Strategic Opportunistic Charging for Electric Vehicles Traveling on a Predetermined Route

Abstract

A method, system, and computer-readable storage medium for improving charging efficiency of an electric vehicle (EV) or fleet of EVs following a predetermined route, wherein the charging efficiency is improved by receiving telemetry data from the EVs, receiving charger data from a plurality of chargers along the predetermined route, determining a charging plan based on a total cost per distance (TCD) of travel over each of a plurality of route segments comprising the predetermined route, and controlling particular chargers along the predetermined route to charge particular EVs according to the charging plan.

Description

[Technical Field]

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 436,419, filed December 30, 2022, U.S. Application No. 18 / 131,189, filed April 5, 2023, and U.S. Application No. 18 / 131,193, filed April 5, 2023, which are incorporated by reference as if fully set forth.

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

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

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

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

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

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

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

[0009] These and other beneficial features and advantages of the present invention will become apparent from the following detailed description taken in conjunction with the accompanying drawings. [Figure 1] Figure 1 shows a graph of the relationship between the EV driving range from the start of charging and the battery's state of charge (SoC). [Figure 2] Figure 2 shows a graph of an example of extending EV range by charging along the route. [Figure 3] Figure 3 shows a graph of an example of extending driving range and battery life through strategic opportunistic charging. [Figure 4] FIG. 4 is a graph of electricity prices over a 24-hour period in one example. [Figure 5] Figure 5 plots the electricity prices of the first and second utilities that share the same geographic market as the deployed WPT system and offer different electricity rates based on time of day and generating capacity. [Figure 6] Figure 6 shows an example where two regions are traversed by an EV route. [Figure 7] FIG. 7 illustrates an exemplary geographic distribution of wireless chargers along a bus route. [Figure 8] FIG. 8 illustrates an example state machine for a transit bus with wireless charging. [Figure 9] FIG. 9 is a diagram that schematically illustrates a configuration in which a single charger is arranged corresponding to a first and second transportation route, and provides service to a plurality of electric vehicles (EVs) traveling on these routes. [Figure 10] FIG. 10 is a high-level diagram illustrating the communication paths available for data collection from electric vehicles (EVs) and wireless power transfer (WPT) chargers. [Figure 11] FIG. 11 is a diagram illustrating an example of a wireless charger site. [Figure 12] FIG. 12 is a flow chart illustrating a method for strategic opportunity charging in an example configuration. [Figure 13]FIG. 13 is a flow chart illustrating an entire wireless charging session. [Figure 14] FIG. 14 is a diagram showing an example of a billing period including a capacity charge and a demand charge. [Figure 15] FIG. 15 is a plan view of a charging station equipped with multiple chargers capable of simultaneously charging multiple vehicles. [Figure 16] FIG. 16 is a diagram showing a plurality of electric vehicles and a plurality of chargers in an area where service is provided by an electric power company that applies a demand charge. [Figure 17] FIG. 17 illustrates a geographic application of strategic opportunity charging to manage utility demand charges. [Figure 18] FIG. 18 is a diagram showing an example in which a single electric bus operates on a single bus route. [Figure 19] FIG. 19 is a diagram showing an example in which a plurality of electric buses operate on a single bus route. [Figure 20] FIG. 20 is a diagram showing an example in which multiple electric buses operate on multiple bus routes and share a common machine charging infrastructure. [Figure 21] FIG. 21 illustrates an example of a drayage yard equipped with electric freight vehicles (not shown) using strategic wireless opportunistic charging. [Figure 22] FIG. 22 is a geographical diagram illustrating an electric delivery vehicle route that minimizes travel costs using strategic opportunity charging. Summary of the Invention

[0010] A detailed description of exemplary embodiments is provided with reference to Figures 1 to 22. While this specification provides a detailed description of possible implementations, it should be noted that these details are intended to be illustrative and in no way limit the scope of the inventive subject matter.

[0011] Strategic Opportunity: Wireless charging enables the expanded deployment of electric transit vehicles with no range or time limitations. Electric buses are emission-free, quieter, and with strategic opportunity wireless charging, can operate without having to go offline or deroute for refueling or charging. EV transit fleets are key to cleaning urban air, reducing traffic noise, and achieving decarbonization. Strategic Wireless Opportunity: Key to mass adoption and eventual automation of transportation.

[0012] Electric vehicles (EVs) use electric traction motors and batteries instead of internal combustion engines and chemical fuels. EVs are used in both battery electric vehicles (BEVs) and a variety of hybrid battery, internal combustion engine vehicles (HBEVs). Batteries or battery packs nominally include rechargeable chemical batteries, but can also include one or more of a capacitor bank, reversible fuel cells, solid-state batteries, or hybrid combinations of the foregoing. Improvements in energy storage technologies (e.g., solid-state batteries, hybrid batteries, ultracapacitors) can also take advantage of rapid opportunistic charging using high-power wireless power transfer.

[0013] Typically, an EV battery pack is made up of electrochemical cells. The commonly used lithium-ion secondary battery cells typically have a lifespan of 300-500 charge cycles. One charge cycle is the period of use from a fully charged state until the battery is fully discharged and then fully charged again.

[0014] These rechargeable lithium-ion batteries have a limited lifespan and their ability to hold a charge gradually decreases. This loss of capacity (battery aging) is irreversible. As the battery loses capacity, it can power the bus for a shorter period of time, thereby limiting its range and duration.

[0015] Much has been written about wireless charging's ability to reduce the required battery size and extend the driving range of electric vehicles. Strategic wireless opportunistic charging's ability to preserve the lifespan of lithium-ion batteries by maintaining the charge level of rechargeable batteries between high and low state-of-charge (SoC) thresholds has been field-proven to extend battery life. Charge rate, battery temperature before charging, and battery temperature during charging also affect lithium-ion battery life. Even solid-state batteries, which can last 1,000 to 10,000 charge / discharge cycles, benefit from managing charge level, charge rate, and battery temperature. In a mixed fleet, some EVs may be equipped with lithium-ion batteries and other solid-state batteries (or even multiple battery technologies in the same EV). In such a mixed fleet, it is beneficial to have individualized EV charging and performance data for modeling and machine learning.

[0016] Wireless mechanical charging describes how electric vehicles can utilize wireless chargers located at service areas or along routes, allowing them to be used along said routes without the "empty run" time of returning to a depot or garage to charge, saving both time and battery charge.

[0017] Opportunistic charging allows for the use of smaller batteries in EVs, resulting in reduced weight, longer driving range, and larger cargo capacity over that range.

[0018] Opportunity charging can also be used to maintain the battery's SoC between upper and lower thresholds, extending the battery's lifespan. EV battery packs are expensive to replace, and extending battery life can lower the total cost of distance (TCD).

[0019] Unlike plug-in chargers that require a connection, automated charging improves driver safety and comfort by eliminating the need to leave the vehicle at night or in bad weather. Automatic automated charging also makes electric vehicle charging accessible to people with disabilities.

[0020] In the examples that follow, electric buses that operate on short- to medium-distance routes as part of a local or regional network of publicly scheduled bus services are referred to as "routes." For route buses, a terminal is where a route begins or ends, where drivers briefly disembark or change buses, and also includes stops where passengers board and disembark. A bus depot is a terminal that includes maintenance and vehicle storage facilities. A stop is a transfer stop where passengers can board or disembark.

[0021] In the case of transit buses (and all vehicles using strategic opportunity charging protocols and systems), first-party data is defined as data transmitted from sensors onboard the EV. Second-party data refers to data transmitted from sensors on other EVs or metering stations (such as charging stations). Third-party data is data obtained from an external source that is not the original collector of the data. Third-party data may also be aggregated from multiple sources. Examples of third-party data include maps, traffic conditions, and weather information.

[0022] All electric vehicles (EVs), including battery electric vehicles (BEVs) and hybrids, have a range that can be estimated from the battery SoC. Vehicle wear, battery life reduction, and electricity prices all contribute to the total cost per distance (mile or km). If the electricity price varies over a route or over the course of a day, an additional variable, cost per watt, contributes to the TCD.

[0023] A fleet management system as defined herein may include fleet energy management in which each vehicle reports sensor data using wireless data links and coordinates charging operations with a dispatcher controller. This coordination and information awareness extends across spatially (geographically and by travel route) and temporally distributed charging opportunity capabilities, reducing the total operating cost of the fleet.

[0024] Multiple buses, geographically dispersed charger sites, charger sites with chargers at high-use businesses or locations, and a mix of private and public charger sites are all intended and used to reduce the total cost of mileage for individual EVs and fleets of EVs via fleet management systems.

[0025] The collection, communication, storage, monitoring, and analysis of environmental, vehicle, charger, and charging session data can be used to enhance or optimize existing services for a single EV, or across a fleet of EVs, as well as to develop new services based on the analysis of collected data. Utilizing historical data allows for better inferences to be made for specific routes, vehicles, and drivers.

[0026] One such service is reducing the total cost of travel. Using collected data, it is possible to ensure that every vehicle in an EV transport fleet completes its route at the lowest cost by optimizing the charging of not only individual EVs but the entire fleet. This cost minimization is achieved through near real-time data, models created using historically collected data, energy costs, and knowledge of chargers in different geographic regions (where there may be limited charging power available at each charger site or station).

[0027] The fleet management application used for cost minimization can optimize travel costs for a single EV or for an entire fleet of vehicles. When using cost optimization for individual EVs as the goal, each EV can be optimized for its potential local lowest travel cost based on data collected from the fleet or predictions using data collected from multiple fleets.

[0028] Using fleet-level objectives, fleet optimization is designed for minimum total cost over a wide range, which may differ from a specific regional optimization algorithm designed to minimize the cost of electric buses. The tradeoffs in modeling fleet management applications are accepting charging of selected EVs in fleets with higher power costs during a first time slot to achieve a lower overall system outcome, or managing battery capacity to minimize grid impacts when charging, instead of charging these vehicles during a second time slot when charging costs are lower, to achieve a lower overall fleet cost.

[0029] Efficiency is the ability to do or produce something without wasting materials, time, or energy. Efficiency for electric vehicles for transportation includes electricity usage versus EV range, vehicle cost, and extended battery pack life depending on the scenario.

[0030] The treasure trove of first-, second-, and third-party data collected or otherwise obtained is suitable for both statistical analysis and machine learning (ML) techniques. Statistical analysis can be used to determine trends, patterns, and relationships (both causal and correlational) using labeled quantitative and categorical data. Because the data is sufficiently labeled, ML's supervised learning algorithms are well suited for use when a specific goal or optimization is desired. In some cases, unsupervised learning algorithms can be used to cluster the data and identify patterns, associations, and anomalies in the data.

[0031] Figure 1 shows an example graph of an EV's range on a single charge versus its battery's SoC. The X-axis shows the range 101, and the Y-axis shows the SoC 102 starting at 103. In this example, a simple linear model of travel is used to illustrate the concept.

[0032] In the first example, the EV starts at a starting point SoC 103 (e.g., 100% SoC). As the EV drives 104, its SoC decreases until it reaches 0% SoC in range 107.

[0033] In the second example, the EV starts at a starting point SoC 103 (e.g., 100% SoC). As the EV drives 105, the SoC decreases until the SoC reaches 0% at range 108.

[0034] In a third example, the EV starts at a starting point SoC 103 (e.g., 100% SoC). As the EV drives 106, the SoC decreases until the SoC reaches 0% at range 109.

[0035] Factors that determine the EV range 107, 108, 109 include vehicle characteristics, battery pack characteristics, environmental factors, payload, terrain traveled, and driver ability.

[0036] Vehicle characteristics include the model, make, year, mileage, and repair status. Tire condition and tire selection are also taken into consideration. Aerodynamics (air resistance) is also a vehicle characteristic.

[0037] Battery pack characteristics include make, model, manufacturer, capacity, battery aging (both time-dependent aging and accelerated aging due to battery cycling), historical battery usage, and historical battery storage SoC during driving. Data from sensors monitoring individual battery temperature and voltage levels is expected to be available nearly continuously through the vehicle's battery management system (BMS). The BMS monitors and manages the EV's battery pack, which consists of a series of interconnected cells configured to provide the required voltage and current. The BMS monitors the battery pack's sensors (e.g., current, voltage, temperature) and communicates with the EV's electrical subsystem and external chargers (e.g., WPT chargers). The BMS maintains the battery pack's operational profile and protects it from over-discharge, overheating, and over-charging. The BMS may also optimize battery performance and lifespan by controlling the charging rate and SoC.

[0038] Environmental factors include weather, temperature, and barometric pressure. Temperature not only affects the battery's state of charge, but also the electrical load required for vehicle systems (e.g., battery pack cooling) as well as interior climate control (heating and cooling) for passengers and cargo. Daytime versus nighttime driving can also affect range. Weather conditions (e.g., rain, snow, wind) can also affect range versus SoC. Lighting, including headlights, interior lighting, and exterior lighting, place different loads on the battery pack. In cold climates, the combined heating and lighting loads (for passengers and battery pack) can require a significant percentage of the battery capacity, necessitating additional power transfer (extended charging time or charging at a higher power output), especially if additional safety lighting is required at dusk. Safety margins in the battery SoC may also need to be readjusted to maintain battery life.

[0039] Environmental factors are expected to be available from charger site sensors, vehicle-mounted sensors, and third-party sources such as public weather stations and feeds to ride-dispatch servers.

[0040] The payload, whether passenger or cargo, affects the power consumption on the route, with a heavier payload resulting in a shorter range. Both rolling friction and acceleration are affected by payload.

[0041] The terrain traveled includes curves, speed, stops, and traffic conditions, as well as the slope and deviation of the route. Traffic conditions may be collected from a third-party service via the dispatch server's application programming interface (API). Terrain may be a significant factor in power consumption for a route segment because uphill slopes require additional power to negotiate, while generally downhill route segments may mitigate power consumption and store additional power through regenerative braking.

[0042] Driver capabilities include lane selection to conserve battery resources, smooth acceleration, and smooth braking. "Driver" here also includes the use of automated driving assistance software packages and autonomous driving systems.

[0043] Figure 2 shows a graph of an example of extending an EV's range through mid-range charging. The x-axis shows range 201, and the y-axis shows SoC 202. This example uses a simple (straight-line) driving model to illustrate the concept. In the initial mid-range charging example, an EV starts with an initial SoC 202 at the start of the trip 204. As the EV travels, its SoC level 203 decreases. At a charging point 205, the EV is recharged, and the recharged EV SoC 206 is used to continue the journey beyond range 201.

[0044] Wireless inductive charging enables wireless inductive charging of EVs. Opportunistic charging is the short-term charging of an electric vehicle while it is in motion. This partial charging strategy contrasts with charging an EV all at once, as shown in Figure 2.

[0045] Figure 3 illustrates an example of range and battery life extension through strategic opportunity charging. The x-axis shows range 301, and the y-axis shows SoC 302. This example uses a simple (straight-line) driving model to illustrate the concept. In this example of strategic static opportunity charging, the EV is not only partially charged at each stop 306, 307, 308, and 309, but the SoC is also maintained between an upper SoC threshold 303 and a lower SoC threshold 304. The SoC profile 305 over the driving route is shown with a varying slope (in this simplified model) to indicate differences in power consumption along the route segments (i.e., between stops with opportunity chargers).

[0046] The upper 303 and lower 304 SoC thresholds are designed to extend the life of the EV battery. While only one set of thresholds is shown here, multiple levels of thresholds can be configured to extend range while minimizing the impact on battery life. Of course, the physical upper threshold 303 of 100% SoC and lower threshold 304 of 0% SoC are always available to extend range at the expense of battery life (e.g., for emergency use).

[0047] 3, the starting point SoC 310 is shown as just below the upper battery charge threshold 303. In some cases, the starting point SoC 310 may be higher (e.g., 100% SoC) or significantly lower (e.g., 40%) when brought in from long-term storage.

[0048] In one configuration, the useful life of a lithium-ion battery pack can be extended by selecting upper and lower thresholds and using opportunity charging to maintain the state of charge between the selected thresholds. Battery life can also be maximized by monitoring the temperature and voltage of individual batteries and varying the charge rate to stay below (and above) the selected thresholds.

[0049] By knowing the vehicle battery's operating thresholds, the charger location, the route, the time of day, the current location, traffic levels, and the estimated time of arrival at the next charger, it is possible to decide whether to charge (and to what SoC level) before arriving at or before arriving at a WPT charger. The cost of electricity from the grid may also vary depending on the time of day and the location of said WPT charger.

[0050] Time-of-Use (TOU) electricity rates are rates that vary depending on the time of day. TOU rates respond to electricity availability, with higher prices during peak demand periods and lower prices during periods of low demand. These rates are typically fixed for a set period of time (week, month, season) to accommodate fluctuations in demand.

[0051] FIG. 4 illustrates a graph of electricity prices over a 24-hour period in one example. In FIG. 4, the x-axis 401 represents the 24-hour period divided into one-hour segments. The y-axis 402 represents the electricity price for each time segment. In this example, from midnight 403 to 8:00 AM 404 on the x-axis 401, the utility sets the price to "off-peak" 405. From 8:00 AM 404 to noon 406, due to increased demand, the utility sets the price to "mid-peak" or "shoulder" 407. From noon 406 to 6:00 PM 408, this is the period of highest demand, so the electricity rate is set to "peak" 409. From 6:00 PM until 11:00 PM 410, due to decreased demand, the rate is set to "mid-peak" 407. After 11:00 PM 410, the rate drops to "off-peak" 405. As can be seen from FIG. 4, pricing changes depending on the increase or decrease in demand.

[0052] EV charging costs (per watt) are lowest during off-peak hours and highest during peak hours. To minimize costs, selective charging of EVs during the day (both charging time and charge level during the charging session) can be achieved.

[0053] Weekend TOU rates may differ from weekday (e.g., Monday through Friday) rates. In some areas, weekend rates are set as "off-peak."

[0054] Depending on the region or market, there may be competitive electric utilities. As shown in FIG. 5, a first and second electric utility share the same geographic market as the deployed WPT system and offer different electricity rates based on the hourly time period and power generation capability and capacity. In FIG. 5, the X-axis 501 is a 24-hour day shown in hourly increments. The Y-axis 502 shows the electricity rate for each time period. In this illustrative example, during a first period 505 (12:00 AM to 7:00 AM), the first electric utility's market rate 503 is lower. During a second period 506 (7:00 AM to 8:00 AM), the rates 503 and 504 of both electric utilities are approximately equal. During a third period 507 (8:00 AM to 2:00 PM), the first electric utility's market rate 503 is lower. During a fourth period 508 (2:00 PM to 11:00 PM), the second electric utility's market rate 504 is more favorable. Finally, in a fifth time period 509 (11 PM - 12 AM), the first electricity provision tariff 503 is again recommended.

[0055] By selecting a cheaper electricity provider's service during charging times, you can reduce the electricity costs for EV charging. By managing charging time, charging duration, and charge level, you can minimize charging during times when electricity costs are high, thereby increasing savings.

[0056] Demand, availability, and cost of electricity can vary significantly from day to day, or even hour to hour, all of which affect charging decisions. Information about electricity prices can come from prior contract terms with local utilities, data feeds from local power exchanges, or spot prices on public electricity markets when multiple suppliers supply the utility. Examples of electricity markets include Day-Ahead-Energy markets and Real-Time Energy markets.

[0057] Electric Power Business Service Area Charger locations may be contiguous along specific routes within different utility service areas, which may require pricing information from multiple sources to be considered in charging decisions. If the chargers are owned by a third party (other than the utility or vehicle operator), the price of electricity for the estimated charging time may need to be obtained from the third party.

[0058] FIG. 6 shows an example in which two regions 601 and 602 are traversed by an EV route 605. In this example, each region 601 and 602 is supplied by a different utility company with different pricing. Therefore, WPT opportunity chargers 603 and 604 may have different electricity costs. By using different charging times and power levels at each WPT charger 603 and 604 during specific times, the total cost of charging for the route can be optimized.

[0059] 7 shows an example distribution of wireless chargers geographically along a bus route. While recharging may occur at transfer or interchange stations between routes with longer dwell times (e.g., rail or airport terminals), the deployment may include recharging stops that are strategically located (e.g., in areas where the predicted battery SoC is predicted to exceed a lower threshold).

[0060] Predictive Modeling By obtaining prices at multiple chargers along the route at multiple estimated arrival times, a travel cost profile for the route can be generated at the start of the day. Cost optimization can be calculated based on upper and lower SoC battery thresholds and recalculated as needed if the original route deviates or the model fails to predict an accurate SoC value. Data used to input the cost optimization model can include training data from the current route, the current fleet, or other fleets using the same or similar electric vehicles. In some cases, a second set of battery SoC thresholds can be involved if there is a substantial cost difference between charging from one location to another or from one time to another.

[0061] The recalculation of the modeled trip cost can include deviations from the schedule for both early and late arrivals. For early arrivals, additional charging time may be available, resulting in a lower offered charging current. For late arrivals, a higher charging current may be available to reduce charging time during a potentially reduced stop.

[0062] We describe a system that enables continuous operation of electric vehicles by utilizing wireless charging while on the move. The vehicle SoC is maintained at an optimal level to promote battery health and lifespan, while also taking into account electricity prices to optimize vehicle operating costs.

[0063] Sensor data is collected repeatedly while driving during a route charging session to facilitate a dynamic charging model that takes into account electric vehicle type (make, model, year), environmental factors (weather, traffic conditions, etc.), driver behavior, and drivetrain and battery pack health, all of which affect wireless charging performance and vehicle operation.

[0064] Data collected across the EV transit bus fleet is used to automatically optimize models that increase or decrease charging power based on these factors, allowing vehicles to continue operating indefinitely while maintaining optimal SoC across the fleet at the lowest possible cost, both in terms of battery life and electricity bills.

[0065] At least one minimum SoC threshold is set for each electric vehicle type (make, model, manufacturer, year, battery pack) or EV class, and the minimum SoC is dynamic and based on a) the minimum SoC to limit battery damage (lifespan degradation), b) the minimum SoC required to reach the next two charging stations in the route, c) the minimum SoC required to complete the route without charging, d) the calculated minimum SoC to abort the route and reach a stop, e) the SoC charge at the start of the route, and f) a manually set SoC threshold.

[0066] In a minimum power system, the EV can only charge to the SoC prediction at each stop with a wireless charger. This minimum power system can be modified based on the estimated electricity prices of WPT chargers along the route.

[0067] As charging networks grow and higher-power wireless charging services are deployed, wireless resonant chargers may be deployed in more geographic locations where short stops can be used for resonant charging, or where charging lanes with dynamic inductive chargers are deployed. Note that both statically and dynamically charging EVs can utilize near-field communications, such as those detailed in U.S. Patent No. 10,135,496, "Near field, full duplex data link for use in static and dynamic resonant induction wireless charging."

[0068] FIG. 7 illustrates one exemplary transit route. Stop 701 is used to accommodate and service electric transit buses when they are not in service. Stop 701 may be part of a terminal where passengers can board before departure. Passengers can board and disembark at each pre-planned stop. Other stops may be introduced interim for passengers to disembark. A first route segment 702 operates to bring the bus to a first stop 703 where passengers can board and disembark. A second route segment 704 brings the bus to a second stop 705 where passengers can board and disembark. A third route segment 706 brings the bus to a first transfer station 707 where passengers can board and disembark for travel on another, intersecting transfer route. The driver may also use this location 707 to take a mandated rest break. Traveling along a fourth route segment 708, the bus arrives at a fifth stop 709 where passengers can board and disembark. After traveling along the fifth route segment 710, the bus arrives at a sixth stop 711 where passengers can board and disembark. After traveling along the sixth route segment 712, the bus arrives at a bus station 714, which is the end of the route in this example. In this example, the longest route segment 712 includes a charger stop 713 where the bus can temporarily stop to charge. Said charger stop 713 may alternatively be a dynamic machine charger where the bus does not need to stop but simply drives over a road surface equipped with chargers.

[0069] Wireless machine chargers may be installed at any of the scheduled stops 703, 705, 707, 709, 711, and 713. Additional charger locations (not shown) may be deployed between stops to increase range while keeping the SoC within the SoC threshold boundaries.

[0070] For electric transit vehicles (e.g., buses), opportunistic charging requires charger locations to be located on or near the route. Charger placement is first achieved by mapping and modeling the route (using either collected data (from test drives), modeled data, or data from similar routes and vehicles) with chargers located at stops and, possibly between stops, where sufficient power is available. To reduce costs, chargers are typically installed at a subset of stops.

[0071] Once the transit system (and accompanying data collection, transmission, and analysis) is operational, the collected data will be used to determine whether 1) additional chargers are needed, 2) fewer chargers are needed, or 3) chargers can be decommissioned and relocated to a different location to better serve transit fleets in order to meet total trip cost targets.

[0072] When chargers and data infrastructure are shared across multiple fleets, driving data from the fleets can be used to rebalance the charger infrastructure to accommodate newly deployed EV transport vehicles, changing vehicle traffic patterns, and shifting users and routes.

[0073] When multiple vehicle fleets share an automated charging infrastructure, ownership of chargers and payment for charging costs are accumulated and negotiated among the fleet. In some scenarios, local or regional authorities own, operate, and service the wireless charger network and ancillary communications and data systems, and allocate the costs to the vehicles they serve.

[0074] In some cases, wireless chargers owned and operated by non-fleet commercial or government operators can be used to supplement the transportation wireless charger network.

[0075] FIG. 8 illustrates an exemplary state machine for a transit bus with wireless charging.

[0076] The state machine in Figure 8 shows events collected at different times and events along the electric vehicle's route.

[0077] In this example, the stop state 801 is encountered at least twice: once at the start and once at the end of the shift. Additional encounters may occur, for example, due to driver shift changes or required vehicle maintenance.

[0078] Upon departure from said depot, the bus system is fully charged (up to the upper SoC threshold) and preheated, cooled, or air-conditioned as needed. Vehicle characteristics (make, model, year, etc.) and the identity of the driver (or driver software) are recorded.

[0079] The on-board data store includes departure (current) time, battery state of charge (SoC), SoC threshold / limit, vehicle empty weight, current location, and route. The planned route defines planned stops (locations) for passengers and chargers. For each charger along the route, electricity tariffs and spot rates are known. The theoretical SoC to be used for each route segment is predicted using modeling based on historical histogram data of the route, similar routes, or simplified example route models. Scheduled events (and detours) along the route are known and are taken into account when pre-planning and modeling the route.

[0080] The en route state 802 is the most general state and is expected to encompass all trips. During the en route state 802, the data store accumulates the current time, current location, SoC, number of passengers, traffic conditions, and vehicle speed.

[0081] The data store can accumulate and store vehicle and route data using periodic or event-driven updates. The accumulated data is tagged with time as well as current location and route segment. Odometer distance can be used as a backup positioning when precise location is unavailable. Data can be uploaded to a dispatch office via a wireless connection (e.g., cellular or satellite modem).

[0082] For charging at a passenger pick-up location 803, the data store is updated with the start and end times of the stop. Passenger pick-up / drop-off counters (or bus weight change as a proxy) are used to update the total number of passengers served, the current number of passengers, and the number of passengers boarding and alighting. As charging occurs, the charging current, start SoC, and end SoC are recorded. The charger can transmit additional charging session data over a communications link, including the state of the charger (and state of the vehicle wireless power receiver) and details about the inductive charging energy transfer (e.g., coupling, frequency, equipment temperature).

[0083] Route information can be updated via the charger location communication system or the bus's wireless communication system. Route-related information includes the distance to the next stop, the stop type, SoC thresholds and limits, charger status and availability at the next stop with a charger, and charger status for at least all chargers along the route.

[0084] For driver breaks involving charging 804, the data store is updated with the start and end times of the stop. Passenger entry / exit counters (or bus weight change as a proxy) are used to update the total number of passengers served, the current number of passengers, and the number of passengers boarding and exiting. As charging occurs, the charging current, start SoC, and end SoC are recorded. The charger may transmit additional charging session data over the communications link, including the state of the charger (and state of the vehicle wireless power receiver) and details about the inductive charging energy transfer (e.g., coupling, frequency, equipment temperature).

[0085] Route information may be updated via the charging location communication system or the bus's wireless communication system. Route-related information includes the distance to the next stop, the type of stop, and the predicted SoC.

[0086] For charger stops 805, the data store is updated with the start and end times of the stop. No passengers are expected to board or disembark at charger stops 805. When charging occurs, the charging current, start SoC, and end SoC are recorded. The charger may transmit additional charging session data over its communications link, including charger status (and vehicle wireless power receiver status), as well as details about the inductive charging energy transfer (e.g., coupling, frequency, device temperature).

[0087] During the charger stops 805, route information may be updated via the charging location communication system or the bus's wireless communication system. Route-related information includes distance to the next stop, stop type, predicted SoC, etc.

[0088] For Driver Break 806, the data store is updated with the start and end times of the stop. Passenger entry / exit counters (or alternatively bus weight change) are used to update the total number of passengers served, the current number of passengers, and counts of passengers boarding and exiting. In the non-charging state of Driver Break 806, the start and end SoCs are recorded. Route information may also be updated via the bus's wireless communication system. Route-related information includes distance to the next stop, stop type, predicted SoC, etc.

[0089] For passenger pick-up locations (no charging) 807, the data store is updated with the start and end times of the stop. Passenger pick-up / drop-off counters (or alternatively bus weight change) are used to update the total number of passengers served, the current number of passengers, and counts of passengers boarding and alighting. In the no-charging state, the start and end SoCs are recorded.

[0090] At passenger pick-up locations (without charging) 807, route information may be updated via the bus's wireless communication system. Route-related information includes distance to the next stop, stop type, SoC thresholds and limits, predicted ending SoC, charger status and availability at the next stop with a charger, and charger status for at least all chargers along the route.

[0091] FIG. 9 illustrates diagrammatically a case where a single charger 901 is used to serve a first transit route 902 and a second transit route 903, and therefore serves multiple EVs serving these routes 902 and 903.

[0092] In some cases, the power available for opportunity charging at a charger may exceed the requested amount of power and / or the number of vehicles requiring charging. This includes the allocation of chargers to match specific vehicle populations and chargers that support non-standard communication protocols or charging signaling.

[0093] Competition for scarce charging resources (power and / or chargers) occurs at cross-route charging stations, such as those shown in Figure 9. Conflicts can also occur at charging stations operated by third parties, at stations experiencing backups due to delays during travel, at stations with disabled chargers, at chargers in areas with low power, or when emergency service preempts a charger.

[0094] Competing charger resources also experience energy prices that vary across space (geography) and time of day. Energy management through distribution planning and forecasting from modeling historical data can be used to manage and allocate limited available power for use at each station or at a station's specific chargers.

[0095] In a cross-route station serving two or more transit routes, such as that shown in FIG. 9, the arrival of transit vehicles may differ from their scheduled arrival and charging times.

[0096] For charging stations operated by third parties, vehicle arrival time, number of vehicles, charge level, total charging demand, and number of modular charging pads per charger and per vehicle (U.S. Patent Application No. 17 / 648,844, "METHOD AND APPARATUS FOR THE SELECTIVE GUIDANCE OF VEHICLES TO A WIRELESS CHARGER") are taken into consideration, even if a prioritization scheme and reservation system (described in U.S. Patent Application No. 17 / 199,234, "OPPORTUNITY CHARGING OF QUEUED ELECTRIC VEHICLES") is implemented.

[0097] For charging stations experiencing backups due to in-transit delays, a queuing scheme based on the scheduled time of departure and vehicle SoC can be implemented. Planning for stations with non-functional chargers adds a layer of complexity in that modular chargers may soft-fail such that a subset of chargers are still available.

[0098] In some cases, power shortages may necessitate power rationing. Vehicles may be assigned power priorities based on their SoC or a ranking system. Power priorities may be served by providing a higher charge rate than lower priority vehicles or by suspending power to lower priority vehicles.

[0099] When an emergency vehicle or a high-priority electric vehicle needs to charge, a currently charging vehicle may take over the charger, or a preemption scheme may be provided in which the next available charger is reserved for the preceding vehicle's use.

[0100] Figure 10 illustrates, at a high level, the communication paths available for data collection from EVs and WPT chargers. Data collection is used to price strategic charging opportunities. Telemetry (including two-way telemetry) uses wired and wireless communications to transfer data and information between remote sources (including mobile sources and users) and remote destinations. Telemetry data flow between source and destination consists of continuous, periodic, polled, or ad-hoc transmission of data. Telemetry includes the automated measurement and wireless transmission of data from remote sources, with collected data routed to receiving equipment at the destination location (e.g., dispatch server 1001) for monitoring, display, recording / storage, post-processing, analysis, and trending.

[0101] The data store is part of a database software with data management software (e.g., IBM Maximo Enterprise Management System) running on processor hardware with a computer operating system with large memory storage. Security and multi-party access control functions are implemented through the data management software. In some implementations, the dispatch server 1001 and associated data store may be implemented as a virtual, hosted (e.g., cloud-based) system, or as an on-premise hardware (with the necessary processors, memory, and fault-tolerant data storage) and software system based on a common high-availability computing platform local to the dispatch office and sized to fit the processing and storage needs. The dispatch server may include (or have interfaces to) redundant, potentially partitioned, and federated databases and geographic information systems (GIS). Interfaces to other third-party information, such as power prices, traffic information, and weather information, may be centralized on the dispatch server 1001.

[0102] The data store resides on the vehicle but uploads accumulated data to the dispatch server 1001. Uploads may be at the request of a dispatch office or may be periodic or event-driven updates (e.g., the start of a WPT charging session). The EV is equipped with a navigation system (e.g., based on Navstar GPS, Galileo, GLONASS, BeiDou, Quasi-Zenith Satellite system (QZSS) (also known as "Michibiki"), or local radio location beacons). The data store is accessible to vehicle systems and battery management systems (BMS) via a local data link (e.g., Controller Area Network (CAN)) bus).

[0103] Data sources include databases of historical data, recorded data, and models using near real-time data. Sources also include near real-time data, including sensor output from sensors containing electrical data (e.g., voltage, current, state of charge) or physical data (e.g., temperature, pressure, mass).

[0104] Telemetry may also include data products such as location, passenger count, timestamp, data source identifier, map updates, route updates, etc. In this application, a vehicle data store may accumulate collected electric vehicle related data and transmit it to the dispatch server 1001 on a near-continuous basis.

[0105] The dispatch server 1001 includes application-specific software, including an API for interfacing with both a data management system, third-party information feeds (e.g., traffic, weather, public charger status), and a communication interface for data originating from the charger locations 1002 and EVs 1003. The charger locations 1002 can use either a wired (not shown) or wireless radio interface 1004 for two-way communication. Depending on the location, such a wireless communication interface can use a public or private cellular data network 1005 using a public or private band radio signal 1006. An alternative or supplemental wireless communication network can be provided by a satellite 1007 using an established satellite band radio signal 1008. A satellite data receiver 1009 can be used to deliver the satellite-transmitted data to the dispatch server 1001.

[0106] Data is generated by the wireless charger locations 1002 and communicated via the wireless charger locations 1002 to the EVs 1003 and / or the dispatch server 1001. Each charger location 1002 has at least a wireless charger 1010 and ancillary equipment 1011 (shown here as an above-ground cabinet but may be installed in an underground vault). The ancillary equipment 1011 may include a wired or wireless backhaul (shown here as a wireless antenna 1012 for a cellular wireless connection 1004).

[0107] The route segments of the transit bus 1003 are pre-planned with arrival and departure times at predetermined geographic locations. Using the time, route segments, and odometer readings, a rough level of location can be calculated. A more precise location of the vehicle 1003 is available using an on-board navigation receiver (not shown) for a global navigation satellite system 1013 (e.g., Navstar GPS, Galileo, GLONASS, BeiDou, Quasi-Zenith Satellite system (QZSS) (also known as "Michibiki")) using satellite broadcast signals 1014. Other communications satellite constellations, such as carrier frequencies from the Starlink system (a high-speed, low-latency broadband internet carrier designed for remote and rural areas around the world), can also be used for positioning.

[0108] Alternatively, geographically local radio location beacons (beacons having a known frequency, known bandwidth, known or broadcast transmitter location, and transmitted identification information (ID)) can be used to obtain accurate positioning, if deployed or available.

[0109] On route EV1003, a radio receiver and transceiver 1015 is used to receive GNSS or local beacon positioning signals, communicate via a land-side cellular network, and potentially use a satellite communication system for receiving and transmitting information.

[0110] The dispatch office and server 1001 may be owned or managed by a third-party, non-fleet party (host) offering charging as a service (CAAS), especially in areas with multiple fleets and shared or third-party wireless charging resources. A charging as a service program removes the burden of ownership and maintenance from charging EV fleets by allowing the host to provide turnkey wireless charging stations, management software, communications infrastructure, 24 / 7 support, professional field maintenance of charger resources, planning and modeling for deploying new chargers when a need for a change in charger location or an increase (or decrease) in charger capacity is detected at an existing charger installation, and modernizing existing charger deployments.

[0111] An example of a wireless charger site 1100 is depicted in Figure 11. A wireless charger 1101 for charging a transit vehicle 1102 is shown installed level with the pavement 1103. A pedestrian area 1104 may be provided if passengers board or disembark at the depicted site 1100. Conduit 1105 provides interconnection to the wireless charger for cooling lines from a cooling structure 1106 and wired or optical communication lines (not shown) to a wireless transceiver and antenna 1107.

[0112] The wireless charger 1101 provides wireless communications between the wireless charger 1101 and the vehicle 1102. These communications may be as described in U.S. Patent No. 10,135,496l: "Near field, full duplex data link for use in static and dynamic resonant induction wireless charging," issued November 20, 2018. Static inductive charging relies on the EV maintaining its position during charging to pair the primary and secondary coils. Dynamic inductive charging uses a series of nearly continuous primary coils (often buried in the road) to charge secondary coils attached to a moving vehicle. Semi-dynamic charging uses the same primary and secondary coils as a static inductive charging system, but extends the operating angle over which the secondary coils are positioned, extending the total charging time for each primary coil.

[0113] The wireless charger 1101 in this example configuration is powered via a wired DC connection 1108 to a local utility grid (not shown).

[0114] In some cases, a mechanical actuator system can be used to connect physical wires for machine charging. The pantograph system 1109 shown is one such alternative system that uses a physical connector.

[0115] FIG. 12 is a flowchart of an example method 1200 for strategic opportunity charging in an example configuration.

[0116] As shown, method 1200 includes creating a model based on historical data, similar routes, near real-time sensor data, and third-party data for use in creating optimal routes for EVs used in commercial and non-commercial environments at 1210. The collected data relates to the environment, EV characteristics, power usage, charger characteristics, charging sessions, energy cost data, traffic, and route data for a single EV or a fleet of EVs.

[0117] Once the data model is created at 1210, the collected data is processed through the data model to provide an initial estimate of the total cost per distance (TCD) of travel on the anticipated route segment at 1220.

[0118] Routes and charging along route segments are optimized at 1230 to reduce the TCD on a route of the EV or fleet of EVs.

[0119] The EV then travels along the route segment, and the telemetry system and third party system collect 1240 near real-time data about the environment, the EV, power usage, power costs, travel time, etc. as the EV travels along the route segment.

[0120] When the current route segment ends, the TCD is calculated at 1250. The TCD is calculated using a data model based on data collected as the EV passes through the route segment, such as environmental, EV, power usage, power cost, travel time, and traffic volume.

[0121] An updated estimate for the next route segment is calculated at 1260 based on variations in the environment, EV, power usage, power cost, travel time, traffic, etc. The updated estimate further includes when / where the EV should charge along the next route segment to obtain optimal TCD using available chargers and available stop times.

[0122] Steps 1240-1260 are repeated for each route segment until the process is reset.

[0123] Figure 13 is a high-level graph of wireless charging operation during a timed stop at a wireless charger. The EV first arrives at 1301 in its initial state of charge (SoC) and is directed to the wireless charger. This direction can arrive via the wireless link, but can also arrive via indicator lights, signs, or auto-steering assistance (see, for example, U.S. Pat. No. 10,040,360; "METHOD AND APPARATUS THE ALIGNMENT OF VEHICLES PRIOR TO WIRELESS CHARGING INCLUDING A TRANSMISSION LINE THAT LEAKS A SISGNAL FOR ALIGNMENT" and U.S. Patent Application No. 17 / 646,844; "METHOD AND APPARATUS THE SELECTIVE GUIDANCE OF VEHICLES TO A WIRELESS CHARGER").

[0124] During pre-charging 1302, the ground charger and vehicle power receiver are coordinated for achieved alignment and efficient wireless transfer across the air gap. Information is exchanged for authorization and billing via the wireless connection. For modular ground chargers, multiple frequencies and phases (see, e.g., U.S. Patent Application No. 17 / 207,257; "MODULAR MAGNETIC CONTROL") can be configured.

[0125] The vehicle battery management system and ground charger controller (not shown) negotiate the supply current during charging 1303. The ground controller sets the initial maximum current to be supplied according to commands from the dispatch controller (based on a data model), and can then change the supply current according to new commands during charging.

[0126] When you leave the charging session, the EE leaves the charging station with a new SoC.

[0127] In the example of a route bus, the EV has a set arrival time and departure time, and therefore a preset total stop time 1305. The charging interval 1306 is a subset of the total stop time 1305.

[0128] The charging manager 1401 is an application running on the dispatch server 1001 or a local control device (charging station server (originally U.S. Patent Application No. 17 / 199,234; "OPPORTUNITY CHARGING OF QUEUED ELECTRIC VHEICLES"; filed March 11, 2011, and incorporated herein by reference). The charging station server includes the charging manager 1401 software for managing the electricity supply (from the utility and local storage), the charging station's internal communication links (both bridging and routing) with wireless chargers 1402, and the charging station's interconnections to entities external to the charging station (servers, data repositories, cloud instances). In this example, all messaging is paired, with each transmission having an acknowledgment.

[0129] In the current embodiment, the ground charger assembly (GCA) 1402 includes a near-field communication interface (described in detail in U.S. Pat. No. 11,121,740; "NEARFEILD FULL DUPLEX DATA LINK FOR RESONANT INDUCTION WIRELESS CHARGING," incorporated herein by reference). A physically corresponding vehicle receiving assembly (VRA) 1403 must be present across an air gap 1404 for charging when using the near-field communication interface. Alternative or supplemental wireless communication links based on wireless local area network (W-LAN) technologies (e.g., IEEE 802.11, Zigbee, Bluetooth) can also be used.

[0130] Before a wireless charging session 1406 begins, the GCA 1402 and VRA 1403 exchange messaging for authorization, mutual authentication (in this model, neither the GCA 1402 nor the VRA 1403 is trusted), and accounting. The operation of a battery management system (BMS) in the current example is included in the functionality and pass-through messaging of the VRA 1403.

[0131] Standard messages for wireless charging of electric vehicles (EVs) have been published by the International Engineering Consortium (IEC) as IEC 61980 Parts 1, 2, and 3. More specifically, IEC 61980-3:2022; "ELECTRIC VEHICLE WIRELESS POWER TRANSFER (WPT) SYSTEMS - Part 3: Specific requirements for magnetic field wireless power transfer systems" (published November 2022) is useful for generalized explanation purposes, as the use cases and messaging supported therein vary.

[0132] Immediately prior to charging, wireless messaging 1405 may be exchanged to measure and ensure alignment between the GCA 1402 and VRA 1403, measure the magnetic gap, determine the efficient magnetic transmission frequency (see U.S. Patent Application No. 17 / 643,764; "Charging Frequency Determination for Wireless Power Transfer," incorporated herein by reference), and exchange capabilities and limitations.

[0133] Once preliminary messaging 1405 ends and charging session 1406 begins, the VRA 1403 and GCA 1402 begin exchanging information messages 1407 that contain electrical, temperature, and / or wireless sensor data of the associated GCA 1402 and VRA 1403 and provide a periodic heartbeat. The information messaging 1407 can include BMS-supplied information regarding the voltage, temperature, and state of charge of the battery pack. The information message streaming 1407 continues for the duration of wireless power transfer (while magnetic flux is generated).

[0134] The VRA 1403 then initiates power request / response messaging 1408 to the charging manager 1401 via the GCA 1402 by wireless signaling over the air interface 1404. The power request can include a requested current level, and the power response can include a granted current value. The power request can also include a preferred current level and a maximum current level, and the power response can include a granted current value below the requested or maximum current level.

[0135] The GCA 1402 sends a message to the VRA 1403 confirming the initial current level assignment 1409 and energizing the charging signal. During the power transfer 1410, heartbeat / telemetry 1407 messaging continues.

[0136] In this example, the charging manager 1401 sends an evaluation command 1411 to the GCA 1402 involved in the charging session 1406. The evaluation command 1411 includes a current level above or below an initial current level (the current level may be zero, which will pause or terminate the charging session 1406 early). The updated current level is passed to the VRA 1403 before the charging signal is changed. The VRA 1403 confirms the updated current level in its response 1409, which can request a new maximum allowed current level or any current level below the updated current level.

[0137] During a second wireless power transfer duration 1413, the GCA 1402 provides a magnetic signal to the VRA 1403 to generate a new allowable current level. The EV terminates the wireless power transfer by setting the requested current level to zero via the BMS and the VRA 1403. The GCA 1402 interrupts the charging signal and notifies the charging server 1401 that the session has ended via a termination notification 1414. The GCA 1402 uses the termination notification 1414 to pass collected time, sensor, and performance data to the charging server 1401 for storage and analysis.

[0138] Figure 15 shows utility billing rates for business customers. This example is for a single charging station or multiple charging stations.

[0139] X-axis 1501 shows time and Y-axis 1502 shows power consumption (kW). Power consumption varies over the billing period from a baseline 1504 to peak demand 1506. An average power consumption 1505 can be determined over the billing period 1503.

[0140] Electricity charges from power companies for wireless power transmission are expected to consist of both volume charges and demand charges.

[0141] The quantity component is typically measured in kilowatt-hours (kWh).

[0142] The demand component is based on the maximum amount of power needed during a time period (e.g., an hour or a set fraction of an hour) within a billing period, and is typically measured in kilowatts (kW).

[0143] Efficient geographic distribution of chargers as well as charger scheduling, which adjusts the charging schedules of individual vehicles to limit simultaneous charging, can be used to reduce the demand charging component from EV fleets.

[0144] Strategic placement of charger stations can limit the number of chargers to less than one per bus stop (on average), minimizing infrastructure costs, including T&D. Such charger placement allows for multiple chargers to be installed at bus stops serving multiple EVs.

[0145] Prioritized charging, which uses wireless opportunistic charging to control the SoC during the day while the EV is parked, can be used to minimize electricity costs by charging only to reach the next charger (with backup) during times when electricity prices are highest. Prioritized charging also involves charging more at stations with higher electricity prices (increasing the EV's SoC).

[0146] 16 is an overhead view of a charging station at a bus stop (in this example, a transit bus stop). In this example, wireless power charging station 1601 has three chargers 1602, 1603, and 1604 arranged to simultaneously service up to three electric vehicles. In this example, each charger 1602, 1603, and 1604 serves a transit bus 1605, 1606, and 1607.

[0147] All three chargers are supplied via an underground electrical connection (not shown) to power electronics 1608, which is connected to the utility grid via drop 1609. The power company can supply AC, DC, or AC three-phase power via drop 1609.

[0148] A local energy storage unit 1610 (see U.S. Patent Application Publication No. US20220368161A1, filed October 30, 2020, entitled "Contactless Swappable Battery System" for one practical example of such a battery system) enables "peak shaving," where energy is stored (trickle charged) during times of low electricity costs and used during peak electricity costs. The battery may be physically swappable (for temporary or emergency use) or may be charged from a utility drop 1609 during times of low electricity rates. Alternative power sources, such as wind or solar power plants, may also be used to charge the local energy storage device 1610.

[0149] To reduce demand and avoid exceeding a desired maximum demand threshold, the charging site controller can:

[0150] a. Allocate the available power equally to each EV.

[0151] b. Prioritize power delivery to EVs that need it most (SoC needed to reach the next charger)

[0152] c. Prioritize arrival and departure times so that power supply is tailored to the arrival and departure of each vehicle.

[0153] d. Prioritize power and optimize energy consumption based on time-of-use billing rates.

[0154] Demand charges can be site-specific (size of transformer or wire to meter) or region-specific. Other methods provide region-level control (e.g., openADR - Automatic Demand Response).

[0155] Demand charges may be aggregated for a single customer across the service territory served by the electric utility.

[0156] Furthermore, available power may be set by local or national government action rather than by a desired concurrent demand threshold to minimize utility demand charges.

[0157] Figure 17 illustrates geographically the ability to use strategic opportunity charging to lower (or at least manage) the utility demand charging portion of electricity costs. In region 1701, chargers are distributed to serve a fleet of EVs on a route (which may be fixed (pre-planned) or ad hoc (changeable)).

[0158] This area includes one charger station 1702, 1703, 1704, two charger stations 1705, 1706, and three charger stations 1707, 1708. These stations 1702, 1703, 1704, 1705, 1706, 1707, 1708 and the number of chargers per station are installed based on predicted vehicle charging needs. Geographically, stations 1702, 1703, 1704, 1705, 1706, 1707, 1708 are located at various distances from each other, as well as from passenger or delivery stops (not shown).

[0159] By adjusting the scheduling of charging sessions for each fleet vehicle 1709, 1710, 1711, 1712, the total demand for electricity can be kept below the threshold at which larger utility charges would be incurred. This geospatial approach to total simultaneous demand minimization can be further optimized for a fleet by adding well-placed opportunity charging stations to minimize the changing needs of EVs at specific charging sites and charging stations.

[0160] Figure 18 shows one electric bus (at a time) traveling on one route. An EV bus 1801 travels along a route 1802 mapped to local roads 1803. An access road 1804 equipped with wireless machine chargers 1805 allows the EV bus 1801 to charge at a set power level until the scheduled bus stop time.

[0161] Information regarding the bus 1801 status, operating conditions, load, schedule adherence, and power consumption may be collected and transmitted in near real time along the route 1802 or at stops 1806 .

[0162] Figure 19 shows multiple electric buses operating on a route at the same time. This arrangement is done to reduce passenger waiting times and / or to provide sufficient capacity to serve the route.

[0163] A first electric bus 1901 and a second electric bus 1902 travel along a route 1903 mapped to a local road 1904. An access road 1905 for a bus stop 1906 is equipped with a wireless machine charger 1907, allowing the first and second electric buses 1901, 1902 to charge at a set power level while passengers board and disembark until the scheduled bus stop time.

[0164] Information about the status, running conditions, load, schedule adherence, and power consumption of the buses 1901, 1902 may be collected and transmitted in near real time along the route 1903 or at the stops 1906.

[0165] Figure 20 shows a case where multiple electric buses operate on multiple routes simultaneously. The buses may be the same or different vehicles. In this example, the wireless charging infrastructure is shared.

[0166] A first route 2001 and a second route 2002 share the same local road network 2003. The first route 2001 is provided by a first electric bus 2004 and a second electric bus 2005, which travel along the route 2001 according to a first schedule. The second route 2002 is operated by a first electric bus 2006 and a second electric bus 2007, which travel along the second route 2002 according to a second schedule.

[0167] Wireless machine chargers 2008 are installed along the first route 2001 and the second route 2002. The first and second schedules must be coordinated to ensure sufficient charging time for each of the electric buses 2004, 2005, 2006, and 2007. The power consumption and power delivered via the wireless machine chargers 2008 (and other shared or non-shared chargers, such as wireless machine charger 2009) must be coordinated (by the dispatch server 1001) to avoid exorbitant power usage and utility demand charges and to avoid overstressing the cooling capacity of the wireless chargers during and between charging sessions.

[0168] Information regarding the status, driving conditions, load, schedule adherence, and power consumption of the buses 2004, 2005, 2006, 2007 may be collected and transmitted in near real time along the routes 2001, 2002 or at parked wireless chargers 2008, 2009. Information regarding the wireless chargers 2008, 2009 themselves may also be collected and transmitted in near real time, periodically, or triggered by an event (e.g., before, after, or during a charging session).

[0169] Figure 21 illustrates an example of a drayage yard 2101 using electric freight vehicles (not shown) with strategic wireless machine charging. In this example, both payload weight and travel distance are primary determinants of battery consumption for the electric vehicles (e.g., forklifts, side loaders, reach trucks). In Figure 12, the movement of containerized cargo is described as an example.

[0170] Containers may be loaded and unloaded from the freight rail system 2102 by yard vehicles. Dedicated container handling equipment 2103 is used to transfer freight containers from visiting rail vehicles to local rail yard stacks 2104, which are accompanied by transfer vehicles equipped with WPT receivers that can move the containers to truck yard stacks 2105, dock yard stacks 2106, or temporary storage stacks 2107. As each stack (2104, 2105, 2106, 2107) is frequently visited by transfer vehicles, wireless chargers can be placed at each based on container usage levels and wait times. In this example, rail yard chargers 2108, shipyard chargers 2109, and truck yard chargers 2110 are installed. In this example, storage stack 2107 does not have an associated charger.

[0171] The truck yard stack 2105 is added to by unloading trucks using crane equipment 2111, added to by transferred containers, or reduced by loading containers onto trucks, or by diverting containers to other transport or storage 2107 using transfer vehicles.

[0172] The dockyard stack 2106 is added to by unloading trucks using cargo cranes 2112, added to by transferred containers, or reduced by loading containers onto ships or barges (not shown), or by diverting containers to other transport or storage facilities 2107 using transfer vehicles.

[0173] Storage stacks 2107 can be added or removed by the transfer of containers to each of the transport yard stacks 2104, 2105, 2106.

[0174] A transport management application (similar to the software and database used by the dispatch center 1001) uses near real-time data regarding cargo container weight (either manifest weight or weight from sensors mounted on the transport vehicle), cargo container location, transport vehicle location, transport vehicle state of charge, container destination (and therefore distance traveled), and current queues at the source and destination stacks to manage opportunity charging schedules for each charger 2108, 2109, 2110, as well as the charge level and charging time for each charging session, minimizing downtime and travel costs.

[0175] The drayage yard 2101 also includes a rest and repair stop 2113 with an associated wireless charger 2114.

[0176] A spur of Highway 2115 leads to Drayage Yard 2101, serving both freight trucks and employee vehicles.

[0177] Figure 22 depicts the geographical route of an electric delivery vehicle taking advantage of strategic opportunities to minimize travel costs.

[0178] A delivery vehicle is stored and maintained at a stop 2201 a distance 2202 from a first distribution center 2203. A wireless machine charger may be installed at the first distribution center 2203. The delivery vehicle has a first route 2204 having multiple stops. The stops may be delivery only, pick-up delivery, or both, depending on the type of delivery service provided. The first route 2204 returns the vehicle to the first distribution center 2203 where it is loaded and unloaded as needed during a machine charging session.

[0179] The second delivery route 2205 includes a third party public or contract wireless charger 2206 that can provide charging if necessary or desired to maintain the delivery vehicle's battery within the desired SoC range.

[0180] At the end of the second delivery route 2205, the vehicle returns to the first distribution center 2203, where loading and unloading of cargo occurs as needed between charging sessions.

[0181] A third delivery route 2207 includes, among its scheduled stops, a visit to a second distribution center 2208. The second distribution center 2208 may include a wireless machine charger that can be used to charge the delivery vehicle. The third delivery route 2207 terminates at the first distribution center 2203, where the vehicle is unloaded and recharged to an optimal charge level for overnight storage at the stop 2201, taking into account the charge used to travel the distance 2202 to the stop 2201.

[0182] While examples have been provided for commercial vehicles such as buses, trucks, and delivery vehicles, it will be understood that the methods described herein can also be applied to non-commercial vehicles such as privately driven EVs, with or without software-based driver assistance. These same methods can also be used for autonomously driven vehicles.

Claims

1. A charging management method for an electric vehicle (EV) traveling along a predetermined route, comprising: collecting data related to EV characteristics, battery pack characteristics, environmental conditions, power usage of the EV, charger characteristics, energy cost data, and route data for the EV; creating a data model from the collected data for use in route planning for the electric vehicle; processing, by a server, the collected data with the data model to provide an initial estimate of a total cost per distance (TCD) for travel by the EV along a projected route segment of the predetermined route, the initial estimate of the TCD for travel by the EV along the predetermined route including when and at which one or more stops the EV should charge along the route segment of the predetermined route based on available chargers along the predetermined route; receiving, by the server, telemetry data and third party data from the EV as the EV travels along a route segment of the predetermined route, the telemetry data and third party data relating to characteristics of the EV, the environmental conditions, power usage of the EV, energy cost data, and travel time; calculating, by the server, the TCD for the predetermined route segment using the data model, received telemetry data, and the third party data collected as the EV travels along the route segment of the predetermined route; processing, by the server, the received telemetry data and the third-party data variations along the route segments with the data model to provide an updated estimate of the TCD of the EV's travel on the remaining route segment of the predetermined route, the updated estimate including when and at which of the one or more stops the EV should charge along the remaining route segment to improve the TCD for the predetermined route using available chargers along the remaining route segment; enabling, by the server, chargers at the one or more stops along the predetermined route to charge the EV according to the updated estimate; providing, by the server, an updated estimate of the TCD along which the EV will travel one or more remaining route segments, and repeating the process of enabling the chargers at the one or more stops along the predetermined route to charge the EV according to the updated estimate of the TCD until the predetermined route is completed; A method comprising:

2. The method of claim 1 further comprising: receiving, by the server, charger data from the chargers at the one or more stops along the predetermined route; determining, by the server, a charging plan for the EV based on the TCD, the telemetry data, the third-party data, and the charger data for travel along each route segment of the predetermined route; receiving, by the server, an indication that the EV is located at a particular one of the chargers at the one or more stops along a predetermined route; In response to the instruction, causing the server to charge the EV according to the charging plan at the specific charger; The method of claim 1,

3. The method of claim 2 further comprising: modifying, by the server, a modified charging plan for the EV based on an actual amount of charge supplied to the EV by the specific charger; The method, wherein the modified charging plan for the EV is utilized by subsequent chargers along the predetermined route to charge the EV.

4. 3. The method of claim 2, wherein the charging plan is determined for the plurality of EVs based on the TCDs traveled on each of the route segments of the predetermined route for each EV of the plurality of EVs and the telemetry data and third-party data collected by each EV of the plurality of EVs.

5. 5. The method of claim 4, wherein the charging plan is determined for the plurality of EVs in the fleet based on the TCDs that have traveled each of the route segments of a plurality of predetermined routes for each EV of the plurality of EVs and the telemetry data and third-party data collected by each EV of the plurality of EVs.

6. 2. The method of claim 1, wherein the TCD for a trip on each of the route segments of the predetermined route is calculated based on at least one of the collected data, charging session data, terrain data, payload, driver ability, or traffic data along the predetermined route, wherein the terrain data includes at least one of slope, deviation, curves, speed, stops, or traffic signals along the predetermined route.

7. The method of claim 1 further comprising: storing, by the server, the characteristics of the EV in a database; The method, wherein the characteristics of the EV include at least one of the make, model, manufacturer, age, mileage, repair status, tire condition, tire selection, or aerodynamic characteristics of the EV.

8. The method of claim 1 further comprising: The method includes a step of storing characteristics of the battery pack of the EV in a database by the server, The battery pack characteristics include the make, model, manufacturer, capacity, battery age, past battery usage, or past journey battery state of charge of the EV.

9. The method of claim 2 , wherein determining the charging plan for the EVs comprises minimizing a total TCD over all route segments of the predetermined route.

10. 3. The method of claim 2, wherein determining the charging schedule for the EV includes maintaining a state of charge of a battery of the EV within upper and lower state of charge thresholds as the EV travels the predetermined route.

11. The method of claim 2 further comprising: The method includes the steps of: obtaining, by the server, tariffs and spot electricity rates at a plurality of chargers along the predetermined route having different estimated arrival times for the EV; and calculating cost optimization when the EV departs the predetermined route or when the data model cannot predict the exact state of charge of the EV's battery pack.

12. The method of claim 2 further comprising: The method comprising recalculating the TCD for the predetermined route if the particular charger is arrived at early or late.

13. 3. The method according to claim 2, wherein the charging plan includes, in the step of charging at the specific charger, performing prioritized charging that supplies a sufficient amount of charge for the EV to reach the next charger along a predetermined route with a reserve according to an electricity price at the specific charger.

14. 3. The method of claim 2, wherein determining the charging plan for the EV is based on a minimum state of charge (SoC) of the EV, the minimum state of charge being: Minimum SoC to limit damage to the battery, Reduced battery life, the minimum SoC required to reach the next two chargers on said predetermined route; the minimum SoC required to complete the predetermined route without charging; the calculated minimum SoC to abort the predetermined route and arrive at a stop; Charging the SoC when the predetermined route is initiated; or Manually set SoC threshold The method is established based on at least one of the following:

15. 10. The method of claim 1, wherein the telemetry data includes at least one of an interior temperature of the EV, an exterior temperature, an interior lighting condition, an exterior lighting condition, a battery pack temperature, and a load on the EV.

16. A system for managing charging of electric vehicles (EVs) traveling on a predetermined route, a communication interface; a memory for storing instructions; a processor communicatively coupled to the communication interface and the memory, collecting data related to EV characteristics, environmental conditions, power usage by the EV, charger characteristics, energy cost data, and route data for the EV via the communication interface; creating a data model from the collected data for use in generating routes for the electric vehicle; processing the collected data with the data model to provide an initial estimate of a total cost per distance (TCD) for travel by the EV along a predicted route segment of the predetermined route, the initial estimate of the TCD for travel by the EV along the predetermined route including when and at which one or more stops the EV should charge along a route segment of the predetermined route based on available chargers along the predetermined route; receiving telemetry data and third party data from the EV as the EV travels a route segment of the predetermined route, related to characteristics of the EV, environmental conditions, power usage by the EV, energy cost data, and travel time; calculating the TCD for the route segment of the predetermined route using the data model, received telemetry data, and the third-party data collected as the EV travels the predetermined route segment; processing the received telemetry data and the variance in the third-party data with the data model along the route segment to provide an updated estimate of the TCD of the EV's travel along the remaining route segment of the predetermined route, the updated estimate including when and at which of the one or more stops along the remaining route segment the EV should charge using available chargers along the remaining route segment to improve the TCD for the predetermined route; enabling chargers at the one or more stops along the predetermined route to charge the EV according to the updated estimate; providing an updated estimate of the TCD as the EV travels one or more remaining route segments, and repeating the process to enable the chargers at the one or more stops along the predetermined route to charge the EV according to the updated estimate of the TCD until the predetermined route is completed. the processor configured to execute the instructions. A system having:

17. 17. The system of claim 16, further comprising: It has additional instructions, The additional instructions, when executed by the processor, receiving charger data from the chargers at the one or more stops along the predetermined route; determining a charging plan for the EV based on the TCDs traveling along each of the route segments of the predetermined route, the telemetry data, the third-party data, and the charger data; receiving an indication that the EV is located at a particular charger at the one or more stops along the predetermined route; In response to the instruction, causing the specific charger to charge the EV according to the charging schedule; The system is configured to perform operations including:

18. 18. The system of claim 17, wherein the additional instructions, when executed by the processor, cause the processor to: modifying the charging plan for the EV based on an actual amount of charge provided to the EV by the particular charger; configured to perform actions including the revised charging plan for the EV is utilized by subsequent chargers to charge the EV along the predetermined route. system.

19. 18. The system of claim 17, wherein the processor determines the charging plan for the plurality of EVs in the fleet based on the TCDs that have traveled each of the route segments of the predetermined route for each EV of the plurality of EVs and the telemetry data and third-party data collected by each EV of the plurality of EVs.

20. 20. The system of claim 19, wherein the processor determines the charging plan for the plurality of EVs in the fleet based on the TCDs that have traveled each of the route segments of a plurality of predetermined routes for each EV of the plurality of EVs and the telemetry data and third-party data collected by each EV of the plurality of EVs.

21. 17. The system of claim 16, wherein the calculating, by the processor, the TCD for each of the route segments of the predetermined route is based on at least one of the collected data, charging session data, terrain data, loads carried, driver performance, or traffic data along the route, wherein the terrain data includes at least one of slope, deviation, curves, speed, stops, or traffic signals along the predetermined route.

22. 17. The system of claim 16, further comprising: The system includes a database for storing characteristics of the EV, the characteristics of the EV including at least one of the make, model, manufacturer, year, mileage, repair status, tire condition, tire selection, or aerodynamic characteristics of the EV, and wherein calculating, by the processor, the TCD for travel on each of the route segments of the predetermined route is based on the characteristics of the EV.

23. 17. The system of claim 16, further comprising: the system further comprising a database for storing characteristics of the EV's battery pack, the characteristics of the battery pack including the EV's make, model, manufacturer, capacity, battery age, past battery usage, or past battery state of charge during a journey, and wherein the processor's calculating the TCD for travel along each of the route segments of the predetermined route is based on the battery pack characteristics.

24. 20. The system of claim 17, wherein the processor determining the charging plan for the EV comprises minimizing a total TCD over all route segments of the predetermined route.

25. 18. The system of claim 17, wherein the processor determining a charging plan for the EV includes maintaining a state of charge of a battery of the EV within upper and lower state of charge thresholds as the EV traverses the predetermined route.

26. 18. The system of claim 17, further comprising: It has additional instructions, The system configures the processor to perform operations executed by the processor, including obtaining tariffs and electricity spot rates at multiple chargers along the predetermined route at different estimated arrival times of the EV, and calculating cost optimization when the predetermined route is departed by the EV or the data model cannot predict the exact state of charge of the EV's battery pack.

27. 18. The system of claim 17, further comprising: It has additional instructions, The additional instructions, when executed by the processor, configure the processor to perform operations including recalculating the TCD for the predetermined route depending on whether the particular charger is arrived at early or late.

28. 18. The system of claim 17, further comprising: It has additional instructions, The additional instructions, when executed by the processor, configure the processor to perform operations including implementing prioritized charging, where charging at a particular charger provides the EV with enough charge to reach the next charger along the predetermined route with a reserve that depends on the electricity price at the particular charger.

29. 18. The system of claim 17, wherein the processor determines the charging plan for the EV based on a minimum state of charge (SoC) established for the EV, the minimum SoC being: Minimum SoC to prevent damage to the battery, Reduced battery life, the minimum SoC required to reach the next two chargers on said predetermined route; the minimum SoC required to complete the predetermined route without charging; the calculated minimum SoC to abort the predetermined route and arrive at a stop; charging the SoC when the predetermined route is initiated; or Manually set SoC threshold The system is established based on at least one of the following:

30. 17. The system of claim 16, wherein the telemetry data includes at least one of an interior temperature of the EV, an exterior temperature, an interior lighting condition, an exterior lighting condition, a battery pack temperature, and a load on the EV.

31. 17. The system of claim 16, wherein the EV is a route bus, and the predetermined route is a route of the route bus.

Images