Strategic mechanical charging for electric vehicles in motion

Abstract

[Solution] Electric vehicles (EVs) for pickup, delivery, or shuttle services, utilizing machine charging, have routes set in response to task requests related to the pickup and delivery of goods or the transportation of people. Candidate EVs for a task are determined based on a preliminary route analysis of the route between the pickup or pick-up location and the delivery or drop-off location, including estimated travel time and power consumption between points for each candidate EV. The candidate EVs are narrowed down by determining which EVs are available within the service time frame and are charged to at least the pickup or pick-up location. A route calculation is performed for each of the remaining candidate EVs to determine the individual route between the pickup or pick-up location and the delivery or drop-off location, and the estimated power consumption along the individual route. Candidate EVs that are charged to meet the estimated power consumption along the individual route with minimal charging delay are assigned to handle the task request.

Description

Technical Field

[0001] The present disclosure relates to wireless power transmission, and more specifically, to apparatuses, systems, and methods related to wireless power transmission to remote systems such as vehicles including batteries. More specifically, the present disclosure relates to minimizing the total cost of driving an electric vehicle on a known route by utilizing opportunity charging via wireless power transmission.

Background Art

[0002] In recent years, there has been a shift towards electric traction motors and battery drives in public transportation, drayage vehicles, taxis, and delivery vehicles.

[0003] A route bus is a public transportation service on a fixed route that operates according to a fixed (pre - announced) schedule, which includes arrival and departure times at geographically dispersed bus stops where passengers board and alight. A terminal is the starting or ending point of a public transportation route, where a driver may take a temporary break or change. A terminal may also include a bus stop where passengers board and alight. A bus depot is not only a terminal that functions as a bus stop for passengers but may also be a connection point between different bus routes. At a depot, it is possible to charge the battery, perform vehicle maintenance, and store the vehicle, and it also provides a rest and waiting area for bus drivers.

[0004] Drayage is a short - distance cargo transportation process. Drayage vehicles operate within a limited area, collecting, moving, and delivering cargo between multiple destinations while changing routes as needed.

[0005] Delivery vehicles are operated in several ways depending on the type of service. Package delivery vehicles depart fully loaded at a loading area, depot, or terminal and deliver to one or more delivery locations via public roads. Package delivery vehicles can load packages at any delivery location within the service area or along a pre-set route, or at a pre-set pickup location. Delivery routes may be fixed routes, variable routes (including both pre-set stops and new stops added during delivery), or temporary routes (including the next stop determined during or after delivery between current stops). Taxi passenger services or rideshares are good examples of entirely temporary delivery services.

[0006] Static, mechanical charging of electric vehicles (EVs) during short stops, loading and unloading of cargo, and cargo sorting is a key application of wireless power transmission (WPT). Electric vehicles (EVs) may be human-driven, use driver-assistance automation systems, or be fully autonomous.

[0007] Wireless power transfer (WPT) enables fully automated power supply to electric vehicles (EVs) without the need for physical (wired) power connections. When using wireless power transfer (WPT), the driver does not need to get out of the vehicle to connect a power cable (even if they are permitted to get out of the vehicle for charging or while charging). Also known as inductively coupled power transfer (WPT), wireless power transfer (WPT) functions as an open-core transformer with a primary coil (ground side) and a secondary coil (vehicle side), and transmits power through an air gap according to Faraday's first law of electromagnetic induction. [Brief explanation of the drawing]

[0008] The above-mentioned and other beneficial features and advantages of the present invention are expected to become apparent from the following detailed description, along with the accompanying drawings. [Figure 1] Figure 1 is a graph showing the relationship between the driving range of an electric vehicle (EV) based on its initial charge and the charge state of its battery (SoC). [Figure 2] Figure 2 is a graph illustrating an example of extending the driving range of an electric vehicle (EV) through charging during the journey. [Figure 3] Figure 3 is a graph illustrating examples of extending driving range and battery life through strategic opportunity charging. [Figure 4] Figure 4 is a graph showing the electricity price over a 24-hour period in one example. [Figure 5] Figure 5 is a graph showing the electricity rates of a first and second power company that share a wireless power transmission (WPT) system deployed in the same geographical market and offer different rates based on the time of day and generation capacity. [Figure 6] Figure 6 shows an example where the route of an electric vehicle (EV) spans two regions. [Figure 7] Figure 7 is a geographical diagram illustrating an exemplary arrangement of wireless chargers along a bus route. [Figure 8] Figure 8 shows an exemplary state machine diagram of a route bus equipped with wireless charging capabilities. [Figure 9] Figure 9 schematically illustrates a configuration in which a single charger 901 is used to provide service to a first public transport line and a second public transport line, thereby providing service to multiple electric vehicles (EVs) that serve these lines. [Figure 10] Figure 10 is a schematic diagram showing the communication paths available for data collection from electric vehicles (EVs) and wireless power transmission (WPT) chargers. [Figure 11] Figure 11 shows an exemplary wireless charger installation location. [Figure 12]Figure 12 shows a flowchart of an exemplary method for strategic opportunity charging in an exemplary configuration. [Figure 13] Figure 13 is a flowchart illustrating a complete wireless charging session. [Figure 14] Figure 14 is a timing diagram showing the interaction between the charger and the vehicle. [Figure 15] Figure 15 is a graph showing an example billing period for a wireless charging customer, including usage-based and demand-based charges. [Figure 16] Figure 16 is a top view of a charging station having multiple chargers capable of supplying power to multiple vehicles simultaneously. [Figure 17] Figure 17 is a diagram showing the geographical capacity to manage public demand-based charges through strategic opportunity charging. [Figure 18] Figure 18 shows an example where a single electric bus operates on a single route. [Figure 19] Figure 19 shows an example where multiple electric buses operate simultaneously on a single route. [Figure 20] Figure 20 shows an example where multiple electric buses share a wireless charging infrastructure and operate on multiple routes simultaneously. [Figure 21] Figure 21 shows an exemplary drayage yard using an electric freight transport vehicle (not shown) with strategic wireless machine charging capabilities. [Figure 22] Figure 22 is a geographical diagram showing the routes of electric delivery vehicles that utilize strategic opportunity charging to minimize driving costs. [Figure 23] Figure 23 is a typical diagram showing additional charging components in public and private charging stations. [Figure 24] Figure 24 shows an exemplary configuration of a ride-hailing service utilizing electric vehicles (EVs) and machine charging. [Figure 25] Figure 25 illustrates an exemplary scenario for rescheduling and rerouting electric delivery vehicles (EVs) using wireless and / or wired machine charging. [Figure 26] FIG. 26 is a flowchart showing the selection and assignment of event sequences of an electric distribution or shuttle vehicle for handling transportation requests of people or goods by an electric service vehicle using opportunity charging in an exemplary configuration.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Exemplary embodiments will be described in detail with reference to FIGS. 1-26. It should be noted that this description provides details of possible embodiments, but these details are intended as examples and do not limit the scope of the subject matter of the present invention.

[0010] Strategic Opportunity Wireless Charging enables the expansion of the introduction of public transportation electric vehicles without range or time limitations. Electric buses are zero-emission, quieter, and with Strategic Opportunity Wireless Charging, they can function without stopping operations or deviating from routes for refueling or charging. Public transportation electric vehicles play an important role in urban air purification, traffic noise reduction, and decarbonization. Strategic wireless charging plays an important role in the widespread use and ultimate automation of public transportation.

[0011] An electric vehicle (EV) uses an electric traction motor and a battery instead of an internal combustion engine and chemical fuel. As used herein, the electric vehicle (EV) relates to both battery electric vehicles (BEV) and various hybrid vehicles (HBEV) that combine a battery and an internal combustion engine. The battery or battery pack typically includes a rechargeable chemical battery, but may also include one or more of a capacitor bank, a reversible fuel cell, a solid-state battery, or a hybrid combination thereof. Advancements in energy storage technologies (e.g., solid-state batteries, hybrid batteries, ultracapacitors, etc.) have made it possible to utilize rapid opportunity charging using high-power wireless power transmission.

[0012] As a general example, the battery pack of an electric vehicle (EV) consists of electrochemical batteries. The lifespan of commonly used rechargeable lithium-ion batteries is typically 300 to 500 charge cycles. One charge cycle refers to the period of use from a fully charged state to a fully discharged state and then back to a fully recharged state.

[0013] These rechargeable lithium-ion batteries have a limited lifespan and gradually experience a decrease in charge capacity. This capacity reduction (aging of the battery) is irreversible. As the battery capacity decreases, the power supply time to the bus is shortened, and as a result, the driving range and driving time are limited.

[0014] Many studies have reported on the ability of wireless opportunistic charging to reduce the required battery size and extend the driving range of electric vehicles. Strategic wireless opportunistic charging has the ability to extend the lifespan of lithium-ion batteries by maintaining the charge level of the secondary battery between the thresholds of the high state of charge (SoC) and the low state of charge (SoC), and its effect has been demonstrated in the field. The charging rate, battery temperature before and during charging are factors that affect the lifespan of lithium-ion batteries. Even in solid-state batteries capable of 1,000 to 10,000 charge and discharge cycles, the lifespan can be extended by controlling factors such as the charge level, charging rate, and battery temperature. In mixed fleets, some electric vehicles (EVs) may be equipped with lithium-ion batteries, while others may be equipped with solid-state batteries (in some cases, a single electric vehicle (EV) may be equipped with multiple battery technologies). In such mixed fleets, it is advantageous to individualize the charging data and performance data of electric vehicles (EVs) for modeling and machine learning.

[0015] Wireless mechanical charging refers to a method by which electric vehicles (EVs) can utilize wireless chargers installed within their operating section or along their route. EVs using mechanical charging can save both time and battery charge because they do not have "dead-head" time (time spent returning to a depot or garage for charging), allowing these savings to be utilized for route operation.

[0016] Mechanical charging allows for the use of smaller batteries in electric vehicles (EVs), resulting in benefits such as reduced weight and extended range or increased payload capacity over the range.

[0017] Mechanical charging can also be used to extend battery life by maintaining the battery's state of charge (SoC) between upper and lower thresholds. Since electric vehicle (EV) battery packs are expensive to replace, extending battery life can reduce the total cost per mile.

[0018] Automatic mechanical charging eliminates the need to leave vehicles unattended at night or in bad weather, unlike plug-in chargers that require cables, thus improving driver safety and comfort. Automatic mechanical charging also makes it possible for people with disabilities to use electric vehicle charging.

[0019] In the following exemplary embodiments, electric buses operating on short to medium-distance routes as part of a regional or local bus network operating on a pre-set public timetable are referred to as “route buses.” In the case of route buses, a terminal is the starting or ending point of a public transport route and a place where drivers temporarily disembark or change vehicles. Terminals may also include stops where passengers board and alight vehicles. A bus terminal is a terminal that includes vehicle maintenance and storage facilities. A stop is any public transport stop where passengers can board and alight public transport vehicles.

[0020] For route buses (and any vehicles utilizing strategic opportunity charging protocols and systems), first-party data is defined as data transmitted from sensors mounted on electric vehicles (EVs). Second-party data is defined as data transmitted from sensors on other electric vehicles (EVs) or measurement stations (e.g., charging stations). Third-party data is data obtained from external sources that are not the source of data collection. Third-party data can be aggregated from multiple sources. Examples of third-party data include maps, traffic conditions, and weather information.

[0021] All electric vehicles (EVs), including battery electric vehicles (BEVs) and hybrid vehicles, have a range that can be estimated from the state of charge (SoC). Vehicle wear and tear, battery life reduction, and electricity prices are all factors in the total cost per distance (TCD). If electricity prices vary by route or day of operation, an additional variable, the cost per watt, becomes a factor in the total cost per distance (TCD).

[0022] The fleet management system as defined herein may include fleet energy management for each vehicle to report sensor data using a wireless data link and to coordinate charging operations with control devices at the dispatch facility. Such coordination and information sharing also impacts the ability to perform spatially and temporally distributed opportunity charging (depending on geography and route), thereby reducing the total operating cost of the fleet over time.

[0023] The use of multiple buses, geographically dispersed charging stations, charging stations co-located with frequently used stores or facilities, and privately and publicly shared charging stations is being considered in general. These will be used through a fleet management system to reduce the total cost per mile for individual electric vehicles (EVs) and fleets of electric vehicles (EVs).

[0024] By collecting, communicating, storing, monitoring, and analyzing data on the environment, vehicles, chargers, and charging sessions for a single electric vehicle (EV), a group of EVs, or multiple EVs, it is possible to improve and optimize existing services, and to provide new services based on the analysis of the collected data. Leveraging historical data enables more accurate estimations regarding specific routes, vehicles, and drivers.

[0025] One such service is the reduction of total operating costs. By utilizing collected data to optimize charging for individual electric vehicles (EVs) and the entire fleet, it is ensured that all vehicles in a fleet of electric vehicles (EVs) complete their routes at minimal cost. This cost minimization is achieved through near real-time data, models built using historically collected data, knowledge of energy costs, and knowledge of chargers installed in different geographical areas where the available charging power at each charging location or station may be limited.

[0026] Vehicle fleet management applications used for cost minimization can optimize the running costs of a single electric vehicle (EV) and an entire fleet. When the goal is to optimize the cost of individual EVs, the potential local minimum running cost of each EV can be optimized based on predictions using data collected from a fleet or multiple fleets.

[0027] When targeting a fleet level, fleet optimization is designed to minimize global total cost, which may differ from local optimization algorithms designed to minimize the cost of a single electric bus. A trade-off in modeling a fleet management application is that, in order to improve the overall performance of the system, it may be necessary to allow electric vehicles selected within the fleet to be charged at a higher power cost during a first period, rather than charging selected electric vehicles during a second period when charging costs are lower. This either reduces the overall cost of the fleet or minimizes the impact on the power grid by managing power resources during charging.

[0028] Furthermore, electric vehicles (EVs) used for pickup, delivery, or pick-up services utilizing machine charging can set routes according to task requests related to the pickup and delivery of goods or the transportation of people. Candidate EVs for a task can be determined by a preliminary route analysis of the routes between pickup and delivery or pick-up points, which includes the estimated travel time and power consumption between points for each candidate EV. Candidate EVs are selected by determining which EVs available within the service time frame are charged enough to reach at least the pickup or pick-up location. For each remaining candidate EV, a route calculation is performed to calculate individual routes between the pickup or pick-up location and the delivery or drop-off location, as well as the predicted power consumption along each route. Candidate EVs that are charged enough to meet the predicted power consumption along each route and have minimal charging delays are assigned to handle the task request.

[0029] Efficiency is the ability to perform any action or produce any product without wasting materials, time, or energy. In the case of electric vehicles in public transport, efficiency may include the amount of electricity used relative to the range of the electric vehicle (EV), vehicle costs, and / or the extended lifespan of the battery pack depending on the circumstances.

[0030] The vast amounts of first-party, second-party, and third-party data collected or acquired are suitable for both statistical analysis and machine learning (ML) techniques. Statistical analysis can be used with labeled quantitative and categorical data to determine trends, patterns, and relationships (both causal and correlational). Because the data is well-labeled, supervised learning algorithms for machine learning (ML) are suitable when specific goals or optimizations are required. In some cases, the data is used in combination with unsupervised learning algorithms to perform data clustering and the identification of patterns, associations, or anomalies based on the data.

[0031] Figure 1 Figure 1 is a graph showing an example of the relationship between the driving range and the battery charge state (SoC) of an electric vehicle (EV), measured based on a single charge. The x-axis represents the driving range 101, and the y-axis represents the charge state (SoC) 102 at the starting point 103. In this example, a simple linear model of driving is used to explain the concept.

[0032] In the first example, the electric vehicle (EV) starts with a starting state of charge (SoC) 103 (for example, a 100% SoC). As the EV travels 104, the SoC decreases until it reaches 0% at a range 107.

[0033] In the second example, the electric vehicle (EV) starts from a starting charge state 103 (for example, a 100% charge state (SoC)). As the EV travels 105, the charge state (SoC) decreases until it reaches 0% at a range 108.

[0034] In the third example, the electric vehicle (EV) starts from a starting charge state 103 (for example, a 100% charge state (SoC)). As the EV travels, the charge state (SoC) decreases 1046 until it reaches 0% at a range of 109.

[0035] Factors determining the range of an electric vehicle (EV) (107, 108, 109) include vehicle characteristics, battery pack characteristics, environmental factors, load capacity, driving terrain, and driver skill. Vehicle characteristics include vehicle type, model, manufacturer, year of manufacture, mileage, and repair status. Tire condition and tire selection are also considered. Aerodynamics (air resistance) is also considered a vehicle characteristic.

[0036] Battery pack characteristics include type, model, manufacturer, battery degradation (both aging degradation and accelerated degradation due to charging and discharging), past battery usage, and the charge state (SoC) of the battery over past mileage. Data from sensors that monitor the temperature and voltage levels of individual batteries is considered to be available almost continuously via the vehicle's Battery Management System (BMS). The Battery Management System (BMS) monitors and manages the vehicle's battery pack. A battery pack typically consists of multiple batteries configured and interconnected to supply the required voltage and current. The Battery Management System (BMS) monitors the battery pack's sensors (e.g., current, voltage, temperature sensors) and communicates with the electric vehicle's (EV) electrical subsystem and external chargers (such as wireless power transmission (WPT) chargers). The Battery Management System (BMS) maintains the battery pack's operating profile and protects against over-discharge, overheating, and overcharging. The Battery Management System (BMS) can also optimize battery performance and lifespan by controlling the charging rate and charge state.

[0037] Environmental factors include weather, temperature, and atmospheric pressure. Temperature affects not only the battery's charge state but also the electrical load required for cabin air conditioning (heating and cooling) for passengers and luggage, and for vehicle systems (e.g., cooling of the battery pack). Daytime and nighttime driving can also affect the driving range. Weather conditions (rain, snow, strong winds, etc.) can also affect the driving range relative to the state of charge (SoC). Lighting, such as headlights, interior and exterior lighting, places a variable load on the battery pack. In cold climates, especially in the evening when it gets dark and additional safety lighting is needed, the heating load (for passengers and the battery pack) and lighting load can overlap, potentially consuming a large portion of the battery capacity, thus requiring additional power transmission (by extending charging time or high-power charging). To extend battery life, the safety margin of the battery's state of charge (SoC) may also need to be recalibrated.

[0038] This data is likely to be available from third-party sources, such as sensors at charging stations, on-board sensors, and feeds to public weather stations and dispatch servers.

[0039] Power consumption along the route is affected by the passenger or cargo load, and the range relative to the state of charge (SoC) decreases as the load increases.

[0040] The terrain includes uphill and downhill slopes, turns, speed, stops, and traffic conditions along the route. Traffic conditions can be collected from third-party organizations via the dispatch server's application programming interface (API). While uphill sections require additional power to overcome, downhill sections generally reduce power consumption, and regenerative braking allows for additional power storage; therefore, terrain can be a significant factor in power consumption along a route.

[0041] Driver capabilities include saving battery resources through lane selection, smooth acceleration, and smooth deceleration (braking). In this specification, “driver” includes the use of automated driving assistance software packages and autonomous driving systems.

[0042] Figure 2 Figure 2 is a graph illustrating an example of extending the range of an electric vehicle (EV) through charging during transit. The x-axis represents the range 201, and the y-axis represents the state of charge (SoC) 202. In this example, the concept is explained using a simple linear model of driving. In the first example of charging during transit, the EV starts from a first state of charge (SoC) 202 at the starting point 204. As the EV drives, its state of charge (SoC) 203 decreases. The EV is charged at the charging station 205, and the driving continues for a range of 201 using the new state of charge (SoC) 206.

[0043] Figure 3 Wireless inductive charging enables disconnected charging of electric vehicles (EVs). Chance charging refers to charging an EV for short periods while driving (and with smaller increments in the State of Charge (SoC)). This partial charging strategy is in contrast to the method of charging an EV in one go, as shown in Figure 2.

[0044] Figure 3 is a graph illustrating an example of extending driving range and battery life through strategic opportunity charging. The x-axis represents driving range 301, and the y-axis represents the state of charge (SoC) 302. In this example, the concept is explained using a simple linear model of driving. In this example of strategic static opportunity charging, the electric vehicle (EV) is not only partially charged at temporary stops 306, 307, 308, and 309, but the state of charge (SoC) is maintained between the upper threshold 303 and the lower threshold 304 of the state of charge (SoC). The state of charge (SoC) profile 305 along the driving path shows different slopes (in this simplified model), which indicate different power consumption in the path sections (i.e., between stops where opportunity charging takes place).

[0045] The upper and lower thresholds 303 and 304 for the state of charge (SoC) are designed to extend the battery life of an electric vehicle (EV). While this example shows only one set of thresholds, it is also possible to set multiple levels of thresholds to extend the range while minimizing the impact on battery life. Naturally, it is always possible to utilize the physical upper threshold 303 (100% SoC) and lower threshold 304 (0% SoC) to extend the range at the expense of battery life (for example, in emergency situations).

[0046] In the example in Figure 3, the starting charge state (SoC) 310 is shown just below the battery's upper threshold 303. Depending on the circumstances, such as when operation is started from a state where the battery has been stored for a long period of time, the starting charge state (SoC) 310 may be higher than this (e.g., 100% charge state (SoC)) or considerably lower (e.g., 40%).

[0047] In one configuration, the lifespan of a lithium-ion battery pack can be extended by selecting upper and lower thresholds and utilizing opportunity charging to maintain the charge state between the selected thresholds. Monitoring the temperature and voltage of individual batteries and varying the charging rate to keep them below (or above) the selected thresholds also contributes to maximizing battery life.

[0048] By understanding the vehicle's battery operating threshold, charger locations, routes, time of day, current location, traffic volume, and estimated arrival time at the next charger, it is possible to decide whether or not to charge (and the level of charge state (SoC)) upon arrival at or before arrival at a wireless power transmission (WPT) machine charger. The cost of electricity from the power grid may also vary depending on the time of day or the location of the wireless power transmission (WPT) charger.

[0049] Figure 4 Time-of-Use (TOU) electricity rates are a pricing system where electricity rates vary depending on the time of day. TOU rates fluctuate according to electricity supply conditions, being higher during peak demand periods and lower during off-peak hours. These rates are typically fixed for a set period (weekly, monthly, or seasonal) to accommodate demand fluctuations.

[0050] Figure 4 is a graph showing the electricity price over a 24-hour period in one example. In Figure 4, the X-axis 401 represents a 24-hour period divided into one-hour segments, and the Y-axis 402 shows the electricity price for each time segment. In this example, from 0:00 AM 403 to 8:04 AM on the X-axis 401, the electricity company sets the price to "off-peak" 405. From 8:04 AM to 12:00 PM 406, the electricity price is set to "semi-peak" or "shoulder time" 407 due to increased demand. From 12:06 PM to 6:08 PM 408, the electricity price is set to "peak" 409 because this is the time of highest demand. From 6:08 PM 408 to 11:00 PM 410, the electricity price is set to "semi-peak" 407 due to decreased demand. After 11:00 PM 410, the price returns to "off-peak" 405. As shown in Figure 4, the price fluctuates according to increases and decreases in demand.

[0051] The cost of charging an electric vehicle (EV) (per watt) is lowest during off-peak hours and highest during peak hours. By selectively charging your EV throughout the day (both the time of day and the level of charge at each time), you can minimize costs.

[0052] Weekend time-of-use (TOU) electricity rates are not shown, but they may differ from weekday rates (e.g., Monday through Friday). In some areas, weekend rates are set as "off-peak."

[0053] Figure 5 In some regions and markets, competing power companies may exist. Figure 5 is a graph showing the electricity rates of a first and second power company that share a wireless power transmission (WPT) system deployed in the same geographic market and offer different rates based on the time of day and generation capacity. As shown in Figure 5, the first and second power companies share a wireless power transmission (WPT) system deployed in the same geographic market and offer different rates based on the time of day and generation capacity. In Figure 5, the X-axis 501 represents a 24-hour period divided into one-hour intervals. The Y-axis 502 shows the electricity price for each time interval. In this example, in the first period 505 (midnight to 7am), the market price 503 of the first power company is cheaper. In the second period 506 (7am to 8am), the prices of the first power company 503 and the second power company 504 are approximately the same. In the third period 507 (8am to 2pm), the price of the first power company 503 is cheaper. In the fourth period 508 (2pm to 11pm), the price of the second power company 504 is more advantageous. Finally, in the fifth period 509 (11pm to midnight), the price of the first power company 503 becomes more advantageous again.

[0054] By choosing a cheaper electricity provider when charging, it is possible to reduce the electricity costs associated with charging electric vehicles (EVs). Savings can be further enhanced by managing charging times, durations, and levels to minimize charging during peak hours when electricity costs are higher.

[0055] Electricity demand, supply, and cost can fluctuate significantly from day to day or even hour to hour, so all of these factors are influencing the decision to charge. Information on electricity rates can be obtained from pre-contractual terms with the local power company, data feeds from the local electricity exchange, or, if multiple suppliers are supplying electricity to the power company, from public spot market prices. Examples of electricity markets include the Day-Ahead Energy Market and the Real-Time Energy Market.

[0056] Figure 6 Power company service area Depending on the location of the chargers, they may be installed consecutively along specific routes within the service areas of multiple power companies, and it is necessary to obtain and consider pricing information from multiple sources when deciding where to charge. If the chargers are owned by a third party (someone other than the power company and vehicle group manager), it may be necessary to obtain electricity charges for the estimated charging time from that third party.

[0057] Figure 6 shows an example where an electric vehicle (EV) route 605 spans two regions 601 and 602. In this example, regions 601 and 602 are serviced by different power companies and have different pricing structures. Therefore, the power costs of the wireless power transmission (WPT) charging stations 603 and 604 may differ. The total charging cost for the route can be optimized by using different charging times and power levels at specific times of day at each wireless power transmission (WPT) charging station 603.

[0058] Figure 7 Figure 7 is a geographical diagram illustrating an exemplary arrangement of wireless chargers along a bus route. Charging can also be performed for longer durations at transfer or connecting stations between routes (e.g., railway or airport terminals), but charger stops can also be placed in more advantageous locations (e.g., along the range where the predicted battery state of charge (SoC) is expected to exceed a lower threshold).

[0059] Predictive Modeling By obtaining charges for multiple estimated arrival times at multiple chargers along a route, a cost profile for that route can be generated at the start of the day. Cost optimization values ​​can be calculated based on upper and lower thresholds for the battery's State of Charge (SoC), and these values ​​can be recalculated as needed if the route is deviated from or if the model fails to accurately predict the SoC value. The data used to create the cost optimization model can include training data from the current route, the current fleet, or other fleets using the same or similar electric vehicles. In some cases, a second set of SoC thresholds may be applied because charging costs can vary significantly depending on the charging location or time.

[0060] The recalculation of modeled running costs may include deviations from the timetable (both early and late arrivals). In the case of an early arrival, additional charging time becomes available, which may result in a reduced supply of charging current. In the case of a late arrival, stopping time may be reduced, which may result in a higher charging current being available to shorten the charging time.

[0061] This document describes a system that enables continuous operation of electric vehicle fleets using wireless charging on the railway line. The vehicle's State of Charge (SoC) is maintained at an optimal level to promote battery health and lifespan, and to optimize vehicle operating costs by considering electricity rates.

[0062] Sensor data is repeatedly collected throughout the entire operation during on-road charging, which facilitates the construction of dynamic charging models that take into account the type of electric vehicle (manufacturer, model, year), environmental factors (e.g., weather, traffic conditions), driver behavior, wireless charging performance, and the health of the drivetrain and battery pack that affect vehicle operation.

[0063] Data collected across the entire fleet of electric route buses is used to automatically optimize models that increase or decrease charging power based on these factors, enabling the vehicles to operate indefinitely while maintaining the optimal State of Charge (SoC) across the entire fleet at the lowest possible cost in terms of both battery life and electricity costs.

[0064] For each type of electric vehicle (i.e., vehicle type, model, year, manufacturer, battery pack) or electric vehicle (EV) class, at least one minimum State of Charge (SoC) threshold is set. This minimum State of Charge (SoC) threshold is dynamic and may be based on a) the minimum State of Charge (SoC) required to prevent battery damage (shortening of lifespan), b) the minimum State of Charge (SoC) required to reach the next two charging stations on the route, c) the minimum State of Charge (SoC) required to complete the route without charging, d) the minimum State of Charge (SoC) calculated to arrive at the departure / arrival point with intermittent route operation, e) the State of Charge (SoC) at the start of route operation, and a manually set minimum State of Charge (SoC) threshold.

[0065] In minimum power mode, electric vehicles (EVs) are only allowed to charge up to the predicted State of Charge (SoC) at each stop equipped with a wireless charger. This minimum power mode can be changed based on the estimated electricity cost of wireless power transmission (WPT) chargers along the route.

[0066] As charging networks expand and higher-power wireless charging services are deployed, it is expected that mechanical chargers will be more widely installed in geographical locations where mechanical charging is available even during short stops, or in geographical locations equipped with charging lanes featuring dynamic induction chargers. It should be noted that both statically and dynamically charged electric vehicles can utilize near-field, full-duplex data link, as detailed in U.S. Patent No. 10,135,496, entitled "Near field, full-duplex data link for use in static and dynamic resonant induction wireless charging."

[0067] Figure 7 shows an exemplary public transport route. Departure point 701 is used to house and maintain electric route buses that are not currently in operation. Departure point 701 may also be part of a terminal, and passengers can board before departure. Passengers can board and alight at each pre-planned stop. Additional stops for passenger alighting may also be set up as needed. The first route section 702 runs to move the bus to the first stop 703 where passengers board and alight. The second route section 704 moves the bus to the second stop 705 where passengers board and alight. The third route section 706 moves the bus to the first transfer station 707 where passengers board and alight to travel to another intersecting public transport route. The driver may also use this transfer station 707 to take a mandatory rest. By traveling through the fourth route section 708, the bus moves to the fifth stop 709 where passengers board and alight. By traveling along the fifth route section 710, the bus moves to the sixth stop 711 where passengers board and alight. By traveling along the sixth route section 712, the bus moves to the bus terminal 714, which is the terminus of the route in this example. In this example, the longest route section 712 includes a charger stop 713 where the bus can make a short stop for charging. Alternatively, the charger stop 713 may be a dynamic mechanical charger that can be charged simply by driving over the road surface equipped with the charger without the bus having to stop.

[0068] Wireless charging stations can be installed at any of the planned stops 703, 705, 707, 709, 711, and 713. By deploying additional charging locations (not shown) between stations, it is possible to extend the range while maintaining the charge state (SoC) within the threshold boundary.

[0069] For electric vehicles used in public transport (e.g., buses), charging stations must be located on or near the route for machine charging to be possible. First, the placement of chargers is determined by mapping and modeling routes (using data collected from test runs, modeled data, or data obtained from similar routes and vehicles) where chargers are installed at stations and stops (or, in some cases, between stops) where sufficient power can be obtained. To reduce costs, chargers are typically installed in a subset of stops.

[0070] Once the public transport system (and associated data collection, transmission, and analysis) is operational, the collected data can be used to determine whether 1) additional chargers are needed, 2) the number of chargers needs to be reduced, or 3) chargers should be deactivated and moved to another location to achieve the target total operating cost for the fleet of vehicles.

[0071] When chargers and data infrastructure are shared among multiple vehicle groups, driving data from these groups can be used to readjust the balance of the charger infrastructure and adapt to newly deployed electric public transport vehicles, changing vehicle traffic patterns, and changes in passenger numbers and routes.

[0072] When multiple vehicle groups share charging infrastructure, ownership of chargers and payment of charging costs can be accumulated and negotiated among the vehicle groups. In some cases, local or regional authorities may own, operate, and service the wireless charging network and auxiliary communication / data systems, and allocate the costs to the vehicle groups receiving the service.

[0073] In some cases, wireless chargers owned and operated by commercial operators or government agencies other than the vehicle fleet may be used to complement the wireless charger network of public transport.

[0074] Figure 8 Figure 8 shows an exemplary state machine diagram of a route bus equipped with wireless charging capabilities.

[0075] The exemplary state transition diagram in Figure 8 shows data collected at different times and events along the route of an electric vehicle.

[0076] In this example, departure / arrival condition 801 occurs at least twice: once at departure and once at the end of the workday. Additional departure / arrival conditions may occur due to driver changes and vehicle maintenance.

[0077] Upon departure from a terminal, the bus system is fully charged (up to the upper threshold of the charge state (SoC)), preheated, cooled, or air-conditioned, depending on the needs of the day's operation. Vehicle characteristics (e.g., make, model, year) and driver (or driver software) identification information are also recorded.

[0078] The onboard data store includes departure time (current time), battery state of charge (SoC), SoC threshold / limit, vehicle empty weight, current location, and route. Planned routes have scheduled passenger and charger stops (locations). For each charger along the route, the fare schedule and spot electricity rates are known. The theoretical SoC used for each route segment is predicted by modeling based on historical histogram data for the route, similar routes, or simplified exemplary route models. Planned events along the route (and detours) are known and considered during route planning and modeling.

[0079] Enroute State 802 is expected to be the most common state and encompasses all types of travel. While in Enroute State 802, the data store stores the current time, current location, charge status (SoC), number of passengers, traffic conditions, and vehicle speed.

[0080] The data store can accumulate and store vehicle and route data by performing periodic or event-driven updates. Stored data is tagged with time, current location, and route section. If precise location information is unavailable, odometer readings can be used as alternative positioning. Data can be uploaded to dispatch facilities via wireless connectivity (e.g., mobile phone or satellite modem).

[0081] For passengers on board (803) with charging, the data store is updated with the start and end times of the stop. The total number of passengers, the current number of passengers, and the number of passengers getting on and off are updated using the passenger boarding / alighting counter (or by using the bus's weight change as an alternative). In addition, during charging, the charging current and the charging state (SoC) at the start and end of charging are recorded. The charger can transmit additional charging data via the communication link, including the status of the charger (and the status of the vehicle's wireless power receiver), as well as details about inductive charging energy transmission (e.g., coupling, frequency, equipment temperature, etc.).

[0082] Route information is updated via the communication system at the charging station or the bus's wireless communication system. Route information includes the distance to the next stop, the type of stop, the threshold and limit values ​​for the State of Charge (SoC), the status of the charger and the availability of chargers at the next stop, and the status of at least all chargers along the route.

[0083] During driver rest periods involving charging (804), the data store is updated with the start and end times of the stop. Using the passenger boarding / alighting counter (or alternatively using the bus's weight change), the total number of passengers on board, the current number of passengers, and the number of passengers boarding and alighting are updated. Also, during charging, the charging current and the charging state (SoC) at the start and end of charging are recorded. The charger can transmit additional charging data via the communication link, including the status of the charger (and the status of the vehicle's wireless power receiver), as well as details about inductive charging energy transmission (e.g., coupling, frequency, equipment temperature, etc.).

[0084] Route information is updated via the communication system at the charging station or the bus's wireless communication system. Route information includes the distance to the next stop, the type of stop, and the predicted state of charge (SoC).

[0085] At charger stop 805, the data store is updated with the start and end times of the stop. Passenger boarding and alighting are not expected at charger stop 805. During charging, the charging current and the charge state (SoC) at the start and end of charging are recorded. The charger may transmit additional charging data via the communication link, including the status of the charger (and the status of the vehicle's wireless power receiver), as well as details about inductive charging energy transmission (e.g., coupling, frequency, equipment temperature, etc.).

[0086] While stopped at charging station 805, route information is updated via the charging station's communication system or the bus's wireless communication system. Route information includes the distance to the next stop, the type of stop, and the predicted state of charge (SoC).

[0087] During driver rest stop 806, the data store is updated with the start and end times of the stop. The total number of passengers, the current number of passengers, and the number of passengers getting on and off are updated using the passenger boarding / alighting counter (or by using the bus's weight change as an alternative). No charging occurs during driver rest stop 806; instead, the state of charge (SoC) at the start and end of the rest is recorded. Route information is updated via the bus's wireless communication system. Route information includes the distance to the next stop, the type of stop, and the predicted state of charge (SoC).

[0088] For passenger boarding (without charging) in case 807, the data store is updated with the start and end times of the stop. The total number of passengers, the current number of passengers, and the number of passengers boarding and alighting are updated using the passenger boarding / alighting counter (or by using the bus weight change as an alternative). With no charging taking place, the charge state (SoC) at the start and end of the boarding is recorded.

[0089] For passenger rides (without charging) of vehicle 807, route information is updated via the bus's wireless communication system. Route information includes the distance to the next stop, the type of stop, the State of Charge (SoC) thresholds and limits, the predicted end-of-ride SoC and the availability of chargers at the next stop, and the status of at least all chargers along the route.

[0090] Figure 9 Figure 9 schematically illustrates a configuration in which a single charger 901 is used to provide service to a first public transport route 902 and a second public transport route 903, thereby providing service to multiple electric vehicles (EVs) that serve routes 902 and 903.

[0091] In some cases, the amount of power required, the number of vehicles requiring charging, or both may exceed the available charging power on a charger. This situation includes allocating chargers to suit a particular group of vehicles, or allocating chargers that support non-standard communication protocols or charging signals.

[0092] As shown in Figure 9, competition for limited charging resources (power and / or chargers) occurs at charging stations on intersecting routes. Competition can also occur due to third-party operated charging stations, charging stations experiencing congestion due to operational delays, charging stations with unusable chargers, chargers located in power-scarce areas, and priority use of chargers for emergency services.

[0093] Competing charger resources are also affected by energy prices, which fluctuate geographically and temporally. By utilizing power allocation planning and energy management based on predictions derived from modeling historical data, the limited power available at each charging station or specific chargers within a charging station can be managed and allocated.

[0094] As shown in Figure 9, at charging stations on intersecting routes connecting two or more public transport lines, the arrival of public transport vehicles may differ from the scheduled arrival time and charging time.

[0095] Even in third-party operated charging stations where prioritization methods and reservation systems (described in U.S. Patent Application No. 17 / 199,234, "OPPORTUNITY CHARGING OF QUEUED ELECTRIC VEHICLES") are in place, factors such as vehicle arrival time, number of vehicles, charge level, total charge demand, and the number of modular charging pads per charger and vehicle (described in U.S. Patent Application No. 17 / 646,844, "METHOD AND APPARATUS FOR THE SELECTIVE GUIDANCE OF VEHICLES TO A WIRELESS CHARGER") may be taken into consideration.

[0096] If delays during operation cause congestion at charging stations, a queuing scheme can be implemented based on the scheduled departure time and the vehicle's state of charge (SoC). Planning for charging stations that include faulty chargers becomes even more complex because modular chargers may soft-fail while some functions are still available.

[0097] In some cases, power shortages may necessitate power distribution. Power supply priorities are set for vehicles based on their charge status (SoC) and a prioritization system. Power supply priorities are achieved by supplying power at a higher charging rate to lower-priority vehicles or by cutting off power supply to lower-priority vehicles.

[0098] In emergencies or when charging of other high-priority electric vehicles is required, a priority system may be provided that either forcibly allocates the charger currently being charged to a vehicle, or reserves the next available charger for use by the priority vehicle.

[0099] Figure 10 Figure 10 schematically shows the communication paths available for data collection from electric vehicle (EV) and wireless power transmission (WPT) chargers. The data collection is used for pricing strategic opportunity charging. Telemetry (including bidirectional telemetry) transmits data and information between a remote source (including mobile sources and users) and a remote destination using wired and wireless communication. Telemetry data streams between source and destination can consist of continuous, periodic, polling, or ad-hoc data transmissions. Telemetry includes automated measurement and wireless transmission of data from a remote source, and the collected data is routed to a receiving device at the destination location (e.g., dispatch server 1001) for monitoring, display, recording / storage, post-processing, analysis, and trend analysis.

[0100] The datastore is part of database software that has high-capacity storage and data management software (e.g., IBM Maximo Enterprise Management System) running on processor hardware with a computer operating system. Security and multi-party access control functions are performed through this data management software. In some embodiments, the dispatch server 1001 and its associated datastore can be implemented as a virtual-hosted (e.g., cloud-based) system or as an on-premises hardware and software system (with the necessary processors, memory, and fault-tolerant data storage) based on a general-purpose, highly available computing platform sized to fit the local processing and storage needs of the dispatch facility. The dispatch server may include (or interface to) redundant and potentially partitioned and federated databases and geographic information systems (GIS). Interfaces to information from other third parties, such as electricity rates, traffic information, and weather information, may be centralized in the dispatch server 1001.

[0101] The data store is implemented within the vehicle, but the stored data is uploaded to the dispatch server 1001. Uploads can be performed upon request from the dispatch facility, periodically, or event-driven (e.g., at the start of wireless power transmission (WPT) charging). Electric vehicles (EVs) are equipped with a navigation system (e.g., Navstar GPS, Galileo, GLONASS, BeiDou, Quasi-Zenith Satellite System (QZSS) (also known as "Michibiki"), or a system based on local radio positioning beacons). The data store can access the vehicle system and battery management system (BMS) via a local data link (e.g., a Controller Area Network (CAN) bus).

[0102] The data source can include a database of historical data, recorded data, and models using near real-time data. The data source can also include near real-time data, including sensor outputs from sensors that contain either electrical data (such as voltage, current, or charge status) or physical data (such as temperature, pressure, or mass).

[0103] The telemetry device may also include data products such as location information, passenger count, timestamp, data source identifier, map updates, and route updates. In this application, the vehicle data store can store data related to the collected electric vehicles and transmit this data to the dispatch server 1001 almost continuously.

[0104] The dispatch server 1001 may include application-specific software, including a data management system, an API for interfacing with third-party information feeds (e.g., traffic, weather, public charger status), and a communication interface for data originating from the charger location 1002 and the electric vehicle (EV) 1003. The charger location 1002 may use either a wired (not shown) or wireless interface 1004 for bidirectional communication. Depending on the installation, such a wireless interface may use a public or private mobile data communication network 1005 using radio signals 1006 in a public or private band. An alternative or supplemental wireless network may be provided from a satellite 1007 using radio signals 1008 in an existing satellite communication band. A satellite data receiver 1009 may be used to transmit satellite communication data to the dispatch server 1001.

[0105] The data is generated by the wireless charger installation location 1002 and transmitted via the wireless charger installation location 1002 to the electric vehicle (EV) 1003 and / or the dispatch server 1001. Each charger installation location 1002 has at least a wireless charger 1010 and auxiliary equipment 1011 (shown here as an above-ground housing, but may be underground). The auxiliary equipment 1011 may include wired or wireless backhaul (shown here as a wireless antenna 1012 for a mobile wireless connection 1004).

[0106] On the route of bus 1003, arrival and departure times are predetermined for each geographically defined point. The approximate location can be calculated from the time, route section, and odometer readings. A more precise location of vehicle 1003 can be obtained by a vehicle-mounted navigation receiver (not shown) for Global Navigation Satellite Systems (GNSS) 1013 (e.g., Navstar GPS, Galileo, GLONASS, BeiDou, Quasi-Zenith Satellite System (QZSS) (also known as "Michibiki")) using satellite broadcast signals 1014. Broadcasts from other communication satellite constellations (e.g., frequencies provided by the Starlink system (a high-speed, low-latency broadband internet service provider designed for remote and rural areas worldwide)) can also be used for location determination.

[0107] Alternatively, if geographically local radio positioning beacons (which have a known frequency, known bandwidth, known transmitter or broadcast transmitter location, and a transmit identifier (ID)) are deployed or available, they can be used to obtain accurate location information.

[0108] In the public transport electric vehicle (EV) 1003, a radio receiver and transceiver 1015 are used to receive Global Navigation Satellite System (GNSS) or local beacon positioning signals, communicate via a land-based mobile network, and, in some cases, to send and receive information via a satellite communication system.

[0109] In particular, in areas with multiple vehicle fleets and shared or third-party wireless charging resources, the dispatch facilities and dispatch servers 1001 may be owned or managed by a third party (host) that is not the operator of the vehicle fleet and provides Charging as a Service (CAAS). The Charging as a Service (CAAS) program removes the burden of owning and maintaining chargers from electric vehicle fleets by having the host provide ready-to-deliver wireless charging stations, management software, communication infrastructure, 24 / 7 support, and professional on-site maintenance of charger resources. The host also provides planning and modeling for deploying new chargers and reconfiguring existing chargers if the need for changing the location of chargers or increasing (or decreasing) charger capacity is detected at existing charger locations.

[0110] Figure 11 Figure 11 shows an exemplary wireless charger installation location 1100. It shows a wireless charger 1101 for charging a public transport vehicle 1102, installed at the same height as the sidewalk 1103. A pedestrian area 1104 may be provided if passengers board or alight at this installation location 1100. Conduit 1105 provides interconnection between the wireless charger, cooling lines from a cooling structure 1106, and wired or optical communication lines (not shown) to a wireless transceiver and antenna 1107.

[0111] The wireless charger 1101 provides wireless communication between the wireless charger 1101 and the vehicle 1102. These communications are described in U.S. Patent No. 10,135,496, issued November 20, 2018, “Near field, full duplex data link for use in static and dynamic resonant induction wireless charging.” In static induction charging, the primary and secondary coils are paired as the electric vehicle (EV) maintains its position during charging. In dynamic induction charging, secondary coils attached to a moving vehicle are charged using a nearly continuous primary coil (often embedded in the road) consisting of coils extended in the direction of travel. In semi-dynamic charging, the same primary and secondary coils as in the static induction charging system are used, but the charging time by each primary coil is extended because the operating angle at which the secondary coil can be charged is increased.

[0112] In this exemplary configuration, the wireless charger 1101 is powered by a wired DC connection 1108 to the local power grid (not shown).

[0113] In some cases, a mechanical actuator system for connecting physical wiring can be used for mechanical charging. The illustrated pantograph system 1109 is one alternative system that uses physical connections.

[0114] Figure 12 A flowchart of exemplary method 1200 for strategic opportunity charging in an exemplary configuration is shown.

[0115] As illustrated, Method 1200 includes the step of creating a model based on historical data, similar routes, near real-time sensor data, and third-party data to generate optimal routes for electric vehicles (EVs) used in commercial and non-commercial environments in Method 1210. The collected data relates to the environment, electric vehicle (EV) characteristics, power consumption, charger characteristics, charging sessions, energy cost data, traffic volume, and route data for a single electric vehicle (EV) or a group of electric vehicles (EVs).

[0116] After the data model is created in 1210, the collected data is processed using the data model, and in 1220, an initial estimate of the total cost per distance (TCD) over the predicted route segment is provided.

[0117] In 1230, the route and charging along the route segments are optimized to reduce the total cost per distance (TCD) over the entire route of an electric vehicle (EV) or group of electric vehicles (EVs).

[0118] Next, the electric vehicle (EV) begins traveling along the route. In 1240, while the electric vehicle (EV) is traveling along the route, telemetry devices and third-party systems collect data on the environment, the electric vehicle (EV), power consumption, power costs, and time of day of travel in near real time.

[0119] At the end of the current route segment, the Total Cost per Distance (TCD) is calculated at 1250. The Total Cost per Distance (TCD) is calculated using a data model generated based on data such as the environment, electric vehicle (EV), power consumption, power cost, time of day, and traffic volume collected as the electric vehicle (EV) travels through the current route segment.

[0120] In step 1260, updated estimates are calculated for the next route section based on fluctuations in the environment, electric vehicles (EVs), power consumption, power costs, time of day, and traffic volume. The updated estimates further include the times and locations along the next route section where the electric vehicle (EV) should charge to achieve the optimal total cost per distance (TCD) using available chargers and available stopping time.

[0121] Steps 1240-1260 are repeated for each path segment until the process is reset.

[0122] Figure 13 Figure 13 is a schematic flowchart illustrating the wireless charging operation at a time-limited parking location using a wireless charger. The electric vehicle (EV) first arrives in a first charging state (SoC) (1301) and is guided to the wireless charger. This guidance may be performed via a wireless link, but can also be performed by indicator lights, signs, or automatic steering assistance (see, for example, U.S. Patent No. 10,040,360, entitled "Method and Apparatus for the Alignment of Vehicles Prior to Wireless Charging Including a Transmission Line That Leaks a Signal for Alignment," and U.S. Patent Application No. 17 / 646,844, entitled "Method and Apparatus for the Selective Guidance of Vehicles to a Wireless Charger").

[0123] Before charging begins (1302), the ground charger and vehicle power receiver are adjusted to ensure efficient wireless transmission at a fixed alignment position and air gap. Information for authentication and billing is exchanged via the wireless connection. In the case of modular ground chargers, multiple frequencies and phases can be set (see, for example, U.S. Patent Application No. 17 / 207,257, entitled "MODULAR MAGNETIC FLUX CONTROL").

[0124] During charging (1303), the vehicle's battery management system and the ground charger's control unit (not shown) adjust the supply current. The ground control unit sets an initial maximum supply current according to the dispatch control unit's instructions (based on a data model), and then changes the supply current according to new instructions during charging.

[0125] Once the charging session is complete, the electric vehicle (EV) departs the charging station with a new charge state (SoC) (1304).

[0126] In the example of a route bus, electric vehicles (EVs) have set arrival and departure times, and therefore have a predetermined total stopping time (1305). The charging interval 1306 is part of the total stopping time 1305.

[0127] Figure 14 Figure 14 is a timing diagram showing the interaction between the charger and the vehicle. In Figure 14, the charging manager 1401 may be an application running on the dispatch server 1001, or a local control device (for example, a charging station server (first disclosed in U.S. Patent Application No. 17 / 199,234, filed March 11, 2021, entitled “OPPORTUNITY CHARGING OF QUEUED ELECTRIC VEHICLES,” which is incorporated herein by reference)). The charging station server includes the charging manager 1401 software and manages power supply (from the power company and local storage), the connection between the charging station's internal communication links (both bridging and routing) and the wireless charger 1402, and the interconnection of the charging station with external entities (servers, data repositories, cloud instances). All messages in this example are paired, and each transmission is accompanied by an acknowledgment.

[0128] In this example, the ground charger assembly (GCA) 1402 includes a near-field wireless communication interface (detailed in U.S. Patent No. 11,121,740, “NEAR FIELD, FULL DUPLEX DATA LINK FOR RESONANT INDUCTION WIRELESS CHARGING,” which is incorporated herein by reference). When using the near-field wireless communication interface, a physically corresponding vehicle receiver assembly (VRA) 1403 must be located on the opposite side across the air gap 1404 in order to charge. Alternative or auxiliary wireless communication links based on wireless local area network (W-LAN) technology (e.g., IEEE 802.11, Zigbee, Bluetooth®, etc.) may also be used.

[0129] Before the wireless charging session 1406 is initiated, the ground charger assembly (GCA) 1402 and the vehicle receiver assembly (VRA) 1403 exchange messages for authorization, mutual authentication (in this model, the reliability of both the ground charger assembly (GCA) 1402 and the vehicle receiver assembly (VRA) 1403 has not been established), and billing. The operation of the battery management system (BMS) in this example is included in the functions and pass-through messages of the vehicle receiver assembly (VRA) 1403.

[0130] Standardized messaging for charging wireless power transfer systems for electric vehicles (EVs) has been published by the International Engineering Consortium (IEC) as IEC 61980, Parts 1, 2, and 3. Although it differs from the uses and messaging supported herein, "IEC 61980-3:2022; ELECTRIC VEHICLE WIRELESS POWER TRANSFER (WPT) SYSTEMS - Part 3: Specific requirements for magnetic field wireless power transfer systems," published in 2022, is useful as a general description.

[0131] Immediately before charging, by exchanging wireless messages 1405, the alignment between the ground charger assembly (GCA) 1402 and the vehicle receiver assembly (VRA) 1403 can be measured and ensured, the magnetic gap can be measured, the efficient magnetic transmission frequency can be determined (see U.S. Patent Application No. 17 / 643,764, "Charging Frequency Determination for Wireless Power Transfer," which is incorporated herein by reference), and information regarding capabilities and limitations can be exchanged.

[0132] Once the preliminary message transmission 1405 is completed and the charging session 1406 has begun, the vehicle receiver assembly (VRA) 1403 and the ground charger assembly (GCA) 1402 begin exchanging informational messages 1407, which include electrical data, temperature data, and / or radio sensor data related to the ground charger assembly (GCA) 1402 and the vehicle receiver assembly (VRA) 1403, and provide a periodic heartbeat. Informational messages 1407 include information on the voltage, temperature, and charge status of the battery pack provided by the battery management system (BMS). Streaming of informational messages 1407 continues throughout the wireless power transmission (while magnetic flux is being generated).

[0133] Next, the vehicle receiver assembly (VRA) 1403 initiates the transmission of a power request / response message 1408 to the charge manager 1401 via the ground charger assembly (GCA) 1402 by radio signal transmission over the air interface 1404. The power request may include a requested current level, and the power response may include an permitted current value. The power request may also include a preferred current level and a maximum current level, and the power response may include an permitted current value less than or equal to the requested value or the maximum current level.

[0134] The ground charger assembly (GCA) 1402 sends a message to the receiver assembly (VRA) 1403 confirming the initial current level assignment 1409 and energizing the charging signal. Message transmission from the heartbeat / telemetry device 1407 continues during power transmission 1410. In this example, the charging manager 1401 sends a rating command 1411 to the ground charger assembly (GCA) 1402 involved in the charging session 1406. The rating command 1411 includes a current level that is either above or below the initial current level (if the current level is zero, the charging session 1406 may be paused or terminated prematurely). The updated current level is passed to the vehicle receiver assembly (VRA) 1403 before the charging signal is changed. The vehicle receiver assembly (VRA) 1403 acknowledges the updated current level in response 1409 and may request a new maximum allowable current level or any current level below the updated current level.

[0135] During the second wireless power transmission period 1413, the ground charger assembly (GCA) 1402 provides a magnetic signal to the receiver assembly (VRA) 1403 to generate a new allowable current level. The electric vehicle terminates wireless power transmission by setting the requested current level to zero via the battery management system (BMS) and the receiver assembly (VRA) 1403. The ground charger assembly (GCA) 1402 pauses the charging signal and notifies that the session has ended with an termination notice 1414. The termination notice 1414 causes the ground charger assembly (GCA) 1402 to pass data regarding collection time, sensors, and performance to the charging server 1401 for storage and analysis.

[0136] demand charging Figure 15 Figure 15 is a graph showing the public utility charge rate for corporate customers. This example can target a single charging station or multiple charging stations together.

[0137] The x-axis (1501) represents time, and the y-axis (1502) represents power consumption (kW). Power consumption fluctuates from a baseline (1504) to peak demand (1506) throughout the billing period. Average power consumption (1505) can be determined over the billing period (1503). Electricity charges for wireless power transmission from the power company are assumed to consist of both usage-based and demand-based charges.

[0138] Usage-based charges consist of fixed and variable charges, namely transmission and distribution charges (T&D) for infrastructure and supply charges based on consumption during the billing period. Note that supply charges may vary depending on the time of day and season (known as time-of-use electricity rates (TOU)). Usage-based charges are typically measured in kilowatt-hours (kWh).

[0139] Demand-based pricing is based on the maximum amount of electricity required during a specific period within the billing cycle (for example, one hour or a certain percentage of one hour). Demand-based pricing is typically measured in kilowatts (kW).

[0140] By utilizing the charging schedule settings of electric vehicles, it is possible to adjust the charging schedules of individual vehicles in a group to limit simultaneous charging, and further optimize the geographical distribution of chargers, thereby reducing demand-based charges for electric vehicle groups.

[0141] By strategically positioning charging stations, it is possible to limit the number of chargers at each stop to less than one on average, minimizing infrastructure costs, including transmission and distribution (T&D) charges. In such a charging arrangement, it is also possible to install multiple chargers at stops where multiple electric vehicles (EVs) are parked.

[0142] By prioritizing charging throughout the day for electric vehicles (EVs) and using wireless charging while stationary to control the State of Charge (SoC), it is possible to minimize electricity costs by charging only the amount of power necessary to reach the next charger (with a backup charge) during peak electricity rates. Prioritizing charging also includes increasing the charge (increasing the SoC of the electric vehicle (EV)) at charging stations with favorable electricity rates.

[0143] Figure 16 Figure 16 is a top view of a charging station located at a bus stop (in this example, a bus stop). The wireless power charging station 1601 in this example has three chargers 1602, 1603, and 1604 that can supply power to up to three electric vehicles simultaneously. In this example, each charger 1602, 1603, and 1604 charges the buses 1605, 1606, and 1607.

[0144] All three chargers are connected to power electronics unit 1608 via underground electrical connections (not shown). Power electronics unit 1608 is connected to the power grid via service drop 1609. The power company can supply AC, DC, or AC three-phase power via service drop 1609.

[0145] The power electronics 1608 may include a local power storage device 1610 (e.g., a battery) used to prevent exceeding the simultaneous demand threshold.

[0146] The local energy storage unit 1610 (see U.S. Patent Application Publication No. 2022-0368161, filed October 30, 2020, entitled "Contactless swappable battery system") enables "peak shaving," where energy is stored (trickle charged) during off-peak hours and used during peak hours. The battery storage device may be physically replaceable (for temporary or emergency use), or it may be charged from the power company's service drop 1609, preferably during off-peak hours. The local energy storage unit 1610 can also be charged using alternative power sources such as wind or solar power plants.

[0147] To curb demand and prevent it from exceeding a desired maximum demand threshold, the charging station control device can perform the following actions:

[0148] a. Distribute the available power equally among all electric vehicles (EVs).

[0149] b. Prioritize supplying power to electric vehicles (EVs) with the highest demand (the charge state (SoC) required to reach the next charger).

[0150] c. Prioritize arrival and departure times, and adjust the power supply according to the arrival and departure of each vehicle.

[0151] d. Optimize power supply based on time-of-day billing rates.

[0152] Demand-based pricing may be location-specific (e.g., size of the transformer and meter) rather than regional. Regional-level control can also be provided through other means (e.g., openADR: Automatic Demand Response).

[0153] Demand-based pricing may also be calculated by aggregating charges across the entire service area where a power company provides services to a single customer.

[0154] Furthermore, available electricity may be set by local or national government measures, rather than by a desirable simultaneous demand threshold, in order to minimize electricity demand.

[0155] Figure 17 Figure 17 is a geographical diagram showing the ability to reduce (or at least manage) the public demand-based portion of electricity costs through strategic opportunity charging. In region 1701, chargers are located on fixed routes (pre-planned) or temporary routes (changeable) to serve a population of electric vehicles (EVs).

[0156] The area includes single charging stations 1702, 1703, and 1704, two charging stations 1705 and 1706, and three charging stations 1707 and 1708. These charging stations 1702, 1703, 1704, 1705, 1706, 1707, and 1708, and the number of chargers per charging station, are positioned based on the projected charging demand of the vehicle fleet. Geographically, charging stations 1702, 1703, 1704, 1705, 1706, 1707, and 1708 are located at different distances from each other, as are passenger or delivery stops (not shown).

[0157] By coordinating the charging session schedules for each vehicle in the fleet (1709, 1710, 1711, and 1712), the total power demand can be kept below a threshold that would lead to increased electricity costs. This geospatial simultaneous demand minimization technique can be further optimized for the entire fleet by adding opportunity charging stations in appropriate locations to minimize fluctuations in electric vehicle (EV) demand at specific charging locations or stations.

[0158] Figure 18 Figure 18 shows an example of a single electric bus operating on a single route. The electric bus vehicle 1801 is traveling on route 1802, which is mapped to local road 1803. A connecting road 1804 equipped with a wireless machine charger 1805 allows the electric bus vehicle 1801 to be charged for a set power level and scheduled stop time.

[0159] Information regarding the status, running conditions, load status, adherence to the operating schedule, and power consumption of bus 1801 is collected and transmitted in near real-time along route 1802 or at stop 1806.

[0160] Figure 19 Figure 19 shows an example where multiple electric buses operate simultaneously on a single route. This configuration aims to reduce passenger waiting times and / or ensure sufficient capacity to operate the route.

[0161] The first electric bus vehicle 1901 and the second electric bus vehicle 1902 are traveling on route 1903, which is mapped to local road 1904. Connecting road 1905, where wireless machine chargers 1907 are installed, allows the first and second electric bus vehicles 1901 and 1902 to be charged at set power levels and during scheduled stops for passenger boarding and alighting.

[0162] Information regarding the status, running conditions, load status, adherence to the operating schedule, and power consumption of buses 1901 and 1902 is collected and transmitted in near real-time along route 1903 or at stop 1906.

[0163] Figure 20 Figure 20 shows an example where multiple electric buses operate on multiple routes simultaneously. These buses may belong to the same vehicle group or to different vehicle groups. In this example, the wireless charging infrastructure is shared.

[0164] Route 1, Route 2001, and Route 2,

[0165] Wireless charging stations 2008 are installed along the first route 2001 and the second route 2002. The first and second timetables need to be adjusted to provide sufficient charging time for each electric bus vehicle 2004, 2005, 2006, and 2007. In addition, power consumption and the power supplied by wireless charging stations 2008 (and other shared or unshared chargers such as wireless charging stations 2009) need to be adjusted (by the dispatch server 1001). This will avoid exorbitant time-of-use and demand-based electricity rates, as well as prevent excessive strain on the cooling capacity of the wireless chargers during charging and between charging sessions.

[0166] Information regarding the status, driving conditions, load status, adherence to the operating schedule, and power consumption of buses 2004, 2005, 2006, and 2007 is collected and transmitted in near real-time along routes 2001 and 2002, or at wireless chargers 2008 and 2009 at bus stops. Information regarding wireless chargers 2008 and 2009 themselves is also transmitted in near real-time, or collected and transmitted periodically or triggered by predetermined events (e.g., before, after, and during charging sessions).

[0167] Additional Embodiments Figure 21 Figure 21 shows an example of a drayage yard 2101 using electric freight transport vehicles (not shown) with strategic wireless machine charging capabilities. In this example, both load weight and distance traveled are the main factors determining the battery consumption of the electric transport vehicles (e.g., forklifts, side loader forklifts, reach trucks, etc.). Figure 21 shows the movement of container cargo as an example.

[0168] Containers are loaded and unloaded from the freight rail system 2102 by yard vehicles. A dedicated container handling machine 2103 is used to transport freight containers from arriving rail vehicles to storage locations 2104 within the local rail yard. Near the stacked containers 2104 within the local rail yard, transport vehicles equipped with wireless power transmission (WPT) receivers are positioned, which can move containers to or from stacked containers 2105 in the truck yard, stacked containers 2106 in the shipyard, or stacked containers 2107 in the temporary storage area. Since transport vehicles frequently arrive at each of the stacked containers 2104, 2105, 2106, and 2107, it is possible to install wireless chargers at each storage location based on the container usage and waiting time. In this example, rail yard chargers 2108, shipyard chargers 2109, and truck yard chargers 2110 are installed. In this example, no shared charger is installed at temporary storage location 2107.

[0169] The stacked containers 2105 in the truck yard are added by unloading trucks using crane equipment 2111, added by transported containers, or reduced by loading containers onto trucks, or by using transport vehicles to move containers to other means of transport or storage locations 2107.

[0170] The stacked containers 2106 within the shipyard are added by unloading trucks using cargo crane equipment 2111, added by transported containers, or reduced by loading containers onto ships or barges (not shown), or by using transport vehicles to move containers to other means of transport or storage locations 2107.

[0171] The stacked containers 2107 within the storage area can be added to or removed by transporting containers to or from the stacked containers within each transport yard.

[0172] The transport management application (similar to the software and database used in dispatch center 1001) minimizes downtime and running costs by managing the opportunity charging schedule, charge level, and charging time for each charging session of each charger 2108, 2109, and 2110, using near real-time data on the weight of the cargo container (weight on the cargo manifest or weight measured by sensors on the transport vehicle), the location of the cargo container, the location of the transport vehicle, the charge status of the transport vehicle, the destination of the container (and therefore the distance traveled), and the current waiting status of the stacked containers at the source and destination.

[0173] The drayage yard 2101 also includes a rest and repair depot 2113 and associated wireless chargers 2114.

[0174] A branch of Highway 2115 connects to Drayage Yard 2101, which serves as a connecting road for both freight trucks and employees' personal vehicles.

[0175] Figure 22 Figure 22 is a geographical diagram showing the routes of electric delivery vehicles that utilize strategic opportunity charging to minimize driving costs.

[0176] Delivery vehicles are stored and maintained at depot 2201, located a distance 2202 from the first distribution center 2203. Wireless machine chargers may be installed at the first distribution center 2203. Delivery vehicles travel along a first route 2204, which includes multiple stops. At the stops, delivery only, pickup, or pickup and delivery takes place, depending on the type of delivery service provided. The vehicles return to the first distribution center 2203 via the first route 2204, where cargo is loaded or unloaded during machine charging sessions as needed.

[0177] The second delivery route 2205 includes a third-party public or subscription-based wireless charger 2206 that can charge the delivery vehicle's battery as needed or desired to keep it within a desired charge state (SoC) range.

[0178] From the end point of the second delivery route 2205, the vehicle returns to the first delivery center 2203, where loading or unloading of goods takes place during the opportunity charging session, as needed.

[0179] The third delivery route 2207 includes a stop at the second delivery center 2208 as one of its regular stops. The second delivery center 2208 includes a wireless machine charger that can be used to charge the delivery vehicles. The third delivery route 2207 ends at the first delivery center 2203, where the vehicles are unloaded and charged to an optimal charge level for overnight storage at depot 2201, taking into account the amount of charge required to travel the distance 2202 to depot 2201.

[0180] Implementation of temporary delivery service Strategic opportunity charging in electric taxis, courier services, and freight transport services differs from charging at known routes, known stops, and pre-deployed charging stations in the embodiments described above. In these electric vehicles, it is not possible to pre-plan routes and stops for passengers, cargo, and opportunity charging for a given day of service.

[0181] Alternatively, in geographically dispersed vehicle fleets, a combination of private and commercial onboard chargers is used throughout the service area, and temporary routes are estimated using modeled and stored historical data, traffic data, vehicle range efficiency, and locations (vehicles, pickup or pick-up, delivery or drop-off, and chargers). Radio data communication between ride-hailing services, customers, and vehicles is essential for such temporary services. Exemplary temporary services apply to electric vehicles (EVs) with a driver on board, electric vehicles (EVs) equipped with driver assistance software, or fully autonomous electric vehicles (EVs).

[0182] The selection of electric vehicles (EVs) for passenger or cargo pick-up services can be performed by selecting candidate EVs based on service criteria (pick-up or pick-up time, drop-off or delivery time, and power efficiency) using location data, estimated travel time, and estimated power consumption.

[0183] Since dedicated charging stations are not envisioned, potential charging stations may include wired and wireless facilities, and the selection of charging stations will be based on scheduling, estimated detour distance, estimated charging time, and compatibility between electric vehicles (EVs) and chargers.

[0184] Figure 23 Figure 23 is a typological diagram of additional charging components in public and private charging stations. Figure 23 shows new charging and communication functions added to the diagram in Figure 10. Additional facilities may be utilized by hybrid wireless / wired electric vehicles (EVs) 2301 configured to support bidirectional charging functions in order to support charging of both wireless and wired electric vehicles (EVs) (wired electric vehicles (EVs) may include conventional plug-in chargers and the pantograph system shown in Figure 11).

[0185] For example, as shown in the figure, a charging station 2302 equipped with a wired plug-in device or a pantograph, which has a power electronics device 2303 that supports one or more charge levels using AC charging and / or DC charging, communicates with a dispatch facility 1001 via a data communication network 1005 using a wireless data connection 1004 via an antenna 1012. A bidirectional wired station 2304 can also be used for charging (or discharging) electric vehicles (EVs). In a bidirectional embodiment, the power electronics device 2305 can draw power from a local battery facility or a power grid (not shown). A wireless bidirectional station 2306 equipped with a bidirectional wireless charger 2307 and a bidirectional power electronics device 2308 that can draw power from or supply power to a local battery power storage facility or a power grid (not shown) can also be provided for use in charging (or discharging) electric vehicles (EVs) 2301.

[0186] Figure 24 Figure 24 illustrates an exemplary scenario in an exemplary configuration of a ride-hailing service utilizing electric vehicles (EVs) and machine charging. Figure 24 topographically depicts an ad hoc delivery service with added pickup and delivery locations and times (or dates and times). This scenario applies to both package delivery services and taxi services using electric vehicles (EVs). In this example, immediate pickup and the fastest possible delivery are required. Fastest arrival at the destination means the most time-efficient route. Economical arrival at the destination means the most cost-efficient route. The shortest route is either the fastest route or the shortest distance route.

[0187] The dispatch facility 1001 receives a request to request pickup at a designated pickup location 2401 at a predetermined pickup time. The dispatch facility 1001 detects all available electric vehicles (EVs) that are within (or scheduled to enter) the geographical area 2402 near the pickup location 2401. Some electric vehicles (EVs) may not be available at the dispatch facility 1001 due to being in operation or due to scheduling conflicts. The dispatch facility 1001 queries the status of available electric vehicles (EVs) 2403, 2404, 2405, and 2406, including their current state of charge (SoC). Electric vehicles (EVs) 2403, 2404, and 2405 report their respective status, including their current state of charge (SoC). Electric vehicle (EV) 2406, which is charging at charger 2407, can also report its current state of charge (SoC) and its expected state of charge (SoC) at the end of scheduled charging.

[0188] Based on the current charge status (SoC), the capabilities of the geographic information system (GIS), and traffic forecasts, dispatch facility 1001 calculates the amount of charge required for each candidate electric vehicle (EV) 2403, 2404, 2405, and 2406 to travel to pickup location 2401, travel to delivery location 2409 using the shortest route 2408, and then proceed to the charging station 2410 closest to delivery location 2409. The selection of a charger after delivery (if necessary) is determined by the distance traveled from delivery location 2409 based on power consumption, thereby generating a geographical search area 2411 for chargers. Based on the fastest pickup and delivery option, electric vehicles (EVs) with sufficient charge to complete the journey and that can receive the package with minimal delay after dispatch facility 1001 receives the request are selected for delivery.

[0189] If none of the candidate electric vehicles (EVs) 2403, 2404, 2405, and 2406 have enough charge to complete their individual routes, it is possible to incorporate opportunity charging sessions at available chargers 2412 by modifying the individual routes. In this case, all candidate electric vehicles (EVs) are guided to charger 2412 near the intermediate point using the shortest route 2413 between collection point 2401 and charger 2412. Additionally, the shortest route 2414 from charger 2412 to delivery point 2409 is generated for each electric vehicle (EV).

[0190] Alternatively, the charger 2415 located near the collection point 2401 can be scheduled to charge any candidate electric vehicles (EVs) 2403, 2404, 2405, and 2406 before and after collection. This allows the vehicle to reach the intermediate charger 2412 with minimal delay in collection time, or reach the destination 2409 without additional charging.

[0191] Based on the fastest pickup and delivery options, an electric vehicle (EV) is selected that can receive the package with minimal delay from the time dispatch facility 1001 receives the request and complete the journey in the shortest possible time (including charging time).

[0192] Figure 25 Figure 25 illustrates an exemplary scenario for rescheduling and rerouting a delivery electric vehicle (EV) using wireless and / or wired machine charging. In this scenario, delivery electric vehicle (EV) 2501 departs from depot 2502 on a planned itinerary that includes route segments 2503, 2505, 2507, destinations 2504, 2508, and a charging station 2506. After departing depot 2502, a new parcel pickup location 2509 within a general service area 2511 is added to the itinerary, requiring a recalculation of the route, arrival time, departure time, and the state of charge (SoC) required to complete each route segment.

[0193] The dispatch facility 1001 adds two route segments 2512 and 2513 to the itinerary to reach the new destination 2509. The additional destination 2509 has a charging station 2510 located adjacent to it. Based on the schedule, distance, and calculated charge status, the dispatch facility 1001 cancels the charging session scheduled at the previous charging station 2506 and the two associated route segments 2505 and 2507.

[0194] The new itinerary includes all previously scheduled and new destinations, within the scope of the goals of minimizing distance traveled, minimizing schedule interruptions, and maintaining the State of Charge (SoC) between thresholds. The availability of charger 2514 at depot 2502 allows electric vehicle (EV) 2501 to depart at maximum SoC operating capacity and return to depot 2502 at minimum SoC operating capacity.

[0195] Figure 26 Figure 26 is a flowchart illustrating the selection and assignment of event sequences for electric delivery or shuttle vehicles to handle requests for transporting people or goods by electric service vehicles using machine charging in an exemplary configuration.

[0196] In Figure 26, the dispatch facility 1001 receives a task request 2601 relating to the collection and delivery of goods or the transportation of people. The task request 2601 includes the collection or pick-up location and the delivery or drop-off location, time constraints for either or both of the collection or pick-up and delivery or drop-off, the requester's preferences regarding the handling of goods or people (e.g., vehicle type, vehicle equipment (e.g., refrigeration equipment, ramps, wheelchair lifts, mileage and service fees for electric vehicles (EVs)), and type of service (e.g., courier service, limousine, taxi, public transport, light / medium / heavy freight trucks)). Based on the request, a set of candidate electric vehicles (EVs) is determined.

[0197] Based on the client's preferences and specified time in request 2601, the dispatch facility 1001 performs a preliminary route analysis 2602 regarding the route between the pickup or pick-up location and the delivery or drop-off location. Estimates of travel time and power consumption (for a particular vehicle class) between the points are calculated.

[0198] The number of candidate electric vehicles (EVs) capable of performing the task is narrowed down using the availability of electric vehicles (EVs) 2603 within the service time frame (determined by the time constraints in task request 2601). Here, electric vehicles (EVs) that are unable to perform pickup or pick-up and delivery or drop-off scheduling are excluded from further consideration.

[0199] Localization 2604 is used to further narrow down the list of suitable electric vehicle (EV) candidates for the task. Localization 2604 takes into account both the estimated current consumption and the estimated time required to arrive at the pickup or pick-up point.

[0200] For each of the remaining candidate electric vehicles (EVs), route calculation 2605 is performed by the dispatch facility 1001 to calculate an individual route. Each route includes the scheduled time and location for pickup or pick-up, the scheduled time and location for delivery or drop-off, and power consumption. To maintain the electric vehicle's (EV) charge state within the threshold of the operating charge state (SoC), opportunity charging sessions may be incorporated into the route plan, each opportunity charging session having an scheduled arrival time and departure time, and a total power transmission amount (represented by the charge state (SoC)). Electric vehicles (EVs) on routes that cannot meet the scheduled pickup or pick-up time, or the scheduled delivery or drop-off time, are excluded from the candidates.

[0201] From the remaining candidate electric vehicles (EVs), one EV is assigned to process the request at 2606. In the final selection, both adherence to time constraint parameters and minimization of predicted power consumption are considered.

[0202] While examples of commercial vehicles such as buses, drayage vehicles, and delivery vehicles have been given, it should be understood that the methods described herein are also applicable to non-commercial vehicles such as electric vehicles (EVs) driven by individuals, with or without software-based driver assistance. The same methods are also applicable to autonomous vehicles.

[0203] conclusion While various embodiments have been described above, it should be understood that these embodiments are presented for illustrative purposes only and are not limiting. For example, any of the elements related to the systems and methods described above may adopt any of the desirable functions described above. Therefore, the scope of preferred embodiments should not be limited by any of the exemplary embodiments described above.

[0204] As described herein, logic, commands, or instructions for implementing aspects of the methods described herein can be provided in computing systems including any number of form factors for computing systems such as desktop or notebook personal computers, tablets, netbooks, and mobile devices such as smartphones, client terminals, and server-hosted machine instances. Another embodiment described herein involves incorporating the techniques described herein into another form, which includes another form of programmed logic, hardware configuration, or special component or module, including an apparatus having each means for performing the functions of the techniques. Each algorithm used to perform the functions of such techniques may include a sequence of some or all of the electronic operations described herein, or other forms described in the accompanying drawings and detailed description. A system and computer-readable medium containing instructions for performing the methods described herein also constitute an exemplary embodiment.

[0205] The processing functions described herein can be implemented in software in one embodiment. The software may consist of computer-readable media such as one or more non-temporary memories or other types of hardware-based storage devices, or computer-executable instructions stored in computer-readable storage devices, whether local or network-connected. Furthermore, such functions may correspond to modules and may be software, hardware, firmware, or any combination thereof. Multiple functions may be performed in one or more modules as needed, but the embodiments described are merely examples. The software may run on a computer system such as a personal computer, a server, or other computer system modified to be programmed for a specific purpose, using a digital signal processor, an ASIC (Application-Specific Integrated Circuit), a microprocessor, or other type of processor.

[0206] As described herein, these embodiments may include, or operate on, a processor, logic, or a number of components, modules, or mechanisms ("modules"). A module is a tangible entity (e.g., hardware) capable of performing a specified operation and may be configured or arranged in a particular manner. For example, a circuit may be arranged in a manner designated as a module (e.g., internally or relative to an external entity such as another circuit). In an example, one or more computer systems (e.g., standalone, client, or server computer systems), or all or part of one or more hardware processors, may consist of firmware or software (e.g., instructions, part of an application, or an application) as a module that operates to perform a specified operation. In an example, the software may reside on a machine-readable medium. The software is executed by the underlying hardware of the module, thereby causing the hardware to perform the specified operation.

[0207] Accordingly, the term “module” is understood to encompass tangible hardware and / or software entities that are physically constructed or configured for a specific purpose (e.g., implemented by hardware), or temporarily configured (e.g., programmed) to operate in a specified manner or to perform some or all of any operations described herein. Considering an example where a module is temporarily configured, each module does not need to be instantiated at any given moment. For example, if a module consists of general-purpose hardware processors configured using software, the general-purpose hardware processors may be configured as different modules at different points in time. Thus, software can configure hardware processors, for example, to constitute a particular module at one point in time and different modules at another.

Claims

1. A method for setting routes for electric vehicles (EVs) for collection, delivery, or shuttle services using machine charging, A process of receiving a task request relating to the collection and delivery of goods or the transportation of people, wherein the task request includes a collection or pick-up location, a delivery or drop-off location, and at least one of the collection or pick-up time, or the delivery or drop-off time, A step of determining at least one candidate electric vehicle (EV) for the delivery of goods or transport of people based on the task requirements, A step of performing a preliminary route analysis on one or more routes between the pickup or pickup location and the delivery or drop-off location based on at least one of the pickup or pick-up time or the delivery or drop-off time in the task request, and determining estimated travel time and power consumption between points for the at least one candidate electric vehicle (EV); A step of determining from the at least one candidate electric vehicle (EV) which is available within the service time frame determined by at least one of the pickup or pick-up time or the delivery or drop-off time in the task request, A step of determining, among the at least one electric vehicle (EV) available within the service time frame, at least one electric vehicle (EV) that is sufficiently charged to reach the pickup or pick-up location, and an electric vehicle (EV) that requires charging along the way. A step of performing a route calculation for each of the at least one candidate electric vehicle (EV) to calculate an individual route between the pickup or pick-up location and the delivery or drop-off location, wherein the individual route includes an estimated pickup or pick-up time at the pickup or pick-up location, an estimated delivery or drop-off time at the delivery or drop-off location, and an estimated power consumption along the individual route. A step of assigning one electric vehicle (EV) from the at least one candidate electric vehicle (EV) in order to process the task request, wherein the electric vehicle (EV) is in a state of charge sufficient to meet the predicted power consumption on its individual route with minimal charging delay, and A method having

2. A method according to claim 1, wherein the step of assigning an electric vehicle (EV) to process the task request comprises the step of assigning an electric vehicle (EV) that minimizes the predicted power consumption from among the at least one candidate electric vehicle (EV).

3. In the method described in claim 1, further, A step of determining the minimum threshold charge state for each electric vehicle (EV) of the at least one candidate electric vehicle (EV), A step of identifying at least one charger located on or near the individual route for each electric vehicle (EV) of the at least one candidate electric vehicle (EV), The process involves incorporating a chance charging session by the at least one charger into the route calculation in order to maintain the charge state of each electric vehicle (EV) of the at least one candidate electric vehicle (EV) at a value higher than the minimum threshold, and A method having

4. A method according to claim 3, wherein the step of incorporating the machine charging session into a route calculation includes the step of estimating, for the machine charging session, the arrival time and departure time to the at least one charger, and the total amount of power transmitted during the machine charging session.

5. A method according to claim 1, wherein the task request further includes at least one preference relating to the handling of luggage or people, including the type of electric vehicle (EV), the electric vehicle (EV) equipment, the electric vehicle (EV) mileage and service charges, or the type of electric vehicle (EV) service.

6. The method according to claim 5, wherein the electric vehicle (EV) equipment includes the availability of at least one of a refrigeration unit, a ramp, or a wheelchair lift.

7. The method according to claim 5, wherein the type of service includes at least one of courier service, limousine, taxi, public transport, or freight truck.

8. In the method described in claim 1, further, To process the aforementioned task request, the process includes receiving a second task request for the assigned electric vehicle (EV) after the assigned electric vehicle (EV) has departed along the individual route, The process involves recalculating the route of the assigned electric vehicle (EV) required to complete each route segment, including the pickup or pick-up location and the delivery or drop-off location, in the second task request. If the assigned electric vehicle (EV) is in a sufficiently charged state to meet the predicted power consumption along the recalculated route, the steps include assigning the assigned electric vehicle (EV) to process the second task request, A method having

9. In the method according to claim 8, the step of recalculating the route of the assigned electric vehicle (EV) is: A step of performing a preliminary route analysis on one or more routes between the pickup or pickup location and the delivery or drop-off location in the second task request, based on at least one of the pickup or boarding time or delivery or drop-off time in the second task request, to determine an estimate of the travel time and power consumption between points for the assigned electric vehicle (EV); A step of determining whether the assigned electric vehicle (EV) is available within a service time frame determined by at least one of the pickup or pick-up time or the delivery or drop-off time in the second task request, A step of determining whether the assigned electric vehicle (EV) is charged to a level sufficient to reach the collection or pick-up location, A step of performing route calculations to calculate updated individual routes between the pickup or pick-up location and the delivery or drop-off location, The updated individual route includes the estimated pickup or pick-up time at the pickup or pick-up location, the estimated delivery or drop-off time at the delivery or drop-off location, and the predicted power consumption along the updated individual route. The updated individual route includes the pickup or pick-up location and the delivery or drop-off location in the task request, and the pickup or pick-up location and the delivery or drop-off location in the second task request. The calculation process described above A method having

10. A method according to claim 9, wherein the updated individual routes are optimized to minimize at least one of the distance traveled by the assigned electric vehicle (EV), the total power consumption, and the schedule interruption.

11. A non-temporary computer-readable medium having instructions stored on the computer-readable medium, and when such instructions are executed by one or more processors, the one or more processors, A process of receiving a task request relating to the collection and delivery of goods or the transportation of people, wherein the task request includes a collection or pick-up location, a delivery or drop-off location, and at least one of the collection or pick-up time, or the delivery or drop-off time, A step of determining at least one candidate electric vehicle (EV) for the delivery of goods or transport of people based on the task requirements, A step of performing a preliminary route analysis on one or more routes between the pickup or pickup location and the delivery or drop-off location based on at least one of the pickup or pick-up time or the delivery or drop-off time in the task request, and determining estimated travel time and power consumption between points for the at least one candidate electric vehicle (EV); A step of determining from the at least one candidate electric vehicle (EV) which is available within the service time frame determined by at least one of the pickup or pick-up time or the delivery or drop-off time in the task request, A step of determining, among the at least one electric vehicle (EV) available within the service time frame, at least one electric vehicle (EV) that is sufficiently charged to reach the pickup or pick-up location, and an electric vehicle (EV) that requires charging along the way. A step of performing a route calculation for each of the at least one candidate electric vehicle (EV) to calculate an individual route between the pickup or pick-up location and the delivery or drop-off location, wherein the individual route includes an estimated pickup or pick-up time at the pickup or pick-up location, an estimated delivery or drop-off time at the delivery or drop-off location, and an estimated power consumption along the individual route. A step of assigning one electric vehicle (EV) from the at least one candidate electric vehicle (EV) in order to process the task request, wherein the electric vehicle (EV) is in a state of charge sufficient to meet the predicted power consumption on its individual route with minimal charging delay, and A computer-readable medium that implements a dispatch system for setting routes for electric vehicles (EVs) for pickup, delivery, or shuttle services, utilizing machine charging, by executing a process that includes the following.

12. A computer-readable medium according to claim 11, wherein, in order to process the task request, an instruction for assigning one electric vehicle (EV) is, when executed by the one or more processors, an instruction for performing the step of assigning an electric vehicle (EV) that minimizes the predicted power consumption from among the at least one candidate electric vehicle (EV).

13. In the computer-readable medium according to claim 11, further, When executed by the aforementioned one or more processors, the aforementioned one or more processors, A step of determining the minimum threshold charge state for each electric vehicle (EV) of the at least one candidate electric vehicle (EV), A step of identifying at least one charger located on or near the individual route for each electric vehicle (EV) of the at least one candidate electric vehicle (EV), The process involves incorporating a chance charging session by the at least one charger into the route calculation in order to maintain the charge state of each electric vehicle (EV) of the at least one candidate electric vehicle (EV) at a value higher than the minimum threshold, and A computer-readable medium containing instructions that cause additional processing to be performed, including [specific processing].

14. The computer-readable medium according to claim 13, wherein the instructions for incorporating the machine charging session into a route calculation include, when executed by the one or more processors, instructions that provide an estimated arrival time and departure time to the at least one charger for the machine charging session, and an estimated total power transmission amount during the machine charging session.

15. A computer-readable medium according to claim 11, wherein the task request further includes at least one preference relating to the handling of luggage or people, including the type of electric vehicle (EV), the electric vehicle (EV) equipment, the electric vehicle (EV) mileage and service charges, or the type of electric vehicle (EV) service.

16. The computer-readable medium according to claim 15, wherein the electric vehicle (EV) equipment includes the availability of at least one of a refrigeration unit, a ramp, or a wheelchair lift.

17. A computer-readable medium according to claim 15, wherein the type of service includes at least one of courier services, limousines, taxis, public transport, or freight trucks.

18. In the computer-readable medium according to claim 11, further, When executed by the aforementioned one or more processors, the aforementioned one or more processors, To process the aforementioned task request, the process includes receiving a second task request for the assigned electric vehicle (EV) after the assigned electric vehicle (EV) has departed along the individual route, The process involves recalculating the route of the assigned electric vehicle (EV) required to complete each route segment, including the pickup or pick-up location and the delivery or drop-off location, in the second task request. If the assigned electric vehicle (EV) is in a sufficiently charged state to meet the predicted power consumption along the recalculated route, the steps include assigning the assigned electric vehicle (EV) to process the second task request. A computer-readable medium containing instructions that cause additional processing to be performed, including [specific processing].

19. In the computer-readable medium according to claim 18, when an instruction for recalculating the route of the assigned electric vehicle (EV) is executed by the one or more processors, the one or more processors shall A step of performing a preliminary route analysis on one or more routes between the pickup or pickup location and the delivery or drop-off location in the second task request, based on at least one of the pickup or boarding time or delivery or drop-off time in the second task request, to determine an estimate of the travel time and power consumption between points for the assigned electric vehicle (EV); A step of determining whether the assigned electric vehicle (EV) is available within a service time frame determined by at least one of the pickup or pick-up time or the delivery or drop-off time in the second task request, A step of determining whether the assigned electric vehicle (EV) is charged to a level sufficient to reach the collection or pick-up location, A step of performing route calculations to calculate updated individual routes between the pickup or pick-up location and the delivery or drop-off location, The updated individual route includes the estimated pickup or pick-up time at the pickup or pick-up location, the estimated delivery or drop-off time at the delivery or drop-off location, and the predicted power consumption along the updated individual route. The updated individual route includes the pickup or pick-up location and the delivery or drop-off location in the task request, and the pickup or pick-up location and the delivery or drop-off location in the second task request. The calculation process described above A computer-readable medium containing instructions that cause additional processing to be performed, including [specific processing].

20. In the computer-readable medium according to claim 19, further, When executed by the aforementioned one or more processors, the aforementioned one or more processors, A computer-readable medium having instructions to perform additional processing, including the step of optimizing the updated individual routes to minimize at least one of the assigned electric vehicle (EV) mileage, total power consumption, and schedule interruptions.

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