Strategy opportunity charging for in-transit electric vehicles

Abstract

Electric vehicles (EVs) for pick-up and delivery services are routed using opportunistic charging in response to task requests for pick-up and delivery of parcels or persons. Candidate EVs for a task are determined based on a preliminary route analysis of a route between a loading location and a delivery location, the preliminary route analysis including an estimate of point-to-point travel time and power consumption for each candidate EV. Candidate EVs are screened by determining which candidate EV is available during a service time window, with sufficient state of charge to reach at least a pick-up location. Route planning calculations are performed on each remaining candidate to develop an individual route between the pick-up location and the arrival location and predicted power consumption for the individual route. A candidate EV is assigned to serve a task request that has sufficient state of charge to meet predicted power consumption for its individual route with minimum recharge delay.

Description

Technical Field

[0001] The present disclosure relates generally to wireless power transmission and, more particularly, to devices, systems, and methods related to wireless power transmission to a remote system (e.g., a vehicle including a battery). More specifically, the present disclosure relates to achieving the lowest total cost of travel for an electric vehicle over a known route using opportunistic charging with wireless power transmission. Background Art

[0002] An increasing number of public transportation, short-haul transport, taxis and delivery vehicles are moving towards electric traction motors and battery power.

[0003] A public bus is a fixed-route public transportation service that operates according to a set (pre-announced) schedule that includes arrival and departure times for geographically distributed passenger stops where passengers can board and alight. A terminal or terminus is the starting or ending point of a public transportation route and a place where drivers can briefly disembark or change shifts. A terminal may also include a stop where passengers board and alight. A bus depot can be a terminal (stop) serving bus passengers or a junction between different bus routes and bus lines. A depot can also provide battery charging, vehicle maintenance, vehicle storage, and refreshments, rest breaks, and assembly areas for bus drivers.

[0004] Short-haul transportation is the process of transporting goods over short distances. Short-haul vehicles operate within a confined area, changing their routes as needed to pick up, move, and deliver goods between multiple destinations.

[0005] Delivery vehicles can be operated in several ways depending on the type of service. A package delivery vehicle can depart fully loaded at a loading dock, yard, or terminal and then deliver to a single or multiple unloading locations via public roads. Package vehicles can also be loaded at any unloading location or pre-set pickup location within the service area or along a pre-set route. Delivery routes can be fixed, extensible (both pre-set and new stops are added during a delivery run), or ad hoc (the next stop is determined during or after the current stop is completed). Taxi passenger services or ridesharing are good examples of completely ad hoc delivery services.

[0006] Static opportunity charging of electric vehicles (EVs) during short stops or while vehicles are loading, unloading, and sorting packages is an important application of wireless power transfer (WPT). EVs can be human-operated, use driver-assist automation, or be fully autonomous.

[0007] WPT provides fully automated power transmission for EVs without the need for a physical (wired) power connection. With WPT, drivers do not need to exit their vehicles to connect a power cable (if charging is permitted or if they are exiting the vehicle while charging). Also known as inductively coupled power transmission, WPT operates as an open-core transformer with a primary (ground-side) coil and a secondary (vehicle-side) coil, transmitting power across an air gap according to Faraday's first law of electromagnetic induction. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The foregoing and other advantageous features and advantages of the present invention will become apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0009] Figure 1 is a chart that graphically depicts an example of EV range versus battery state of charge (SoC) from the start of charging.

[0010] Figure 2 is a chart that graphically depicts an example of EV range extension through mid-trip recharging.

[0011] Figure 3 is a chart that graphically depicts examples of range extension and battery life extension through strategic opportunity charging.

[0012] Figure 4 is a chart that graphically depicts electricity prices over a 24-hour period in an example.

[0013] Figure 5 is a graph graphically depicting electricity prices from a first utility and a second utility that share the same geographic market with a deployed WPT system and offer different electricity rates based on the hourly time of day and generation capability and capacity.

[0014] Figure 6 is a diagram depicting an example of an EV route crossing two areas.

[0015] Figure 7 is a graph depicting an exemplary distribution of wireless chargers along bus routes in a geographical manner.

[0016] Figure 8 is a diagram depicting an example state machine for a public bus with wireless charging.

[0017] Figure 9 is a diagram that diagrammatically illustrates the use of a single charger to serve a first public transportation route and a second public transportation route, and thus to serve multiple EVs serving these routes.

[0018] Figure 10A diagram depicting at a high level the communication paths that can be used for data collection from EVs and WPT chargers.

[0019] Figure 11 is a diagram depicting an example wireless charger site.

[0020] Figure 12 is a flow chart of a sample method for strategic opportunity charging in an example configuration.

[0021] Figure 13 is a flow chart depicting a complete wireless charging session.

[0022] Figure 14 is a timing diagram depicting the interaction between the charger and the vehicle.

[0023] Figure 15 is a chart illustrating an example billing period for a wireless charging customer having a usage charging component and an on-demand charging component.

[0024] Figure 16 is a diagram depicting an overhead view of a charging station, where multiple chargers are capable of servicing multiple vehicles simultaneously.

[0025] Figure 17 is a diagram that illustrates geographically the ability of a utility to manage demand charging using strategic opportunity charging.

[0026] Figure 18 is a diagram showing an example in which a single electric bus serves a single bus route.

[0027] Figure 19 is a diagram showing an example in which a plurality of electric buses serve a single bus route.

[0028] Figure 20 is a diagram illustrating an example of multiple electric buses serving multiple bus routes utilizing a shared opportunistic charger infrastructure.

[0029] Figure 21 is a diagram depicting an example short-haul yard utilizing electric cargo transfer vehicles (not shown) with strategic wireless opportunistic charging.

[0030] Figure 22 A diagram geographically depicting electric delivery vehicle routes using strategic opportunistic charging to minimize driving costs.

[0031] Figure 23 Figure 1 is a diagram showing typologically the additional charging components for public and private charging stations.

[0032] Figure 24is a diagram illustrating an example scenario for a scheduled pickup and delivery service using EVs and opportunity charging in a sample configuration.

[0033] Figure 25 is a diagram illustrating an example scenario for rescheduling and rerouting pick-up and delivery EVs using wireless and / or wired opportunity charging in a sample configuration.

[0034] Figure 26 is a flow chart illustrating a sequence of events for selecting and assigning EV pick-up and delivery vehicles for processing requests for people or package deliveries using opportunity-charged EV service vehicles in a sample configuration. DETAILED DESCRIPTION

[0035] Reference Figures 1 to 26 Detailed Description of Illustrative Embodiments is Described.While this description provides detailed descriptions of possible implementations, it should be noted that these details are intended to be exemplary and in no way limit the scope of the inventive subject matter.

[0036] Strategic wireless charging allows for increased deployment of electric public transportation vehicles without range or time constraints. Electric buses have zero tailpipe emissions, are quieter, and, with strategic wireless charging, can operate without being removed from the line or route for refueling or recharging. EV public transportation vehicles are key to cleaner urban air, reduced traffic noise, and decarbonization. Strategic wireless charging is key to the large-scale adoption and eventual realization of public transportation automation.

[0037] An electric vehicle (EV) uses an electric traction motor and batteries instead of an internal combustion engine and chemical fuels. EV is used here for both battery electric vehicles (BEVs) and various hybrid battery and internal combustion engine vehicles (HBEVs). Batteries or battery packs nominally include rechargeable chemical batteries, but may also include one or more of capacitor banks, reversible fuel cells, solid-state batteries, or hybrid combinations of the above. Improvements in energy storage technologies (e.g., solid-state batteries, hybrid batteries, and supercapacitors) can also be used to take advantage of rapid opportunity charging using high-power wireless power transfer.

[0038] In a typical example, an EV battery pack is made up of electrochemical cells. The lifespan of a commonly used rechargeable lithium-ion battery cell is typically quoted as 300 to 500 charge cycles. A charge cycle is the period of use from full charge to full discharge and then back to full recharge.

[0039] These rechargeable lithium-ion batteries have a finite lifespan and will gradually lose their ability to hold a charge. This capacity loss (battery aging) is irreversible. As the battery loses capacity, the length of time it can power the bus decreases, resulting in a more limited driving range and operating time.

[0040] Much has been written about the ability of wireless opportunity charging to reduce the required battery size and extend the range of electric vehicles. The ability of strategic wireless opportunity charging to maintain the life of lithium-ion batteries by maintaining the charge level of the rechargeable battery between a high state of charge (SoC) threshold and a low state of charge (SoC) threshold has been demonstrated in the field targeting battery life extension. Charging rate and battery temperature before and during charging are also considerations for lithium-ion battery life. Solid-state batteries that can be charged and discharged 1000 to 10,000 times can still benefit from controlled charge level, charging rate and battery temperature considerations. In a mixed fleet, some EVs may contain lithium-ion batteries and other solid-state batteries (in some cases, the same EV may have multiple battery technologies). Such a mixed fleet will benefit from the personalization of EV charging and performance data for modeling and machine learning.

[0041] Wireless opportunity charging describes how electric vehicles can utilize wireless chargers deployed in their service system or along their routes. Since opportunity-charged EVs have no "idle" time when returning to a depot or garage for charging, they can save time and battery charging for service routes.

[0042] Opportunity charging allows for the use of smaller batteries in EVs, resulting in reduced weight and, beneficially, longer driving range or greater cargo capacity within that range.

[0043] Opportunity charging can also be used to maintain the battery SoC between upper and lower thresholds to extend battery life. EV battery packs can have high replacement costs, and extending battery life can reduce total cost of distance (TCD).

[0044] Opportunity charging allows for improved driver safety and comfort, as there is no need to leave the vehicle at night or in inclement weather conditions, as is required with plug-in chargers that require a cord. Automated opportunity charging also makes electric vehicle charging accessible to people with disabilities.

[0045] In the following illustrative examples, electric buses serving short- and medium-distance routes as part of a local or regional network of a pre-arranged government-managed bus service system are referred to as "public buses." For public buses, a terminal or terminus is the starting or ending point of a public transportation route and a location where drivers can briefly disembark or change shifts. A terminal may also include a stop where passengers board and disembark. A bus depot is a terminal that includes maintenance and vehicle storage facilities. A stop is any public transportation stop where passengers can board and disembark from public transportation vehicles.

[0046] For public buses (and indeed for any vehicle using strategic opportunity charging protocols and systems), first-party data is defined as data sent from sensors mounted on the EV. Second-party data is defined as data sent from other EVs or from sensors at meter stations (e.g., charging stations). Third-party data is acquired from external sources that are not the original data collectors. Third-party data can be aggregated from multiple sources. Examples of third-party data include maps, traffic conditions, and weather information.

[0047] All EVs, including battery electric vehicles (BEVs) and hybrids, have an estimated range based on the battery's state of charge (SoC). Vehicle wear and tear, battery life degradation, and electricity prices all factor into the total cost per mile or kilometer of distance traveled. When electricity prices vary across routes or service days, the additional variable cost per watt becomes factored into the TCD.

[0048] A fleet management system as defined herein may include fleet energy management, where each vehicle uses a radio data link to report sensor data and charging operations are coordinated with a dispatch office controller. This coordination and information awareness extends to opportunity charging capabilities distributed spatially (geographically and by travel route) and temporally to reduce overall fleet operating costs.

[0049] The following are all considered and used via a fleet management system to reduce the total distance cost for individual EVs and EV fleets: multiple buses, geographically distributed charger stations, charger stations with chargers co-located with high-usage businesses or locations, and a mix of private and public use charging stations.

[0050] The collection, communication, storage, monitoring, and analysis of environmental, vehicle, charger, and charging session data for a single EV or electric vehicle fleet or multiple electric vehicle fleets can be used to both enhance or optimize existing services and to provide new services based on analysis of the collected data. The use of historical data can be used to make better estimates for specific routes, vehicles, and drivers.

[0051] One such service is reducing total driving costs. The collected data can be used to ensure that all vehicles in an EV public transportation fleet complete their service routes at the lowest cost by optimizing charging for both individual EVs and the fleet as a whole. This cost minimization is achieved through near-real-time data, models populated with historically collected data, and an understanding of energy costs and chargers located in different geographic areas with potentially limited charging power available at each charger station or station.

[0052] Fleet management applications for cost minimization can optimize the driving cost of an individual EV or an entire fleet. Using individual EV cost optimization as the goal, each EV can be optimized for the potential local minimum driving cost based on predictions using data collected from the fleet or multiple fleets.

[0053] Using fleet-level objectives, fleet optimization is designed for a global minimum total cost, which may differ from a local optimization algorithm designed for minimizing the cost of an EV bus. Trade-offs modeled in fleet management applications may include accepting charging select EVs in the fleet at a higher electricity cost during a first time period to achieve a better overall outcome for the system, rather than charging those vehicles during a second time period when the charging cost is lower to achieve an overall lower cost for the fleet, or managing power resources to minimize the impact on the utility grid while charging.

[0054] Electric vehicles (EVs) for pick-up and delivery services using opportunity charging can also plan routes in response to task requests for package or person pickup and delivery. Candidate EVs for the task can be determined based on a preliminary route analysis of the route between the pick-up location and the delivery location, including estimates of the point-to-point travel time and power consumption for each candidate EV. Candidate EVs can be screened by determining which candidate EV is available during the service time window and has a sufficient state of charge to at least reach the pick-up location. Route planning calculations are performed for each remaining candidate to develop a separate route between the pick-up location and the delivery location and a predicted power consumption for the separate route. Candidate EVs that have a sufficient state of charge to meet the predicted power consumption for their separate routes with minimal recharging delays are assigned to serve the task request.

[0055] Efficiency is the ability to do or produce something without wasting material, time, or energy. In the context of public transportation, the efficiency of electric vehicles can include the relationship between electricity usage and the extension of EV range, vehicle cost, and / or battery pack life (depending on the scenario).

[0056] The treasure trove of first-party, second-party, and third-party data collected or otherwise obtained is well-suited to both statistical analysis and machine learning (ML) techniques. Statistical analysis can be used to identify trends, patterns, and relationships (both causal and correlational) using labeled quantitative and categorical data. Because the data is well-labeled, supervised learning algorithms for ML are well-suited for use when specific goals or optimizations are desired. In some cases, the data can be used in conjunction with unsupervised learning algorithms to cluster the data and identify patterns, associations, or anomalies from the data.

[0057] Figure 1

[0058] Figure 1 This is a graph that graphically depicts an example of EV range versus battery SoC from a single charge. The x-axis shows range 101, while the y-axis shows SoC 102 starting at 103. In this example, a simple linear driving model is used to illustrate the concept.

[0059] In a first example, an EV starts with a starting SoC 103 (eg, 100% SoC). As the EV travels 104 , its SoC decreases until it reaches 0% SoC at a cruising range 107 .

[0060] In a second example, an EV starts with a starting SoC 103 (eg, 100% SoC). As the EV travels 105 , its SoC decreases until it reaches 0% SoC at a cruising range 108 .

[0061] In a third example, an EV starts with a starting SoC 103 (eg, 100% SoC). As the EV travels 106 , its SoC decreases until it reaches 0% SoC at a cruising range 109 .

[0062] Factors that determine the EV range 107 , 108 , 109 may include vehicle characteristics, battery pack characteristics, environmental factors, load carried, terrain traversed, and driver ability.

[0063] Vehicle characteristics include the vehicle's make, model, manufacturer, age, mileage, and state of repair. Tire condition and tire selection are also considered. Aerodynamics (air resistance) are also considered a vehicle characteristic.

[0064] Battery pack characteristics include brand, model, manufacturer, capacity, battery aging (both time-dependent aging and accelerated aging due to battery cycling), past battery usage, and past trip-to-trip battery storage SoC. Data from sensors used to monitor individual battery cell temperatures and individual battery cell voltage levels are expected to be available nearly continuously via the vehicle's battery management system (BMS). The BMS provides supervision and management for the EV's battery pack. The battery pack nominally includes an array of battery cells that are configured and interconnected to provide the required voltage and the required current. The BMS monitors battery pack sensors (e.g., current, voltage, temperature), and communicates with the EV electrical subsystem and external chargers (e.g., WPT chargers). The BMS maintains a battery pack operating profile, as well as prevents over-discharge, overheating, and overcharging. The BMS can also optimize battery performance and life by controlling the charging rate and SoC.

[0065] Environmental factors include weather, air temperature, and air pressure. Air temperature affects not only the battery state of charge, but also the electrical loads required by interior climate control (heating and cooling) for passengers and / or cargo, and vehicle systems (e.g., cooling the battery pack). Daytime versus nighttime driving may also affect range. Weather conditions (e.g., rain, snow, wind) may also affect range and SoC. Lighting such as headlights, interior and exterior lighting place variable loads on the battery pack. In colder climates, especially when additional safety lighting is required in the dark at night, the combined heating (for passengers and battery pack) and lighting loads may require a large portion of the battery capacity, necessitating additional power delivery (either longer charging times or charging at higher power). The battery SoC safety margin may also need to be recalibrated to maintain battery life.

[0066] Environmental factors are expected to be obtained from charger site sensors, onboard sensors, and via third parties such as public weather stations, and fed to the dispatch server.

[0067] The load carried (whether passengers or cargo) affects power consumption along the route, with heavier loads reducing range and SoC. Rolling friction and acceleration are both affected by the load carried.

[0068] The terrain traversed includes the incline and slope of the driving route, as well as curves, speeds, stops, and traffic conditions. Traffic conditions can be collected from third-party services via an application programming interface (API) at the dispatch server. Terrain can be a significant factor in power consumption along a route segment, as uphill slopes will require additional power to overcome, while generally downhill route segments will both reduce power consumption and store additional power due to regenerative braking.

[0069] Driver capabilities include conserving battery resources through lane selection, smooth acceleration, and smooth deceleration (braking). “Driver” here includes the use of automated driver assistance packages and autonomous driving systems.

[0070] Figure 2

[0071] Figure 2 2 is a graph that graphically depicts examples of EV range extension through mid-trip recharging. The x-axis shows range 201, while the y-axis shows SoC 202. In this example, a simple (linear) driving model is used to illustrate the concept. In a first mid-trip recharging example, an EV begins at a starting point 204 of a trip with a first SoC 202. As the EV travels, the EV SoC level 203 decreases. At a charging point 205, the EV is recharged and then uses the recharged EV SoC 206 to continue the trip on the range 201.

[0072] Figure 3

[0073] Wireless inductive charging allows for connectionless charging of EVs. Opportunity charging involves charging the electric vehicle in short bursts (with small SoC increments) throughout its journey. This partial recharging strategy is similar to Figure 2 Shown is a comparison of one-time versus EV recharging development.

[0074] Figure 3 is a chart that graphically depicts an example of range extension and battery life extension through strategic opportunity charging. The x-axis shows range 301 and the y-axis shows SoC 302. In this example, a simple (linear) driving model is used to illustrate the concept. In this example of strategic static opportunity charging, not only is the EV partially recharged at each temporary station 306, 307, 308 and 309, but the SoC is also maintained between the SoC upper threshold 303 and the SoC lower threshold 304. The SoC curve 305 over the driving route is shown as a slope change (in this simplified model), showing the different power consumption over the route segments (i.e., between stations with opportunity chargers).

[0075] The upper SoC threshold 303 and the lower SoC threshold 304 are designed to increase EV battery life. A single threshold set is shown here, but multiple thresholds can be set to extend range while minimizing the impact on battery life. Of course, a physical upper threshold 303 of 100% SoC and a lower threshold 304 of 0% can always be used to extend range (e.g., for emergency use), at the expense of battery life.

[0076] exist Figure 3In the example shown, the starting SoC 310 is shown as being slightly below the upper battery charge threshold 303. In some cases, when removed from long-term storage, the starting SoC 310 may be higher (e.g., 100% SoC) or much lower (e.g., 40%).

[0077] In one configuration, selecting upper and lower thresholds and using opportunity charging to maintain the state of charge between the selected thresholds can extend the useful life of a lithium-ion battery pack. Monitoring the temperature and voltage of individual cells and varying the charge rate to keep both below (and above) the selected thresholds can also help maximize battery life.

[0078] By understanding the vehicle battery availability threshold, charger location, route, time of day, current location, traffic levels, and estimated time to reach the next charger, a decision can be made to charge (and to what SoC level) upon or before reaching a WPT opportunity charger. The cost of electricity from the utility grid may also vary depending on the time of day or at the WPT charger location.

[0079] Figure 4

[0080] Time-of-use (TOU) electricity rates are billing arrangements where the price of electricity varies based on the time of day. TOU rates are responsive to electricity availability, being more expensive during peak demand periods and less expensive during low demand periods. These rates typically remain constant over a period of time (week, month, quarter) to respond to changes in demand.

[0081] Figure 4 is a graph that graphically depicts the price of electricity over a 24-hour period in an example. Figure 4 , the X-axis 401 is a 24-hour day labeled in 1-hour segments. The Y-axis 402 shows the price of electricity for each hourly segment. In this illustrative example, starting at midnight 403 on the X-axis 401 and continuing until 8:00 a.m. 404, the electricity utility sets prices to "off-peak" 405. Between 8:00 a.m. 404 and noon 406, rising demand sets the utility price to "mid-peak" or "shoulder" 407. Between noon 406 and 6:00 p.m. 408, during the period of highest demand, the utility price is set to "peak" 409. After 6:00 p.m. 408 and until 11:00 p.m. 410, falling demand sets the utility price to "mid-peak" 407. After 11:00 p.m. 410, the rate falls back to "off-peak" 405. As shown in Figure 4 As can be seen in Figure 2, prices change as demand rises and falls.

[0082] EV charging (per watt) will be cheapest during off-peak hours and most expensive during peak hours. Selective charging of EVs (both the time of charging and the charge level at the charging session) can be implemented throughout the day to minimize costs.

[0083] Not shown is the weekend TOU rate which may be different from the weekday (e.g., Monday through Friday). In some areas, weekend rates are set to "off-peak."

[0084] Figure 5

[0085] In some regions and markets, there may be competing utilities. Figure 5 is a graph graphically depicting electricity prices from a first utility and a second utility that share the same geographic market with the deployed WPT system and offer different electricity rates based on the hourly time of day and generation capability and capacity. Figure 5 As shown, the first utility and the second utility share the same geographic market with the deployed WPT system and offer different electricity rates based on the hourly time of day and the generation capability and capacity. Figure 5 , the X-axis 501 is a 24-hour day marked in 1-hour segments. The Y-axis 502 shows the price of electricity for each hour segment. In this illustrative example, in the first time period 505 (midnight to 7 a.m.), the first utility unit market rate 503 is cheaper. During the second time period 506 (7 a.m. to 8 a.m.), the rates of the two utility units 503 and 504 are roughly equal. The third time period 507 (8 a.m. to 2 p.m.) shows that the first utility unit rate 503 is cheaper. During the fourth time period 508 (2 p.m. to 11 p.m.), the second utility unit rate 504 is favorable. Finally, in the fifth time period 509 (11 p.m. to midnight), the first utility unit supply rate 503 is again preferred.

[0086] By choosing service from a less expensive utility during charging times, the cost of electricity for EV charging can be reduced. Managing charging times, durations, and charge levels can increase these savings by minimizing charging during times of the day when electricity costs are higher.

[0087] Because the demand, availability, and cost of electricity can vary significantly from day to day or even hour to hour, these factors all factor into charging decisions. Information about electricity rates can be obtained from the terms of a pre-existing contract with the local utility, from data feeds from a local electricity exchange, or from the public market spot price of electricity when multiple suppliers feed into the utility. Examples of electricity markets include day-ahead energy markets and real-time energy markets.

[0088] Figure 6

[0089] Public unit service area

[0090] Charger targeting allows for the placement of successive chargers in different utility service areas along a specific route. In these areas, pricing information from multiple sources may need to be obtained and factored into charging decisions. In cases where the chargers are owned by a third party (other than the utility and fleet operator), it may be necessary to obtain electricity prices from the third party for the estimated charging time.

[0091] Figure 6 The diagram depicts an example of an EV route 605 traversing two areas 601 and 602. In this example, each area 601 and 602 is served by a different utility, each with a different price. Therefore, WPT opportunity chargers 603 and 604 can have different electricity costs. By using different charging durations and power levels at each WPT charger 603 and 604 at specific times of day, the total charging cost of the route can be optimized.

[0092] Figure 7

[0093] Figure 7 Figure 1 is a diagram depicting an exemplary geographical distribution of wireless chargers along bus routes. While recharging can occur at inter-route public transportation stops or interconnection stops (e.g., at rail hubs or airport terminals) with longer hold times, deployments can include recharging stops that are advantageously located (e.g., at ranges where the predicted battery SoC is predicted to cross a lower threshold).

[0094] Predictive Modeling

[0095] By obtaining prices for multiple chargers along the route at multiple estimated arrival times, a driving cost profile for that route can be generated at the beginning of the day. Based on the upper and lower SoC battery thresholds, a cost optimization can be calculated and recalculated as needed when the original route deviates or the model cannot predict accurate SoC values. The data used to populate 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, significant cost differences in charging from one location to another or from one time to another may trigger a second set of battery SoC thresholds.

[0096] The recalculation of the modeled travel costs can include deviations from the schedule for both early and late arrivals. In the case of an early arrival, additional charging time is available, potentially resulting in a lower supplied charging current. In the case of a late arrival, a higher charging current can be supplied to shorten charging time within a potentially shortened dwell duration.

[0097] A system is described that enables continuous operation of electric fleet vehicles using on-the-go wireless charging. The vehicle SoC is maintained at an optimal level to promote battery health and extend life, while also taking into account electricity prices to optimize vehicle operating costs.

[0098] Sensor data is repeatedly collected throughout operations during route charging sessions to facilitate a dynamic charging model that accounts for electric vehicle type (make, model, year), environmental factors (e.g., weather, traffic conditions), driver behavior, and drivetrain and battery pack health that impact wireless charging performance and vehicle operation.

[0099] Data collected across the EV bus fleet is used to automatically optimize the model to increase or decrease charging power based on these factors, allowing the vehicles to remain in operation indefinitely while also maintaining the optimal SoC across the fleet at the lowest possible cost in terms of both battery life and electricity cost.

[0100] For each electric vehicle type (i.e., brand, model, manufacturer, year, battery pack) or EV class, at least one minimum SoC threshold is established. The minimum SoC is dynamic and can be based on: a) the minimum SoC to limit battery damage (life reduction); 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 recharging; d) the minimum SoC calculated for aborting the route and reaching the depot; e) the SoC charge at the beginning of the route; and f) a manually set SoC threshold.

[0101] In the minimum power regime, EVs are only allowed to charge to the predicted value of SoC using wireless chargers for each stop. This minimum power regime can be changed based on the estimated electricity price at the WPT chargers along the route.

[0102] As recharging networks develop and higher-power wireless charging services are deployed, wireless opportunity chargers can be deployed in more geographic locations where even brief stops can be used for opportunity charging, or where charging lanes equipped with dynamic induction chargers are deployed. Note that both static and dynamic charging EVs can utilize near-field communication, such as that 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.”

[0103] Figure 7 An exemplary public transportation route is shown. A depot 701 is used to accommodate and service electric buses when not in service. Depot 701 can be part of a terminal, where passengers can board buses before departure. Passengers can board and alight at each pre-planned stop. Additional stops can be temporarily introduced for passenger alighting. A first route segment 702 is driven to bring the bus to a first stop 703, where passengers can board and alight. A second route segment 704 brings the bus to a second stop 705, where passengers can board and alight. A third route segment 706 brings the bus to a first transfer stop 707, where passengers can board and alight to continue on another intersecting public transportation route. Drivers can also use this stop 707 as an opportunity to take a mandatory rest break. A fourth route segment 708 is driven to a fifth stop 709, where passengers can board and alight. Driving the fifth route segment 710 brings the bus to the sixth stop 711, where passengers can board and disembark. Driving the sixth route segment 712 brings the bus to bus stop 714, which in this example is the end of the route. In this example, the longest route segment 712 includes a charger station 713, where the bus can make a brief stop to recharge. Charger station 713 can alternatively be a dynamic opportunistic charger, where the bus simply drives along a road equipped with a charger without stopping.

[0104] A wireless opportunistic charger may be installed at any of the planned docking stations 703, 705, 707, 709, 711, and 713. Additional charger stations (not shown) may be deployed between docking stations to increase range while keeping the SoC within the SoC threshold boundary.

[0105] For electric public transportation vehicles (e.g., buses), opportunity charging requires charging stations to be located on or near the driving route. Charger placement is initially accomplished through route mapping and modeling (using data collected (via test drives), modeled, or derived from similar routes and vehicles), with chargers placed at depots and stops (or, in some cases, between stops) where sufficient power is available. To reduce costs, chargers will typically be placed at a subset of stops.

[0106] Once the public transportation system (and the data collection, transmission, and analysis that comes with it) is operational, the collected data can be used to determine: 1) whether additional chargers are needed; 2) whether fewer chargers are needed; or 3) whether chargers can be decommissioned and moved to another site to better serve the public transportation fleet in terms of meeting total cost of travel targets.

[0107] Where charger and data infrastructure is shared between multiple fleets, the use of driving data from multiple fleets can be used to rebalance the charger infrastructure to accommodate newly deployed EV public transit vehicles, changing vehicle traffic patterns, and changes in ridership and routes.

[0108] When multiple fleets share opportunity charging infrastructure, charger ownership and payment for charging costs can be aggregated and negotiated between fleets. In some scenarios, local or regional agencies will own, operate, and service the wireless charger network and supporting communications and data systems, and distribute the costs among the fleets they serve.

[0109] In some cases, wireless chargers owned and operated by non-fleet commercial or government operators may be used to supplement the public transportation wireless charger network.

[0110] Figure 8

[0111] Figure 8 is a diagram depicting an example state machine for a public bus with wireless charging.

[0112] Figure 8 The state machine shows data collected at different times and events along the electric vehicle route.

[0113] In this example, the yard state 801 is experienced at least twice. Once at departure and once at the end of the day. Additional experiences may occur, for example, due to driver shift changes or necessary vehicle maintenance.

[0114] Upon departure from the depot, the bus systems are fully charged (to the SoC upper threshold), preheated, cooled, or air-conditioned as needed for the start of the day. Vehicle characteristics (e.g., make, model, year) and driver (or driver software) identity are recorded.

[0115] On-board data storage includes departure (current) time, battery state of charge (SoC) and SoC thresholds / limits, vehicle empty weight, current location and route. For the planned route, planned stops (locations) for passengers and chargers are established. For each charger on the route, the rate table and the spot rate for electricity are known. Modeling is used to predict the theoretical SoC used for each route segment based on past histogram data for the route, similar routes or simplified exemplary route models. The calendar and scheduled events along the route (and detours) are known and are taken into account during route pre-planning and modeling.

[0116] The en route state 802 is expected to be the most common state and covers all driving. During the en route state 802, the data storage accumulates the current time, current location, SoC, passenger count, traffic conditions and vehicle speed.

[0117] The data store can accumulate and store vehicle and route data using periodic or event-driven updates. Stored data is tagged with the time, current location, and route segment. Odometer mileage can be used as a backup fix when precise location is unavailable. Data can be uploaded to the dispatch office via a wireless connection (e.g., cellular or satellite modem).

[0118] For passenger pickup with charging 803, the data store updates the start and end time of the stop. Using the passenger pickup / drop-off counter (or bus weight change as a proxy), the total number of passengers served, the current number of passengers, and the number of passengers getting on and off the bus are updated. In the event of charging, the charging current, start and end SoC are recorded. The charger can send additional charging session data via its communication link, including charger status (and vehicle wireless power receiver status) and details about the inductive charging energy transfer (e.g., coupling, frequency, device temperature).

[0119] Route information can be updated via the charging station communication system or the bus's radio communication system. Route-related information includes the distance to the next stop, the type of stop, SoC thresholds and limits, the status and availability of chargers at the next stop with chargers, and the charger status of at least all chargers along the route.

[0120] For driver breaks with charging 804, the data store updates the start and end times of the stops. Using a passenger boarding / disembarking counter (or bus weight change as an alternative), the total number of passengers served, the current number of passengers, and the number of passengers boarding and disembarking are updated. When charging occurs, the charging current, start and end SoCs are recorded. The charger can send additional charging session data via its communication link, including charger status (and vehicle wireless power receiver status) and details about inductive charging energy transfer (e.g., coupling, frequency, device temperature).

[0121] Route information can be updated via the charging station communication system or the bus's radio communication system. Route-related information includes the distance to the next stop, the type of stop, the predicted SoC, etc.

[0122] For charger station 805, the data store is updated with the station's start and end times. No passengers are expected to board or exit the vehicle at charger station 805. When charging occurs, the charging current, start and end SoCs are recorded. The charger can send additional charging session data via its communication link, including charger status (and vehicle wireless power receiver status) and details about inductive charging energy transfer.

[0123] During the charging stop 805, route information may be updated via the charging station communication system or the bus's radio communication system. Route related information includes the distance to the next stop, the type of stop, the predicted SoC, etc.

[0124] For driver rest 806, the data store is updated with the start and end times of the stop. Using a passenger boarding / disembarking counter (or bus weight change as an alternative), the total number of passengers served, the current number of passengers, and the number of passengers boarding and disembarking are updated. In the absence of charging at driver rest 806, the start and end SOCs are recorded. Route information can also be updated via the bus's radio communication system. Route-related information includes distance to the next stop, stop type, predicted SOC, etc.

[0125] For passenger pickups (without charging) 807, the data store is updated with the start and end times of the stop. Using the passenger pickup / drop-off counter (or bus weight change as an alternative), the total number of passengers served, the current number of passengers, and the number of passengers getting on and off the bus are updated. In the event that charging does not occur, the start and end SoCs are recorded.

[0126] At passenger pickup (not charging) 807, route information may be updated via the bus's radio communication system. Route-related information includes distance to next stop, stop type, SoC thresholds and limits, predicted end SoC, charger status and availability at next stop with a charger, and charger status of at least all chargers along the route.

[0127] Figure 9

[0128] Figure 9 is a diagram that diagrammatically illustrates a situation where a single charger 901 is used to serve a first public transportation route 902 and a second public transportation route 903 and thus to serve a plurality of EVs serving these routes 902 and 903 .

[0129] In some cases, the amount of power requested, the number of vehicles to be charged, or both, may exceed the power available at the charger for opportunity charging. This includes assigning chargers that match a specific number of vehicles or that support non-standard communication protocols or charging signaling.

[0130] For cross-route charging stations, there will be competition for scarce charging resources (power and / or chargers), such as Figure 9 Contention may also occur for charging stations operated by third parties, stations experiencing congestion due to en-route delays, stations with disabled chargers, chargers in areas with power shortages, and in cases where emergency services have priority over chargers.

[0131] Competing charger resources also experience energy prices that fluctuate with space (geography) and time of day. Distribution planning and energy management via forecasts from historical data modeling can be used to manage and allocate the limited available power used at each station or by a specific charger at that station.

[0132] For example, Figure 9 In cross-route stations serving two or more public transportation routes, the arrival of public transportation vehicles may differ from the pre-scheduled arrival and charging times.

[0133] In the case of charging stations operated by a third party, even if a prioritization scheme and reservation system is implemented (as described in U.S. patent application serial number 17 / 199,234; “OPPORTUNITY CHARGING OF QUEUED ELECTRIC VEHICLES”), vehicle arrival time, number of vehicles, charge level, total charging demand, and the number of modular charging pads per charger and per vehicle (as described in U.S. patent application serial number 17 / 646,844; “METHOD AND APPARATUS FOR THE SELECTIVE GUIDANCE OF VEHICLES TO A WIRELESS CHARGER”) may also be considered.

[0134] At charging stations that experience congestion due to in-transit delays, a queuing scheme based on pre-scheduled departure times and vehicle SoC can be implemented. Planning for stations where chargers are inoperative has an additional level of complexity, as modular chargers may soft-fail while a subset of chargers remain available.

[0135] In some cases, power shortages may require power rationing. Vehicles can be assigned power priorities based on a SoC or ranking system. Power priorities can be served by providing higher charging rates to lower-priority vehicles or by suspending power to lower-priority vehicles.

[0136] In situations where an emergency or other high-priority electric vehicle requires charging, a priority scheme may be provided in which the charger of the currently charging vehicle is commandeered, or the next available charger is reserved for use by the priority vehicle.

[0137] Figure 10

[0138] Figure 10 The diagram depicts, at a high level, the communication paths that can be used for data collection from EV and WPT chargers. Data collection is used to strategically opportunity charge pricing. Telemetry (including two-way telemetry) uses wired and wireless communications to transmit data and information between remote sources (including mobile sources and users) and distant destinations. The telemetry data stream between the source and the destination can include continuous, periodic, polled, or ad hoc data transmission. Telemetry includes the automatic measurement and wireless transmission of data from a remote source, where the collected data is routed to a receiving device at the destination site (e.g., dispatch server 1001) for monitoring, display, recording / storage, post-processing, analysis, and trending.

[0139] The data store is part of the database software, where the data management software (e.g., IBM Maximo enterprise management system) runs on processor hardware with a computer operating system that has a large memory storage unit. Security and multi-party access control functions are via the data management software. In some implementations, the dispatch server 1001 and associated data store can be implemented as a virtual, hosted (e.g., cloud-based) system, or as a pre-built hardware (with the necessary processors, memory, and fault-tolerant data storage devices) and software system based on a general-purpose high-availability computing platform that is sized to suit the processing and storage needs of the dispatch office locally. The dispatch server can include (or have interfaces with) redundant, and potentially partitioned and federated databases; and a geographic information system (GIS). Interfaces to other third-party information such as electricity prices, traffic, and weather information can be centralized at the dispatch server 1001.

[0140] The data storage is stored in the vehicle, but the accumulated data is uploaded to the dispatch server 1001. The upload can be at the request of the dispatch office, or it can be a periodic or event-driven update (e.g., when a WPT charging session starts). 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 positioning beacons). The data storage accesses the vehicle system and the battery management system (BMS) via a local data link (e.g., a controller area network (CAN) bus).

[0141] Data sources may include historical data databases, usage log data, and models of near real-time data. Sources may also include near real-time data, which may include sensor outputs from sensors, including electrical data (e.g., voltage, current, or state of charge) or physical data (e.g., temperature, pressure, mass).

[0142] Telemetry may also include data products such as location, passenger counts, timestamps, data source identifiers, map updates, and route updates. In this application, vehicle data storage accumulates and can transmit collected electric vehicle related data to the dispatch server 1001 on a near continuous basis.

[0143] The dispatch server 1001 includes application-specific software that includes a data management system, an API for interfacing with third-party information feeds (e.g., traffic, weather, public charger status), and communication interfaces for data originating from the charger sites 1002 and EVs 1003. The charger sites 1002 can use a wired (not shown) or wireless radio interface 1004 for two-way communication. Depending on the installation, such a wireless radio interface can use a public or private cellular data network 1005 using public or private band radio signals 1006. An alternative or supplemental wireless radio network can be supplied from a satellite 1007 using established satellite communication band radio signals 1008. A satellite data receiver 1009 can be used to transmit satellite communication data to the dispatch server 1001.

[0144] Data can be generated by wireless charger sites 1002 and transmitted to EVs 1003 and / or dispatch servers 1001 via wireless charger sites 1002. Each charger site 1002 has at least a wireless charger 1010 and auxiliary equipment 1011 (shown here as an above-ground cabinet, but can be installed in a basement). Auxiliary equipment 1011 can include a wired or wireless backhaul (shown here as a radio antenna 1012 for a cellular radio connection 1004).

[0145] The route segments of the public bus 1003 are pre-planned with set arrival and departure times for each geographically predetermined stop. The time and route segment, along with the odometer reading, allow a rough horizontal position to be calculated. A more precise position of the vehicle 1003 can be obtained using an onboard navigation receiver (not shown) utilizing a global navigation satellite system 1013 (e.g., Navstar GPS, Galileo, GLONASS, BeiDou, Quasi-Zenith Satellite System (QZSS) (also known as Michibiki)) that broadcasts satellite signals 1014. Other communication satellite constellations broadcasting, for example, 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.

[0146] Alternatively, where deployed or available, geographically localized radiolocation beacons (beacons with known frequency, known bandwidth, known or broadcast transmitter location, and transmitted identification (ID)) may be used to obtain precise positioning.

[0147] In the public transportation EV 1003, a radio receiver and transceiver 1015 is used to receive GNSS or local beacon positioning signals to communicate via a land-side cellular network, and possibly using a satellite communication system to receive and send information.

[0148] The dispatch office and server 1001 (particularly in areas with multiple fleets and shared or third-party wireless charging resources) can be owned or managed by a third-party, non-fleet party (host) that provides Charging as a Service (CAAS). CAAS programs eliminate the ownership and maintenance burdens of charging EV fleets, with the host providing things like turnkey wireless charging stations, management software, communication infrastructure, 24 / 7 support, professional on-site maintenance of charger resources, and planning and modeling for deploying new chargers and retrofitting existing charger deployments when a need to change charger locations or increase (or decrease) charger capacity is detected at any existing charger site.

[0149] Figure 11

[0150] Figure 11 An example wireless charger station 1100 is depicted in FIG. A wireless charger 1101 for charging a public transportation vehicle 1102 is shown mounted flush with the road surface 1103. If passengers board and alight at this depicted station 1100, a pedestrian area 1104 can be co-located. Conduit 1105 provides interconnection with the wireless charger for cooling lines from a cooling structure 1106 and for wired or optical communication lines (not shown) to a radio transceiver and antenna 1107.

[0151] The wireless charger 1101 provides radio communications between the wireless charger 1101 and the vehicle 1102. These communications can be as described in U.S. Patent No. 10,135,496, “Near field, full duplex datalink for use in static and dynamic resonant induction wireless charging,” issued on November 20, 2018. Static induction charging relies on the EV maintaining its position during charging to pair the primary and secondary coils. Dynamic induction charging uses a near-continuous series of primary coils (typically buried in the road)—typically including coils extending in the direction of travel—to charge a secondary coil attached to the moving vehicle. Semi-dynamic charging uses the same primary and secondary coils as static induction charging systems, but expands the operating angle over which the secondary coil can be serviced and, therefore, increases the total charging time per primary coil.

[0152] The wireless charger 1101 in this example configuration is powered via a wired DC connection 1108 to a local utility grid (not shown).

[0153] In some cases, a mechanical actuator system used to connect physical wiring may be used for opportunity charging.The pantograph system 1109 shown is one such alternative system that uses a physical connector.

[0154] Figure 12

[0155] Figure 12 is a flow diagram of an example method 1200 for strategic opportunity charging in an example configuration.

[0156] As shown, method 1200 includes creating a model based on historical data, similar routes, near real-time sensor data, and third-party data for 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.

[0157] Once the data model has been created at 1210 , the collected data is processed at 1220 using the data model to provide an initial estimate of the total cost per distance (TCD) for traveling over the intended route segment.

[0158] At 1230 , the route and charging along the route segments are optimized to reduce the TCD of the EV or fleet of EVs over the route.

[0159] The EV then drives along the route segment. At 1240 , the telemetry system and the third-party system collect data related to the environment, the EV, electricity usage, electricity cost, travel time, etc. in near real time as the EV drives along the route segment.

[0160] At the end of the current route segment, TCD is calculated at 1250. TCD is calculated using a data model based on the collected environmental, EV, electricity usage, electricity cost, travel time, traffic, etc. data collected while the EV traverses the route segment.

[0161] At 1260 , an updated estimate for the next route segment is calculated based on changes in the environment, EV, power usage, power cost, travel time, traffic, etc. The updated estimate also includes when / where the EV should charge along the next route segment using available chargers and available dwell durations to achieve optimal TCD.

[0162] Steps 1240 through 1260 are repeated for each route segment until the process is reset.

[0163] Figure 13

[0164] Figure 13is a flow chart that graphically depicts, at a high level, wireless charging operations while on a wireless charger at a limited-time dwell. The EV first arrives at 1301 at a first state of charge (SoC) and is directed to the wireless charger. This direction can be reached via a radio link, but can also be reached via indicators, signs, or automatic steering assistance (e.g., see U.S. Patent No. 10,040,360 “METHOD AND APPARATUS FOR THE ALIGNMENT OF VEHICLES PRIOR TOWIRELESS CHARGING INCLUDING A TRANSMISSION LINE THAT LEAKS ASIGNAL FOR ALIGNMENT” and U.S. Patent Application No. 17 / 646,844 “METHOD AND APPARATUS FOR THE SELECTIVE GUIDANCE OF VEHICLES TO A WIRELESS CHARGER”).

[0165] During pre-charging 1302, the ground charger and vehicle power receiver are tuned for efficient wireless transmission under the achieved alignment and air gap. Authorization and billing information are exchanged via the radio connection. In the case of a modular ground charger, multiple frequencies and phases can be set (see, for example, U.S. Patent Application Serial No. 17 / 207,257 "MODULAR MAGNETIC FLUX CONTROL").

[0166] During charging 1303, the vehicle battery management system and the ground charger controller (not shown) negotiate the supplied current. The ground controller can set the initial maximum supplied current according to the dispatch controller instructions (based on the data model) and then change the supplied current according to the new instructions during charging.

[0167] Leaving the charging session, the EV departs from the charging station at 1304 with a new SoC.

[0168] In the public bus example, the EV has set an arrival time and a departure time, and therefore a preset total dwell duration 1305. The charging interval 1306 is a subset of the total dwell duration 1305.

[0169] Figure 14

[0170] Figure 14 is a timing diagram depicting the interaction between the charger and the vehicle. Figure 14In the example, the charging manager 1401 can be an application running on the dispatch server 1001, or a local controller (e.g., a charging station server (first disclosed in U.S. patent application serial number 17 / 199,234, “OPPORTUNITY CHARGING OF QUEUED ELECTRIC VEHICLES,” filed on March 11, 2021, and incorporated herein by reference). The charging station server contains the charging manager 1401 software to manage the power supply (from the utility and local storage), the internal communication link (both bridging and routing) between the charging station and the wireless charger 1402, and the interconnection with entities external to the charging station (servers, data repositories, cloud instances). In this example, all messaging is paired, with each source having an acknowledgement response.

[0171] In the present example, a ground charger assembly (GCA) 1402 includes a near-field radio communication interface (as detailed in U.S. Patent No. 11,121,740, “NEAR FIELD, FULL DUPLEX DATA LINK FOR RESONANT INDUCTION WIRELESS CHARGING,” which is incorporated herein by reference). When using the near-field radio communication interface, a physically corresponding vehicle receiver assembly (VRA) 1403 must be present across the air gap 1404 for charging to occur. An alternative or supplemental wireless communication link based on wireless local area network (W-LAN) technology (e.g., IEEE 802.11, Zigbee, Bluetooth) may also be used.

[0172] Before the wireless charging session 1406 begins, the GCA 1402 and VRA 1403 exchange messages for authorization, mutual authentication (in this model, neither the GCA 1402 nor the VRA 1403 is trusted), and billing. In the current example, battery management system (BMS) operations are included in the VRA 1403 functionality and message delivery is transparent.

[0173] The International Engineering Consortium (IEC) has published standardized messaging for wireless power transfer charging of electric vehicles (EVs) 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 in November 2022), can be used for general description purposes, as the use cases and messaging supported in this document are different.

[0174] Immediately prior to charging, radio messaging 1405 may be exchanged to measure and ensure alignment between the GCA 1402 and the VRA 1403, measure the magnetic gap, determine the effective magnetic transfer frequency (see U.S. patent application Ser. No. 17 / 643,764, “Charging Frequency Determination for Wireless Power Transfer,” incorporated herein by reference), and exchange capabilities and limits.

[0175] Once the initial messaging 1405 is complete and the charging session 1406 begins, the VRA 1403 and GCA 1402 begin exchanging information messages 1407, which contain relevant GCA 1402 and VRA 1403 electrical, temperature, and / or radio sensor data and provide a periodic heartbeat. Information messaging 1407 can include information about battery pack voltage, temperature, and state of charge supplied by the BMS. The information message flow 1407 continues throughout the duration of the wireless power transfer (while magnetic flux is being generated).

[0176] The VRA 1403 then initiates power request / response messaging 1408 via radio signaling over the air interface 1404, via the GCA 1402, and to the charge manager 1401. The power request may include a requested current level, and the power response may include an allowed current value. The power request may also include a preferred current level and a maximum current level, and the power response may include an allowed current value that is equal to or lower than the requested value or the maximum current level.

[0177] The GCA 1402 will send a message to the VRA 1403 confirming the initial current level assignment 1409 and activating the charging signal. During the duration of the power transfer 1410, heartbeat / telemetry 1407 messaging continues.

[0178] In this example, the charge manager 1401 sends a rating command 1411 to the GCA 1402 participating in the charging session 1406. The rating command 1411 includes a current level that may be higher or lower than the initial current level (the current level may be zero, thereby prematurely pausing or ending the charging session 1406). Before the charging signal changes, the updated current level is passed to the VRA 1403. The VRA 1403 acknowledges the updated current level in its response 1409, and the VRA 1403 may request a new maximum allowed current level or any current level lower than the updated current level.

[0179] For the second wireless power transfer duration 1413, GCA 1402 provides a magnetic signal to generate a new permitted current level in VRA 1403. The EV, via the BMS and VRA 1403, ends the wireless power transfer by setting the requested current level to zero. GCA 1402 pauses the charging signal and notifies charging server 1401 of the end of the session via termination notification 1414. GCA 1402 uses termination notification 1414 to communicate the collected time, sensor, and performance data to charging server 1401 for storage and analysis.

[0180] Charging on demand

[0181] Figure 15

[0182] Figure 15 is a chart showing utility billing rates for enterprise customers. This example can be for a single charging station or a collection of charging stations.

[0183] The x-axis 1501 shows time, while the y-axis 1502 shows power consumed in kW. Power consumption varies over a billing period from a baseline 1504 to a peak demand 1506. An average power consumption 1505 can be determined over a billing period 1503.

[0184] Wireless power transmission charges from utilities are expected to include both a usage-based component and a demand-based component.

[0185] The usage-based component includes fixed and variable charges: transmission and distribution (T&D) charges for infrastructure; and supply charges based on consumption during the billing interval. Note that supply charges can vary by time of day and season (referred to as time-of-use (TOU)). The usage-based component is typically measured in kilowatt-hours (kWh).

[0186] The on-demand component is based on the maximum amount of power required during a time period within the billing interval (e.g., a single hour or a set portion of an hour). The on-demand component is typically measured in kilowatts (kW).

[0187] The use of charger scheduling, i.e., coordinating the charging schedule of individual fleet vehicles to limit simultaneous charging, and efficient geospatial distribution of chargers can be used to reduce the on-demand charging component of EV fleets.

[0188] Strategic placement of charger stations can be used to minimize infrastructure costs, including T&D, by limiting the number of chargers to less than one per docking station (on average). With this charger placement, multiple chargers can be installed at docking stations served by more than one EV.

[0189] Preferential charging, using wireless opportunity charging during stops, and controlling SoC can be used during the EV day to minimize electricity costs by charging only during the highest cost times until reaching the next charger (with backup). Preferential charging can also include charging more at stations with better electricity rates (increasing EV SoC).

[0190] Figure 16

[0191] Figure 16 16 is a diagram showing an overhead view of a charging station at a bus stop (in this example, a public bus stop). In this example, a wireless power charging station 1601 has three chargers 1602, 1603, and 1604, which are arranged to simultaneously serve up to three electric vehicles. In this example, each charger 1602, 1603, and 1604 serves a public bus 1605, 1606, and 1607.

[0192] All three chargers are serviced via an underground electrical connection (not shown) to power electronics 1608. Power electronics 1608 is connected to the utility grid via drop 1609. The utility may supply AC, DC, or AC three-phase power via drop 1609.

[0193] The power electronics 1608 may include local power storage 1610 (eg, a battery), which may be used to prevent excursions beyond a concurrent demand threshold.

[0194] The local energy storage unit 1610 (see U.S. Patent Application Publication No. US20220368161A1, filed October 30, 2020, “Contactless swappable battery system,” for a working example of one such battery system) allows for “peak shaving,” where energy is stored (trickle charged) during low electricity cost times and used during peak electricity cost times. The battery storage device can be physically swappable (for occasional or emergency use) or preferably charged from the utility connection 1609 during low electricity cost periods. Alternative power sources such as wind or solar farms can be used to charge the local energy storage unit 1610.

[0195] To reduce demand, and therefore not exceed a desired maximum demand threshold, the charging site controller may:

[0196] a. Allow each EV to share the available electricity equally.

[0197] b. Prioritize powering the EV with the greatest demand (SoC required to reach the next charger)

[0198] c. Prioritize arrival and departure times so that power delivery varies with the arrival and departure of each vehicle

[0199] d. Prioritize electricity optimization energy based on time of day billing rates

[0200] On-demand charging can be site-specific (wiring size to transformer and then meter) rather than regional. Other approaches provide regional level control (e.g., openADR - Automatic Demand Response).

[0201] Demand charging can be aggregated for individual customers within the service area served by the utility.

[0202] Alternatively, available power could be set by local or national government action, rather than by desired concurrent demand thresholds, to minimize utility demand charging.

[0203] Figure 17

[0204] Figure 17 17 is a diagram showing the ability of a utility to use strategic opportunity charging to reduce (or at least manage) electricity costs using the on-demand charging portion of its geographic location. In region 1701, chargers are distributed to serve the EV fleet on routes (routes can be fixed (pre-planned) or ad hoc (modifiable)).

[0205] The region includes single-charger stations 1702, 1703, 1704, dual-charger stations 1705, 1706, and triple-charger stations 1707, 1708. These stations 1702, 1703, 1704, 1705, 1706, 1707, 1708 and the number of chargers at each station are configured based on the projected demand for fleet charging. Geographically, the distances between stations 1702, 1703, 1704, 1705, 1706, 1707, 1708 vary, as do passenger or delivery stations (not shown).

[0206] By coordinating the scheduling of charging sessions for each fleet vehicle 1709, 1710, 1711, 1712, the total power demand can be kept below a threshold that would result in higher utility unit charges. This geospatial approach to total concurrent demand minimization can be further optimized for the fleet by adding additional, well-sited opportunistic charging stations to minimize the varying demands of EVs at any particular charging site or stations.

[0207] Figure 18

[0208] exist Figure 18, a single electric bus serving a single route (at a time) is shown. EV bus 1801 is traveling on route 1802 mapped to local roads 1803. Accessible roads 1804 equipped with wireless opportunistic chargers 1805 allow EV bus 1801 to charge to a set power level and for the duration of the scheduled bus stop.

[0209] Information about bus 1801 status, driving conditions, load, adherence to schedule, and power consumption may be collected and transmitted in near real time along route 1802 or at stops 1806 .

[0210] Figure 19

[0211] exist Figure 19 , multiple electric buses are shown serving a single route simultaneously. This arrangement is intended to reduce passenger waiting time and / or provide sufficient capacity to serve the route.

[0212] A first EV bus 1901 and a second EV bus 1902 are traveling on a route 1903 mapped to a local road 1904. Accessible roads 1905 for a bus stop 1906 are equipped with wireless opportunity chargers 1907, allowing the first EV bus 1901 and the second EV bus 1902 to charge to a set power level as passengers board and alight, and for the duration of the scheduled bus stop.

[0213] Information about the status of buses 1901 , 1902 , driving conditions, loads, adherence to schedules, and power consumption may be collected and transmitted in near real time along the route 1903 or at stops 1906 .

[0214] Figure 20

[0215] exist Figure 20 In Figure 2, multiple electric buses are shown serving multiple routes simultaneously. These buses can be from the same fleet or from different fleets. In this example, the wireless charging infrastructure is shared.

[0216] A first route 2001 and a second route 2002 share the same local road network 2003. The first route 2001 is served by a first EV bus 2004 and a second EV bus 2005, which move along the route 2001 according to a first schedule. The second route 2002 is served by a first EV bus 2006 and a second EV bus 2007, which move along the second route 2002 according to a second schedule.

[0217] Arranged along the first route 2001 and the second route 2002 are wireless opportunity chargers 2008. The first and second arrangements must be consistent to allow sufficient charging time for each EV bus 2004, 2005, 2006, 2007. The power supplied and consumed by the wireless opportunity chargers 2008 (and other shared or non-shared chargers such as wireless opportunity chargers 2009) must also be coordinated (by the dispatch server 1001) to avoid excessive time-of-use electricity rates and utility demand charges, as well as to not overstrain the cooling capabilities of the wireless chargers during and between charging sessions.

[0218] Information about the status, driving conditions, load, adherence to schedules, and power consumption of buses 2004, 2005, 2006, 2007 can be collected and transmitted in near real time along routes 2001, 2002 or at stops at wireless chargers 2008, 2009. Information about the wireless chargers 2008, 2009 themselves can also be transmitted in near real time, or collected and transmitted periodically, or triggered by events (e.g., before, after, and during a charging session).

[0219] Additional Implementations

[0220] Figure 21

[0221] Figure 21 is a diagram depicting an example short-haul yard 2101 using electric cargo transfer vehicles (not shown) with strategic wireless opportunistic charging. In this example, both load weight and travel distance are the primary determinants of battery consumption for electric transfer vehicles (e.g., forklifts, side loaders, forklifts). Figure 21 , the movement of containerized cargo is described as an illustrative example.

[0222] Containers can be loaded and unloaded onto and from the cargo rail system 2102 by yard vehicles. Dedicated container handling machines 2103 are used to transfer cargo containers from incoming railcars to local railyard stacks 2104. Local railyard stacks 2104 are staffed by transfer vehicles equipped with wireless power transmission (WPT) receivers, which can move containers to and from truckyard stacks 2105, shipyard stacks 2106, or temporary storage stacks 2107. Because each stack 2104, 2105, 2106, and 2107 is frequently visited by transfer vehicles, wireless chargers can be placed at various locations based on usage levels and container wait times. In this example, railyard chargers 2108, shipyard chargers 2109, and truckyard chargers 2110 are installed. In this example, storage stack 2107 is not equipped with a co-located charger.

[0223] Truck yard stacks 2105 may be added by unloading trucks using crane devices 2111 , added by transshipping containers, or reduced by loading containers onto trucks or by redirecting containers to other transport or storage 2107 using transshipment vehicles.

[0224] Dock stacks 2106 may be added by unloading trucks using cargo cranes 2112, added by transshipping containers, or reduced by loading containers onto ships or barges (not shown) or by redirecting containers to other transportation or storage 2107 using transshipment vehicles.

[0225] Storage stacks 2107 can be increased or decreased by transferring containers into and out of each shipping yard stack 2104 , 2105 , and 2106 .

[0226] The transfer management application (similar to the software and database used in the dispatch center 1001) uses near real-time data about the cargo container weight (either manifest weight or weight obtained via sensors on the transfer vehicle), cargo container location, transfer vehicle location, transfer vehicle charge status, container destination (and therefore travel distance), and current queues at the source and destination stacks to manage the opportunistic charging schedule for each charger 2108, 2109 and 2110 as well as the charge level and charging duration of each charging session to minimize downtime and travel costs.

[0227] The short-haul yard 2101 also contains a rest and repair yard 2113, which itself has an associated wireless charger 2114.

[0228] Highway 2115 spur serves Short Haul Yard 2101, which is used for both truck access and employee personal vehicle access.

[0229] Figure 22

[0230] Figure 22 A graph that geographically depicts electric delivery vehicle routes using strategic opportunities to minimize driving costs.

[0231] Delivery vehicles are stored and maintained at a depot 2201, which may be spaced a distance 2202 from a first distribution center 2203. A wireless opportunity charger may be installed at the first distribution center 2203. The delivery vehicle has a first route 2204 with multiple stops. Depending on the type of delivery service provided, the stops may be for deliveries only, pickups, or both. First route 2204 returns the vehicle to the first distribution center 2203, where packages may be loaded or unloaded during an opportunity charging session, if desired.

[0232] The second delivery route 2205 includes a third party public or subscription wireless charger 2206, on which charging can be completed if needed or desired to keep the battery of the delivery vehicle within the desired SoC range.

[0233] At the end of the second delivery route 2205, the vehicle returns to the first distribution center 2203 where package loading or unloading can occur during the opportunity charging session, if necessary.

[0234] The third delivery route 2207 includes a visit to a second distribution center 2208 among the regular stops. The second distribution center 2208 may include a wireless opportunity charger that can be used to recharge the delivery vehicle. The third delivery route 2207 ends at the first distribution center 2203, where the vehicle is unloaded and recharged to a charge level optimal for overnight storage at the depot 2201 (taking into account the amount of power used to travel the distance 2202 to the depot 2201).

[0235] Temporary delivery service implementation method

[0236] Strategic opportunity charging for EV taxi, courier, and package delivery services is different from strategic opportunity charging with known routes, known stops, and pre-installed charging stations in the above embodiments. For these EV vehicles, routes and stops for passengers, cargo, and opportunity charging cannot be pre-planned for the service day.

[0237] Instead, a roughly geographically distributed fleet uses a mix of private and commercial opportunistic chargers within a service area, where modeled and accumulated historical data, traffic data, vehicle range efficiency, and location (of the vehicle, pickup, drop-off, and charger) are used to estimate temporary routes. A dispatch service and radio data communication between the customer and the vehicle are essential for this service. Examples of temporary services apply to EVs with a driver, driver assistance software, or fully autonomous EVs.

[0238] The selection of an EV to serve a pickup (of passengers or packages) may use location data, estimated travel time, and estimated power consumption to select among candidate EVs based on service criteria (pick-up time, delivery time, power efficiency).

[0239] Since dedicated charging stations are not anticipated, potential charging stations may include wired and wireless facilities, with station selection based on scheduling, estimated off-route distance, estimated charging time, and EV compatibility with the charger.

[0240] Figure 23

[0241] Figure 23 Figure 1 is a diagram showing typologically the additional charging components for public and private charging stations. Figure 23 Shown is added to Figure 10 To support both wireless and wired EV charging (wired EV charging can include conventional plug-in chargers as well as Figure 11 Both the wired pantograph system shown) and the hybrid wireless / wired EV 2301 adapted to support bidirectional charging capabilities may use additional facilities.

[0242] For example, a charging station 2302 equipped with a wired plug or pantograph with power electronics 2303 uses AC and / or DC charging as shown to support one or more charge levels, using a wireless data connection 1004 via antenna 1012 to communicate with the dispatch office 1001 via a wireless data network 1005. The two-way wired station 2304 can also be used by the EV 2301 to charge (or discharge). The power electronics 2305 in the two-way case will be able to draw power from or feed power to a local battery facility or power grid (not shown). A wireless two-way station 2306 equipped with a two-way wireless charger 2307 and two-way power electronics 2308 can also be provided for the EV 2301 to use for charging (or discharging), and the two-way power electronics 2308 can draw power from or feed power to a local battery power storage facility or power grid (not shown).

[0243] Figure 24

[0244] Figure 24 is a diagram illustrating an example scenario for a scheduled pickup and delivery service using EVs and opportunity charging in a sample configuration. Figure 24 This topographically depicts a temporary delivery service with a pickup location and time (or time and date). This scenario covers both a package delivery service using electric vehicles (EVs) and a taxi service. In this example, the request is for an immediate pickup and the fastest possible delivery. The "fastest" designation indicates the most time-efficient route. The "economy" designation indicates the most efficient charging. The "shortest route" can be either the fastest or the shortest distance.

[0245] The dispatch office 1001 receives a request for a pickup at a pickup location 2401 at a pickup time. The dispatch office 1001 finds all dispatchable EVs within (or scheduled for) a geographic area 2402 near the pickup point 2401. Some EVs may be en route or have scheduling conflicts and are therefore unavailable to the dispatch office 1001. The dispatch office 1001 then queries the available EVs 2403, 2404, 2405, and 2406 for status, including the current state of charge (SoC). EVs 2403, 2404, and 2405 report their status (including the current SoC). EV 2406, which is charging at a charger 2407, may also report its current SoC and its expected SoC at the end of the scheduled charging.

[0246] Using the reported current SOC, GIS capabilities, and traffic estimates, for each candidate EV 2403, 2404, 2405, and 2406, the dispatch office calculates the amount of power required to travel to the pickup location 2401, use the shortest path 2408 to the delivery point 2409, and then travel to the charging station 2410 closest to the delivery point 2409. The choice of a charger after delivery (if required) is determined by the power consumption distance from the delivery point 2409, resulting in a geographic charger search area 2411. Based on the fastest pickup and delivery options, the EV with sufficient power to complete the journey that can pick up the package with minimal delay after the dispatch office 1001 receives the request will be selected for delivery.

[0247] If a candidate EV 2403, 2404, 2405, or 2406 does not have enough charge to complete its individual route, the route can be modified to accommodate an opportunity charging session at an available charger 2412, which will change each individual route. In this case, all candidate EVs will be directed to the near-midpoint charger 2412 using the shortest route 2413 between the pickup point 2401 and the charger 2412. The shortest route 2414 from the charger 2412 to the delivery 2409 will also be drawn for each EV.

[0248] Alternatively, any candidate EV 2403, 2404, 2405, or 2406 may be arranged to use a charger 2415 located near the pickup point 2401 for charging before or after pickup, so that the midpoint charger 2412 can be reached with minimal delay relative to the pickup time, or the destination 2409 can be reached without additional charging.

[0249] Based on the fastest pickup and delivery option, after the dispatch office 1001 receives the request, the EV that takes the least time (including mid-range charging events) to complete the journey can be selected to pick up the package with minimal delay.

[0250] Figure 25

[0251] Figure 25 is a diagram illustrating an example scenario for rescheduling and rerouting a pickup and delivery EV using wireless and / or wired opportunity charging in a sample configuration. In this scenario, a pickup and delivery EV 2501 departs a depot 2502 with the following planned route: route segments 2503, 2505, and 2507, destinations 2504 and 2508, and a charging station 2506. Sometime after leaving depot 2502, a new package pickup location 2509 within a general service area 2511 is added to the route, requiring recalculation of the route, arrival time, departure time, and the SOC required to complete each route segment.

[0252] Dispatch office 1001 adds two route segments 2512 and 2513 to the route to new destination 2509. Added destination 2509 has a co-located charging station 2510. Based on the schedule, distance, and calculated state of charge, dispatch office 1001 cancels the previously scheduled charging session at charging station 2506 and the two associated route segments 2505 and 2507.

[0253] The new route plan includes all previously scheduled destinations and the new destination, with the goal of minimizing travel distance, minimizing scheduling interruptions, and maintaining SoC between thresholds. The availability of chargers 2514 at the depot 2502 allows the EV 2501 to depart with the maximum SoC available capacity and return to the depot 2502 with the minimum SoC available capacity.

[0254] Figure 26

[0255] Figure 26 is a flow chart illustrating a sequence of events for selecting and assigning EV pick-up and delivery vehicles for processing requests for people or package deliveries using opportunity-charged EV service vehicles in a sample configuration.

[0256] exist Figure 26 In the example, dispatch office 1001 receives a task request 2601 for package or person pickup and delivery. Request 2601 includes the pickup and delivery locations, the time sensitivity of one or both of the pickup and delivery events, and the requester's preferences for package or person handling (e.g., vehicle type, vehicle amenities (e.g., refrigeration, ramp, wheelchair lift), EV range and service cost, and service type (e.g., courier delivery, limousine, taxi, public transportation, light / medium / heavy truck)). Based on the request, a set of candidate EVs is determined.

[0257] Based on the request, the dispatch office 1001 performs a preliminary route analysis 2602 for the route between the pickup location and the delivery location based on the preferences and times from the request 2601. Point-to-point travel time and power consumption (for the specified vehicle class) estimates are calculated.

[0258] The number of EVs considered for servicing tasks is reduced using the availability 2603 of each EV during the service time window (as determined by the time sensitivity in the task request 2601). EVs that cannot be scheduled for pickup and delivery are excluded from further consideration.

[0259] EV candidates for the mission are further filtered using localization 2604. The localization 2604 takes into account the estimated time required to reach the pickup point and the estimated power consumption.

[0260] For each remaining candidate EV, the dispatch office 1001 performs a route calculation 2605 to develop a separate route. Each route includes a pickup time estimate and location, a delivery time estimate and location, and power consumption. To keep the EV's state of charge within the available SoC threshold, opportunity charging sessions can be included in the route planning, each with arrival and departure estimates and total power transferred (expressed as SoC). EVs with routes that cannot meet the scheduled pickup or delivery times are excluded from the candidate list.

[0261] One EV will be assigned from the remaining candidate EVs to serve the request at 2606. Both adhering to the time sensitivity parameters and minimizing the predicted power consumption will be factors in the final selection.

[0262] While examples have been provided for commercial vehicles such as buses, short-haul delivery vehicles, and delivery vehicles, it should be understood that the methods described herein can also be applied to non-commercial vehicles such as EVs driven by individuals with or without software-based driver assistance. These same methods can be used for autonomous vehicles.

[0263] in conclusion

[0264] Although various implementations have been described above, it should be understood that they are presented by way of example only and not limitation. For example, any element associated with the systems and methods described above may employ any desired functionality described above. Therefore, the breadth and scope of preferred implementations should not be limited by any of the example implementations described above.

[0265] As discussed herein, logic, commands, or instructions for implementing various aspects of the methods described herein may be provided in a computing system, including any number of form factors for computing systems such as desktop or notebook personal computers, mobile devices such as tablet computers, netbooks, and smartphones, client terminals, and server-hosted machine instances. Another embodiment discussed herein includes incorporating the techniques discussed herein into other forms, including into other forms of programmed logic, hardware configurations, or dedicated components or modules, including devices having corresponding means for performing the functions of such techniques. The corresponding algorithms for implementing the functions of such techniques may include some or all of the sequences of electronic operations described herein, or other aspects depicted in the accompanying drawings and detailed description below. Such systems and computer-readable media including instructions for implementing the methods described herein also constitute example embodiments.

[0266] In one embodiment, the processing functions described herein can be implemented with software. Software may include computer-executable instructions stored on a local or networked computer-readable medium or computer-readable storage device (e.g., one or more non-transient memories or other types of hardware-based storage devices). In addition, such functions correspond to modules, and modules may be software, hardware, firmware, or any combination thereof. As needed, multiple functions may be performed in one or more modules, and the described embodiments are only examples. Software may be executed on a digital signal processor, an ASIC, a microprocessor, or other types of processors operating on a computer system such as a personal computer, a server, or other computer system, thereby turning such a computer system into a specially programmed machine.

[0267] As described herein, examples may include a processor, logic or multiple components, modules or mechanisms (herein "modules"), or examples may operate on a processor, logic or multiple components, modules or mechanisms. A module is a tangible entity (e.g., hardware) that can perform a specified operation and can be configured or arranged in some way. In an example, a circuit can be arranged in a specified manner as a module (e.g., internally or relative to an external entity such as other circuits). In an example, all or part of one or more computer systems (e.g., a stand-alone, client or server computer system) or one or more hardware processors can be configured by firmware or software (e.g., instructions, application parts or applications) to operate as a module that performs a specified operation. In an example, the software can reside on a machine-readable medium. The software causes the hardware to perform the specified operation when executed by the underlying hardware of the module.

[0268] Thus, the term "module" is understood to include tangible hardware and / or software entities, i.e., entities that are physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g., temporarily) configured (e.g., programmed) to operate in a specified manner or to perform part or all of any of the operations described herein. Considering an example in which modules are temporarily configured, each of the modules need not be instantiated at any one time. For example, where the modules include a general-purpose hardware processor configured using software, the general-purpose hardware processor can be configured as various different modules at different times. Thus, software can configure a hardware processor to, for example, constitute a particular module at one instance of time, and to constitute different modules at different instances of time.

Claims

1. A method for routing an electric vehicle (EV) for a pickup and delivery service using opportunity charging, comprising: receiving a task request for picking up and delivering a package or a person, the task request including: a pick-up location, a delivery location, at least one of a pick-up time and a delivery time, and at least one preference for handling the package or the person; Determining at least one candidate EV for picking up and delivering the package or person based on the task request; performing a preliminary route analysis on one or more routes between the pickup location and the delivery location based on the at least one processing preference from the task request and at least one of the pickup time and the delivery time to determine an estimate of a point-to-point travel time and power consumption for the at least one candidate EV; determining which EV of the at least one candidate EV is available during a service time window determined according to at least one of the pick-up time and the delivery time in the task request; determining which EV of at least one EV available during the service time window has a sufficient state of charge to reach at least the pick-up location; For each EV of the at least one candidate EV, performing a route planning calculation to develop an individual route between the pick-up location and the delivery location, the individual route including a pick-up time estimate at the pick-up location, a delivery time estimate at the delivery location, and a predicted power consumption for the individual route; and An EV of the at least one candidate EV is assigned to service the mission request, the EV having a sufficient state of charge to meet a predicted power consumption for an individual route of the assigned EV with minimal recharging delay.

2. The method according to claim 1, wherein Assigning an EV to serve the task includes assigning an EV among the at least one candidate EV that minimizes the predicted power consumption.

3. The method according to claim 1, further comprising: determining a minimum state of charge threshold for each EV of the at least one candidate EV; identifying at least one charger on or near an individual route of each of the at least one candidate EV; and incorporating opportunistic charging sessions at the at least one charger into the route planning calculation to maintain each of the at least one candidate EV above the minimum state of charge threshold.

4. The method according to claim 3, wherein: Including the opportunity charging session in the route planning calculation includes estimating an arrival time and a departure time at the at least one charger for the opportunity charging session and a total power transfer during the opportunity charging session.

5. The method according to claim 1, wherein The at least one preference for package or personnel handling includes at least one of EV type, EV amenities, EV range, and service cost, or a type of service performed by the EV.

6. The method according to claim 5, wherein: The EV amenities include availability of at least one of refrigeration, steps, or a wheelchair lift.

7. The method according to claim 5, wherein: The service type includes at least one of courier delivery, limousine, taxi, public transportation, or truck.

8. The method according to claim 1, further comprising: receiving a second mission request for the assigned EV after the assigned EV has begun servicing the mission request along its individual route; recalculating a route for the assigned EV required to complete each route segment including the pickup location and the drop-off location in the second task request; as well as The assigned EV is assigned to service the second task request when the assigned EV has a sufficient state of charge to meet the predicted power consumption for the recalculated route of the assigned EV.

9. The method according to claim 8, wherein Recalculating the assigned EV's route includes: performing a preliminary route analysis on one or more routes between a pickup location and a delivery location in the second task request based on at least one processing preference and at least one of a pickup time and a delivery time from the second task request to determine an estimate of a point-to-point travel time and power consumption for the assigned EV; determining whether the assigned EV is available during a service time window determined based on at least one of a pick-up time and a delivery time in the second task request; determining whether the assigned EV has a sufficient state of charge to reach at least the pickup location; and A route planning calculation is performed to develop an updated individual route between the pick-up location and the drop-off location, the updated individual route including a pick-up time estimate at the pick-up location, a drop-off time estimate at the drop-off location, and a predicted power consumption for the updated individual route, wherein the updated individual route includes the pick-up location and the drop-off location in the task request and the pick-up location and the drop-off location in the second task request.

10. The method according to claim 9, wherein: The updated individual routes are optimized to minimize at least one of a driving distance and a dispatch disruption for the assigned EV.

11. A non-transitory computer-readable medium comprising instructions stored thereon, the instructions, when executed by one or more processors, causing the one or more processors to implement a dispatch system for routing electric vehicles (EVs) for a pick-up and delivery service using opportunity charging by performing operations comprising: receiving a task request for picking up and delivering a package or a person, the task request including: a pick-up location, a delivery location, at least one of a pick-up time and a delivery time, and at least one preference for handling the package or the person; Determining at least one candidate EV for picking up and delivering the package or person based on the task request; performing a preliminary route analysis on one or more routes between the pickup location and the delivery location based on the at least one processing preference from the task request and at least one of the pickup time and the delivery time to determine an estimate of a point-to-point travel time and power consumption for the at least one candidate EV; determining which EV of the at least one candidate EV is available during a service time window determined according to at least one of the pick-up time and the delivery time in the task request; determining which EV of at least one EV available during the service time window has a sufficient state of charge to reach at least the pick-up location; For each EV of the at least one candidate EV, performing a route planning calculation to develop an individual route between the pick-up location and the delivery location, the individual route including a pick-up time estimate at the pick-up location, a delivery time estimate at the delivery location, and a predicted power consumption for the individual route; and An EV of the at least one candidate EV is assigned to service the mission request, the EV having a sufficient state of charge to meet a predicted power consumption for an individual route of the assigned EV with minimal recharging delay.

12. The medium according to claim 11, wherein The instructions for assigning an EV to serve the task include instructions that, when executed by the one or more processors, assign an EV among the at least one candidate EV that minimizes the predicted power consumption.

13. The medium of claim 11, further comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform additional operations, the additional operations comprising: determining a minimum state of charge threshold for each EV of the at least one candidate EV; identifying at least one charger on or near an individual route of each of the at least one candidate EV; and incorporating opportunistic charging sessions at the at least one charger into the route planning calculation to maintain each of the at least one candidate EV above the minimum state of charge threshold.

14. The medium according to claim 13, wherein The instructions for incorporating the opportunity charging session into the route planning calculation include instructions that, when executed by the one or more processors, provide estimates of an arrival time and a departure time at the at least one charger for the opportunity charging session and a total power transfer during the opportunity charging session.

15. The medium according to claim 11, wherein The at least one preference for package or personnel handling includes at least one of EV type, EV amenities, EV range, and service cost, or a type of service performed by the EV.

16. The medium according to claim 15, wherein The EV amenities include availability of at least one of refrigeration, steps, or a wheelchair lift.

17. The medium according to claim 15, wherein The service type includes at least one of courier delivery, limousine, taxi, public transportation, or truck.

18. The medium of claim 11, further comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform additional operations, the additional operations comprising: receiving a second mission request for the assigned EV after the assigned EV has begun servicing the mission request along its individual route; recalculating a route for the assigned EV required to complete each route segment including the pickup location and the drop-off location in the second task request; as well as The assigned EV is assigned to service the second task request when the assigned EV has a sufficient state of charge to meet the predicted power consumption for the recalculated route of the assigned EV.

19. The medium according to claim 8, wherein The instructions for recalculating the route of the assigned EV include instructions that, when executed by the one or more processors, cause the one or more processors to perform additional operations, the additional operations including: performing a preliminary route analysis on one or more routes between a pickup location and a delivery location in the second task request based on at least one processing preference and at least one of a pickup time and a delivery time from the second task request to determine an estimate of a point-to-point travel time and power consumption for the assigned EV; determining whether the assigned EV is available during a service time window determined based on at least one of a pick-up time and a delivery time in the second task request; determining whether the assigned EV has a sufficient state of charge to reach at least the pickup location; and A route planning calculation is performed to develop an updated individual route between the pick-up location and the drop-off location, the updated individual route including a pick-up time estimate at the pick-up location, a drop-off time estimate at the drop-off location, and a predicted power consumption for the updated individual route, wherein the updated individual route includes the pick-up location and the drop-off location in the task request and the pick-up location and the drop-off location in the second task request.

20. The medium of claim 19, further comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform additional operations, the additional operations comprising: The updated individual routes are optimized to minimize at least one of a driving distance and a dispatch disruption for the assigned EV.

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