Providing electric vehicle charging plans

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 on 28 February 2023, the contents and elements thereof being incorporated herein by reference for all purposes.

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

[0003] Electrification is progressing in mass transit networks (e.g., buses, trains, trams, and light railway vehicles) to reduce pollution associated with vehicles operating on these networks and to enable efficient operation using electricity generated from renewable energy sources. One form of electrification involves equipping such mass transit vehicles with batteries to store the energy necessary for driving the vehicles and to execute their planned routes.

[0004] Compared to mass transit vehicles powered by internal combustion engines (ICE), mass transit battery electric vehicles (EVs) typically have a shorter range before needing refueling / charging, and the EV recharging process takes longer than refueling an ICE mass transit vehicle. Therefore, in addition to developing driving plans for each mass transit vehicle in a network, when mass transit battery EVs are used in a mass transit network, a relevant charging plan can be developed to determine when and, if necessary, where EVs will be charged so that their batteries are sufficiently charged to execute their driving plans. When developing charging plans for mass transit electric vehicles (EVs) in relation to their driving plans, the traditional focus has naturally been on preventing power shortages for EVs during times when the vehicles are available for charging and on utilizing available charging facilities for EVs.

[0005] Because mass transit vehicles typically have longer lifespans and higher lifetime mileage than private vehicles, the batteries in mass transit battery-powered electric vehicles (EVs) are expected to undergo more charge-discharge cycles over their lifetime than those in private vehicles. However, batteries are known to degrade with increasing charge-discharge cycles, and this degradation generally manifests as a decrease in energy storage capacity. Therefore, the batteries in mass transit EVs may have a shorter lifespan (i.e., the battery's service life before its energy storage capacity deteriorates to a level where the EV can no longer perform its planned route) than other components of the EV's drivetrain, such as the motor and inverter, and may even have a shorter lifespan than the internal combustion engines (ICEs) of conventional vehicles. In other words, the lifespan of an EV may be effectively determined by the lifespan of its battery. Therefore, electric vehicles (EVs) for public transport may be configured to allow for the replacement of batteries when the existing battery's energy storage capacity degrades to the point where it is no longer sufficient to perform the planned route.

[0006] Similar concerns arise with other forms of electric vehicle fleets, such as electric micromobility vehicles like electric scooters and electric bicycles. This invention was conceived in view of these considerations. [Overview of the Initiative]

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

[0008] Accordingly, in a first embodiment, a computer implementation method is provided that provides an optimized future charging plan for charging the batteries of electric vehicles in a fleet of electric vehicles using electric vehicle chargers. The method includes the steps of: obtaining a future driving plan for each electric vehicle; obtaining battery data for the electric vehicle batteries, the battery data including the state of charge (SOC) and state of health (SOH) of the battery for each electric vehicle; and performing an optimization process for determining a future charging plan for charging the electric vehicles using electric vehicle chargers, wherein the optimization process has one or more constraints, including a first constraint that requires the future charging plan to increase the SOC of the battery so that the electric vehicles can perform their respective future driving plans; wherein, within one or more constraints, the optimization process seeks to minimize the rate of degradation of the SOH of the battery; and so it is possible to provide a future charging plan that reduces the rate at which the SOH of the batteries of electric vehicles in a fleet of electric vehicles degrades and extends the battery life.

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

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

[0011] The steps of executing the optimization process may include determining one or more constraints based on the obtained data, for example, future driving plans and constraints based on battery data.

[0012] The steps of obtaining the future driving plans for each electric vehicle and obtaining battery data for the electric vehicle's battery may be performed simultaneously or sequentially in any order.

[0013] If the electric vehicle charger is powered by the power grid, the method may further include a step of obtaining future charging capacity data of the power grid, and the optimization process may have a second constraint requiring that the future charging plan fits within the future charging capacity of the power grid. An advantage of this is that it is possible to prevent the future charging plan created by the method from overloading the power grid. The charging capacity of the power grid can change over time, and therefore the second constraint may not be a fixed value but a time-dependent function.

[0014] The method may further include a step of acquiring charging capacity data, which may include the maximum charging rate of each electric vehicle (and possibly each electric vehicle charger). If such data is acquired, the optimization process may include further constraints requiring that future charging plans stay within the maximum charging rate of the electric vehicle battery (and possibly the maximum charging rate of the electric vehicle charger). This can reduce damage to the electric vehicle battery and electric vehicle charger due to excessive current during charging.

[0015] If electricity prices from the power grid fluctuate over time, the charging capacity data may further include the maximum electricity price for one or more selected items from each electric vehicle, each electric vehicle charger, and a fleet of electric vehicles, and the vehicles and / or chargers can only be charged when the electricity price is less than or equal to the maximum electricity price.

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

[0017] The optimization process may attempt to determine a future charging plan that converges the states of health (SOH) of batteries so that all batteries reach a predetermined cutoff SOH simultaneously. Typically, a predetermined cutoff SOH is the SOH at which, under any charging plan, a given battery is too low for the associated EV to perform its driving plan. Reaching the cutoff SOH allows for the simultaneous replacement of batteries in most or all EVs in the fleet, reducing maintenance costs associated with battery replacement and increasing the efficiency of maintenance associated with battery replacement.

[0018] When attempting to converge the SOHs of batteries toward each other so that all batteries reach a predetermined cutoff SOH simultaneously, the optimization process may include determining the future charge plans for the batteries sequentially in ascending order of their respective SOHs, starting with the battery with the lowest SOH, and updating one or more constraints for the remaining batteries in the ascending sequence after the future charge plan for each battery in the ascending sequence has been determined, and before determining the future charge plan for the next battery in the ascending sequence, updating one or more constraints for the remaining batteries in the ascending sequence to take into account the determined future charge plan for each battery. An advantage of this is that it 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 we try to converge the SOHs of the batteries to each other so that all batteries reach a predetermined cutoff SOH at the same time, the optimization process may include generating an equation for the overall degradation rate of the battery SOH, which is a weighted linear combination of the battery degradation rates, with the weights set according to the battery SOH, and running the optimization process with the aforementioned equation included in the objective function. An advantage of this is that it improves the computational efficiency of the optimization process because it reduces the risk of having to restart the optimization process because a solution could not be reached. If the optimization objective function is directly proportional to the degradation rate of the battery SOH, the weights may be negatively correlated with the battery SOH, for example, the weights may decrease in proportion to the battery health. If the optimization objective function is inversely proportional to the degradation rate of the battery SOH, the weights may be positively correlated with the battery SOH.

[0020] The future driving 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 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 the optimization process may be carried out by changing 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 the battery charging rate, the charge state at the end of the charging period, the average charge state of the battery during the period during which the vehicle is available for charging, the charging frequency, the average charge state of the battery while the future driving plan is being executed, the start time of the charging period, and the end time of the charging period.

[0021] The optimization process may include accessing a database that includes a plurality of charging profiles related to a predetermined battery SOH and that are lines on a plot of battery SOC versus time, and the associated battery SOH degradation rate, and constructing a future charging plan by allocating from the database one or more charging profiles for which the associated battery SOH matches the battery SOH of the electric vehicle, to one or more time periods during which the electric vehicle is chargeable. Thereby, the optimization process can be made faster by providing a predefined charging profile for which the degradation rate of the associated battery SOH has already been determined.

[0022] The database of charging profiles may be filtered so that only charging profiles that match the battery SOH of an electric vehicle for which a future charging plan has been determined are accessible, and may be further filtered based on the starting SOC, the ending SOC, and the period during which charging is possible 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 being charged under that charging profile. Advantageously, this is generally more efficient than alternative options of conducting physical experiments on the battery.

[0024] When the 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 SOH of the battery, and the optimization process can further include providing SOH information to the database. Thus, the battery SOH degradation rate associated with the charging profile can be better adapted to the battery targeted by the future charging plan.

[0025] 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 targeted. Thus, by providing the electrochemical characteristics of the battery for which a future charging plan is to be formulated, the provided battery SOH degradation rate associated with the charging profile can be better adapted to that battery.

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

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

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

[0029] For example, in a third embodiment, the system may comprise a constraint determination unit configured as follows: acquires a future driving plan for each electric vehicle; acquires battery data for the electric vehicle batteries, the battery data including the battery charge state and the battery's state of health for each electric vehicle. The system may further comprise a future charging plan determination unit configured to perform an optimization process for determining a future charging plan for charging the electric vehicles using an electric vehicle charger, the optimization process having one or more constraints, including a first constraint requiring the future charging plan to increase the battery charge state so that the electric vehicles can perform their respective future driving plans. Thus, a system is provided that can determine a future charging plan that reduces the degradation rate at which the battery health of electric vehicles in a fleet deteriorates and extends battery life. The constraint determination unit may comprise a power management unit configured to acquire battery data and a charging time management unit configured to acquire future driving plans.

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

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

[0032] A future charging plan determination unit may communicate with a database containing multiple charging profiles and associated battery SOH degradation rates, where each charging profile is a line plotting the battery's charge state over time, relative to a given battery SOH. The database may reside within or outside the operation management system.

[0033] In a fourth aspect, a computer program is provided which, when executed on a computer, is configured to perform the method of the first aspect.

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

[0035] A fleet of electric vehicles that provides or is configured to provide an optimized charging profile, as described in the first, second, third, fourth, and fifth aspects of this disclosure, 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. Alternatively, the fleet of vehicles may be electric taxis or electric micromobility vehicles (such as electric scooters and electric bicycles). The fleet of vehicles may consist 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, unless such combinations are clearly unacceptable or explicitly avoided. [Brief explanation of the drawing]

[0037] Next, embodiments illustrating the principles of this disclosure will be described with reference to the attached figures. Figure 1 shows the interconnection of computer-implemented driving management systems configured to provide an optimized future charging plan for charging the batteries of electric vehicles in an electric vehicle fleet. Figure 2 is an information flow diagram of the computer-implemented operation management system shown in Figure 1. Figure 3 is a flowchart illustrating a method for providing an optimized future charging plan for charging the batteries of an EV fleet. Figure 4 schematically shows the future travel plans for the three EVs, along with a graph of the available power for charging from the power supply network. Figures 5A to 5F schematically show various possible charging profiles for EV batteries; Figure 6 schematically shows the future travel and charging plans for three EVs, along with graphs of available charging power from the power grid and the power consumption of electric vehicle chargers. Figure 7 shows a graph of the charging status of the three EVs, along with a schematic representation of their future travel plans and future charging plans. Figure 8 is a flowchart showing the implementation of the optimization process. Figures 9A and 9B are schematic plots of the battery health over time for three EVs, with and without an optimized future charging plan. [Modes for carrying out the invention]

[0038] Next, aspects and embodiments of the present invention will be described with reference to the accompanying drawings. Further aspects and embodiments will be obvious to those skilled in the art.

[0039] Figure 1 shows 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 EV90 is equipped with a battery 91 and a communication unit 92 that receives data from the battery 91. The communication unit 92 of each EV90 communicates with the operation management system 1 via a network, and can transfer data from the EV90 to the system 1, or vice versa. The battery 91 of each EV90 shown in Figure 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 an EV90 at a given time can be defined as the electrochemical energy stored in the battery 90 at that time divided by the energy capacity of the battery 90 at the same time.

[0041]

number

[0042] The electric vehicle charger 81 in Figure 1 belongs to the same charging station 80. The charging station 80 is equipped with a power source 82 that supplies the power necessary to charge the battery 91 of the EV90 connected to the charging station 80. Typically, the power source 82 is a power grid, but the power source 82 can also take other forms such as a generator, solar cells, or wind turbine, or it may be a combination of one or more of these forms. The charging station 80 is network-communicated with the operation management system 1 so that data is transferred from the charging station 80 to system 1 or vice versa. Although only a single charging station 80 and its associated charger 81 are illustrated in Figure 1, system 1 may be network-communicated with multiple charging stations 80, and each charging station 80 may consist of one or more chargers 81 for the EV90.

[0043] By connecting the operation management system 1 to the EV90 and the charging station 80, the system 1 acquires input data from these sources, uses it to generate an optimized future charging plan, outputs the optimized future charging plan, and makes it available for execution when charging the EV90 at the charging station 80.

[0044] The operation management system 1 includes a constraint determination unit 50 configured to acquire data from the EV90 and the charging station 80 and define one or more constraints on the optimization process. A first constraint on the optimization process requires that in future charging plans, the battery's SOC be increased to a level that allows the EV90 to execute each future driving plan. 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 the future charging plan for the EV80's battery 91. Among the one or more constraints, the optimization process requires that the degradation rate of the battery's SOH be minimized so that the battery life can be extended while allowing the EV90 to execute future driving plans.

[0045] The future charging plan determination unit 60 is also connected to a database 100 which contains multiple charging profiles and associated battery state of health (SOH) degradation rates. The role of database 100 and its role in optimizing the future charging plan for the EV90 battery will be further explained in relation to the charging profiles shown in Figure 5. In Figure 1, database 100 is located outside the operation management system 1 and is networked with system 1 so that data can be transferred from database 100 to system 1 and vice versa.

[0046] Figure 2 is an information flow diagram of the computer-implemented operation management system 1 and its input / output shown in Figure 1. In Figure 2, features corresponding to those in Figure 1 are given the same reference numbers. Details of the information flow shown in Figure 2 are described below with reference to Figures 3 to 7. Figure 3 is a flowchart of a method for providing an optimized future charging plan 70 for charging the batteries of an EV fleet. Figure 4 is a schematic diagram showing the future driving plans of three EVs, along with a graph of the available charging power from the power grid. Figures 5A to 5F schematically show different charging profiles for EV batteries. Figure 6 is a schematic diagram showing the future driving plans of three EVs, along with a graph of the available charging power from the power grid. Figure 7 is a schematic diagram showing the driving and charging plans of three EVs, along with a graph of the SOC information for the three EVs.

[0047] Figure 2 shows four types of input data to System 1: each future driving plan 10 for the EV90, battery data 20 about the EV90's battery, future charging capacity data 30, and charging capability data 40, which System 1 uses to optimize the future charging plan 70. To provide an optimized future charging plan 70, the constraint determination unit 50 in System 1 acquires at least the future driving plans 10 and battery data 20, and typically also acquires the future charging capacity data 30 and charging capability data 40. The constraint determination unit 50 in Figure 2 comprises a charging time management unit 51 configured to acquire the future driving plans 10 and charging capability data 40, and a power management unit 52 configured to acquire the battery data 20 and charging capacity data 30.

[0048] The future driving plan 10 indicates the energy consumption required for each EV90 configured so that the system 1 provides an optimized future charging plan 70, enabling that EV90 to perform the planned driving. The future driving plan 10 for an EV90 may simply include the route the EV is scheduled to travel and the timing of the trip, from which an estimated energy consumption can be calculated based on, for example, the distance and elevation changes of the route, the mass of the EV, traffic conditions, and the required average speed, which may be provided along with the future driving plan 10. Alternatively, the future driving plan 10 may include one or more times within the future driving plan when the EV90 is in use, a pre-calculated estimated energy consumption profile, and / or a pre-calculated estimated power consumption profile.

[0049] Typically, in a fleet of EV90s, each having a future travel plan 10, each future travel plan 10 can include one or more trips that the EV90 is scheduled to make and the times at which the EV90 is scheduled to make those trips. Furthermore, the future travel plans 10 are usually repeated periodically; for example, Figure 4 provides a 24-hour future travel plan 10 repeated every 24 hours for three EV90s: EV1, EV2, and EV3. The EV90s in Figure 4 are part of a fleet of buses. For each EV90, the future travel plan 10 indicates the period during which the vehicle is in motion and therefore cannot be charged at the charging station 80. The time between trips is defined as the time during which the EV90 is available for charging. In the example in Figure 4, each EV90 has multiple scheduled travel periods within a day (i.e., the EV's future travel plan includes multiple trips), and the length of these periods differs for each EV90, meaning that even assuming a constant energy consumption rate for the EVs, the minimum State of Charge (SOC) required for the EV90's battery to perform each trip in plan 10 without running out of charge will differ. Figure 4 further illustrates the cases in which multiple EV90s can be charged during certain time periods of the day, all EV90s can be charged, none of the EV90s can be charged during certain time periods of the day, and only one EV90 can be charged during certain time periods of the day.

[0050] Battery data 20 is data specific to each EV90 and includes at least the State of Charge (SOC) and State of Health (SOH) of the battery for that EV90. The SOH of the EV90's battery at a given point in time can be defined as the battery's energy capacity at that point in time divided by either the battery's original energy capacity or its rated energy capacity.

[0051]

number

[0052] The battery data 20 is used by the constraint determination unit 50 to determine the estimated energy usage and the minimum charge state (SOC) that the EV90 battery should have to execute the future driving plan.min Used to convert to ).

[0053]

number

[0054] The battery's original energy capacity may be provided with battery data 20, or it may already be known by system 1. If the future travel plan 10 includes multiple travel periods (as shown in Figure 4), the minimum SOC can be calculated at the start of each travel period. Thus, the minimum SOC effectively acts as a first constraint on the optimization process for determining the future charging plan of the EV90, and the future charging plan 70 must increase the battery's SOC so that the EV90 can execute each future travel plan 10 (i.e., the minimum battery SOC value must be achieved at the relevant point in time).

[0055] In the embodiments shown in Figures 1 and 2, the power source 82 for the charging station 80 is the power grid, and future charging capacity data 30 is acquired by the power management unit 52. The future charging capacity data 30 indicates the charging power available from the power grid and essentially indicates the upper limit of the amount of power that can be drawn from the grid supply to recharge a fleet of EV90s at a given point in time. Figure 4 includes a diagram that provides an illustration of the charging power available from the power grid 400 over a 24-hour period of the future driving plan in Figure 4. If the charging station 80 is powered by the power grid and has a power limit, the charging power available from the power grid to that charging station 80 becomes a second constraint on the optimization of the future charging plan 70 by System 1, that is, the total power consumption based on the future charging plan 70 for multiple EV90s at a given point in time should remain within the charging capacity of the power grid 400 at that point in time.

[0056] In the embodiments shown in Figures 1 and 2, charging capacity data 40 is acquired by the charging time management unit 51. The charging capacity data 40 includes, for at least each EV battery 91, the maximum charging rate on which the battery 91 is rated. It can be understood that the charging rate (i.e., the rate at which the battery's SOC increases) can affect the degradation rate of the battery's SOH d(SOH) / dt. The charging capacity data 40 can also be obtained 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 properties of the battery 91, but generally, a faster charging rate results in a faster degradation rate of the battery SOH. 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 charge rate may impose further constraints on future charging plans 70, meaning that future charging plans 70 should ensure that the planned battery 91 charge rate falls within the maximum charge rates of the battery 91 and the charger 81.

[0057] The constraint determination unit 50 is configured to determine one or more constraints on the optimization process based on data acquired by the charging time management unit 51 and the power management unit 52. As described above, the first constraint on the optimization process is that the future charging plan 70 increases 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 is that the future charging plan 70 stays within the charging capacity of the power grid. A further constraint that may be imposed on the optimization process is that the future charging plan 70 stays within the maximum charging rate of either the EV battery 91 or the electric vehicle charger 81.

[0058] One or more constraints defined by the constraint determination unit 50 are provided to the future charging plan determination unit 60. The future charging plan determination unit 60 is configured to perform an optimization process using the constraints to determine the future charging plan 70 for the EV90. The optimization process may take the form of a nonlinear programming method.

[0059] Within one or more constraints, the optimization process seeks to minimize the degradation rate of the battery's State of Health (SOH). In other words, the future charge plan 70 determined by the future charge plan determination unit 60 is determined by finding a future charge plan 70 that reduces the degradation rate of the battery's SOH compared to the base case. This is typically done by performing the optimization process based on an objective function that includes the formula d(SOH) / dt for the degradation rate of the battery's SOH. Such objective functions are described in more detail in relation to Figure 8. The future charge plan determination unit 60 is connected to a database 100 and can obtain charge profiles and associated battery SOH degradation rates from the database 100 for use in determining the optimized future charge plan 70. This is described in more detail in relation to the charge profile shown in Figure 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 EV90s and the charging stations 80 that charge those EV90s, so that the future charging plan 70 determined by system 1 can be automatically implemented by the EV90s and the chargers 81.

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

[0062] If the EV90's future driving plan 10 includes multiple trips and multiple time periods between those trips during which the EV90 can be charged, the EV90's future charging plan 70 consists of multiple charging profiles, each corresponding to one of these time periods during which the EV90 can be charged, relating to a given battery SOH, and becoming a line on a battery SOC vs. time plot. When performing an optimization process to determine the future charging plan 70 for minimizing the battery SOH degradation rate (step S200), the impact of charge-discharge cycles on the battery's SOH can be adjusted by setting one or more control variables of the EV90's charging profile within the future charging plan 70. Figures 5A to 5F provide illustrations of different possible charging profiles, and Table 1 below shows the numerical data related to the charging profiles in Figures 5A to 5F. Each of Figures 5A to 5F also includes an estimated energy usage profile for the time periods during which the electric vehicle is in use. Figure 5A is for the “basic case”, and Figures 5B to 5F are for variations numbered 1 to 5 from that basic case, respectively.

[0063] Each charging profile is specific to the change in State of Charge (SOC) required over a certain period. That is, the starting and ending SOCs of a charging profile are fixed, and the charging profile then defines how the SOC increases from the starting SOC to the ending 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 degrades as a result of running 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 degradation rate of its SOH when a given charging profile is applied to that battery. Thus, each charging profile associated with a battery SOH degradation rate is related to a given battery SOH.

[0064] [Table 1]

[0065] The base case profile in Figure 5A assumes that the EV is charged at the maximum charge rate as soon as it becomes available, and after the SOC reaches 100%, the EV is moved and remains stationary for a certain period of time with an SOC of 100% until it is discharged. Figure 5A shows two such charge-discharge cycles, in which the EV performs two moves as part of a future driving plan (the energy consumption profiles of these moves are shown in Figure 5A by the non-linear portion of the SOC line where the SOC decreases), starting the first move with a full charge, fully charging during the first period the EV is available to charge between the first and second moves, performing the second move, and fully recharging during the second period the EV is available to charge at the end of the second move. Table 1 lists the EV's battery SOC at 50% at the start of the charging period and the end of the discharging period.

[0066] Next, Figures 5B to 5F illustrate different charging profiles in which one or more variables related to the basic charging profile are changed, in order to provide a new charging profile with a reduced battery SOC degradation rate compared to the basic charging profile in Figure 5A.

[0067] Charging profile No. 1 in Figure 5B differs from the base case in Figure 5A in that the average SOC of the battery is reduced while the future driving plan is being executed (i.e., during periods when the vehicle is moving). Alternatively, the modification in Figure 5B can be understood as reducing the average SOC of the battery during periods when the vehicle is rechargeable (i.e., during periods when the vehicle is not moving). That is, instead of charging and discharging the battery so that the SOC value is always between 50% and 100% (see Table 1), charging profile No. 1 in Figure 5B is designed so that the SOC varies between 0% and 50%. Starting each trip in the EV's future driving plan at 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 driving plan. By reducing the average SOC of the battery and avoiding charging to high SOC values, the battery's SOH degradation rate can be suppressed compared to the base case shown in Figure 5A. Charging plan No. 1 is rated "High" in Table 1 for its effect on suppressing the battery SOH degradation rate.

[0068] Charging profile No. 2 in Figure 5C differs from the basic case in Figure 5A in that the battery charging rate is reduced. Alternatively, the modification in Figure 5C can be understood as reducing the average SOC of the battery during periods when the vehicle is rechargeable (i.e., when the vehicle is not moving). In Table 1, the charging current in the basic case is 100 amps, while the charging rate in charging profile No. 2 is 50 amps. This modification to the charging profile means that in Figure 5B, the battery SOC reaches 100% only just before the EV sets off on the next leg of its future driving plan. Therefore, the time the battery SOC is at or near 100% is reduced, and the rate of increase in energy stored in the battery decreases, thus suppressing the degradation rate of the battery SOH. Charging profile No. 2 is rated as "low" in Table 1 for its effect on suppressing the SOH degradation rate. Reducing the charging rate as shown in Figure 5C is not as effective in suppressing the battery SOH degradation rate as reducing the average SOC of the battery while the future driving plan is being executed, as shown in Figure 5B.

[0069] Charging profile No. 3 in Figure 5D differs from the basic case in Figure 5A in that the frequency of battery charging is reduced; instead of charging the battery before every trip by the EV, the battery is charged only after every other trip. Alternatively, the changes in Figure 5D can be understood as reducing the average SOC of the battery during periods when the vehicle is rechargeable (i.e., periods when the vehicle is not moving), or reducing the average SOC of the battery while executing a future trip plan (i.e., periods when the vehicle is moving). The battery's SOC is 50% after the first trip, and after starting the trip with an SOC of 100%, the EV completes further trips and reaches a charging station with an SOC of 0%, and then the battery is recharged from SOC 0% to 100% at the maximum charge rate, so that the EV is fully charged in time for the next trip in the upcoming trip schedule.

[0070] Therefore, although EVs have time to charge between trips, they are not charged at all, and the State of Charge (SOC) remains constant at 50%. As a result, the time the battery's SOC is at or near 100% is shortened, and the battery capacity is used over a wider range (0% to 100%). Charging profile No. 3 is rated as "medium" in Table 1 for its effect on suppressing battery SOH degradation. Reducing the charging frequency as shown in Figure 5D does not have as high an effect on suppressing battery SOH degradation as reducing the average battery SOC as shown in Figure 5B, but because the average battery SOC in charging profile No. 3 is lower than the average battery SOC in charging profile No. 2, it has a higher effect on 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 start and end times of charging are shifted to within the period when the EV is able to be charged. Alternatively, the changes in Figure 5E can be understood as reducing the average SOC of the battery during the period when the vehicle is able to be charged (i.e., the period when the vehicle is not moving). Instead of waiting for the EV's SOC to be held at 100% at the end of charging, as in the base case scenario, the charging start and end times are set so that the EV is moved as soon as its SOC reaches 100%, shortening the period during which the EV is at 100% SOC. Charging profile No. 4 receives a "high" rating in Table 1 for its effect on suppressing the battery's SOH degradation rate.

[0072] The difference between charging profile No. 5 in Figure 5F and the base case in Figure 5A is that in charging profile No. 5, each charging period in the base case is divided into two charging periods separated by periods when no charging or discharging occurs. As a result, the average SOC of the battery during the period when the electric vehicle is chargeable (i.e., the period when the electric vehicle is not running) is lower compared to the base case. In other words, instead of the battery being charged and discharged so that the SOC value increases linearly from 50% to 100% during charging (see Table 1) and then maintained at 100% for a certain period until the EV is delivered, charging profile No. 5 in Figure 5F initially increases the SOC from 25% to 50%, maintains it at 50% for a certain period, and then increases it from 50% to 75% just before the EV is delivered. Therefore, charging profile No. 5 allows the EV battery to spend more time at around 50% SOC, and can reduce the rate of SOH degradation of the battery compared to the time spent at near 0% or 100% SOC. Charging profile No. 5 received a "High" rating in Table 1 for its effectiveness in suppressing the battery's SOH degradation rate.

[0073] Based on the above discussion regarding the charging profiles in Figures 5A to 5F, it can be understood that, in order to influence the effect of charge-discharge cycles on the battery's SOH, the control variables for the EV's charging profile that can be set within the future charging plan (i.e., future charging plan variables) include one or more variables selected from the following group: Battery charge rate, Charging status at the end of the charging period, Charging frequency, The average charge level of the battery during the period when the vehicle is rechargeable. Average battery charge level during the execution of future driving plans, The start time of the charging period, and The end time of the charging period.

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

[0075] If unavailable from other sources, the SOH degradation rate of a battery under a given charge profile can be determined by either a physical experiment involving repeating charge cycles according to that charge profile, or a computer simulation of a battery being charged under that charge profile.

[0076] Typically, when performing the optimization process described herein, the system is connected to a database containing multiple charge profiles similar to those illustrated in Figures 5A to 5F, and their associated battery SOH degradation rates, where each charge profile is associated with a given battery SOH. The charge profiles provide information on whether and how the battery should be charged during the time periods when the vehicle is available for charging (lines on the plot of battery SOH against time). In executing the optimization process, a future charging plan determination unit can access the database containing multiple charge profiles and associated battery SOH degradation rates and assign one or more charge profiles to one or more time periods in a future charging plan where the EV is available for charging. Specifically, firstly, the system filters the multiple charge profiles in the database using one or more constraints in the optimization process to eliminate those that do not satisfy the constraints, and secondly, by selecting the charge profile with the lowest associated battery SOH degradation rate from the filtered charge profiles, a charge profile whose associated battery SOH matches the EV's battery SOH is selected from the database and assigned to a time period in the future charging plan. The battery SOH degradation rate associated with a given charge profile is a function of the battery's SOH and its electrochemical properties. Therefore, the battery's SOH (state) and electrochemical properties are provided to the database so that the battery's SOH degradation rate associated with a given charge profile is specific to the battery related to the future charge plan.

[0077] Figure 6 is similar to Figure 4, but further includes details of the future charging plan developed by System 1, along with the future driving plan. Figure 6 includes a representation of the time each EV spends charging within the period during which it is rechargeable (outline-only boxes) (solid black boxes). Figure 6 also includes a diagram showing the power demand for charging each EV at different points in time (demands 601, 602, and 603 corresponding to EV1, EV2, and EV3, respectively) and the total power demand 604 when executing the plan, compared to the power available from the power grid 600. Most notably in Figure 6, due to the second constraint of staying within the charging capacity of the power grid 600, the future charging plan does not show a point in time when multiple EVs are charging simultaneously, as there is no point in time when enough power is available from the power grid 600 to allow multiple EVs to charge at the same time.

[0078] As is evident from the information on future charging plans shown in Figure 6, future charging plans are optimized by selecting charging profiles with controlled start and end times, compared to a base case charging profile where EVs are charged at the maximum charging rate as soon as they arrive at a charging station: EV1 in Figure 6 is available to charge from 22:00 to 06:00, but is only charged between 03:00 and 06:00, and immediately thereafter is dispatched for the next trip in the future travel plan. However, due to a second constraint that the power grid 600 cannot be secured to charge multiple EVs simultaneously, EV2 and EV3 are charged earlier in the available charging time. EV2 and EV3 are available to charge between 21:00 and 04:00 and 22:00 and 06:30 respectively, but are charged early within these time slots, and then have a period of stationary time (e.g., EV3 is stationary from 06:30 to 10:00) after being sufficiently charged for the next trip in the future travel plan. As mentioned above in relation to Figure 5E, from the perspective of battery SOH, it is desirable that the charging start and end times are set so that the EV can start driving as soon as it reaches the target SOC, and the charging profile minimizes the period spent at a high SOC. However, this is not the case for EV2 and EV3 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 as possible towards the end of the time period in which EV2 can be charged. For EV3, due to constraints, the charging start and end times cannot be moved further ahead than the charging profile of the basic case, where charging starts as soon as the EV becomes chargeable, towards the end of the time period in which EV3 can be charged.

[0079] Figure 6 further demonstrates that the method and system of the present invention can provide an optimized future charging profile that converges the states of health (SOH) of the batteries relative to each other. In Figure 6, EV1 has the lowest SOH, followed by EV2, and EV3 has the highest SOH. Therefore, in the future charging plan determined by the optimization process, maintaining the SOH of EV1 is prioritized over that of EV2, and maintaining the SOH of EV2 is prioritized over that of EV3. This is also evident from the fact that the charging time for EV1, which has the lowest SOH (03:00-06:00), is shifted most towards the end of the time period when all three EVs can be charged (22:00-06:00). 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 has the lowest battery SOH degradation rate, followed by the charging profile for this portion of EV2's future charging plan, and then the charging profile for this portion of EV3's future charging plan, which worsens the battery SOH the most among the three charging profiles.

[0080] Figure 7 shows further details of the future charging plan developed by System 1, in relation to the future driving plan. The future charging plans for EV1, EV2, and EV3 in Figure 7 are the same as those shown in Figure 6, but Figure 7 also includes plots 701, 702, and 703 showing the change in the State of Charge (SOC) of the batteries of EV1, EV2, and EV3 over time. In the plots, the slope of the line indicates the charge and discharge rate of the battery 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 on future charging plans shown in Figure 7 illustrates another way in which future charging plans are optimized. Figure 7 shows how the battery charging rate is controlled compared to a base-case charging profile where the EV charges at the maximum charging rate as soon as it arrives at the charging station. As mentioned above, the slope of the lines in the plots in Figure 7 represents the battery charging rate. We can see that the charging rate of EV1 from 03:00 to 06:00 is lower than the charging rate of EV2 from 00:00 to 03:00. Similarly, the charging rate of EV2 between 15:00 and 16:30 is lower than the charging rate of EV3 between 17:00 and 18:00. As discussed in relation to Figure 5C, reducing the charging rate as much as possible can reduce the rate of battery SOH degradation caused by future charging plans.

[0082] Figure 7 further illustrates how this method and system can provide optimized future charging profiles that converge the states of health (SOH) of batteries. Similar to Figure 6, EV1 has the lowest SOH, followed by EV2 and then EV3. Comparing the charging rates of EV1, EV2, and EV3 in Figure 7 for each charging period from 21:00 to 06:00, we can see that EV1's SOH is preferred over the SOH of EV2 and EV3, and EV2's SOH is preferred over EV3's SOH. EV1 has the lowest charging speed profile during this period, EV2's charging profile has a higher charging speed than EV1 during this period, and EV3's charging profile has a higher charging speed than both EV1 and EV2 during this period.

[0083] As described above in relation to Figure 2, in order for the optimization process to seek to minimize the degradation rate of the battery SOH, the optimization process can operate based on an objective function that includes the equation d(SOH) / dt for the degradation rate of the battery SOH.

[0084] Furthermore, the optimization process should ideally converge the states of health (SOH) of the batteries and determine a future charging plan such that all batteries reach a predetermined health cutoff state simultaneously. This means that the SOH of each battery will be similar or the same when it reaches a point where the SOH of the batteries is too low for the relevant EV to execute its driving plan under any charging plan (i.e., the battery's SOH is too low to store enough energy to complete a single trip under that driving plan). This is desirable because, since all these batteries are degrading similarly, it is possible to replace the batteries of multiple, or all, EVs in the fleet simultaneously. This reduces the maintenance costs associated with battery replacement.

[0085] One method for providing an optimization process with an objective function to achieve the above objective is to generate an equation for the overall degradation rate of the battery SOH by taking the degradation rates of each battery in the EV as a weighted linear combination.

[0086]

number

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

[0088] The optimization process determines the battery health degradation rate d(SOH). T) To determine a future charging plan that converges the SOHs of batteries towards minimizing / dt, the weighting xi in the above function is set to be negatively correlated with the SOH of the battery. That is, the lower the SOH of the battery, the larger the corresponding weighting. The correlation may be a simple linear relationship between SOH i and x i (e.g., x i = 1 - SOH i ). Since the battery with the lowest SOH has the largest weighting, minimizing this equation prioritizes reducing d(SOH i ) / dt of the degradation rate of the battery with the lowest SOH. Due to the weighting, the terms of the above objective function related to these batteries contribute the most to the value of d(SOH T ) / dt.

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

[0090] First, in step S210, N batteries of an EV for which an optimized future driving plan is to be determined are ranked based on SOH. The battery with the lowest SOH is ranked 1st, the battery with the second - lowest SOH is ranked 2nd, and so on. In step S220, an iterative loop is started and the first battery in the ranking is selected (iteration counter, i = 1). Then, in step S230, a future charging plan for the battery ranked 1st is determined independently of the other batteries by an optimization that attempts to minimize the degradation rate of the SOH of the battery ranked 1st. For this optimization, an objective function including the equation d(SOH1) / dt of the degradation rate of the SOH of the battery ranked 1st is used.

[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 executed, 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 executed (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 determined is for the battery with the lowest State of Health (SOH), and at that point, the feasible region defined by the charging plan constraints is the largest because there are no other future charging plans that modify the original constraints and reduce the size of the feasible region. However, when determining future charging plans for subsequent batteries in the ranking with larger SOHs, the feasible region becomes smaller, and certain local minimums 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 in the updated feasible region. Therefore, by determining future charging plans for batteries sequentially in descending order of SOH, reducing the degradation rate of batteries with lower SOHs takes precedence over reducing the degradation rate of batteries with higher SOHs. In other words, by operating a fleet of EVs under an optimized future charging plan, the SOHs of the batteries converge toward each other.

[0095] Figures 9A and 9B illustrate the impact of implementing an optimized future charging plan on the battery degradation rates of EV1, EV2, and EV3, as described in relation to Figures 4, 5, and 6, and the ability of the method and system described herein to converge the states of health (SOH) of the batteries toward each other so that all batteries reach a predetermined health cutoff state simultaneously.

[0096] Figure 9A is a schematic plot showing the relationship between the battery's SOH and time over multiple charge-discharge cycles for EV1, EV2, and EV3 in Figure 4, when they are operated under a charging plan that has not undergone an optimization process to minimize the degradation rate of the battery's SOH (for example, charging the battery to 100% as soon as each vehicle completes its planned run). The SOH of the three EVs' batteries diverges over time, and the steep slope of the straight line for EV1 indicates that the SOH of EV1's battery is degrading faster than that of EV2's battery, and the SOH of EV2's battery is degrading faster than that of EV3's battery. As mentioned above, a minimum value is set for the battery's SOH so that the EVs can execute the running plan regardless of the charging plan (a state where the battery's SOH is too low to store the energy required for one run in the running plan). This minimum SOH is shown as the "SOH cutoff" in Figures 9A and 9B. Figure 9A shows that when a fleet of EVs is operated under a charging plan not optimized in accordance with this disclosure, the time it takes for the batteries of those EVs to reach SOH cutoff will vary significantly from one another.

[0097] Figure 9B is a graph showing the relationship between battery SOH and time over multiple charge-discharge cycles for EV1, EV2, and EV3, when (i) initially operated with the same charging plan as in Figure 9A, and (ii) subsequently operated with the charging plans in Figures 5 and 6, which have undergone an optimization process to minimize the battery SOH degradation rate (the plots in Figure 9B are annotated to indicate when the optimized future charging plan was implemented). Comparing Figure 9A and Figure 9B, it is clear that implementing the optimized future charging plan reduces the battery SOH degradation rate for all three EVs compared to the degradation rate of each battery SOH in the charging plan implemented in Figure 9A: the time until each battery reaches SOH cutoff is significantly longer in Figure 9B than in Figure 9A. Furthermore, the battery SOH degradation rates of the batteries in Figure 9B after the optimized charging plan has been implemented are more similar to each other than in Figure 9A, and the battery SOH degradation rates of each battery are converging towards each other. This convergence occurs because the objective of the optimization process is to determine a future charging plan such that all batteries reach a predetermined "SOH cutoff" simultaneously. Therefore, the optimized future charging plan provided by the system and method of this disclosure can not only extend the lifespan of the batteries on which the charging plan is implemented, but also reduce the maintenance costs associated with replacing EV batteries, as all batteries in all EVs can be replaced simultaneously.

[0098] As used herein, the term "battery" includes an electrochemical device containing multiple electrochemical cells, where the cells may be arranged in multiple cell modules.

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

[0100] The term "computer system" includes hardware, software, and data storage devices for implementing the systems or methods described above.

[0101] For example, a computer system consists of 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 consists of 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 through that network; in such cases, it may be called a computer network.

[0102] The method of the above embodiment can be provided as a computer program, or as a computer program product or computer-readable medium carrying a computer program, which is configured to execute the above method when run on a computer.

[0103] The term "computer-readable media" includes, but is not limited to, any non-transient media or media that can be directly read and accessed by a computer or computer system. Media include, but are not limited to, magnetic storage media such as floppy disks, hard disks, and magnetic tapes; optical storage media such as optical discs and CD-ROMs; electrical storage media 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 embodiments described above have been described as being implemented on the systems of the embodiments described, the methods and systems of this disclosure do not need to be implemented in relation to each other and can each be implemented on alternative systems or using alternative methods.

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

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

[0107] To avoid any doubt, all theoretical explanations provided herein are provided for the purpose of improving the reader's understanding. The inventors do not wish to be bound by these theoretical explanations.

[0108] The headings of any section used herein are for organizational purposes only and should not be construed as limiting the subject matter described herein.

[0109] Throughout this Specification, including in the subsequent claims, unless otherwise required by context, the words “comprise” and “include,” as well as variations such as “comprises,” “comprising,” and “including,” are understood to mean including the integer or step or group of integers or steps described, but not to mean excluding other integers or steps or groups of integers or steps.

[0110] It should be noted that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly indicates otherwise. Ranges may be expressed herein as “about” a certain value to and / or “about” another certain value. Where such ranges are expressed, in another embodiment they include a range from a certain value to and / or another certain value. Similarly, where values ​​are expressed as approximations, the use of the antecedent “about” is understood to mean that a particular value forms another embodiment. The term “about” with respect to numbers is arbitrary and means, for example, ±10%. [Explanation of symbols]

[0111] 1. Operation Management System 10. Future Driving Plans 20 Battery Data 30 Charge Capacity Data 40 Charging capacity data 50 Constraint Determination Units 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 Chargers 82 Power supply 90 Electric vehicles 91 EV batteries 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. A computer implementation method that provides an optimized future charging plan for charging the batteries of electric vehicles in a fleet of electric vehicles using electric vehicle chargers, The aforementioned computer implementation method is The steps include obtaining the future driving plan for each of the aforementioned electric vehicles, A step of acquiring battery data of the battery of the electric vehicle, wherein the battery data includes, for each electric vehicle, the charge state of the battery and the health state of the battery. An optimization process for determining a future charging plan for charging the electric vehicle using an electric vehicle charger, comprising the steps of performing the optimization process having one or more constraints, including a first constraint that requires the future charging plan to increase the charge state of the battery so that the electric vehicle can perform each of the future driving plans; Includes, Within the one or more constraints described above, the optimization process attempts to minimize the rate of battery health degradation. The optimization process attempts to determine a future charging plan that converges the health states of the batteries so that all of them reach a predetermined cutoff health state simultaneously. The optimization process includes the step of determining the future charging plan for the batteries in ascending order of their respective health statuses, starting with the battery with the lowest health status, After the future charging plan is determined for each of the batteries in the ascending sequence, and before the future charging plan is determined for the next battery in the ascending sequence, update one or more constraints for the remaining batteries in the ascending sequence to take into account the future charging plan for which one or more constraints for the remaining batteries have been determined. including, Computer implementation method.

2. In the computer implementation method described in claim 1, Electric vehicle chargers are supplied with power from the power grid. The computer implementation method further includes the step of acquiring future charging capacity data of the power grid, The optimization process has a second constraint requiring that the future charging plan fits within the future charging capacity of the power grid. Computer implementation method.

3. In the computer implementation method according to claim 1 or claim 2, The aforementioned computer implementation method is The process further includes the step of acquiring charging capacity data, The aforementioned charging capacity data includes the maximum charging rate of the electric vehicle's battery for each electric vehicle. The optimization process has a further constraint that the future charging plan must fall within the range of the maximum charging rate of the electric vehicle's battery. Computer implementation method.

4. In the computer implementation method described in claim 1, The optimization process operates on an objective function that includes an equation for the degradation rate of the battery's health. Computer implementation method.

5. A computer implementation method for providing an optimized future charging plan for charging the batteries of electric vehicles in a fleet of electric vehicles using an electric vehicle charger, The aforementioned computer implementation method is The steps include obtaining the future driving plan for each of the aforementioned electric vehicles, A step of acquiring battery data of the battery of the electric vehicle, wherein the battery data includes, for each electric vehicle, the charge state of the battery and the health state of the battery. An optimization process for determining a future charging plan for charging the electric vehicle using an electric vehicle charger, comprising the steps of performing the optimization process having one or more constraints, including a first constraint that requires the future charging plan to increase the charge state of the battery so that the electric vehicle can perform each of the future driving plans; Includes, Within the one or more constraints described above, the optimization process attempts to minimize the rate of battery health degradation. The optimization process attempts to determine a future charging plan that converges the health states of the batteries so that all of them reach a predetermined cutoff health state simultaneously. The optimization process operates on an objective function that includes an equation for the degradation rate of the battery's health state, The optimization process described above is A step of generating an equation for the overall degradation rate of the battery's health, which is a weighted linear combination of the degradation rates of the battery's health, wherein the weights are set according to the battery's health. The steps include: executing an optimization process using the formula included in the objective function; Computer implementation methods including

6. In the computer implementation method described in claim 1, The aforementioned future driving plans for each electric vehicle are: A display of one or more time periods during which the electric vehicle is in use, The display of one or more time periods during which the electric vehicle can be charged, The estimated energy use and / or power use profile for one or more periods during which the electric vehicle is in use, including, Computer implementation method.

7. In the computer implementation method described in claim 1, The aforementioned future charging plan includes one or more future charging plan variables, The optimization process determines the future charging plan by changing one or more future charging plan variables. The future charging plan variables are selected from the group consisting of the battery charging rate, the charging state at the end of the charging period, the average charging state of the battery during the period in which the electric vehicle can be charged, the charging frequency, the average charging state of the battery while the future driving plan is being executed, the start time of the charging period, and the end time of the charging period. Computer implementation method.

8. In the computer implementation method described in claim 1, The optimization process described above is A step of accessing a database containing multiple charge profiles and associated battery health deterioration rates, wherein each charge profile is a line on a plot of battery charge status over time, relating to a predetermined battery health state. The steps include constructing the future charging plan by assigning one or more charging profiles from the database, in which the health status of the relevant battery matches the health status of the electric vehicle, to one or more time periods during which the electric vehicle can be charged, including, Computer implementation method.

9. In the computer implementation method described in claim 1, The step further includes communicating the future charging plan to the corresponding electric vehicle and / or the electric vehicle charger in order to automatically implement the future charging plan, Computer implementation method.

10. A procedure for charging the batteries of the electric vehicles in the electric vehicle fleet using the electric vehicle charger, The above procedure is, A step of carrying out the method according to claim 1, By implementing the future charging plan determined by the method described above, the battery of the electric vehicle is charged using the electric vehicle charger, Procedures including the above.

11. A computer-based operation management system configured to carry out the method according to any one of claims 1, 2, 4, 5, 6, 7, 8, 9, and 10.

12. A computer program configured to execute on a computer the method according to any one of claims 1, 2, 4, 5, 6, 7, 8, 9, and 10.

13. A computer-readable storage medium storing the computer program described in claim 12.

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