ranked battery, independent of the other batteries. This optimization uses an objective function that includes the SOH degradation rate expression for the first-ranked battery, d(SOH1) / dt.

[0091] Next, in step S240, the method consists of checking whether a charging plan has been determined for all batteries. If there is still a charging plan to be determined (i < N), after steps S250 and S260 are performed, the iterative loop starts again.

[0092] After determining the future charging plan for the first battery in the ranking and determining that there is still a future charging plan to be determined, in step S250, the constraints on the future charging plans for batteries 2 to N in the ranking are updated based on the future charging plan for the first battery. For example, if the optimization process has a second constraint on the future charging plan to stay within the charging capacity of the power grid supplying power to the electric vehicle charger, the available effective charging capacity for batteries 2 to N according to the second constraint can be updated to reflect the charging capacity already allocated to the first battery. Then, the iteration counter i is incremented in step S260, and the process of determining the future charging plan for the second battery in the ranking is performed (step S230).

[0093] The iterative loop continues until there is no need to determine a future charging plan anymore (i = N). At that point, the method proceeds to step S270 and the optimization process is completed.

[0094] The first future charging plan is determined for the battery with the lowest SOH. At that time, the feasible region defined by the charging plan constraints is the largest because there are no other future charging plans that have changed the original constraints to reduce the size of the feasible region. However, when determining future charging plans for subsequent batteries with larger SOHs, the feasible region becomes smaller, and a specific local minimum of the objective function that existed in the feasible region when determining the future charging plan for the first battery in the ranking may no longer exist within the updated feasible region. Therefore, by determining future charging plans for batteries in descending order of SOH, reducing the deterioration rate of batteries with low SOH is prioritized over reducing the deterioration rate of batteries with high SOH. In other words, by operating a fleet of EVs under the optimized future charging plan, the SOHs of the batteries converge to each other.

[0095] 9A and 9B illustrate the effect of implementing the optimized future charging schedule described in connection with FIGS. 4, 5, and 6 on the degradation rates of the batteries of EV1, EV2, and EV3, and the ability of the methods and systems described herein to cause the SOH of the batteries to converge with each other so that they all reach a predetermined health cutoff state simultaneously.

[0096] Figure 9A is a schematic plot of the battery SOH versus time for EV1, EV2, and EV3 in Figure 4 over multiple charge / discharge cycles when the three EVs are operating on a charging schedule that does not undergo an optimization process to minimize the rate of battery SOH degradation (e.g., charging each vehicle's battery to 100% as soon as it completes its planned trip). The battery SOH of the three EVs diverges over time, and the steeper slope of EV1 indicates that its battery SOH is degrading at a faster rate than that of EV2, which in turn is degrading at a faster rate than that of EV3. As mentioned above, a minimum battery SOH is set to ensure that the EVs can execute any planned trip with any charging schedule (i.e., the battery SOH is too low to store the energy required for one trip of the planned trip). This minimum SOH is shown as the "SOH cutoff" in Figures 9A and 9B. FIG. 9A shows that when a fleet of EVs are operated under a non-optimized charging schedule according to the present disclosure, the times at which the batteries of the EVs reach the SOH cutoff vary significantly from one another.

[0097] FIG. 9B is a graph of battery SOH versus time for EV1, EV2, and EV3 over multiple charge / discharge cycles when (i) initially operated with the same charging schedule as in FIG. 9A and (ii) subsequently operated with the charging schedules of FIGS. 5 and 6 that have undergone an optimization process to minimize the battery SOH degradation rate (the plots in FIG. 9B are annotated to indicate when the optimized future charging schedules were implemented). Comparing FIGS. 9A and 9B, it is clear that implementing the optimized future charging schedules reduces the battery SOH degradation rates for all three EVs compared to their respective battery SOH degradation rates for the charging schedules implemented in FIG. 9A: the time to reach the SOH cutoff for each battery is significantly longer in FIG. 9B than in FIG. 9A. Furthermore, the battery SOH degradation rates for the batteries in FIG. 9B after the optimized charging schedules are implemented are more similar to each other than in FIG. 9A, and the battery SOH degradation rates for each battery also converge toward each other. This convergence occurs because the goal of the optimization process is to determine a future charging plan that will cause the batteries to all reach a predetermined "SOH cutoff" at the same time. Therefore, the optimized future charging plan provided by the system and method of the present disclosure can not only extend the lifespan of the batteries implementing the charging plan, but also reduce the maintenance costs associated with replacing EV batteries, since all batteries in all EVs can be replaced simultaneously.

[0098] As used herein, the term "battery" includes an electrochemical device that includes a plurality of electrochemical cells, which may be arranged in a plurality of cell modules.

[0099] The systems and methods of the above embodiments, in addition to the structural components and user interactions described, can be implemented in a computer system (particularly computer hardware or computer software).

[0100] The term "computer system" includes hardware, software, and data storage devices for implementing a system or performing a method according to the above-described embodiments.

[0101] For example, a computer system comprises a central processing unit (CPU), input means, output means, and data storage. A computer system may have a monitor that provides a visual output display. Data storage may comprise RAM, disk drives, or other computer-readable media. A computer system may include multiple computing devices connected by a network and capable of communicating with each other over the network, and in such cases may be referred to as a computer network.

[0102] The methods of the above embodiments may be provided as a computer program or as a computer program product or computer readable medium carrying a computer program arranged to perform the above methods when run on a computer.

[0103] The term "computer-readable medium" includes, but is not limited to, any non-transitory medium or media that can be read and accessed directly by a computer or computer system, including, but not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape, optical storage media such as optical disks and CD-ROMs, memory such as RAM, ROM, and flash memory, and hybrids or combinations of the above, such as magnetic / optical storage media.

[0104] In particular, although the methods of the above embodiments are described as being implemented on the systems of the described embodiments, the methods and systems of the present disclosure need not be implemented in conjunction with each other and may each be implemented on alternative systems or using alternative methods.

[0105] The features disclosed in the foregoing description, or the following claims, or the accompanying drawings, may be expressed in their specific form, or in terms of means for performing a disclosed function, or a method or process for obtaining a disclosed result, as appropriate, and may be utilized to realize the invention in various of its forms, either separately or in any combination of such features.

[0106] While the present invention has been described in conjunction with the exemplary embodiments set forth above, many equivalent modifications and variations will be apparent to those skilled in the art given this disclosure. Accordingly, the exemplary embodiments of the invention set forth above are considered to be illustrative and not limiting. Various changes can be made to the described embodiments without departing from the spirit and scope of the invention.

[0107] For the avoidance of doubt, all theoretical explanations provided herein are provided for the purpose of enhancing the understanding of the reader, and the inventors do not wish to be bound by these theoretical explanations.

[0108] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0109] Throughout this specification, including in the claims which follow, unless the context otherwise requires, the words "comprise" and "include," and variations such as "comprises," "comprising," and "including," will be understood to imply the inclusion of a stated integer or step or group of integers or steps, but not the exclusion of other integers or steps or groups of integers or steps.

[0110] It should be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations, by use of the antecedent "about," it is understood that the particular value forms another embodiment. The term "about" with respect to numerical values ​​is arbitrary and means, for example, ±10%. [Explanation of symbols]

[0111] 1. Operational management system 10 Future driving plans 20 Battery Data 30 Charging capacity data 40 Charging capacity data 50 Constraint Determination Unit 51 Charging time management unit 52 Power Management Unit 60 Future charging plan decision unit 70 Future charging plans 80 charging stations 81 Electric vehicle charger 82 Power supply 90 Electric Vehicles 91 EV battery 92 Communication Unit 100 databases 400,600 power supply 601,602,603 ​​Electric vehicle charging power demand 604 Total charging power demand 701,702,703 EV charging status

Claims

1. 1. A computer-implemented method for providing an optimized future charging plan (70) for charging batteries (91) of electric vehicles (90) of a fleet of electric vehicles (90) using electric vehicle chargers (81), comprising: The computer-implemented method comprises: obtaining a future trip plan (10) for each of the electric vehicles (90); acquiring battery data (20) of the batteries (91) of the electric vehicles (90), the battery data (20) including, for each electric vehicle (90), a battery state of charge (701, 702, 703) and a battery state of health; running an optimization process for determining a future charging plan (70) for charging the electric vehicle (90) using an electric vehicle charger (81), the optimization process having one or more constraints, including a first constraint requiring the future charging plan (70) to increase the state of charge of the battery (91) so that the electric vehicle (90) can execute each of the future driving plans (10); Including, and wherein, within the one or more constraints, the optimization process seeks to minimize the rate of deterioration of the battery's (91) state of health. Computer-implemented methods.

2. 10. The computer-implemented method of claim 1, The electric vehicle charger (81) is supplied with power from the power grid, The computer-implemented method further includes obtaining future charging capacity data (30) for the power grid; the optimization process has a second constraint requiring the future charging plan (70) to fit within the future charging capacity (30) of the power grid. Computer-implemented methods.

3. 10. A computer-implemented method according to any preceding claim, comprising: The computer-implemented method comprises: further comprising the step of obtaining charging capability data (40); The charging capability data (40) includes, for each electric vehicle (90), a maximum charging rate for the electric vehicle's battery (81); the optimization process has a further constraint requiring the future charging plan (70) to fall within a maximum charging rate of the electric vehicle's battery (91). Computer-implemented methods.

4. 10. A computer-implemented method according to any preceding claim, comprising: the optimization process operates on an objective function that includes an expression for a degradation rate of the battery's state of health; Computer-implemented methods.

5. 10. A computer-implemented method according to any preceding claim, comprising: The computer-implemented method, wherein the optimization process attempts to determine a future charging plan (70) that causes the health states of the batteries (91) to converge with each other such that the batteries (91) all reach a predetermined cutoff health state simultaneously.

6. 6. The computer-implemented method of claim 5, executing the optimization process, wherein the optimization process determines the future charging plan (70) for the batteries (91) in ascending order of their respective health states, starting with the battery (91) with the lowest health state; After the future charging plan (70) has been determined for each battery (91) in the ascending sequence, before determining the future charging plan (70) for the next battery (91) in the ascending sequence, updating one or more constraints for the remaining batteries (91) in the ascending sequence to take into account the future charging plan (70) for which the one or more constraints for the remaining batteries (91) have been determined; Including, Computer-implemented methods.

7. 6. The computer-implemented method of claim 5, Run the optimization process, The optimization process comprises: generating an overall deterioration rate equation for the battery's (91) state of health, the equation being a weighted linear combination of the deterioration rates of the battery's (91) state of health, the weighting being set according to the state of health of the battery; running an optimization process using the equation contained in the objective function; 10. A computer-implemented method comprising:

8. A computer-implemented method according to any preceding claim, comprising: The future trip plan (10) for each electric vehicle (90) comprises: an indication of one or more time periods during which the electric vehicle (90) is in use; and an indication of one or more time periods during which the electric vehicle (90) is available for charging; and an estimated energy and / or power usage profile for one or more periods during which the electric vehicle (90) is in use; and Including, Computer-implemented methods.

9. 10. A computer-implemented method according to any preceding claim, comprising: the future charging plan (70) including one or more future charging plan variables; the optimization process determining the future charging plan (70) by varying one or more future charging plan variables; The future charging plan variables are selected from the group consisting of a charging rate of the battery (91), a charging state (701, 702, 703) at the end of a charging period, an average charging state (701, 702, 703) of the battery (91) during a period in which the electric vehicle (90) is chargeable, a charging frequency, an average charging state (701, 702, 703) of the battery (91) during execution of the future driving plan (10), a start time of a charging period, and an end time of a charging period. Computer-implemented methods.

10. 10. A computer-implemented method according to any preceding claim, comprising: The optimization step comprises: accessing a database (100) containing a plurality of charging profiles and associated battery health degradation rates, each charging profile being associated with a predetermined battery health state and being a line on a plot of battery charge state (701, 702, 703) versus time; constructing the future charging plan (70) by assigning one or more of the charging profiles from the database (100) whose associated battery health status matches the health status of the electric vehicle (90) to one or more time slots during which the electric vehicle (90) is available for charging; Including, Computer-implemented methods.

11. 10. A computer-implemented method according to any preceding claim, comprising: and communicating the future charging plan (70) to a corresponding electric vehicle (90) and / or an electric vehicle charger (81) for automatically implementing the future charging plan (70). Computer-implemented methods.

12. A procedure for charging the batteries (91) of the electric vehicles (90) of a fleet of the electric vehicles (90) using the electric vehicle charger (81), comprising: The procedure comprises: performing a method according to any preceding claim; Charging the battery (91) of the electric vehicle (90) using the electric vehicle charger (81) by implementing the future charging plan (70) determined by the method; Includes:

13. A computer-implemented operations management system (1) configured to carry out the method according to any of claims 1 to 11.

14. A computer program arranged to carry out on a computer the method of any one of claims 1 to 11.

15. A computer-readable storage medium storing the computer program according to claim 14.

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