Strategic Opportunity Charging for Electric Vehicles Traveling on Predetermined Routes

Abstract

A method, system, and computer-readable storage medium for improving charging efficiency of electric vehicles (EVs) following a predetermined route are disclosed. The charging efficiency of the electric vehicles is improved by receiving telemetry data from the EVs and charger data from multiple chargers along the predetermined route, determining a charging plan for the EVs based on a total cost-per-distance (TCD) of travel over each of the multiple route segments comprising the predetermined route, and controlling specific chargers along the predetermined route to charge the EVs according to the charging plan. To enable power arbitrage, both the EVs and ground-based wireless charging stations are equipped with bidirectional charging capabilities. Individual EVs (e.g., transit buses traveling a set route and schedule) can store, transfer, and discharge power via bidirectional chargers using a predetermined charging strategy.

Description

[Technical Field]

[0001] This application claims priority to U.S. Patent Application No. 18 / 232,659, filed August 10, 2023, U.S. Patent Application No. 18 / 131,189, filed April 5, 2023, and U.S. Provisional Patent Application No. 63 / 436,419, filed December 30, 2022, all of which are incorporated by reference as if fully set forth herein.

[0002] The present disclosure relates to wireless transmission, and more particularly, to devices, systems, and methods relating to wireless transmission to remote systems, such as vehicles that include batteries. More particularly, the present disclosure relates to using wireless power transfer opportunity charging to minimize the total cost of traveling an electric vehicle on a known route. [Background technology]

[0003] Increasingly, transport vehicles, carriers, taxis, delivery vehicles and the like are being developed to be powered by electric motors and batteries.

[0004] A local bus is a public transport service on a fixed route that operates according to a set (pre-published) schedule, including arrival and departure times at geographically dispersed stops where passengers can board and disembark. A terminal is a location where a route begins or ends, where drivers temporarily disembark or change buses. A terminal also includes stops where passengers board and disembark vehicles. A bus depot is not only a terminal that serves bus passengers, but may also be a junction between different bus routes. The depot may also provide facilities for battery charging, vehicle maintenance, vehicle storage, food and beverages for bus drivers, rest, shifting, and waiting.

[0005] Drayage is the process of transporting freight over short distances. Drayage vehicles operate within a limited area, changing routes as needed to pick up, move, and deliver freight between multiple destinations.

[0006] Delivery vehicles can operate in several modes depending on the type of service. A package delivery vehicle can start fully loaded at a loading dock, depot, or terminal and deliver to one or more drop-off points via public roads. Package delivery vehicles can also load cargo at any drop-off point or at predetermined pickup points within a service area or on a pre-defined route. Delivery routes can be fixed, variable (including both pre-defined stops and new stops added during the delivery), or ad-hoc (the next stop is determined during or after the journey between current stops). Taxi passenger transportation services and ride-sharing are good examples of fully ad-hoc delivery services.

[0007] Static, opportunistic charging of electric vehicles (EVs) during loading, unloading, sorting, or short stops is an important application of wireless power transfer (WPT). EVs can be human-driven, equipped with automated driving assistance, or fully autonomous.

[0008] WPT enables fully autonomous power delivery to electric vehicles (EVs) without the need for a physical (wired) power connection. With WPT, drivers don't need to exit the vehicle to connect the charging cable (even if they are permitted to exit the vehicle while charging). WPT, also known as inductively coupled power transfer, uses a primary (ground) coil and a secondary (vehicle) coil that function as an open-core transformer, transferring power through an air gap, according to Faraday's first law of electromagnetic induction. [Brief explanation of the drawings]

[0009] These and other beneficial features and advantages of the present invention will become apparent from the following detailed description taken in conjunction with the accompanying drawings. [Figure 1] Figure 1 shows a graph of the relationship between the EV driving range from the start of charging and the battery's state of charge (SoC). [Figure 2] Figure 2 shows a graph of an example of extending EV range by charging along the route. [Figure 3] Figure 3 shows a graph of an example of extending driving range and battery life through strategic opportunistic charging. [Figure 4] FIG. 4 is a graph of electricity prices over a 24-hour period in one example. [Figure 5] Figure 5 plots the electricity prices of the first and second utilities that share the same geographic market as the deployed WPT system and offer different electricity rates based on time of day and generating capacity. [Figure 6] Figure 6 shows an example where two regions are traversed by an EV route. [Figure 7] FIG. 7 illustrates an exemplary geographic distribution of wireless chargers along a bus route. [Figure 8] FIG. 8 illustrates an example state machine for a transit bus with wireless charging. [Figure 9] FIG. 9 is a diagram that schematically illustrates a configuration in which a single charger is arranged corresponding to a first and second transportation route, and provides service to a plurality of electric vehicles (EVs) traveling on these routes. [Figure 10] FIG. 10 is a high-level diagram illustrating the communication paths available for data collection from electric vehicles (EVs) and wireless power transfer (WPT) chargers. [Figure 11] FIG. 11 is a diagram illustrating an example of a wireless charger site. [Figure 12] FIG. 12 is a flow chart illustrating a method for strategic opportunity charging in an example configuration. [Figure 13]FIG. 13 is a flow chart illustrating an entire wireless charging session. [Figure 14] FIG. 14 is a flow chart showing the interaction of the charger with the vehicle. [Figure 15] FIG. 15 is a diagram showing an example of a billing period including a capacity charge and a demand charge. [Figure 16] FIG. 16 is a plan view of a charging station equipped with multiple chargers capable of simultaneously charging multiple vehicles. [Figure 17] FIG. 17 illustrates a geographic application of strategic opportunity charging to manage electric utility demand charges. [Figure 18] FIG. 18 is a diagram showing an example in which a single electric bus operates on a single bus route. [Figure 19] FIG. 19 is a diagram showing an example in which a plurality of electric buses operate on a single bus route. [Figure 20] FIG. 20 is a diagram showing an example in which multiple electric buses operate on multiple bus routes and share a common machine charging infrastructure. [Figure 21] FIG. 21 illustrates an example of a drayage yard equipped with electric freight vehicles (not shown) using strategic wireless opportunistic charging. [Figure 22] FIG. 22 is a geographical diagram illustrating an electric delivery vehicle route that minimizes travel costs using strategic opportunity charging. [Figure 23] FIG. 23 is an exemplary high-level functional diagram of power flow passing and transformation by a bidirectional wireless power transfer (WPT) system that may be used for power arbitrage in a sample configuration. [Figure 24] FIG. 24 is a diagram illustrating a simplified example configuration for enabling power arbitrage using reverse metering in a sample WPT charging station configuration. [Figure 25] FIG. 25 is a diagram illustrating an example of the configuration of a system that manages a CaaS system at the functional element level. [Figure 26] FIG. 26 is a diagram showing an example of the configuration of a hybrid terminal that charges and discharges parked vehicles. [Figure 27]FIG. 27 is a diagram of a system for enabling power arbitrage, including elements shown as functional entities that may execute on separate or distributed processing hardware and data storage in a sample configuration. [Figure 28] Figure 28 includes a graph showing the capacity of an individual EV (e.g., a transit bus on a set route with a set schedule) used to store, transfer, and discharge power using a conservative charging strategy. [Figure 29] FIG. 29 illustrates a battery pack with ranges and thresholds in a sample configuration. [Figure 30] FIG. 30 is a flowchart showing the control logic for charging and discharging the battery pack in the sample configuration. [Figure 31] FIG. 31 is a flowchart showing the operational flow for controlling charging and discharging from an EV in operation (e.g., a transit bus on a set route with a set schedule, with estimated operating charge levels and power consumption per route section) in a sample configuration. [Figure 32] FIG. 32 includes a graph depicting the capacity used to store, transfer, and discharge electricity for an EV transportation fleet (e.g., multiple buses each operating a set route on a set schedule). DETAILED DESCRIPTION OF THE INVENTION

[0010] Strategic Opportunity: Wireless charging enables the expanded deployment of electric transit vehicles with no range or time limitations. Electric buses are emission-free, quieter, and with strategic opportunity wireless charging, can operate without having to go offline or deroute for refueling or charging. EV transit fleets are key to cleaning urban air, reducing traffic noise, and achieving decarbonization. Strategic Wireless Opportunity: Key to mass adoption and eventual automation of transportation.

[0011] Electric vehicles (EVs) use electric traction motors and batteries instead of internal combustion engines and chemical fuels. EVs are used in both battery electric vehicles (BEVs) and a variety of hybrid battery, internal combustion engine vehicles (HBEVs). Batteries or battery packs nominally include rechargeable chemical batteries, but can also include one or more of a capacitor bank, reversible fuel cells, solid-state batteries, or hybrid combinations of the foregoing. Improvements in energy storage technologies (e.g., solid-state batteries, hybrid batteries, ultracapacitors) can also take advantage of rapid opportunistic charging using high-power wireless power transfer.

[0012] Typically, an EV battery pack is made up of electrochemical cells. The commonly used lithium-ion secondary battery cells typically have a lifespan of 300-500 charge cycles. One charge cycle is the period of use from a fully charged state until the battery is fully discharged and then fully charged again.

[0013] These rechargeable lithium-ion batteries have a limited lifespan and their ability to hold a charge gradually decreases. This loss of capacity (battery aging) is irreversible. As the battery loses capacity, it can power the bus for a shorter period of time, thereby limiting its range and duration.

[0014] Much has been written about wireless charging's ability to reduce the required battery size and extend the driving range of electric vehicles. Strategic wireless opportunistic charging's ability to preserve the lifespan of lithium-ion batteries by maintaining the charge level of rechargeable batteries between high and low state-of-charge (SoC) thresholds has been field-proven to extend battery life. Charge rate, battery temperature before charging, and battery temperature during charging also affect lithium-ion battery life. Even solid-state batteries, which can last 1,000 to 10,000 charge / discharge cycles, benefit from managing charge level, charge rate, and battery temperature. In a mixed fleet, some EVs may be equipped with lithium-ion batteries and other solid-state batteries (or even multiple battery technologies in the same EV). In such a mixed fleet, it is beneficial to have individualized EV charging and performance data for modeling and machine learning.

[0015] Wireless mechanical charging describes how electric vehicles can utilize wireless chargers located at service areas or along routes, allowing them to be used along said routes without the "empty run" time of returning to a depot or garage to charge, saving both time and battery charge.

[0016] Opportunistic charging allows for the use of smaller batteries in EVs, resulting in reduced weight, longer driving range, and larger cargo capacity over that range.

[0017] Opportunity charging can also be used to maintain the battery's SoC between upper and lower thresholds, extending the battery's lifespan. EV battery packs are expensive to replace, and extending battery life can lower the total cost of distance (TCD).

[0018] Unlike plug-in chargers that require a connection, automated charging improves driver safety and comfort by eliminating the need to leave the vehicle at night or in bad weather. Automatic automated charging also makes electric vehicle charging accessible to people with disabilities.

[0019] In the examples that follow, electric buses that operate on short- to medium-distance routes as part of a local or regional network of publicly scheduled bus services are referred to as "routes." For route buses, a terminal is where a route begins or ends, where drivers briefly disembark or change buses, and also includes stops where passengers board and disembark. A bus depot is a terminal that includes maintenance and vehicle storage facilities. A stop is a transfer stop where passengers can board or disembark.

[0020] In the case of transit buses (and all vehicles using strategic opportunity charging protocols and systems), first-party data is defined as data transmitted from sensors onboard the EV. Second-party data refers to data transmitted from sensors on other EVs or metering stations (such as charging stations). Third-party data is data obtained from an external source that is not the original collector of the data. Third-party data may also 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 hybrids, have a range that can be estimated from the battery SoC. Vehicle wear, battery life reduction, and electricity prices all contribute to the total cost per distance (mile or km). If the electricity price varies over a route or over the course of a day, an additional variable, cost per watt, contributes to the TCD.

[0022] A fleet management system as defined herein may include fleet energy management in which each vehicle reports sensor data using a wireless data link and coordinates charging operations with a dispatch room controller. This coordination and information awareness extends across spatially (geographically and by travel route) and temporally distributed charging opportunity capabilities, reducing the total operating cost of the fleet.

[0023] Multiple buses, geographically dispersed charger sites, charger sites with chargers at high-use businesses or locations, and a mix of private and public charger sites are all intended and used to reduce the total cost of mileage for individual EVs and fleets of EVs via fleet management systems.

[0024] The collection, communication, storage, monitoring, and analysis of environmental, vehicle, charger, and charging session data can be used to enhance or optimize existing services for a single EV, or across a fleet of EVs, as well as to develop new services based on the analysis of collected data. Utilizing historical data allows for better inferences to be made for specific routes, vehicles, and drivers.

[0025] One such service is reducing the total cost of travel. Using collected data, charging can be optimized for the entire fleet, not just individual EVs, ensuring that every vehicle in an EV transport fleet completes its route at the lowest cost. This cost minimization is achieved through near real-time data, models created using historically collected data, energy costs, and knowledge of chargers in different geographic regions (where there may be limited charging power available at each charger site or station).

[0026] The fleet management application used for cost minimization can optimize travel costs for a single EV or for an entire fleet of vehicles. When using cost optimization for individual EVs as the goal, each EV can be optimized for its potential local lowest travel cost based on data collected from the fleet or predictions using data collected from multiple fleets.

[0027] Using fleet-level objectives, fleet optimization is designed for minimum total cost over a wide range, which may differ from a specific regional optimization algorithm designed to minimize the cost of electric buses. The tradeoffs in modeling fleet management applications are accepting charging of selected EVs in fleets with higher power costs during a first time slot to achieve a lower overall system outcome, or managing battery capacity to minimize grid impacts when charging, instead of charging these vehicles during a second time slot when charging costs are lower, to achieve a lower overall fleet cost.

[0028] Efficiency is the ability to do or produce something without wasting materials, time, or energy. Efficiency for electric vehicles for transportation includes electricity usage versus EV range, vehicle cost, and extended battery pack life depending on the scenario.

[0029] The treasure trove of first-, second-, and third-party data collected or otherwise obtained is suitable for both statistical analysis and machine learning (ML) techniques. Statistical analysis can be used to determine trends, patterns, and relationships (both causal and correlational) using labeled quantitative and categorical data. Because the data is sufficiently labeled, ML's supervised learning algorithms are well suited for use when a specific goal or optimization is desired. In some cases, unsupervised learning algorithms can be used to cluster the data and identify patterns, associations, and anomalies in the data.

[0030] The systems and methods described herein further provide power arbitration using at least one electric vehicle (EV) following a predetermined route with multiple bidirectional chargers, including, for example, wireless power transfer (WPT) chargers. A charging management system as a service (CaaS) receives EV data, at least one charging schedule for the predetermined route, and charger data for each bidirectional charger along the predetermined route. When the EV requests charging from a bidirectional charger among the multiple chargers, the CaaS determines whether the EV has excess charge beyond the amount of charge required to complete the predetermined route. If the EV is charging more than the power required to travel the predetermined route, the CaaS instructs the EV to discharge to a bidirectional charger among the multiple chargers. On the other hand, if the EV does not have more power than the power required to travel the predetermined route, the CaaS instructs the bidirectional charger to charge the EV according to the charging schedule.

[0031] In one configuration, the bidirectional charger is installed at a mobile charging station for EVs connected to a local micro-generation grid that supplies power from a local generator to the bidirectional charger when the EV is not charging more than the power required to complete the predetermined route, and receives power from the EV for storage in a local battery when the EV is charging more than the power required to complete the predetermined route. A spare EV installed at the mobile charging station can be used as a local power storage and / or a local power generator.

[0032] The CaaS may include at least one processor that executes instructions to use trained machine learning to perform route modeling based on historical EV data and predetermined route data and to generate at least one predictive model for managing charging availability at multiple chargers. The at least one processor of the CaaS may further execute instructions to receive electricity rate data for at least two bidirectional chargers along the predetermined route, calculate electricity rates at the at least two bidirectional chargers along the predetermined route, and, if the electricity rate at a first of the at least two bidirectional chargers is lower than that at another of the at least two bidirectional chargers along the predetermined route, instruct the bidirectional chargers to charge the EV to a charge level greater than the charge level predicted by the at least one predictive model. The at least one processor of the CaaS may further execute instructions to instruct the EV to discharge its charge to another of the at least two bidirectional chargers along the predetermined route when the EV arrives at the other of the at least two bidirectional chargers.

[0033] In another configuration, the at least one processor of the CaaS may execute instructions to receive electricity rate data for bidirectional chargers along a predetermined route, calculate electricity rates at bidirectional chargers along the predetermined route at different times of day, and instruct the EV to discharge at a bidirectional charger if the EV has an excess charge beyond that required to complete the predetermined route and the electricity rate at the time of charging is higher than the electricity rate during a previous charging session. The at least one processor of the CaaS may further execute instructions to send a message to a utility company to adjust power supply from the EV to a utility grid based on at least one of power demand, power ramp-up, or power price, and adjust power storage in the EV based on at least one of power supply or price of the utility grid.

[0034] In another configuration, the at least one processor of the CaaS may further execute an instruction to set a threshold for electricity cost. When an EV requests charging from a bidirectional charger among the multiple chargers, the at least one processor of the CaaS may determine whether the electricity cost at the bidirectional charger is equal to or less than the cost threshold, and instruct the bidirectional charger to charge the EV if the electricity cost at the bidirectional charger is equal to or less than the cost threshold. On the other hand, if the at least one processor of the CaaS executes an instruction to determine that the electricity cost at the bidirectional charger exceeds the cost threshold and determines that the electricity cost exceeds the power required for the EV to travel a predetermined route, the CaaS instructs the EV to discharge to the bidirectional charger.

[0035] In yet another configuration, the at least one processor of the CaaS may further execute instructions to determine which of the at least one additional bidirectional chargers along a predetermined route has a lower electricity price at a time when charging is expected, and may direct charging of the EV to the bidirectional charger with the lower electricity price if the EV does not have a charge amount in excess of a charge amount required to complete the predetermined route but has a charge amount sufficient to reach the at least one additional bidirectional charger along the predetermined route.

[0036] Additionally, the at least one processor of the CaaS may execute instructions to calculate a charging plan based on a total cost per distance (TCD) of travel for each of a plurality of route segments between a plurality of bidirectional chargers along a predetermined route, telemetry data received from the EV, and charger data.

[0037] Additionally, the at least one processor of the CaaS may execute instructions to schedule charging of at least one EV in the fleet of EVs or draining of power from the at least one EV in the fleet of EVs to a utility grid based on the cost of power relative to a cost threshold and a power demand curve for the fleet of EVs at different times of day.

[0038] A detailed description of exemplary embodiments is provided below with reference to Figures 1-32. While this specification provides detailed descriptions of possible implementations, it should be noted that these details are exemplary and do not limit the scope of the inventive subject matter in any way.

[0039] Figure 1 Figure 1 shows an example graph of an EV's range on a single charge versus its battery's SoC. The X-axis shows the range 101, and the Y-axis shows the SoC 102 starting at 103. In this example, a simple linear model of travel is used to illustrate the concept.

[0040] In the first example, the EV starts at a starting point SoC 103 (e.g., 100% SoC). As the EV drives 104, its SoC decreases until it reaches 0% SoC in range 107.

[0041] In the second example, the EV starts at a starting point SoC 103 (e.g., 100% SoC). As the EV drives 105, the SoC decreases until the SoC reaches 0% at range 108.

[0042] In a third example, the EV starts at a starting point SoC 103 (e.g., 100% SoC). As the EV drives 106, the SoC decreases until the SoC reaches 0% at range 109.

[0043] Factors that determine the EV range 107, 108, 109 include vehicle characteristics, battery pack characteristics, environmental factors, payload, terrain traveled, and driver ability.

[0044] Vehicle characteristics include the model, make, year, mileage, and repair status. Tire condition and tire selection are also taken into consideration. Aerodynamics (air resistance) is also a vehicle characteristic.

[0045] Battery pack characteristics include make, model, manufacturer, capacity, battery aging (both time-dependent aging and accelerated aging due to battery cycling), historical battery usage, and historical battery storage SoC during driving. Data from sensors monitoring individual battery temperature and voltage levels is expected to be available nearly continuously through the vehicle's battery management system (BMS). The BMS monitors and manages the EV's battery pack, which consists of a series of interconnected cells configured to provide the required voltage and current. The BMS monitors the battery pack's sensors (e.g., current, voltage, temperature) and communicates with the EV's electrical subsystem and external chargers (e.g., WPT chargers). The BMS maintains the battery pack's operational profile and protects it from over-discharge, overheating, and over-charging. The BMS may also optimize battery performance and lifespan by controlling the charging rate and SoC.

[0046] Environmental factors include weather, temperature, and barometric pressure. Temperature not only affects the battery's state of charge, but also the electrical load required for vehicle systems (e.g., battery pack cooling) as well as interior climate control (heating and cooling) for passengers and cargo. Daytime versus nighttime driving can also affect range. Weather conditions (e.g., rain, snow, wind) can also affect range versus SoC. Lighting, including headlights, interior lighting, and exterior lighting, place different loads on the battery pack. In cold climates, the combined heating and lighting loads (for passengers and battery pack) can require a significant percentage of the battery capacity, necessitating additional power transfer (extended charging time or charging at a higher power output), especially if additional safety lighting is required at dusk. Safety margins in the battery SoC may also need to be readjusted to maintain battery life.

[0047] Environmental factors are expected to be available from charger site sensors, vehicle-mounted sensors, and third-party sources such as public weather stations and feeds to dispatch servers.

[0048] The payload, whether passenger or cargo, affects the power consumption on the route, with a heavier payload resulting in a shorter range. Both rolling friction and acceleration are affected by payload.

[0049] The terrain traveled includes curves, speed, stops, and traffic conditions, as well as the slope and deviation of the route traveled. Traffic conditions may be collected from third-party services via the dispatch server's application programming interface (API). Terrain may be a significant factor in power consumption for a route segment because uphill slopes require additional power to negotiate, while generally downhill route segments may mitigate power consumption and store additional power through regenerative braking.

[0050] Driver capabilities include lane selection to conserve battery resources, smooth acceleration, and smooth braking. "Driver" here also includes the use of automated driving assistance software packages and autonomous driving systems.

[0051] Figure 2 Figure 2 shows a graph of an example of extending an EV's range through mid-range charging. The x-axis shows range 201, and the y-axis shows SoC 202. This example uses a simple (straight-line) driving model to illustrate the concept. In the initial mid-range charging example, an EV starts with an initial SoC 202 at the start of the trip 204. As the EV travels, its SoC level 203 decreases. At a charging point 205, the EV is recharged, and the recharged EV SoC 206 is used to continue the journey beyond range 201.

[0052] Wireless inductive charging enables wireless inductive charging of EVs. Opportunistic charging is the short-term charging of an electric vehicle while it is in motion. This partial charging strategy contrasts with charging an EV all at once, as shown in Figure 2.

[0053] Figure 3 Figure 3 illustrates an example of range and battery life extension through strategic opportunity charging. The x-axis shows range 301, and the y-axis shows SoC 302. This example uses a simple (straight-line) driving model to illustrate the concept. In this example of strategic static opportunity charging, the EV is not only partially charged at each stop 306, 307, 308, and 309, but the SoC is also maintained between an upper SoC threshold 303 and a lower SoC threshold 304. The SoC profile 305 over the driving route is shown with a varying slope (in this simplified model) to indicate differences in power consumption along the route segments (i.e., between stops with opportunity chargers).

[0054] The upper 303 and lower 304 SoC thresholds are designed to extend the life of the EV battery. While only one set of thresholds is shown here, multiple levels of thresholds can be configured to extend range while minimizing the impact on battery life. Of course, the physical upper threshold 303 of 100% SoC and lower threshold 304 of 0% SoC are always available to extend range at the expense of battery life (e.g., for emergency use).

[0055] 3, the starting point SoC 310 is shown as just below the upper battery charge threshold 303. In some cases, the starting point SoC 310 may be higher (e.g., 100% SoC) or significantly lower (e.g., 40%) when brought in from long-term storage.

[0056] In one configuration, the useful life of a lithium-ion battery pack can be extended by selecting upper and lower thresholds and using opportunity charging to maintain the state of charge between the selected thresholds. Battery life can also be maximized by monitoring the temperature and voltage of individual batteries and varying the charge rate to stay below (and above) the selected thresholds.

[0057] By knowing the vehicle battery's operating thresholds, the charger location, the route, the time of day, the current location, traffic levels, and the estimated time of arrival at the next charger, it is possible to decide whether to charge (and to what SoC level) before arriving at or before arriving at a WPT charger. The cost of electricity from the grid may also vary depending on the time of day and the location of said WPT charger.

[0058] Figure 4 Time-of-Use (TOU) electricity rates are rates that vary depending on the time of day. TOU rates respond to electricity availability, with higher prices during peak demand periods and lower prices during periods of low demand. These rates are typically fixed for a set period of time (week, month, season) to accommodate fluctuations in demand.

[0059] FIG. 4 illustrates a graph of electricity prices over a 24-hour period in one example. In FIG. 4, the x-axis 401 represents the 24-hour period divided into one-hour segments. The y-axis 402 represents the electricity price for each time segment. In this example, from midnight 403 to 8:00 AM 404 on the x-axis 401, the utility sets the price to "off-peak" 405. From 8:00 AM 404 to noon 406, due to increased demand, the utility sets the price to "mid-peak" or "shoulder" 407. From noon 406 to 6:00 PM 408, this is the period of highest demand, so the electricity rate is set to "peak" 409. From 6:00 PM until 11:00 PM 410, due to decreased demand, the rate is set to "mid-peak" 407. After 11:00 PM 410, the rate drops to "off-peak" 405. As can be seen from FIG. 4, pricing changes depending on the increase or decrease in demand.

[0060] EV charging costs (per watt) are lowest during off-peak hours and highest during peak hours. To minimize costs, selective charging of EVs during the day (both charging time and charge level during the charging session) can be achieved.

[0061] Weekend TOU rates may differ from weekday (e.g., Monday through Friday) rates. In some areas, weekend rates are set as "off-peak."

[0062] Figure 5 Depending on the region or market, there may be competitive electric utilities. As shown in FIG. 5, a first and second electric utility share the same geographic market as the deployed WPT system and offer different electricity rates based on the hourly time period and power generation capability and capacity. In FIG. 5, the X-axis 501 is a 24-hour day shown in hourly increments. The Y-axis 502 shows the electricity rate for each time period. In this illustrative example, during a first period 505 (12:00 AM to 7:00 AM), the first electric utility's market rate 503 is lower. During a second period 506 (7:00 AM to 8:00 AM), the rates 503 and 504 of both electric utilities are approximately equal. During a third period 507 (8:00 AM to 2:00 PM), the first electric utility's market rate 503 is lower. During a fourth period 508 (2:00 PM to 11:00 PM), the second electric utility's market rate 504 is more favorable. Finally, in a fifth time period 509 (11 PM - 12 AM), the first electricity provision tariff 503 is again recommended.

[0063] By selecting the cheapest electricity provider's service during charging times, you can reduce the electricity costs for EV charging. By managing charging time, charging duration, and charge level, you can minimize charging during times when electricity costs are high, thereby increasing savings.

[0064] Demand, availability, and cost of electricity can vary significantly from day to day, or even hour to hour, all of which affect charging decisions. Information about electricity prices can come from prior contract terms with local utilities, data feeds from local power exchanges, or spot prices on public electricity markets when multiple suppliers supply the utility. Examples of electricity markets include Day-Ahead-Energy markets and Real-Time Energy markets.

[0065] Figure 6 Electric Power Business Service Area Charger locations may be contiguous along specific routes within different utility service areas, which may require pricing information from multiple sources to be considered in charging decisions. If the chargers are owned by a third party (other than the utility or vehicle operator), the price of electricity for the estimated charging time may need to be obtained from the third party.

[0066] FIG. 6 shows an example in which two regions 601 and 602 are traversed by an EV route 605. In this example, each region 601 and 602 is supplied by a different utility company with different pricing. Therefore, WPT opportunity chargers 603 and 604 may have different electricity costs. By using different charging times and power levels at each WPT charger 603 and 604 during specific times, the total cost of charging for the route can be optimized.

[0067] Figure 7 7 shows an example distribution of wireless chargers geographically along a bus route. While recharging may occur at transfer or interchange stations between routes with longer dwell times (e.g., rail or airport terminals), the deployment may include recharging stops that are strategically located (e.g., in areas where the predicted battery SoC is predicted to exceed a lower threshold).

[0068] Predictive Modeling By obtaining prices at multiple chargers along the route at multiple estimated arrival times, a travel cost profile for the route can be generated at the start of the day. Cost optimization can be calculated based on upper and lower SoC battery thresholds and recalculated as needed if the original route deviates or the model fails to predict an accurate SoC value. Data used to input 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 battery SoC thresholds can be involved if there is a substantial cost difference between charging from one location to another or from one time to another.

[0069] The recalculation of the modeled trip cost can include deviations from the schedule for both early and late arrivals. For early arrivals, additional charging time may be available, resulting in a lower offered charging current. For late arrivals, a higher charging current may be available to reduce charging time during a potentially reduced stop.

[0070] We describe a system that enables continuous operation of electric vehicles by utilizing wireless charging while on the move. The vehicle SoC is maintained at an optimal level to promote battery health and lifespan, while also taking into account electricity prices to optimize vehicle operating costs.

[0071] Sensor data is collected repeatedly while driving during a route charging session to facilitate a dynamic charging model that takes into account electric vehicle type (make, model, year), environmental factors (weather, traffic conditions, etc.), driver behavior, and drivetrain and battery pack health, all of which affect wireless charging performance and vehicle operation.

[0072] Data collected across the EV transit bus fleet is used to automatically optimize models that increase or decrease charging power based on these factors, allowing vehicles to continue operating indefinitely while maintaining optimal SoC across the fleet at the lowest possible cost, both in terms of battery life and electricity bills.

[0073] At least one minimum SoC threshold is set for each electric vehicle type (make, model, manufacturer, year, battery pack) or EV class, and the minimum SoC is dynamic and based on a) the minimum SoC to limit battery damage (lifespan degradation), b) the minimum SoC required to reach the next two charging stations in the route, c) the minimum SoC required to complete the route without charging, d) the calculated minimum SoC to abort the route and reach a stop, e) the SoC charge at the start of the route, and f) a manually set SoC threshold.

[0074] In a minimum power system, the EV can only charge to the SoC prediction at each stop with a wireless charger. This minimum power system can be modified based on the estimated electricity prices of WPT chargers along the route.

[0075] As charging networks grow and higher-power wireless charging services are deployed, wireless resonant chargers may be deployed in more geographic locations where short stops can be used for resonant charging, or where charging lanes with dynamic inductive chargers are deployed. Note that both statically and dynamically charging EVs can utilize near-field communications, such as those detailed in U.S. Patent No. 10,135,496, "Near field, full duplex data link for use in static and dynamic resonant induction wireless charging."

[0076] FIG. 7 illustrates one exemplary transit route. Stop 701 is used to accommodate and service electric transit buses when they are not in service. Stop 701 may be part of a terminal where passengers can board before departure. Passengers can board and disembark at each pre-planned stop. Other stops may be introduced interim for passengers to disembark. A first route segment 702 operates to bring the bus to a first stop 703 where passengers can board and disembark. A second route segment 704 brings the bus to a second stop 705 where passengers can board and disembark. A third route segment 706 brings the bus to a first transfer station 707 where passengers can board and disembark for travel on another, intersecting transfer route. The driver may also use this location 707 to take a mandated rest break. Traveling along a fourth route segment 708, the bus arrives at a fifth stop 709 where passengers can board and disembark. After traveling along the fifth route segment 710, the bus arrives at a sixth stop 711 where passengers can board and disembark. After traveling along the sixth route segment 712, the bus arrives at a bus station 714, which is the end of the route in this example. In this example, the longest route segment 712 includes a charger stop 713 where the bus can stop temporarily to charge. Said charger stop 7133 may alternatively be a dynamic machine charger where the bus does not need to stop but simply drives over a road surface equipped with chargers.

[0077] Wireless machine chargers may be installed at any of the scheduled stops 703, 705, 707, 709, 711, and 713. Additional charger locations (not shown) may be deployed between stops to increase range while keeping the SoC within the SoC threshold boundaries.

[0078] For electric transit vehicles (e.g., buses), opportunistic charging requires charger locations to be located on or near the route. Charger placement is first achieved by mapping and modeling the route (using either collected data (from test drives), modeled data, or data from similar routes and vehicles) with chargers located at stops and, possibly between stops, where sufficient power is available. To reduce costs, chargers are typically installed at a subset of stops.

[0079] Once the transit system (and accompanying data collection, transmission, and analysis) is operational, the collected data will be used to determine whether 1) additional chargers are needed, 2) fewer chargers are needed, or 3) chargers can be decommissioned and relocated to a different location to better serve transit fleets in order to meet total trip cost targets.

[0080] When chargers and data infrastructure are shared across multiple fleets, driving data from the fleets can be used to rebalance the charger infrastructure to accommodate newly deployed EV transport vehicles, changing vehicle traffic patterns, and shifting users and routes.

[0081] When multiple vehicle fleets share an automated charging infrastructure, ownership of chargers and payment for charging costs are accumulated and negotiated among the fleet. In some scenarios, local or regional authorities own, operate, and service the wireless charger network and ancillary communications and data systems, and allocate the costs to the vehicles they serve.

[0082] In some cases, wireless chargers owned and operated by non-fleet commercial or government operators can be used to supplement the transportation wireless charger network.

[0083] Figure 8 FIG. 8 illustrates an exemplary state machine for a transit bus with wireless charging.

[0084] The state machine in Figure 8 shows events collected at different times and events along the electric vehicle's route.

[0085] In this example, the stop state 801 is encountered at least twice: once at the start and once at the end of the shift. Additional encounters may occur, for example, due to driver shift changes or required vehicle maintenance.

[0086] Upon departure from said depot, the bus system is fully charged (up to the upper SoC threshold) and preheated, cooled, or air-conditioned as needed. Vehicle characteristics (make, model, year, etc.) and the identity of the driver (or driver software) are recorded.

[0087] The on-board data store includes departure (current) time, battery state of charge (SoC), SoC threshold / limit, vehicle empty weight, current location, and route. The planned route defines planned stops (locations) for passengers and chargers. For each charger along the route, electricity tariffs and spot rates are known. The theoretical SoC to be used for each route segment is predicted using modeling based on historical histogram data of the route, similar routes, or simplified example route models. Scheduled events (and detours) along the route are known and are taken into account when pre-planning and modeling the route.

[0088] The en route state 802 is the most general state and is expected to encompass all trips. During the en route state 802, the data store accumulates the current time, current location, SoC, number of passengers, traffic conditions, and vehicle speed.

[0089] The data store can accumulate and store vehicle and route data using periodic or event-driven updates. The accumulated data is tagged with time as well as current location and route segment. Odometer distance can be used as a backup positioning when precise location is unavailable. Data can be uploaded to a dispatch office via a wireless connection (e.g., cellular or satellite modem).

[0090] For charging at a passenger pick-up location 803, the data store is updated with the start and end times of the stop. Passenger pick-up / drop-off counters (or bus weight change as a proxy) are used to update the total number of passengers served, the current number of passengers, and the number of passengers boarding and alighting. As charging occurs, the charging current, start SoC, and end SoC are recorded. The charger can transmit additional charging session data over a communications link, including the state of the charger (and state of the vehicle wireless power receiver) and details about the inductive charging energy transfer (e.g., coupling, frequency, equipment temperature).

[0091] Route information can be updated via the charger location communication system or the bus's wireless communication system. Route-related information includes the distance to the next stop, the stop type, SoC thresholds and limits, charger status and availability at the next stop with a charger, and charger status for at least all chargers along the route.

[0092] For driver breaks involving charging 804, the data store is updated with the start and end times of the stop. Passenger entry / exit counters (or bus weight change as a proxy) are used to update the total number of passengers served, the current number of passengers, and the number of passengers boarding and exiting. As charging occurs, the charging current, start SoC, and end SoC are recorded. The charger may transmit additional charging session data over the communications link, including the state of the charger (and state of the vehicle wireless power receiver) and details about the inductive charging energy transfer (e.g., coupling, frequency, equipment temperature).

[0093] Route information may be updated via the charging location communication system or the bus's wireless communication system. Route-related information includes the distance to the next stop, the type of stop, and the predicted SoC.

[0094] For charger stops 805, the data store is updated with the start and end times of the stop. No passengers are expected to board or disembark at charger stops 805. When charging occurs, the charging current, start SoC, and end SoC are recorded. The charger may transmit additional charging session data over its communications link, including charger status (and vehicle wireless power receiver status), as well as details about the inductive charging energy transfer (e.g., coupling, frequency, device temperature).

[0095] During the charger stops 805, route information may be updated via the charging location communication system or the bus's wireless communication system. Route-related information includes distance to the next stop, stop type, predicted SoC, etc.

[0096] For Driver Break 806, the data store is updated with the start and end times of the stop. Passenger entry / exit counters (or alternatively bus weight change) are used to update the total number of passengers served, the current number of passengers, and counts of passengers boarding and exiting. In the non-charging state of Driver Break 806, the start and end SoCs are recorded. Route information may also be updated via the bus's wireless communication system. Route-related information includes distance to the next stop, stop type, predicted SoC, etc.

[0097] For passenger pick-up locations (no charging) 807, the data store is updated with the start and end times of the stop. Passenger pick-up / drop-off counters (or alternatively bus weight change) are used to update the total number of passengers served, the current number of passengers, and counts of passengers boarding and alighting. In the no-charging state, the start and end SoCs are recorded.

[0098] At passenger pick-up locations (without charging) 807, route information may be updated via the bus's wireless communication system. Route-related information includes distance to the next stop, stop type, SoC thresholds and limits, predicted ending SoC, charger status and availability at the next stop with a charger, and charger status for at least all chargers along the route.

[0099] Figure 9 FIG. 9 illustrates diagrammatically a case where a single charger 901 is used to serve a first transit route 902 and a second transit route 903, and therefore serves multiple EVs serving these routes 902 and 903.

[0100] In some cases, the power available for opportunity charging at a charger may exceed the requested amount of power and / or the number of vehicles requiring charging. This includes the allocation of chargers to match specific vehicle populations and chargers that support non-standard communication protocols or charging signaling.

[0101] Competition for scarce charging resources (power and / or chargers) occurs at cross-route charging stations, such as those shown in Figure 9. Conflicts can also occur at charging stations operated by third parties, at stations experiencing backups due to delays during travel, at stations with disabled chargers, at chargers in areas with low power, or when emergency service preempts a charger.

[0102] Competing charger resources also experience energy prices that vary across space (geography) and time of day. Energy management through distribution planning and forecasting from modeling historical data can be used to manage and allocate limited available power for use at each station or at a station's specific chargers.

[0103] In a cross-route station serving two or more transit routes, such as that shown in FIG. 9, the arrival of transit vehicles may differ from their scheduled arrival and charging times.

[0104] For charging stations operated by third parties, vehicle arrival time, number of vehicles, charge level, total charging demand, and number of modular charging pads per charger and per vehicle (U.S. Patent Application No. 17 / 648,844, "METHOD AND APPARATUS FOR THE SELECTIVE GUIDANCE OF VEHICLES TO A WIRELESS CHARGER") are taken into consideration, even if a prioritization scheme and reservation system (described in U.S. Patent Application No. 17 / 199,234, "OPPORTUNITY CHARGING OF QUEUED ELECTRIC VEHICLES") is implemented.

[0105] For charging stations experiencing backups due to in-transit delays, a queuing scheme based on the scheduled time of departure and vehicle SoC can be implemented. Planning for stations with non-functional chargers adds a layer of complexity in that modular chargers may soft-fail such that a subset of chargers are still available.

[0106] In some cases, power shortages may necessitate power rationing. Vehicles may be assigned power priorities based on their SoC or a ranking system. Power priorities may be served by providing a higher charge rate than lower priority vehicles or by suspending power to lower priority vehicles.

[0107] When an emergency vehicle or a high-priority electric vehicle needs to charge, a currently charging vehicle may take over the charger, or a preemption scheme may be provided in which the next available charger is reserved for the preceding vehicle's use.

[0108] Figure 10 Figure 10 illustrates, at a high level, the communication paths available for data collection from EVs and WPT chargers. Data collection is used to price strategic charging opportunities. Telemetry (including two-way telemetry) uses wired and wireless communications to transfer data and information between remote sources (including mobile sources and users) and remote destinations. Telemetry data flow between source and destination consists of continuous, periodic, polled, or ad-hoc transmission of data. Telemetry includes the automated measurement and wireless transmission of data from remote sources, with collected data routed to receiving equipment at the destination location (e.g., dispatch server 1001) for monitoring, display, recording / storage, post-processing, analysis, and trending.

[0109] The data store is part of a database software with data management software (e.g., IBM Maximo Enterprise Management System) running on processor hardware with a computer operating system with large memory storage. Security and multi-party access control functions are implemented through the data management software. In some implementations, the dispatch server 1001 and associated data store may be implemented as a virtual, hosted (e.g., cloud-based) system, or as an on-premise hardware (with the necessary processors, memory, and fault-tolerant data storage) and software system based on a common high-availability computing platform local to the dispatch office and sized to fit the processing and storage needs. The dispatch server may include (or have interfaces to) redundant, potentially partitioned, and federated databases and geographic information systems (GIS). Interfaces to other third-party information, such as power prices, traffic information, and weather information, may be centralized on the dispatch server 1001.

[0110] The data store resides on the vehicle but uploads accumulated data to the dispatch server 1001. Uploads may be at the request of a dispatch office or may be periodic or event-driven updates (e.g., the start of a WPT charging session). The EV is equipped with a navigation system (e.g., based on Navstar GPS, Galileo, GLONASS, BeiDou, Quasi-Zenith Satellite system (QZSS) (also known as "Michibiki"), or local radio location beacons). The data store is accessible to vehicle systems and battery management systems (BMS) via a local data link (e.g., Controller Area Network (CAN)) bus).

[0111] Data sources include databases of historical data, recorded data, and models using near real-time data. Sources also include near real-time data, including sensor output from sensors containing electrical data (e.g., voltage, current, state of charge) or physical data (e.g., temperature, pressure, mass).

[0112] Telemetry may also include data products such as location, passenger count, timestamp, data source identifier, map updates, route updates, etc. In this application, a vehicle data store may accumulate collected electric vehicle related data and transmit it to the dispatch server 1001 on a near-continuous basis.

[0113] The dispatch server 1001 includes application-specific software, including APIs for interfacing with both a data management system, third-party information feeds (e.g., traffic, weather, public charger status), and a communication interface for data originating from the charger locations 1002 and EVs 1003. The charger locations 1002 can use either a wired (not shown) or wireless radio interface 1004 for two-way communication. Depending on the location, such a wireless communication interface can use a public or private cellular data network 1005 using a public or private band radio signal 1006. An alternative or supplemental wireless communication network can be provided by a satellite 1007 using an established satellite band radio signal 1008. A satellite data receiver 1009 can be used to deliver the satellite-transmitted data to the dispatch server 1001.

[0114] Data is generated by the wireless charger locations 1002 and communicated via the wireless charger locations 1002 to the EVs 1003 and / or the dispatch server 1001. Each charger location 1002 has at least a wireless charger 1010 and ancillary equipment 1011 (shown here as an above-ground cabinet but may be installed in an underground vault). The ancillary equipment 1011 may include a wired or wireless backhaul (shown here as a wireless antenna 1012 for a cellular wireless connection 1004).

[0115] The route segments of the transit bus 1003 are pre-planned with arrival and departure times at predetermined geographic locations. Using the time, route segments, and odometer readings, a rough level of location can be calculated. A more precise location of the vehicle 1003 is available using an on-board navigation receiver (not shown) for a global navigation satellite system 1013 (e.g., Navstar GPS, Galileo, GLONASS, BeiDou, Quasi-Zenith Satellite system (QZSS) (also known as "Michibiki")) using satellite broadcast signals 1014. Other communications satellite constellations, such as carrier frequencies from the Starlink system (a high-speed, low-latency broadband internet carrier designed for remote and rural areas around the world), can also be used for positioning.

[0116] Alternatively, geographically local radio location beacons (beacons having a known frequency, known bandwidth, known or broadcast transmitter location, and transmitted identification information (ID)) can be used to obtain accurate positioning, if deployed or available.

[0117] On route EV1003, a radio receiver and transceiver 1015 is used to receive GNSS or local beacon positioning signals, communicate via a land-side cellular network, and potentially use a satellite communication system for receiving and transmitting information.

[0118] The dispatch office and server 1001 may be owned or managed by a third-party, non-fleet party (host) offering charging as a service (CAAS), especially in areas with multiple fleets and shared or third-party wireless charging resources. A charging as a service program removes the burden of ownership and maintenance from charging EV fleets by allowing the host to provide turnkey wireless charging stations, management software, communications infrastructure, 24 / 7 support, professional field maintenance of charger resources, planning and modeling for deploying new chargers when a need for a change in charger location or an increase (or decrease) in charger capacity is detected at an existing charger installation, and modernizing existing charger deployments.

[0119] Figure 11 An example of a wireless charger site 1100 is depicted in Figure 11. A wireless charger 1101 for charging a transit vehicle 1102 is shown installed level with the pavement 1103. A pedestrian area 1104 may be provided if passengers board or disembark at the depicted site 1100. Conduit 1105 provides interconnection to the wireless charger for cooling lines from a cooling structure 1106 and wired or optical communication lines (not shown) to a wireless transceiver and antenna 1107.

[0120] The wireless charger 1101 provides wireless communications between the wireless charger 1101 and the vehicle 1102. These communications may be as described in U.S. Patent No. 10,135,496l: "Near field, full duplex data link for use in static and dynamic resonant induction wireless charging," issued November 20, 2018. Static inductive charging relies on the EV maintaining its position during charging to pair the primary and secondary coils. Dynamic inductive charging uses a series of nearly continuous primary coils (often buried in the road) to charge secondary coils attached to a moving vehicle. Semi-dynamic charging uses the same primary and secondary coils as a static inductive charging system, but extends the operating angle over which the secondary coils are positioned, extending the total charging time for each primary coil.

[0121] The wireless charger 1101 in this example configuration is powered via a wired DC connection 1108 to a local utility grid (not shown).

[0122] In some cases, a mechanical actuator system can be used to connect physical wires for opportunity charging. The pantograph system 1109 shown is one such alternative system that uses a physical connector.

[0123] Figure 12 FIG. 12 is a flowchart of an example method 1200 for strategic opportunity charging in an example configuration.

[0124] As shown, method 1200 includes creating a model based on historical data, similar routes, near real-time sensor data, and third party data for use in creating optimal routes for EVs used in commercial and non-commercial environments at 1210. The collected data relates to the environment, EV characteristics, power usage, charger characteristics, charging sessions, energy cost data, traffic, and route data for a single EV or a fleet of EVs.

[0125] Once the data model is created at 1210, the collected data is processed through the data model to provide an initial estimate of the total cost per distance (TCD) of travel on the anticipated route segment at 1220.

[0126] Routes and charging along route segments are optimized at 1230 to reduce the TCD on a route of the EV or fleet of EVs.

[0127] The EV then begins traveling along the route segment, and the telemetry system and third party systems collect 1240 near real-time data about the environment, the EV, power usage, power costs, trip time, etc. as the EV travels along the route segment.

[0128] When the current route segment ends, the TCD is calculated at 1250. The TCD is calculated using a data model based on data collected as the EV passes through the route segment, such as environmental, EV, power usage, power cost, travel time, and traffic volume.

[0129] An updated estimate for the next route segment is calculated at 1260 based on variations in the environment, EV, power usage, power cost, travel time, traffic, etc. The updated estimate further includes when / where the EV should charge along the next route segment to obtain optimal TCD using available chargers and available stop times.

[0130] Steps 1240-1260 are repeated for each route segment until the process is reset.

[0131] Figure 13 Figure 13 is a high-level graph of wireless charging operation during a timed stop at a wireless charger. The EV first arrives at 1301 in its initial state of charge (SoC) and is directed to the wireless charger. This direction can arrive via the wireless link, but can also arrive via indicator lights, signs, or auto-steering assistance (see, for example, U.S. Pat. No. 10,040,360; "METHOD AND APPARATUS THE ALIGNMENT OF VEHICLES PRIOR TO WIRELESS CHARGING INCLUDING A TRANSMISSION LINE THAT LEAKS A SISGNAL FOR ALIGNMENT" and U.S. Patent Application No. 17 / 646,844; "METHOD AND APPARATUS THE SELECTIVE GUIDANCE OF VEHICLES TO A WIRELESS CHARGER").

[0132] During pre-charging 1302, the ground charger and vehicle power receiver are coordinated for achieved alignment and efficient wireless transfer across the air gap. Information is exchanged for authorization and billing via the wireless connection. For modular ground chargers, multiple frequencies and phases (see, e.g., U.S. Patent Application No. 17 / 207,257; "MODULAR MAGNETIC CONTROL") can be configured.

[0133] The vehicle battery management system and ground charger controller (not shown) negotiate the supply current during charging 1303. The ground controller sets the initial maximum current to be supplied according to commands from the dispatch controller (based on a data model), and can then change the supply current according to new commands during charging.

[0134] When you leave the charging session, the EV leaves the charging station with the new SoC.

[0135] In the example of a route bus, the EV has a set arrival time and departure time, and therefore a preset total stop time 1305. The charging interval 1306 is a subset of the total stop time 1305.

[0136] Figure 14 The charging manager 1401 may be an application running on the dispatch server 1001 or a local control device (charging station server (originally U.S. Patent Application No. 17 / 199,234; "OPPORTUNITY CHARGING OF QUEUED ELECTRIC VHEICLES"; filed March 11, 2011, and incorporated herein by reference). The charging station server includes the charging manager 1401 software for managing the electrical supply (from the utility and local storage), the charging station's internal communication links (both bridging and routing) with wireless chargers 1402, and the charging station's interconnections to entities external to the charging station (servers, data repositories, cloud instances). In this example, all messaging is paired, with each transmission having an acknowledgment.

[0137] In the current embodiment, the ground charger assembly (GCA) 1402 includes a near-field communication interface (described in detail in U.S. Pat. No. 11,121,740; "NEARFEILD FULL DUPLEX DATA LINK FOR RESONANT INDUCTION WIRELESS CHARGING," incorporated herein by reference). A physically corresponding vehicle receiving assembly (VRA) 1403 must be present across an air gap 1404 for charging when using the near-field communication interface. Alternative or supplemental wireless communication links based on wireless local area network (W-LAN) technologies (e.g., IEEE 802.11, Zigbee, Bluetooth) can also be used.

[0138] Before a wireless charging session 1406 begins, the GCA 1402 and VRA 1403 exchange messaging for authorization, mutual authentication (in this model, neither the GCA 1402 nor the VRA 1403 is trusted), and accounting. The operation of a battery management system (BMS) in the current example is included in the functionality and pass-through messaging of the VRA 1403.

[0139] Standard messages for wireless charging of electric vehicles (EVs) have been published by the International Engineering Consortium (IEC) as IEC 61980 Parts 1, 2, and 3. More specifically, IEC 61980-3:2022; "ELECTRIC VEHICLE WIRELESS POWER TRANSFER (WPT) SYSTEMS - Part 3: Specific requirements for magnetic field wireless power transfer systems" (published November 2022) is useful for generalized explanation purposes, as the use cases and messaging supported therein vary.

[0140] Immediately prior to charging, wireless messaging 1405 may be exchanged to measure and ensure alignment between the GCA 1402 and VRA 1403, measure the magnetic gap, determine the efficient magnetic transmission frequency (see U.S. Patent Application No. 17 / 643,764; "Charging Frequency Determination for Wireless Power Transfer," incorporated herein by reference), and exchange capabilities and limitations.

[0141] Once preliminary messaging 1405 ends and charging session 1406 begins, the VRA 1403 and GCA 1402 begin exchanging information messages 1407 that contain electrical, temperature, and / or wireless sensor data of the associated GCA 1402 and VRA 1403 and provide a periodic heartbeat. The information messaging 1407 can include BMS-supplied information regarding the voltage, temperature, and state of charge of the battery pack. The information message streaming 1407 continues for the duration of wireless power transfer (while magnetic flux is generated).

[0142] The VRA 1403 then initiates power request / response messaging 1408 to the charging manager 1401 via the GCA 1402 by wireless signaling over the air interface 1404. The power request can include a requested current level, and the power response can include a granted current value. The power request can also include a preferred current level and a maximum current level, and the power response can include a granted current value below the requested or maximum current level.

[0143] The GCA 1402 sends a message to the VRA 1403 confirming the initial current level assignment 1409 and energizing the charging signal. During the power transfer 1410, heartbeat / telemetry 1407 messaging continues.

[0144] In this example, the charging manager 1401 sends an evaluation command 1411 to the GCA 1402 involved in the charging session 1406. The evaluation command 1411 includes a current level above or below an initial current level (the current level may be zero, which will pause or terminate the charging session 1406 early). The updated current level is passed to the VRA 1403 before the charging signal is changed. The VRA 1403 confirms the updated current level in its response 1409, which can request a new maximum allowed current level or any current level below the updated current level.

[0145] During a second wireless power transfer duration 1413, the GCA 1402 provides a magnetic signal to the VRA 1403 to generate a new allowable current level. The EV terminates the wireless power transfer by setting the requested current level to zero via the BMS and the VRA 1403. The GCA 1402 interrupts the charging signal and notifies the charging server 1401 that the session has ended via a termination notification 1414. The GCA 1402 uses the termination notification 1414 to pass collected time, sensor, and performance data to the charging server 1401 for storage and analysis.

[0146] Demand charging Figure 15 Figure 15 shows utility billing rates for business customers. This example is for a single charging station or multiple charging stations.

[0147] X-axis 1501 shows time and Y-axis 1502 shows power consumption (kW). Power consumption varies over the billing period from a baseline 1504 to peak demand 1506. An average power consumption 1505 can be determined over the billing period 1503.

[0148] Electricity charges from power companies for wireless power transmission are expected to consist of both volume charges and demand charges.

[0149] The quantity component is typically measured in kilowatt-hours (kWh).

[0150] The demand component is based on the maximum amount of power needed during a time period (e.g., an hour or a set fraction of an hour) within a billing period, and is typically measured in kilowatts (kW).

[0151] Efficient geographic distribution of chargers as well as charger scheduling, which adjusts the charging schedules of individual vehicles to limit simultaneous charging, can be used to reduce the demand charging component from EV fleets.

[0152] Strategic placement of charger stations can limit the number of chargers to less than one per bus stop (on average), minimizing infrastructure costs, including T&D. Such charger placement allows for multiple chargers to be installed at bus stops serving multiple EVs.

[0153] Prioritized charging, which uses wireless opportunistic charging to control the SoC during the day while the EV is parked, can be used to minimize electricity costs by charging only to reach the next charger (with backup) during times when electricity prices are highest. Prioritized charging also involves charging more at stations with higher electricity prices (increasing the EV's SoC).

[0154] Figure 16 16 is an overhead view of a charging station at a bus stop (in this example, a transit bus stop). In this example, wireless power charging station 1601 has three chargers 1602, 1603, and 1604 arranged to simultaneously service up to three electric vehicles. In this example, each charger 1602, 1603, and 1604 serves a transit bus 1605, 1606, and 1607.

[0155] All three chargers are supplied via an underground electrical connection (not shown) to power electronics 1608, which is connected to the utility grid via drop 1609. The power company can supply AC, DC, or AC three-phase power via drop 1609.

[0156] A local energy storage unit 1610 (see U.S. Patent Application Publication No. US20220368161A1, filed October 30, 2020, entitled "Contactless Swappable Battery System" for one practical example of such a battery system) enables "peak shaving," where energy is stored (trickle charged) during times of low electricity costs and used during peak electricity costs. The battery may be physically swappable (for temporary or emergency use) or may be charged from a utility drop 1609 during times of low electricity rates. Alternative power sources, such as wind or solar power plants, may also be used to charge the local energy storage device 1610.

[0157] To reduce demand and avoid exceeding a desired maximum demand threshold, the charging site controller can:

[0158] a. Allocate the available power equally to each EV.

[0159] b. Prioritize power delivery to EVs that need it most (SoC needed to reach the next charger)

[0160] c. Prioritize arrival and departure times so that power supply is tailored to the arrival and departure of each vehicle.

[0161] d. Prioritize power and optimize energy consumption based on time-of-use billing rates.

[0162] Demand charges can be site-specific (size of transformer or wire to meter) or region-specific. Other methods provide region-level control (e.g., openADR - Automatic Demand Response).

[0163] Demand charges may be aggregated for a single customer across the service territory served by the electric utility.

[0164] Furthermore, available power may be set by local or national government action rather than by a desired concurrent demand threshold to minimize utility demand charges.

[0165] Figure 17 Figure 17 illustrates geographically the ability to use strategic opportunity charging to lower (or at least manage) the utility demand charging portion of electricity costs. In region 1701, chargers are distributed to serve a fleet of EVs on a route (which may be fixed (pre-planned) or ad hoc (changeable)).

[0166] This area includes one charger station 1702, 1703, 1704, two charger stations 1705, 1706, and three charger stations 1707, 1708. These stations 1702, 1703, 1704, 1705, 1706, 1707, 1708 and the number of chargers per station are installed based on predicted vehicle charging needs. Geographically, stations 1702, 1703, 1704, 1705, 1706, 1707, 1708 are located at various distances from each other, as well as from passenger or delivery stops (not shown).

[0167] By adjusting the scheduling of charging sessions for each fleet vehicle 1709, 1710, 1711, 1712, the total demand for electricity can be kept below the threshold at which larger utility charges would be incurred. This geospatial approach to total simultaneous demand minimization can be further optimized for a fleet by adding well-placed opportunity charging stations to minimize the changing needs of EVs at specific charging sites and charging stations.

[0168] Figure 18 Figure 18 shows one electric bus (at a time) traveling on one route. An EV bus 1801 travels along a route 1802 mapped to local roads 1803. An access road 1804 equipped with wireless machine chargers 1805 allows the EV bus 1801 to charge at a set power level until the scheduled bus stop time.

[0169] Information regarding the bus 1801 status, operating conditions, load, schedule adherence, and power consumption may be collected and transmitted in near real time along the route 1802 or at stops 1806 .

[0170] Figure 19 Figure 19 shows multiple electric buses operating on a route at the same time. This arrangement is done to reduce passenger waiting times and / or to provide sufficient capacity to serve the route.

[0171] A first electric bus 1901 and a second electric bus 1902 travel along a route 1903 mapped to a local road 1904. An access road 1905 for a bus stop 1906 is equipped with a wireless machine charger 1907, allowing the first and second electric buses 1901, 1902 to charge at a set power level while passengers board and disembark until the scheduled bus stop time.

[0172] Information about the status, running conditions, load, schedule adherence, and power consumption of the buses 1901, 1902 may be collected and transmitted in near real time along the route 1903 or at the stops 1906.

[0173] Figure 20 Figure 20 shows a case where multiple electric buses operate on multiple routes simultaneously. The buses may be the same or different vehicles. In this example, the wireless charging infrastructure is shared.

[0174] A first route 2001 and a second route 2002 share the same local road network 2003. The first route 2001 is provided by a first electric bus 2004 and a second electric bus 2005, which travel along the route 2001 according to a first schedule. The second route 2002 is operated by a first electric bus 2006 and a second electric bus 2007, which travel along the second route 2002 according to a second schedule.

[0175] Wireless machine chargers 2008 are installed along the first route 2001 and the second route 2002. The first and second schedules must be coordinated to ensure sufficient charging time for each of the electric buses 2004, 2005, 2006, and 2007. The power consumption and power delivered via the wireless machine chargers 2008 (and other shared or non-shared chargers, such as wireless machine charger 2009) must be coordinated (by the dispatch server 1001) to avoid exorbitant power usage and utility demand charges and to avoid overstressing the cooling capacity of the wireless chargers during and between charging sessions.

[0176] Information regarding the status, driving conditions, load, schedule adherence, and power consumption of the buses 2004, 2005, 2006, 2007 may be collected and transmitted in near real time along the routes 2001, 2002 or at parked wireless chargers 2008, 2009. Information regarding the wireless chargers 2008, 2009 themselves may also be collected and transmitted in near real time, periodically, or triggered by an event (e.g., before, after, or during a charging session).

[0177] Additional Examples Figure 21 Figure 21 illustrates an example of a drayage yard 2101 using electric freight vehicles (not shown) with strategic wireless machine charging. In this example, both payload weight and travel distance are primary determinants of battery consumption for the electric vehicles (e.g., forklifts, side loaders, reach trucks). In Figure 12, the movement of containerized cargo is described as an example.

[0178] Containers may be loaded and unloaded from the freight rail system 2102 by yard vehicles. Dedicated container handling equipment 2103 is used to transfer freight containers from visiting rail vehicles to local rail yard stacks 2104, which are accompanied by transfer vehicles equipped with WPT receivers that can move the containers to truck yard stacks 2105, dock yard stacks 2106, or temporary storage stacks 2107. As each stack (2104, 2105, 2106, 2107) is frequently visited by transfer vehicles, wireless chargers can be placed at each based on container usage levels and wait times. In this example, rail yard chargers 2108, shipyard chargers 2109, and truck yard chargers 2110 are installed. In this example, storage stack 2107 does not have an associated charger.

[0179] The truck yard stack 2105 is added to by unloading trucks using crane equipment 2111, added to by transferred containers, or reduced by loading containers onto trucks, or by diverting containers to other transport or storage 2107 using transfer vehicles.

[0180] The dockyard stack 2106 is added to by unloading trucks using cargo cranes 2112, added to by transferred containers, or reduced by loading containers onto ships or barges (not shown), or by diverting containers to other transport or storage facilities 2107 using transfer vehicles.

[0181] Storage stacks 2107 can be added or removed by the transfer of containers to each of the transport yard stacks 2104, 2105, 2106.

[0182] A transport management application (similar to the software and database used by the dispatch center 1001) uses near real-time data regarding cargo container weight (either manifest weight or weight from sensors mounted on the transport vehicle), cargo container location, transport vehicle location, transport vehicle state of charge, container destination (and therefore distance traveled), and current queues at the source and destination stacks to manage opportunity charging schedules for each charger 2108, 2109, 2110, as well as the charge level and charging time for each charging session, minimizing downtime and travel costs.

[0183] The drayage yard 2101 also includes a rest and repair stop 2113 with an associated wireless charger 2114.

[0184] A spur of Highway 2115 leads to Drayage Yard 2101, serving both freight trucks and employee vehicles.

[0185] Figure 22 Figure 22 depicts the geographical route of an electric delivery vehicle taking advantage of strategic opportunities to minimize travel costs.

[0186] A delivery vehicle is stored and maintained at a stop 2201 a distance 2202 from a first distribution center 2203. A wireless machine charger may be installed at the first distribution center 2203. The delivery vehicle has a first route 2204 having multiple stops. The stops may be delivery only, pick-up delivery, or both, depending on the type of delivery service provided. The first route 2204 returns the vehicle to the first distribution center 2203 where it is loaded and unloaded as needed during a machine charging session.

[0187] The second delivery route 2205 includes a third party public or contract wireless charger 2206 that can provide charging as needed or desired to maintain the delivery vehicle's battery within the desired SoC range.

[0188] At the end of the second delivery route 2205, the vehicle returns to the first distribution center 2203, where loading and unloading of cargo occurs as needed between charging sessions.

[0189] A third delivery route 2207 includes, among its scheduled stops, a visit to a second distribution center 2208. The second distribution center 2208 may include a wireless machine charger that can be used to charge the delivery vehicle. The third delivery route 2207 terminates at the first distribution center 2203, where the vehicle is unloaded and recharged to an optimal charge level for overnight storage at the stop 2201, taking into account the charge used to travel the distance 2202 to the stop 2201.

[0190] Figure 23 As discussed above, factors that determine EV range 107, 108, 109 can include vehicle characteristics, battery pack characteristics, environmental factors, payload, terrain traveled, and driver capability. Even in fully modeled systems, excess charge is retained in the battery pack as a range buffer and to preserve battery life. In some cases, managed Charging-as-a-Service (CaaS) can be used with a need-weighted, geographically distributed WPT opportunistic charging system to low-costly accumulate, store, and discharge excess energy (above the surplus energy level) into the utility grid, using power arbitrage to lower total electricity costs. The CaaS system has access to information about the charger locations under its control, including near-real-time environmental and usage data, as well as EV-related data and charging schedules generated by the route model.

[0191] 23 is an exemplary high-level functional diagram of power flow-through and conversion by a bidirectional wireless power transfer (WPT) system 2300 that can be used for power arbitrage in a sample configuration. The bidirectional functionality enables selective wireless charging or discharging of EV battery packs as directed under the CaaS management.

[0192] While certain components are inherently bidirectional and symmetrical (e.g., resonant inductive circuits, also known as open-core transformers) and can be shared, the forward (charging) and reverse (discharging) power transmission paths rely on a branched simplex architecture, requiring a switch 2309, control logic 2310 (see FIG. 30 below), and communication link 2311 to initiate and complete the power transmission path for each of the forward (charging) and reverse (discharging) usage scenarios. While both the forward and reverse paths are presented here, WPT can be implemented (and optimized) using only one path (nominally the forward path).

[0193] In the forward direction, power is nominally supplied from the utility grid 2301. Depending on the grid connection, power may be single-phase alternating current (AC), direct current (DC), or polyphase AC. The utility grid 2301 includes the transformers necessary to step down voltage from high voltage transmission lines. In this example, single-phase AC is supplied by the utility grid 2301 with sufficient capacitance present so that the power factor is regulated to near unity (unity).

[0194] The AC power is converted to DC by AC / DC converter 2302. This function can be performed by an active (switch-based) rectifier or a passive (diode-based) rectifier. DC / AC converter 2303 takes the input DC power and converts it to a high-frequency AC (nominal 85 kHz in this example) sinusoidal signal. The DC / AC conversion operation by DC / AC converter 2303 can be performed using an inverter.

[0195] The AC power signal is passed to a resonant inductive air core transformer 2304 having a primary coil and a secondary coil. The AC power is converted to magnetic flux on the primary side and inductively coupled to the secondary side. The secondary coil converts the received magnetic flux into an AC power signal. The AC power signal is passed to an AC / DC converter 2305. The AC / DC conversion function can be achieved by an active (switch-based) rectifier or a passive (diode-based) rectifier.

[0196] The resulting DC signal is used to charge the power storage device 2306, which is nominally a rechargeable chemical battery, but could also be a capacitor bank, a reversible fuel cell, a solid-state battery, or one or more of the foregoing hybrid combinations. The DC signal can also be used to directly power an electrical device. Being bidirectional, the power storage device 2306 can output stored power as DC back into the power path. The DC power is converted to the required AC power signal by a DC / AC inverter 2307.

[0197] This AC power signal is input to a resonant induction air core transformer 2304. In this reverse path scenario, the coils operate in the opposite direction to the forward path. The AC power is converted to magnetic flux by the primary coil of the resonant induction air core transformer 2304, which is inductively coupled to a secondary coil. The secondary coil converts the received magnetic flux into an AC power signal. The resulting AC power is frequency regulated by an AC / AC converter 2308. In one configuration, an AC / DC / AC converter is used as the AC / AC converter 2308, and AC / AC frequency regulation is achieved using an AC / DC rectifier, followed by DC to AC conversion at the required frequency by an inverter circuit. The utility grid 2301 in this example includes the necessary transformers to convert the AC power to the desired voltage and AC / DC conversion, if necessary, to interface with utility supply power.

[0198] In an alternative configuration, if a DC utility feed is available from a DC utility grid (not shown), the DC / AC converter 2303 can be sized to accept the DC feed directly, eliminating the need for the preceding AC / DC stage 2302.

[0199] Figure 24 Figure 24 illustrates a simplified configuration example for realizing power arbitrage using reverse metering in a WPT charging station. To enable power arbitrage, both the ground-mounted EV and the wireless power station are equipped for bidirectional charging.

[0200] 24 is an overhead view of a wireless power charging station, in this example, a serviced charging station for EVs. The wireless power charging station 2401 in this example has four bidirectional chargers 2402, 2403, 2404, and 2405 arranged to power up to four electric vehicles simultaneously. In this example, each charger 2402, 2403, 2404, and 2405 powers an electric bus 2406, 2407, 2408, and 2409, respectively. Note that depending on the installation location, some or all of the chargers may only be one-way.

[0201] All four chargers 2402, 2403, 2404, 2405 are fed via an underground electrical connection (not shown) to power electronics 2411 of a local micro-generation grid 2415. The power electronics 2411 is connected to a utility grid 2414 via a drop 2410. The utility grid 2414 can provide AC, DC, or AC three-phase power via the drop 2410. In this example configuration, the power electronics 2411 includes the electronics necessary to support both electrical charging from and discharging to the utility grid 2414. The utility drop 2410 includes the necessary transformers and electrical metering to allow for power transmission in either direction. Electricity can be collected from visiting EVs or from local power storage 2412 of the local micro-generation grid 2415 when the dispatch server 1001 determines excess power or when excess power is determined from the dispatch office 1001 of the CaaS management system 2501 (FIG. 25).

[0202] The local micro-grid 2415 may include auxiliary local power storage 2412 (e.g., batteries) that can be used to prevent excursions above concurrent demand thresholds. The local power storage 2412 may also store power from visiting EVs 2406, 2407, 2408, 2409 via bidirectional chargers 2402, 2403, 2404, 2405 if the EVs are identified (by dispatch server 1001) as storing excess power.

[0203] In some remote facilities, local power generation 2413 (e.g., diesel generators, solar generators, wind generators, hydroelectric generators, nuclear reactors (disintegration, fission, or fusion)) of the local micro-power grid 2415 may be used to supplement or replace connection to the utility grid 2414.

[0204] Additionally, a utility communications network 2416 may be provided that allows near real-time signaling to the controllers (shown in FIG. 24 as part of the power electronics 2411) of the local micro-grid 2415. This signaling may include current electricity rates and needs.

[0205] A utility discharge controller 2417 enables the coordination of charging and discharging from the local micro-power grid 2415, which includes eligible EVs, the local power storage 2412, and the local power generation 2413. The utility discharge controller 2417 can signal the need for power input to the grid 2414 via the utility drop 2410.

[0206] Figure 25 For publicly owned electric vehicle (EV) fleets, or vehicles shared in Charging-as-a-Service (CaaS) programs, power arbitrage is enabled by the ability to manage electricity usage and storage over time, including local storage at charging stations (batteries, compressed air, etc.), excess state of charge (SoC) within each EV visiting a charging station, and battery packs in EVs parked at depots and yards.

[0207] Wireless chargers can be made bidirectional by adding parallel charging and discharging circuits on the EV and ground sides, since the inductive coupling between the onboard coil and the ground coil works in either direction. WPT charging and discharging (bidirectional WPT) can be remotely controlled for any EV or vehicle under CaaS management, without driver or passenger interaction. The CaaS system or its dispatch office contains location, usage, and scheduling data for both unidirectional and bidirectional chargers. EVs can be human-driven, driver-assisted (partially autonomous), or fully autonomous. The CaaS and WPT systems automatically handle the coordination of charging, discharging, and storage based on extensive modeling and analysis of route models.

[0208] This server has access to charger data, vehicle data, access to third-party data feeds (e.g., traffic, weather, electricity availability, electricity rates), and geographic and terrain data for calculation of modeled routes and route segments. The dispatch server 1001 also maintains (and updates) charger configuration, availability, charging session scheduling, and power availability and consumption. Both the route modeling and charging management algorithms used by the dispatch server 1001 use trained machine learning to generate predictive models for the CaaS.

[0209] An example of such power arbitrage can be seen in Figure 6, where two local utilities have different electricity pricing (e.g., based on generation method and capacity). In the example scenario, a charging station 603 supplied by a utility grid 601 is able to charge an EV more than predicted by a route model (using a second set of battery SoC thresholds for excess charging). When the excess-charged EV arrives at the charging station 604 (supplied by the utility grid 602), the excess power is transferred via the WPT system to local storage 2412 (for use by other EVs using or planning to use station 604) or to the local grid 602, ensuring that the EV's SoC falls within the first set of battery SoC thresholds.

[0210] In FIG. 17 , by scheduling surplus charging across a managed fleet of EVs 1709, 1710, 1711, and 1712 (or multiple managed fleets), power can be extracted, stored locally, and distributed to manage electricity costs during the service day. Selling power during peak usage hours can further reduce electricity costs to the managed fleet of EVs through power arbitrage. Modeled power consumption and third-party data (e.g., electricity rates) can be used to calculate surplus power at each charging station 1702, 1703, 1704, 1705, 1706, 1707, and 1708. In some cases, surplus EV power may be transferred to local electrical storage 2412 for future use by fleet EVs or for future scheduled sales opportunities if the local storage is calculated to have surplus power of its own.

[0211] Another example of electrical arbitrage can be seen in Figure 21. In this example, unused, underused, or parked vehicles at a depot 2101 can be used as local battery storage facilities during the operating day (within SoC thresholds). Depot WPT bidirectional chargers 2108, 2110, 2114 can be used to profitably store excess power from EVs to the utility grid (not shown) during low-cost times and then send it back during times of high demand.

[0212] FIG. 25 is a diagram of a system for managing a CaaS system at the functional element level in a sample configuration. In this example, CaaS management system 2501 is a processing platform including a processor that performs the functions described herein and interacts with local data storage 2502 and optional remote data storage 2503. An example of optional remote data storage 2503 may be a distributed, decentralized database that stores cached traffic, weather, public charger status, recent charging sessions, and the like. Data communications network 2504 connects CaaS management system 2501 to one or more dispatch offices, including the dispatch server 1001. In this configuration, the dispatch server 1001 provides vehicle data, route data, electricity rate data, charger usage, charger capabilities, charger locations, and third-party data to the CaaS system for use in calculating awards by one or more processors in the CaaS system.

[0213] Figure 26 26 is a block diagram illustrating an example hybrid depot for charging and discharging parked fleet vehicles 2602, 2603, 2604, and 2605. The hybrid depot 2601 uses bidirectional wireless power transfer pads 2606, 2607, and 2608 and wired power transmission 2609 under the control of local power electronics 2610. The local power electronics 2610 interconnects the WPT 2615 and wired 2609 subsystems to a utility grid (not shown) and converts and meters power to and from the depot 2601 under the control of the CaaS management system 2501 via the local power electronics 2610. In this example, the CaaS management system 2501 is located remotely and connected to the local controller 2610 via a data network 2504. The local controller 2601 can also use a wireless (radio) network via an optional antenna 2611.

[0214] In this example depot, pavement 2612 supports EVs and conceals conduits 2613 for power, cooling, and communications between each WPT pad 2606, 2607, and 2608 and the power electronics 2610, thermal management 2614, and the local controller 2616. Alternatively, although not shown, an overhead structural frame or gantry can be used to support the conduits 2613, as well as signs, directional signals, surveillance cameras (see U.S. Patent Application No. 17 / 659,45, entitled "FOREIGN OBJECT DETECTION FOR WIRELESS POWER TRANSFER SYSTEMS"), and lighting.

[0215] Feed 2702 includes spot prices for electricity associated with a geographic region, utility, or service territory. Information from said feed 2702 may be overridden or superseded by contractual terms or active arrangements with a utility.

[0216] An impact database 2703 stores the impact of using EVs and local batteries 2704 for excess storage along with the associated battery lifespan. Each EV and local storage can have a unique lifespan determination model that results in a unique total cost for excess charging.

[0217] Wear on charging station components such as power electronics, cooling systems, chargers, etc. is also maintained in the impact database 2703. For modular chargers, data is maintained for each coil assembly of each charger. For hybrid wired / wireless charging stations 2601, data is also maintained for power cords and connectors. The logistics controller 2701 uses the impact data in arbitrage equations for profit maximization.

[0218] Local storage 2704 is an optional component nominally co-located with an individual charger. Nominally intended to supply power to charger stations and visiting EVs, the local storage 2704 can charge or discharge from the utility grid or charging EVs under the direction of the logistics controller 2701. When EVs are used to store and transport excess power, the local storage 2704 can accept and store power obtained from EVs under the direction of the logistics controller 2701.

[0219] Each EV may have an estimated power level and reserve power level (set by the operator) required for each route segment, potentially making additional excess power capacity available for storage and transportation (excess is the maximum allowed capacity minus the required capacity and reserve). The route segment database 2705 stores the estimated power and reserve power capacity for each route segment.

[0220] The route database 2706 stores the total route segments for each EV under its control. The total cost of distance (TCD) for each EV, each route segment, and each route, as well as actions by the logistics control device 2701 that affect the power charged or discharged from each EV, are stored in the route database 2706.

[0221] Examples of actions performed by the logistics controller 2701 include charging above a nominal threshold, charging above a nominal current, and charging at a temperature above or below the nominal battery temperature. Other examples include discharging below a nominal threshold, discharging above a nominal current / rate, and discharging at a temperature above or below the nominal battery temperature.

[0222] The optional reserve fleet 2707 consists of EVs not currently in operation connected to a charging station via a bidirectional charger. Similar to the optional local storage 2704, the battery packs of the reserve fleet 2707 can be charged and discharged as needed to increase the overall vehicle's electrical efficiency. Charging and discharging can be performed using the utility grid or the local storage 2704. Charge levels can range from a recommended minimum to a recommended maximum state of charge (SoC) for each battery pack. Allowable charge and discharge rates can also be set at nominal or recommended levels for individual battery packs. A portion of the reserve fleet 2707 can be maintained at an operational charge level for use as needed, and therefore will not be discharged below a set operational threshold level.

[0223] Figure 28 Figure 28 includes a graph depicting the capacity of an individual EV (e.g., a transit bus on a set route with a set schedule) used to store, transfer, and discharge power using a conservative charging strategy.

[0224] Graph 2801 shows a relative demand curve 2805 over an exemplary 24 hour period 2804 .

[0225] Graph 2802 shows a time series of charging / discharging spots and trip intervals for a transit bus over an exemplary 24-hour period 2804. Time spent offline (parked at a stop or otherwise) is shown as blocked periods 2806.

[0226] Graph 2803 illustrates an example of power arbitrage using one electric transit bus, showing the estimated power needed for the trip 2807 and the surplus power 2808 at each stop along the route of scheduled stops during an example time period 2804. Because EVs can charge or discharge while stopped, but can only discharge while on the route, the estimated power needed for the trip 2807 is consumed along the route and may be replenished by reallocating surplus power or recharged while stopped.

[0227] Exceptions exist that allow EVs to charge above the operational level of the next route section along the route by using regenerative braking.

[0228] The charging strategy for the bus in this example is designed to maintain maximum excess charge by keeping the stops fully charged, recharging only when costs are below a threshold 2809, and using the excess for operational needs when costs are above the threshold 2809.

[0229] Figure 29 FIG. 29 illustrates a battery pack 2901 with sample configuration ranges and thresholds. The battery pack 2901 is nominally composed of a bank of battery cells with internal cross-connections and electronic circuitry for powering an electric vehicle (EV). Depending on the battery chemistry (e.g., lithium-ion (Li-ion) batteries (including their various configurations and chemistries)) or energy storage technology (electronic battery, reversible fuel cell, solid-state battery), the battery pack 2901 can have a lower 2902 charge threshold to minimize damage from discharging the battery pack 2901. The battery pack 2901 can also have an upper charge threshold 2903 set to minimize damage from charging the battery pack 2901. Between the upper threshold 2903 and the lower threshold 2902 is a nominal charge range 2909. The upper threshold 2903 and the lower threshold 2902 can be recommended by the battery pack manufacturer, set from experience gained from the EV's operating history, or determined by the operator.

[0230] For each route segment, the charge amount that exceeds (at least) the minimum threshold 2902 is set as the operation level 2904. Therefore, the operation charge amount 2905 that is equal to or greater than the minimum threshold 2902 and less than the operation level 2904 is the charge amount required to complete the route segment.

[0231] A fallback fare level 2906 is set (usually as a percentage added to the trip fare 2905) due to potential vagaries when traveling the route segment. The difference between the fallback fare level 2906 and the trip fare level 2904 is the fallback fare 2907.

[0232] Above the reserve charge level 2906 and below the upper charge threshold 2903 is excess battery capacity 2908. The excess battery capacity 2908 charge can be used to reduce the need for EV charging on subsequent route segments, or can be used to store and transport electricity between charging stations to reallocate charge.

[0233] Figure 30 Control logic for charging and discharging from a battery pack is shown in FIG. 30. The control logic may be located in the bidirectional charger 2300, the dispatch server 1001, and / or the CaaS management system 2501. A pre-event 3001 may be any of a number of trigger events that cause a logistics check 3002. The pre-event 3001 may be arrival, alignment, and communication established when an EV arrives at a wireless charger. The pre-event 3001 may also be the plugging in of an EV's wired power connector or the crossing of a threshold charge at a local battery store (or a member of a reserve fleet).

[0234] Alternatively, logistics check 3002 may be initiated by logistics controller 3003. For example, logistics check 3002 may be initiated upon detection of exceeding a utility pricing threshold, reaching a contract boundary, time of day, significant deviation from estimated power consumption, route change (e.g., new route segment), loss of power at a charging station or charger, reduction in charging capacity at a charging station, loss of communication with a charging station or charger, etc.

[0235] The logistics check 3002 queries the status of the battery pack, which is compared to baseline data. The status of the EV or local storage includes the current SoC, battery temperature, and charge / discharge capacity and capability.

[0236] Based on the logistics check 3002 and the fleet demand model, the logistics controller 3003 signals the charger to do nothing or to charge or discharge the battery pack at 3004. The logistics controller 3003 can then set the charging parameters of the EV with a charge command 3005 or set the discharging parameters of the EV with a discharge command 3007. These commands 3005 and 3007 can override the parameters set in the EV's battery management system.

[0237] Once charging 3006 or discharging 3008 is complete, the EV prepares to depart from the charger or is sent a subsequent logistics check 3002 if required and allowable by the EV's schedule.

[0238] If the EV is inactive, the inactive EV reallocates the excess power to operational and reserve power for the next route segment and prepares to move at event complete 3009 .

[0239] If stopped, the inactive EV may be subjected to a subsequent logistics check 3002, and the EV may then receive a charge command 3005 or a discharge command 3007 based on the new data. Once charging 3006 or discharging 3008 is complete, the event is complete at 3009 and the EV is ready to be moved from the charging pad (or unhooked from a wired charger).

[0240] The logistics controller 3003 may also instruct the EV not to charge or discharge at 3010 before completing the event at 3009 .

[0241] It should be understood that the control logic of Figure 30 may be applied to control a single EV, a fleet of EVs, a spare EV, and a fleet of spare EVs, although the spare EV is typically managed to maintain its operational charge in case that EV is called into service.

[0242] Figure 31 An operational flow for controlling charging and discharging from an EV in operation (e.g., a fixed-schedule transit bus on a set route with estimated operating charge levels and estimated power consumption per route segment) is shown in Figure 31 .

[0243] As shown in Figure 31, the EV must first establish communication 3101 with the charger controller. In the case of a wireless or pantograph charger, this can be done via a wireless connection through the charging station infrastructure or by short-range communication with the wireless charger. In the case of a traditional wired system, communication can begin upon physical connection of the plug-in connector.

[0244] Pre-charge messaging 3102 can include alignment and safety verification messaging.

[0245] The EV BMS attempts to start a charging session with a charging request message 3103.

[0246] Based on instructions from the logistics controller 3003, the charger controller denies the charging request and returns a charging override 3104 message providing discharging parameters to the EV BMS instead.

[0247] The EV battery system discharge 3105 to local storage or utility grid continues for a preset time calculated to allow the EV to depart on schedule.

[0248] After discharging 3105 is complete, the EV is uncoupled at 3106 and leaves the stop with at least the necessary charge (and reserve) to continue on the next route segment.

[0249] Figure 32 FIG. 32 includes a graph depicting the capacity used to store, transfer, and discharge power for an EV transit fleet (e.g., multiple buses each serving a set route on a set schedule).

[0250] Graph 3201 shows a relative power cost curve 3202 over an exemplary 24 hour period 3203 from the perspective of a local utility.

[0251] Graph 3204 graphically illustrates the excess power 3205 captured, retained, and returned to the utility grid (or local micro-energy grid) by the fleet of transportation vehicles during the exemplary 24 hour period 3203. The excess power ranges between a maximum value 3206 and a minimum value 3207.

[0252] A route bus consumes power as it travels along each route segment. The consumed power can be recovered at any charging station. The consumed power can be recovered by reallocating the surplus. The surplus power can be added at charging stations with sufficient time and power available. The surplus power can be discharged at any station, provided there is discharge capacity and sufficient time. As mentioned above, the timing of charging / discharging is controlled based on various factors, including the cost of electricity and the power demand curve for the vehicle by time of day (Figure 28).

[0253] Additionally, charging / discharging to / from the utility grid can be coordinated with a local utility grid to minimize power spikes due to unexpected charging / discharging to / from the utility grid. For example, the control logic of Figure 30 may periodically send messages to the local utility grid to provide a schedule of expected charging / discharging to / from the utility grid and allow the utility grid to manage its charging allocation.

[0254] While examples have been provided for commercial vehicles such as buses, trucks, and delivery vehicles, it will be understood that the methods described herein can also be applied to non-commercial vehicles, such as privately driven EVs, with or without software-based driver assistance. These same methods can also be used for autonomously driven vehicles.

Claims

1. 1. A method of providing electricity arbitrage using at least one electric vehicle (EV) traveling along a predetermined route having a plurality of bidirectional chargers, the method comprising: receiving EV data and at least one charging schedule for the predetermined route and charger data for each bidirectional charger along the predetermined route; When the EV requests charging from a bidirectional charger among the plurality of chargers, determining whether the EV has excess charge beyond the charge required to complete the predetermined route; instructing the EV to discharge power to the bidirectional charger among the plurality of chargers if the EV has excess charge beyond the power required to complete the predetermined route; charging the EV according to a charging plan with the bidirectional charger if the EV does not have excess charge beyond the power required to complete the predetermined route; A method comprising:

2. 10. The method of claim 1, wherein one bidirectional charger of the plurality of bidirectional chargers comprises a wireless power transfer (WPT) charger.

3. 3. The method according to claim 2, wherein the WPT charger is installed at a commercial charging station for EVs, and further comprising: charging the EV from a local micro-grid connected to the WPT charger if the EV does not have excess charge beyond the power required to complete the predetermined route; discharging power from the EV to the local micro-grid if the EV has excess charge beyond the power required to complete the predetermined route; The method of claim 1,

4. The method of claim 3 further comprising: Storing power discharged from the EV in a local battery in the local small-scale power grid.

5. The method of claim 3 further comprising: Charging an EV from a generator of the local microgrid.

6. The method of claim 3 further comprising: The method includes using a spare EV as at least one of a local battery or a local generator at the mobile charging station.

7. The method of claim 1 further comprising: The method includes using trained machine learning to perform route modeling based on historical EV data and predetermined route data to generate at least one predictive model for managing charging availability at the plurality of chargers.

8. The method of claim 7 further comprising: receiving electricity price data for at least two bidirectional chargers along the predetermined route; calculating electricity prices for the at least two bidirectional chargers along the predetermined route; and charging the EV to a charge level that is higher than a charge level predicted by the at least one predictive model if a first charger of the at least two bidirectional chargers has a lower electricity price than another charger of the at least two bidirectional chargers along the predetermined route.

9. The method of claim 8 further comprising: Charging the EV when the EV arrives at another of the at least two bidirectional chargers along the predetermined route.

10. The method of claim 8 further comprising: receiving electricity rate data for bidirectional chargers along the predetermined route; calculating electricity rates for different time periods at bidirectional chargers along the predetermined route; and, if the EV has excess charge beyond the power required to complete the predetermined route, discharging at the bidirectional charger when the electricity cost at the time of charging is higher than the electricity cost at the previous time of charging.

11. The method of claim 1 further comprising: supplying power from the EV to a utility grid and coordinating with an electric utility based on at least one of power demand, power ramping, and power price to store power in the EV based on at least one of power supply or price of the utility grid.

12. The method of claim 1 further comprising: The method includes the steps of: when the EV requests charging from the bidirectional charger among the plurality of chargers, setting a cost threshold for electricity; determining whether the electricity cost of the bidirectional charger is equal to or less than the cost threshold; and charging the EV when the electricity cost of the bidirectional charger is below the cost threshold.

13. The method of claim 1 further comprising: setting a cost threshold for electricity; determining whether the cost of electricity at the bidirectional charger exceeds the cost threshold; and discharging electricity to the bidirectional charger if the EV has excess charge beyond the power required to complete the predetermined route and the cost of electricity at the bidirectional charger exceeds the cost threshold.

14. 2. The method of claim 1, further comprising: if the EV does not have excess charge beyond the power required to complete the predetermined route but has sufficient charge to reach at least one bidirectional charger along the predetermined route, determining which of the at least one bidirectional charger along the predetermined route has a lower electricity price at a time when charging is expected; and charging the EV at the bidirectional charger with the lower electricity price.

15. The method of claim 1 further comprising: calculating the charging plan based on a total cost per distance (TCD) of travel for each of a plurality of route segments between the plurality of bidirectional chargers along the predetermined route, telemetry data received from the EV, and the charger data.

16. The method of claim 1 further comprising: scheduling charging of at least one EV in the fleet of EV vehicles or discharging power from the at least one EV in the fleet of EV vehicles to a utility grid based on a cost of electricity relative to a cost threshold and a power demand curve for the fleet of EV vehicles at different times of day.

17. A system for providing electricity arbitrage using at least one electric vehicle (EV) traveling along a predetermined route, comprising: a plurality of bidirectional chargers along the predetermined route; Charging as a service management system (CaaS), receiving EV data and at least one charging schedule for the predetermined route, and charger data for each bidirectional charger along the predetermined route; When an EV requests charging from a bidirectional charger among the plurality of chargers, determining whether the EV has excess charge beyond the power required to complete the predetermined route; instructing the EV to discharge power to the bidirectional charger among the plurality of chargers if the EV has excess charge beyond the power required to complete the predetermined route; If the EV does not have excess charge beyond the power required to complete the predetermined route, instruct the bidirectional charger to charge the EV according to a charging plan. receiving as the service management system (CaaS) having at least one processor executing instructions; A system having:

18. 20. The system of claim 17, wherein a bidirectional charger of the plurality of bidirectional chargers comprises a wireless power transfer (WPT) charger.

19. 20. The system of claim 18, wherein the WPT charger is installed at a mobile charging station for EVs connected to a local micro-power grid that supplies power to the WPT charger when the EV does not have excess charge beyond the power required to complete the predetermined route, and receives power from the EV when the EV has excess charge beyond the power required to complete the predetermined route.

20. 20. The system of claim 19, wherein the local micro-grid comprises a local generator.

21. 21. The system of claim 20, wherein the local micro-grid comprises a local battery.

22. 22. The system of claim 21, wherein at least one of the local battery or the local generator includes a standby EV installed at the mobile charging station.

23. 20. The system of claim 17, wherein the at least one processor of the CaaS executes instructions to perform route modeling based on historical EV data and predetermined route data using trained machine learning to generate at least one predictive model for managing charging availability at the plurality of chargers.

24. 24. The system of claim 23, wherein the at least one processor of the CaaS further executes instructions to receive electricity rate data for at least two bidirectional chargers along the predetermined route, calculate electricity rates at the at least two bidirectional chargers along the predetermined route, and, if a first bidirectional charger of the at least two bidirectional chargers has a lower electricity rate than another bidirectional charger of the at least two bidirectional chargers along the predetermined route, instruct the bidirectional charger to charge the EV to a charge level that is greater than a charge level predicted by the at least one predictive model.

25. 25. The system of claim 24, wherein the at least one processor of the CaaS further executes instructions to instruct the EV, when the EV arrives at another bidirectional charger of the at least two bidirectional chargers, to discharge charge to the other bidirectional charger of the at least two bidirectional chargers along the predetermined route.

26. 25. The system of claim 24, wherein the at least one processor of the CaaS further executes instructions to receive electricity rate data for bidirectional chargers along the predetermined route, calculate electricity rates by time of day at the bidirectional chargers along the predetermined route, and instruct the EV to discharge at the bidirectional charger if the EV has excess charge beyond the power required to complete the predetermined route and if the electricity rate at the time of charging is higher than the electricity rate at the previous time of charging.

27. 20. The system of claim 17, wherein the at least one processor of the CaaS further executes instructions to adjust power supply from the EV to a utility grid based on at least one of power demand, power ramping, or power price, and to send a message to a utility company to adjust power storage in the EV based on at least one of power supply or price of the utility grid.

28. 18. The system of claim 17, wherein the at least one processor of the CaaS further executes instructions to set a cost threshold for electricity when the EV requests charging from the bidirectional charger among the plurality of chargers, determine whether the cost of electricity at the bidirectional charger is less than the cost threshold, and instruct the bidirectional charger to charge the EV if the cost of electricity at the bidirectional charger is less than the cost threshold.

29. 18. The system of claim 17, wherein the at least one processor of the CaaS further executes instructions to set a cost threshold for an electricity rate; determine whether the electricity rate for the bidirectional charger exceeds the cost threshold; and instruct the EV to discharge to the bidirectional charger if the EV has excess charge beyond the power required to complete the predetermined route and the electricity rate for the bidirectional charger exceeds the cost threshold.

30. 18. The system of claim 17, wherein the at least one processor of the CaaS further executes instructions to determine which of at least one additional bidirectional chargers along the predetermined route has a lower electricity rate at a time when charging is expected, and directs charging of the EV to the bidirectional charger with the lower electricity rate if the EV does not have excess charge beyond the power required to complete the predetermined route but has sufficient charge to reach the at least one additional bidirectional charger along the predetermined route.

31. 18. The system of claim 17, wherein the at least one processor of the CaaS further executes instructions to calculate the charging plan based on a total cost per distance (TCD) of travel over each of a plurality of route segments between the plurality of bidirectional chargers along the predetermined route, telemetry data received from the EV, and charger data.

32. 18. The system of claim 17, wherein the at least one processor of the CaaS executes instructions to schedule charging of at least one EV in the fleet of EV vehicles or draining of electricity from the at least one EV in the fleet of EV vehicles to a utility grid based on the cost of electricity relative to a cost threshold and an electricity demand curve for the fleet of EV vehicles at different times of day.

33. 1. A charging station for providing power arbitrage using at least one electric vehicle (EV), comprising: at least one bidirectional charger; a local micro-power grid that supplies power to the at least one bidirectional charger when the EV does not have excess charge beyond the power required to complete a predetermined route at the at least one bidirectional charger, and receives power from the EV when the EV has excess charge beyond the power required to complete the predetermined route; A charging station having

34. 34. The charging station of claim 33, wherein the local micro-grid comprises a local generator that provides power to the bidirectional charger.

35. 35. The charging station of claim 34, wherein the local micro-power grid is a charging station having a local power storage that receives power from

36. 36. The charging station of claim 35, wherein at least one of the local power storage or the local generator includes a spare EV installed at the charging station.

37. 34. The charging station of claim 33, wherein at least one of the bidirectional chargers comprises a wireless power transfer (WPT) charger.

38. 34. The charging station of claim 33, wherein the local micro-grid receives near real-time signaling from an electric utility communication network to control charging of the local micro-grid from an electric utility and discharging of the local micro-grid to the electric utility, the near real-time signaling including at least one of a current electricity price or power needs of the electric utility.

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