VEHICLE RUNNING ON BATTERY IN ANY DIRECTION (RANGE- AREA) FROM ITS CURRENT LOCATION

CROSS REFERENCE

This application claims priority to U.S. Provisional Patent Application No. 63/352293 filed on June 15, 2022, the entire contents of which is incorporated herein by reference in its entirety.

BACKGROUND

[0001] The disclosed embodiments are directed to driving optimization and more specifically to a system and method for determining the available range of a vehicle running on battery in any direction (range-area) from its current location.

[0002] Electric and plug-in hybrid vehicles battery range may be calculated and displayed in linear-miles. The approaches for understanding range in linear miles and available charge vary from using standardized data, past data on energy consumption per mile, or energy consumption per mile considering current temperature.

[0003] Some manufacturers allow drivers to choose to display a range as either a percentage of battery energy remaining, or remaining miles that can be driven. A displayed range may be adapted based on fixed EPA test data, not personal driving patterns. For other manufacturers, an available range is determined in vehicles based on previous energy usage and driving conditions. However, if a recent two hour of driving has been on a flat road yet the next coming hour will be uphill into the mountains consuming electric charge at a far faster rate, the predicted range then will be more than the actual range realized because energy consumption will be greater than before. For some manufacturers, the range is an extrapolated average, meaning that the computer's range prediction is derived from translating the remaining battery charge into kilometers (distance), based on a remaining charge as a percentage of a total battery capacity and how long the vehicle has travelled so far on that charge.

[0004] Processes identified above may be faulty. A difficulty in managing a vehicle range begins before entering the vehicle due to the unrealistic assumptions made when establishing vehicle range. From real-world testing, EVs may be capable of achieving around 85% of a quoted range.

[0005] That is, there may be a variation between stated and actual range of the vehicle across manufacturers. The range of a vehicle running on its battery can vary far more than for a traditional Internal Combustion Engine (ICE) vehicle. For example, in some situations, a 20% reduction in range for an electric vehicle equals only a 12% drop for a gasoline powered vehicle.

[0006] Cold weather also impacts an available range. An efficiency of both electric and gasoline cars operating at 0 degrees Fahrenheit, 32 degrees, and 73 degrees has been tested. When the temperature dropped from 73 degrees to 0, electric cars experienced an average range reduction of 29 percent. Between 73 degrees and 32 degrees, there was a 20- percent drop in range. The gap was lower for gasoline cars. Between 73 degrees and 0, they experienced an average range reduction of 19 percent, and a reduction of 12 percent between 73 degrees and 32 degrees.

[0007] Another potential drain on range was tire inflation. This was found to cut electric-car range by up to 13 percent, and up to 4 percent for gasoline cars.

[0008] In addition, air density was found to shrink range by up to 6 percent in electric cars, and 5 percent in gasoline cars. Due to the sensitivity of vehicles powered by batteries to various conditions, the impact on range can be very significant. One study indicates that built environment variables have a large effect on the energy consumption of BEVs (battery powered EVs, e.g., cars without ICEs). In some cases, drivers could reach an energy reduction of 35% only by shifting driving styles, while shifting routes could lead to 35-50% energy reduction for all tested scenarios. One study showed reductions in range for vehicles powered by batteries, as well, based on driving styles and temperature.

[0009] In one test, three vehicles were tested in cold, moderate, and hot temperatures. Vehicles were tested for city driving to mimic stop-and-go traffic, and to better compare with the Environmental Protection Agency’s ratings. All the vehicles evaluated demonstrated reduced driving range in hot and cold climates; the average EV battery range in the test was 105 miles at 75 degrees Fahrenheit, and dropped to 43 miles when the temperature was 20 degrees Fahrenheit. Taken together — (1) inaccurate baseline range as determined by the EP A, (2) the greater range variation between ICE and electric powered vehicles under the same conditions, and (3) the high impact on range by a wide range of factors — these make estimating range and its impact on driver decisions exceedingly complex and create drive range uncertainty.

[0010] Various factors affect EV range. One factor is speed. Driving at high speeds (65+ MPH) reduces the electric car’ s efficiency. The reason for this is the faster you drive, the more work the electric motor does. One test showed that a driver can expect about 15% loss of range when driving at 75 MPH rather than 65 MPH.

[0011] Wind is another factor that affects electric vehicle range is wind, e.g., headwind. The higher the headwinds, the more resistance the vehicle experiences. Therefore, the motor works harder to combat the negative effect.

[0012] Payload is another factor. The more payload the more the motor works to offset the weight. The more passengers and cargo you load into the vehicle, the less efficient the EV will be.

[0013] Tire Traction is another factor. Poor tire traction results in less efficiency. Factors that contribute to tire traction are tire quality, tire inflation, and road conditions. If a driver drives an EV with tires that are at the end of their life and the roads are wet from rain, then the range slightly decreases. Additionally, under-inflated tires will not perform as well, and will not be as efficient. Poor tire traction plays a part in range loss.

[0014] Cold Weather is another factor. Excessive cold, or excessive heat, will result in loss of range for an EV. During cold ambient temperatures, the battery will need to use energy to heat itself up. With an ICE vehicle, the engine block creates heat, which can heat up the cabin, fuel, or other vehicle parts and functions. With an EV, heat to warm parts and functions is created using energy from the battery. During cold weather (below 50 degrees Fahrenheit), there may be a slight drop in range due to the battery needing to warm up. Below 30 degrees, the range loss may become more apparent. Electric cars may experience a range loss of 12% in 20 degree F temperatures, with the HVAC (Heating Ventilation Air Conditioning) off. Regarding HVAC, utilizing it for heating during cold ambient temperatures may result in range loss. Using the heating system uses energy, therefore, range may be lost from the battery using energy to heat the car rather than to move the wheels. A typical car heating system consists of a radiator, water pump, thermostat, blower motor, and coolant. With an ICE vehicle, the natural heat byproduct from the engine may be used to heat the cabin. With an EV, energy from the battery, just like when the weather is cold, may be used to warm the cabin. EVs can experience as much as a 41% loss of range when using heating when ambient temperatures are 20 degrees. ICE vehicles, too, experience similar, but not as drastic, loss of efficiency with both cold weather and HVAC use. Electric cars may be outfitted with heat pumps rather than the electric resistance heater to combat this loss of range during heating use. A heat pump can be as much as 350% more efficient than electric resistance heaters. This significant increase in the efficiency of the heating system will result in less loss of range during cold weather HVAC use.

[0015] Battery degradation is another factor that affects electric vehicle range. A new electric car will have a State of Health (SOH), which is the amount of battery energy available compared to when new, of 100%. When new, battery degradation may have minimal impact on the range. Over time the battery slowly loses capacity. Unlike cell phone or laptop batteries, electric car batteries include a Thermal Management System (TMS), resulting in EVs experiencing around 2% of battery capacity loss per year.

[0016] Air Density is another factor. Air density was found to shrink range by up to 6 percent in electric cars, and 5 percent in gasoline cars.

[0017] Topography is another factor. Topology has an impact on energy consumption. Driving roughly equivalent mileage, with one route being uphill and the other downhill, the uphill route consumed more power, and the downhill route was able to utilize regenerative braking.

[0018] Traffic Congestion is another factor. Traffic congestion has an impact on vehicles powered by a battery. Traffic conditions impact the vehicle’s efficiency, with additional consumption of approximately 4-5% in the high traffic scenario. By creating and comparing driving in traffic to an underlying base case scenario, the additional range potential by avoiding traffic for a particular vehicle can be quantified as up to seven miles. Patterns of BEV efficiencies emerged, which can show how eco-driving can be strategically improved by selecting trip times and routes that avoid high traffic intensity.

[0019] Given the variables that impact range of vehicles running on batteries a driver could experience significantly less range than they expected. Considering the factors that impact vehicle range, for a vehicle with a 250-mile base range — midpoint for electric vehicles — a person for instance, driving up in the mountains to go skiing on a cold day, hauling cargo in their vehicle, while staying within the optimal manufacturer battery charging limits of 20% minimum and 80% maximum, could see an effective range of 75 to 100 miles.

[0020] As another example, an all-electric vehicle with a 250 mile stated range can drop down to a 130 mile effective range on a long highway trip based on factors like charging limits, lack of regenerative braking, cold weather and speed without also considering vehicle load. On the other hand, ICE vehicles with a median range of 400 miles and less affected by conditions reducing mileage may have greater effective range than an all-electric.

[0021] Moreover, there are fewer charging ports vs. fuel pumps. The longer duration required to recharge (30-40% to go from 20% to 80% charge on a high-speed charging port) makes trip planning more complicated. Thus, understanding range is more important for EVs than with ICE vehicles.

[0022] A trip can be planned with a specific destination and known route. Certain manufactures offer a feature that estimates when you would need to recharge and identifies the appropriate locations to stop on a pre-determined route. This approach may utilize a limited set of assumptions: route and destination. In the real world, trips can be more complex. Trips may not have a specific destination or planned route and/or the plan for a trip can change unexpectedly. In this case, re-entering destination and route information may be problematic, inefficient, requiring a driver to make additional stops to enter the information. A more flexible solution is desired. BRIEF SUMMARY

[0023] Disclosed is a method of determining and displaying an available range of an all-electric vehicle in any direction from its current location, the method including: measuring, via a vehicle monitoring system, a current available charge of the vehicle; determining, via a global positioning system, the current location of the vehicle; collecting data of one or more categories of information from one or more sensors or a cloud service; ingesting and processing the data, to generate the range-area RA for display, on a processor disposed on one or more of: a smartphone; the cloud service; or the vehicle; and displaying the range-area RA, on a monitor on one or more of the smartphone and a display of the vehicle.

[0024] In addition to one or more aspects disclose herein or as an alternate, collecting the one or more categories of information includes collecting one or more of: vehicle speed; wind conditions; vehicle payload; tire traction, weather; HVAC utilization; battery degradation; air density; travel topography; or traffic congestion.

[0025] In addition to one or more aspects disclosed herein or as an alternate, the method includes storing the collected data in a non-transitory memory storage located in one or more of: a cloud service; a smartphone; or the vehicle.

[0026] In addition to one or more aspects disclosed herein or as an alternate, ingesting the data includes one or more of: periodic batch data ingestion; real-time streaming data ingestion; or Lambda architecture data ingestion. [0027] In addition to one or more aspects disclose herein or as an alternate, the method includes processing the ingested data on the processor to generate the range-area RA by one or more algorithms, wherein the one or more algorithms are produced via one or more of: regression; or an AI/ML (artificial intelligence/machine learning) model.

[0028] In addition to one or more aspects disclosed herein or as an alternate, the method includes updating the one or more algorithms either periodically if the data is ingested as batches or continuously if the data is real-time streaming.

[0029] In addition to one or more aspects disclose herein or as an alternate, the method includes determining, by the one or more algorithms executed by the processor, the range-area RA within which the vehicle can travel in any direction until the battery reaches a determined threshold minimum.

[0030] In addition to one or more aspects disclose herein or as an alternate, determining that the threshold minimum is reached when the battery charge reaches: no remaining charge; a charge required to maintain predetermined vehicle services; a driver selected minimum charge; a minimum charge sufficient to return to a predetermined location.

[0031] In addition to one or more aspects disclosed herein or as an alternate, the method includes determining an end point on a road within the range-area RA where the battery charge reaches the threshold minimum. [0032] In addition to one or more aspects disclose herein or as an alternate, the method includes training an AI/ML model and its algorithms with one or more of: a previously generate range-area RA; or actual vehicle range data; and

[0033] In addition to one or more aspects disclosed herein or as an alternate, the method includes updating the AI/ML model and its algorithms based on updated ingested data.

[0034] In addition to one or more aspects disclosed herein or as an alternate, the method includes showing the range-area RA as a delineated area on a digital map surrounding and centered on or near a current location of the vehicle.

[0035] In addition to one or more aspects disclosed herein or as an alternate, the method includes displaying at least one end point on at least one road within the range-area RA where the battery charge reaches a threshold minimum.

[0036] In addition to one or more aspects disclose herein or as an alternate, the method includes varying an appearance of the delineated area such that a distance D between the vehicle illustrated in the delineated area and an outer edge of the delineated area depends on the category of information utilized to calculate the range-area RA.

[0037] In addition to one or more aspects disclose herein or as an alternate: one or more of: the outer edge of the range-area is noncircular; or the delineated area is shaded.

[0038] In addition to one or more aspects disclose herein or as an alternate, the method includes displaying the end points on each of the roads within the range-area RA where the battery charge reaches a threshold minimum; and connecting the end points with straight or arcuate line segments to define the outer edge of the range-area RA.

[0039] In addition to one or more aspects disclosed herein or as an alternate, the method includes moving the range-area RA on the display as the vehicle moves, while a center C of the range-area RA remains on or near the vehicle illustrated in the range-area RA.

[0040] In addition to one or more aspects disclosed herein or as an alternate, the method includes resizing the illustration of the range-area RA while driving, depending on a remining charge and drivable range.

[0041] In addition to one or more aspects disclosed herein or as an alternate, the method includes resizing the illustration of the range-area RA depending on a change to power consumption parameters while driving.

[0042] In addition to one or more aspects disclose herein or as an alternate, resizing the illustration of the range-area RA depending on a change to one or more of: aggressiveness of a driving style; changing speed due to unpredicted and predicted traffic congestion; or utilizing an HVAC system of the vehicle to compensate for changing ambient conditions.

[0043] In addition to one or more aspects disclosed herein or as an alternate, the method includes displaying, on the digital map, charging stations within the range-area RA surrounding the vehicle. [0044] In addition to one or more aspects disclosed herein or as an alternate, the method includes showing the charging stations only at the edge area of the range-area RA.

[0045] Further disclosed is a system including: a vehicle; and a smartphone, wherein the system is configured to perform the method disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements.

[0047] FIG. 1 shows aspects of a system for determining an available range of a vehicle running on battery in any direction (range-area) from its current location, according to an embodiment;

[0048] FIG. 2A is a flowchart that shows aspects of a method, executed by the system, for determining an available range of a vehicle running on battery in any direction (range-area) from its current location, according to an embodiment;

[0049] FIG. 2B shows additional aspects of the step of collecting data shown in FIG. 2A;

[0050] FIG. 2C shows additional aspects of the step of ingesting and processing the data shown in FIG. 2A; and

[0051] FIG. 2D shows additional aspects of the step of displaying the range-area

RA shown in FIG. 2A. DETAILED DESCRIPTION

[0052] Aspects of the disclosed embodiments will now be addressed with reference to the figures. Aspects in any one figure is equally applicable to any other figure unless otherwise indicated. Aspects illustrated in the figures are for purposes of supporting the disclosure and are not in any way intended on limiting the scope of the disclosed embodiments. Any sequence of numbering in the figures is for reference purposes only.

[0053] The disclosed embodiments are directed to a process of creating an algorithm or algorithms to accurately determine the available range-area RA, e g., the area in which a vehicle running on battery, either an all-electric vehicle or a plug-in hybrid, can travel in any direction before running low or out of charge. This process provides a driver with flexibility.

[0054] Turning to FIG. 1, the figure shows various aspects of the operational environment of the embodiments. A system and method (collectively referred to as the system) 5 provides a delineated area 10 on a digital map 20 identifying the available rangearea RA an electric or hybrid vehicle 30, with a battery 35, can travel before the charge dips to a predetermined threshold, which may be running low or out of charge. The delineated area 10 may be shaded to provide a greater distinguishing characteristic compared with the rest of the map 20. The map 20 is centered on the current location of the vehicle 30 and is continuously updated as battery level declines, the vehicle 30 moves, and conditions change. The delineated area 10 has an outer edge 15, the appearance of which is dependent on a distance D to the vehicle 30 illustrated in the delineated area 10. Within the range-area RA streets 22 may be illustrated and, on each street 22, an end point 24 may be identified at which the battery charge dips to the predetermined threshold. The end points 24 may be connected with straight- or arcuate-line segments 17 to define the outer edge 15 of the range-area RA.

[0055] The vehicle 30 may have at least one monitoring system 32, that may include monitors 32A, meters 32B and at least one sensor 33, that monitors the state of the battery 35 and other vehicle systems and components, such as air pressure of the tires 34. The vehicle 30 may also have an HVAC system 37.

[0056] The digital map 10 with the range-area RA would be shown in the display 40 of the vehicle 30 and/or a smartphone app 55 and shown in a display 50 of a smartphone 60. This would allow an operator or driver 70 of the vehicle 30 to have the intelligence and thus a more accurate understanding of how far the vehicle 30 can travel in any direction based on the current location. As travel plans change, new routes or destinations would not have to be entered, simply looking at the map 10 would inform the driver if there is enough charge to reach the destination.

[0057] The system 5 provides the driver 70 with improved situational awareness: being aware of what is happening around the drive 70 in terms of where they are, where they are supposed to be or might be, and whether there are factors such as changes in topography which may increase or decrease available charge that determines their ability to get to the end location. [0058] That is, the system 5 provide the driver 70 of the vehicle 70 running on a battery 35 to see on a digital map 10 the distance the vehicle 30 can drive in any direction within a range-area or RA. RA is shown as a delineated area 20 on the map 10 and is determined by a variety of factors (discussed below) that influence the available RA. The range-area RA would remain centered on the vehicle 30 in the map 10 as the vehicle 30 moves and changes in size as the battery 35 charge declines, and as RA is impacted by other factors affecting range.

[0059] The system 5 includes one or more of a smartphone app (e g., software) 55, cloud-based software 85 on a cloud service 80 and/or the in-vehicle software 45 with the output, the range-area RA, displayed on the display 50 of the smartphone 60 and/or in- vehicle di splay/ screen 40. Each of the vehicle 30, smartphone 60 and cloud service 80 has memory 36, 66, 86 for storing data 100 and the software 45, 55, 86.

[0060] According to a first aspect of the system 5, algorithm(s) 90 are generated in the cloud software 85, in smartphone app 50 and/or in-vehicle software 45 that determine the range-area RA. The system 5 may generate an algorithm (via single or multivariate regression) and/or a set of algorithms (via Artificial Intelligence (Al) for unstructured data and Al’s sub-component of Machine Learning (ML) for structured data). These two approaches to generate algorithms 90 - regression and AI/ML — would change the algorithms 90 as data 100 is ingested either in real-time and/or periodically during use of the vehicle 30, e.g., in one or more trips. [0061] According to a second aspect of the system 5, data 100 representing various type of conditions may be used by the algorithm. The categories of data 100 (vehicle speed, wind conditions, vehicle payload, tire traction, weather, HVAC utilization, battery degradation, air density, travel topography, traffic congestion) may be utilized to improve the accuracy and precision of the distance the vehicle 30 can travel in any direction. The factors (and resulting data) make the output of the algorithm 90 more accurate.

[0062] According to a third aspect of the system 5, a continuous updating of the range-area RA is based on the location of the vehicle 30 as it moves, available charge, changing current (observable) conditions, and/or predicted conditions, leading to updating of the algorithms 90.

[0063] According to a fourth aspect of the system 5, a display of RA on a smartphone 60 and/or in-vehicle screen/di splay 40 is based on the current or recent determination of RA by the algorithm(s) 90.

[0064] According to a fifth aspect of the system 5, locations of most or all charging stations 110 located at the inside edge of the RA. This provides the driver 70 the data needed to find a location to recharge

[0065] Turning to Fig. 2A, the disclosed system 5 provides a process for determining and displaying the available range of an all-electric vehicle 30 in any direction from its current location. [0066] As shown in block 110, the process includes measuring a current available charge of a vehicle. This is performed by ingesting data 100 from monitoring systems 32 of the vehicle 30. There are a few methods that can be used alone or in combination, for determining the remaining charge of a battery including estimation based on voltage, estimation based on current (Coulomb Counting), and estimation from internal impedance measurements. All these methods rely on measuring a parameter that changes as the battery is charged/discharged.

[0067] As shown in block 120, the process includes determining the current location of the vehicle. This is performed by using Google Maps, Apple Maps or third- party mapping software and location data from a smartphone 60, vehicle 30 or GPS receiver, e.g., on the vehicle 30.

[0068] As shown in block 130, the process includes collecting data 100 of one or more categories of information from external sources and/or sensors in the vehicle. Turning to FIG. 3B, as shown in block 130A, collecting the one or more categories of information includes collecting one or more of: vehicle speed; wind conditions; vehicle payload; tire traction, weather; HVAC utilization; battery degradation; air density; travel topography; or traffic congestion. As shown in block BOB collecting the data includes storing the data 100 in a non-transitory memory storage 36, 66, 86, located in the vehicle 30, smartphone 50 and/or cloud 80, so that the data 100 is available to the software used to generate the algorithms. [0069] A first category of information may include, for example, topography (actual) from USGS data and/or Google Maps, Apple Maps, or third-party digital maps to determine the gradient of roads in the area. A second category of information may include a type of roads, highway vs. suburban vs. city, in the area from USDOT, Google Maps, Apple Maps or third-party sources. A third category of information may include traffic conditions such as current vehicle speeds and congestion, e.g., current and/or predicted, and/or speed limits from Google Maps, Apple Maps, or third-party sources to estimate speeds and acceleration/deacceleration, e g., stop and go, over the range-area. A fourth category of information may include weather conditions including wind, e.g., direction and strength, air density, and temperature, e.g., current and/or predicted, from private AccuWeather, public NOAA or other similar sources. A fifth category of information may include vehicle power consumption such as lighting, climate control, digital processing based on sensors 33 such as electric meters 32B or monitors 32A located in the vehicle 30. A sixth category of information may include driving style, e.g., rate of acceleration of the vehicle 30 during the current driving period and/or on past data, based on sensors 33 located in the vehicle 30 such as accelerometers and/or images processed from vehicle cameras and/or battery discharge patterns. A seventh category of information may include remaining battery charge and battery performance/discharge rate of the battery 35, e.g., remaining battery capacity as it changes over time due to the normal degradation of battery, based on sensors 33 such as electric meters 32B or monitors 32A located in the vehicle 30. An eighth category of information may include a weight of the vehicle 30- Gross Vehicle

Weight - such as measured by onboard scales in trucks and/or derived based on vehicle height and/or deacceleration/acceleration and/or stopping distance. A ninth category of information may include tire pressure of the tires 34 based on vehicle sensors 33 located in the wheel 34. A tenth category of information may include current and/or past battery consumption of driver’s own vehicle 30 and/or other vehicles running on battery on the same or similar roads and conditions, or other comparable vehicle battery consumption data adjusted based on several factors such as conditions, road type, topography, vehicle type, and/or battery type and age. Though the categories of information are identified as first through tenth, this is not indictive of a minimum, maximum, or closed set of the categories of data that may be utilized, and it is also not indicative of a relative level of importance for the different categories of identified information.

[0070] Turning back to FIG. 2A, as shown in block 140, the process includes ingesting and processing the data 100 to generate the range-area RA for display, e g., on a smartphone 60 and/or in-vehicle display/screen 40. Turning to FIG. 2C, as shown in block 140A, the ingesting step includes a combination of periodic batch based data ingestion, real-time/streaming data ingestion and/or Lambda based data ingestion architecture. As indicated, the ingested data is placed into computer memory storage 86, 66, 36 located in the cloud system 80, smartphone 60 and/or the vehicle 30 to be accessed by the software creating the algorithm(s) 90. As shown in block MOB, the ingesting step includes processing the ingested data by algorithm(s) 90 produced via regression and/or AI/ML to generate the range-area RA shown on display 50 of the smartphone 60 and/or in-vehicle display/screen 40. As shown in block 140C the method includes updating the algorithm(s) 90 either periodically if the data 100 is ingested as batches or continuously if the data is real-time streaming.

[0071] As shown in block 140D, the processing step includes determining, by the algorithms (90) in the cloud 80, smartphone 60 and/or vehicle 30, the range-area RA (e.g., the distance) within which the vehicle 30 can travel, e.g., in any direction until the battery 35 reaches a threshold minimum. As shown in block 140E, the processing step includes determining that the threshold minimum is reached upon reaching one or more predetermined categories discharge limits. A first category of discharge limits may be when the battery runs out of charge completely. A second category of discharge limits may be when the battery reaches a pre-determined discharge threshold set by the manufacturer, e.g., 1 or 2% to maintain basic functions such as communications. A third category of discharge limits may be when the battery reaches a driver selected discharge threshold such as maintaining a minimum charge of 20%. A fourth category of discharge limits may be when the battery reaches a point where it has sufficient charge left to return to its starting point or some predetermined point entered by the driver or selected from a list of prior trips such as a home or work location. Though the categories of discharge limits are identified as first through fourth, this is not indictive of a minimum, maximum, or closed set of the categories of discharge limits that may be utilized, and it is also not indicative of a relative level of importance for the different categories of discharge limits. [0072] As shown in block 140F, the method includes determining an end point 24 on a road 22 within the range-area R where the battery charge reaches the threshold minimum.

[0073] As shown in block 140G, the method includes training an AI/ML model and its algorithms 90 with one or more of a previously generated range-area RA, or actual vehicle range data. As shown in block 140H, the method includes updating the AI/ML model and its algorithms based on ingested data.

[0074] Turning back to FIG. 2A, as shown in block 150, the method includes displaying, on an in-vehicle digital monitor/display 40 and/or an app 55 on a smartphone 60, the RA. Turning to FIG. 2D, as shown in block 150A, the displaying step includes showing the RA as a delineated area 20 on a digital map 10 surrounding, e.g., encircling, and centered on a current location of the vehicle 30.

[0075] As shown in block 150B, the display step includes illustrating at least one end point 23 on at least one road 22 where the battery charge reaches the threshold minimum.

[0076] As shown in block 150C, the method further includes varying an appearance of the delineated area 20 such that a distance D between the vehicle 30 illustrated in the delineated area 20 and an outer edge 15 of the delineated area 20 depends on the category of information utilized to calculate the range-area RA. For example, a vehicle running on batteries can go further downhill than uphill. Thus, in this example, the shaded edge for a vehicle 15 moving up a hill may be closer in the uphill direction compared to the downhill direction. It is to be appreciated that in this situation, the delineated area 10 would define a non-circular shape.

[0077] As shown in block 150D, the method includes displaying the end points 24 on each of the roads 22 within the range-area RA where the battery charge reaches a threshold minimum. As shown in block 150E, the method further includes connecting the end points 24 with straight- or arcuate-line segments 17 to define the outer edge 15 of the range-area RA.

[0078] As shown in block 150F, the display step further includes moving the rangearea RA on the display as the vehicle 30 moves, while a center C of the range-area RA remaining centered on or near the vehicle 30 illustrated in the range-area RA.

[0079] As shown in block 150G the display step further includes resizing the illustration of the range-area RA while driving depending on a remaining charge and drivable range. For example, the shaded edge of the area shrinks around, e.g., moves closer to, the current location of the vehicle 30. The range-area RA may increase if the vehicle 30 is driven on a route which is more energy efficient, such as driving the vehicle 30 along a downhill road that comes into range if the vehicle 30 travels.

[0080] As shown in block 150H, the method includes resizing the illustration of the range-area RA to reflect a change to power consumption factors of the vehicle while driving that impact an available range. A first power consumption factor includes aggressiveness of a driving style. A second power consumption factor includes changing speed due to unpredicted and predicted traffic congestion. Unpredicted conditions include increased congestion due to an accident. Predicted changes include, for example, increased traffic due to a drive time. As an example, there may be low congestion when leaving a home to drive to work, and congestion may predictably (e.g., statistically) increase during the drive, affecting vehicle range. A third power consumption factor includes utilizing the HVAC system 37 of the vehicle 30 to compensate for changing ambient conditions, such as external temperature. For example, a drop in temperature may require an increase in cabin climate control power consumption. Thus, as shown in block 150i, the method includes resizing the illustration of the range-area RA depending on a change to one or more of: aggressiveness of a driving style; changing speed due to unpredicted and predicted traffic congestion; or utilizing an HVAC system of the vehicle to compensate for changing ambient conditions.

[0081] As shown in block 150j, the method includes displaying, on the digital map 10, charging stations 110 within the range-area RA surrounding the vehicle 30. In one embodiment, as shown in 150k, the method includes showing the charging stations 110 only at the edge area 15 of the range-area RA. The purpose of this is to assist the driver in finding a location to recharge when they run low on charge. Then as the driver 70 drives in the vehicle within and approaches the edge of the RA, the driver 70 can select one to recharge the battery 35. [0082] Thus, the disclosed embodiments provide a system 5 that accounts for dynamic variables established by and during a drive and then translates output, e.g., the displayed map 10, of the system 5 into a range-area RA, showing, effectively, square miles, not just linear miles, and the range-area RA changes as conditions change.

[0083] In other words, according to the disclosed embodiments, the system 5 would first either calculate or ingest from a sensor 33, smartphone app 55 and/or vehicle 30, the estimated range in miles remaining for the vehicle battery 35. The system 5 would then use one of various disclosed approaches to determine the range-area RA.

[0084] A first approach is to create a shape such as an arcuate closed loop that may be circular or non-circular, e.g., an irregular circle, on the digital map 10 centered on the current location of the vehicle 30. The distance D from the illustrated vehicle 30 to the outer edge 15 represents the estimated available range-area RA in miles. The range-area 20 is shaded to make the range-area RA more visually apparent.

[0085] A second approach has a first step of identifying, along multiple roads 22 emanating in all directions from the current location of the vehicle 30, a specific point 24 on each road which represents the end point for the estimated available range in miles. A second step is drawing with line segments 17 a polygon, e.g., straight lines connecting the specific points, and/or an irregular circle, e.g., smoothed lines connecting the specific points, on the digital map, between the end points 24 centered on the vehicle 30 and shade the area to make the range-area RA more visually apparent. [0086] A third approach has a first step that is the same as the first step of the second approach. A second step of the third approach includes updating the range-area RA to make it more accurate based on other factors such as weather, topography, type of road and/or battery consumption of driver’s own and/or other vehicles. The third approach has a third step that is the same as the second step in the second approach.

[0087] A fourth approach has first and second steps that is the same as the first and second steps of the third A third step of the fourth approach includes updating the rangearea RA estimate to the points 24 using factors such as weather, topography, and type of road. The fourth approach has a fourth step that is the same as the third step of the third approach.

[0088] A fifth approach has a first step of utilizing previously generated RAs and/or actual vehicle range data to train an AI/ML model consisting of the algorithm(s) 90, which in turn determines the range-area RA adjusted based on relevant factors such as available charge, weather, topography, type of road and/or battery consumption of driver’s own or other vehicles. The fifth approach includes a second step of drawing an irregular circle on the digital map centered on the vehicle and potentially shade that area to make the RA more visually apparent. The fifth approach has a third step of updating the AI/ML model and its algorithms based on data being dynamically ingested into it either continuously via streaming and/or periodically via batch.

15 [0089] In all approaches, the range-area RA remains centered on the vehicle as the vehicle moves and the range-area RA changes in size, e.g., to reduce in illustrated area, as the remaining battery charge is reduced, and/or based on the factors being measured that impact battery discharge rate. Accordingly, disclosed herein is a process of determining the available range-area (the area in which a vehicle running on battery, either an allelectric vehicle or a plug-in hybrid, can travel in any direction before running low or out of charge) by creating an algorithm (via single or multivariate regression) and/or a set of algorithms (via Artificial Intelligence (Al) for unstructured data and Al’s sub-component of Machine Learning (ML) for structured data) which is used in a smartphone software app, cloud-based software, and/or the in-vehicle software to show the driver as a delineated area on a digital map centered on the current location of vehicle and continuously updated as battery level declines, the vehicle moves, and conditions change. The disclosed approaches to generate algorithms - regression and AI/ML — would change as data is ingested either real-time and/or periodically and stored in the cloud, smartphone and/or vehicle. The data is impacted by factors that are specific to the potential paths of the car, the weather, topography, and other variables. This would allow the driver of the vehicle to have a more accurate understanding of how far they can travel in any direction based their current location based on their current charge and other variables such as weather and topography.

[0090] "The cloud", as utilized herein, refers to servers that are accessed over the Internet, and the software and databases that run on those servers. Cloud servers are in data centers all over the world. [0091] Sensor data identified herein may be obtained and processed separately, or simultaneously and stitched together, or a combination thereof, and may be processed in a raw or complied form. The sensor data may be processed on the sensor (e.g., via edge computing), by controllers identified or implicated herein, on a cloud service, or by a combination of one or more of these computing systems. The sensor may communicate the data via wired or wireless transmission lines, applying one or more protocols as indicated below.

[0092] Wireless connections may apply protocols that include local area network (LAN, or WLAN for wireless LAN) protocols. LAN protocols include WiFi technology, based on the Section 802.11 standards from the Institute of Electrical and Electronics Engineers (IEEE). Other applicable protocols include Low Power WAN (LPWAN), which is a wireless wide area network (WAN) designed to allow long-range communications at a low bit rate, to enable end devices to operate for extended periods of time (years) using battery power. Long Range WAN (LoRaWAN) is one type of LPWAN maintained by the LoRa Alliance and is a media access control (MAC) layer protocol for transferring management and application messages between a network server and application server, respectively. LAN and WAN protocols may be generally considered TCP/IP protocols (transmission control protocol/Internet protocol), used to govern the connection of computer systems to the Internet. Wireless connections may also apply protocols that include private area network (PAN) protocols. PAN protocols include, for example, Bluetooth Low Energy (BTLE), which is a wireless technology standard designed and marketed by the Bluetooth Special Interest Group (SIG) for exchanging data over short distances using short- wavelength radio waves. PAN protocols also include Zigbee, a technology based on Section 802.15.4 protocols from the IEEE, representing a suite of high-level communication protocols used to create personal area networks with small, low- power digital radios for low-power low-bandwidth needs. Such protocols also include Z- Wave, which is a wireless communications protocol supported by the Z-Wave Alliance that uses a mesh network, applying low-energy radio waves to communicate between devices such as appliances, allowing for wireless control of the same.

[0093] Wireless connections may also include radio-frequency identification (RFID) technology, used for communicating with an integrated chip (IC), e.g., on an RFID smartcard. In addition, Sub-lGhz RF equipment operates in the ISM (industrial, scientific, and medical) spectrum bands below Sub IGhz - typically in the 769 - 935 MHz, 315 Mhz and the 468 Mhz frequency range. This spectrum band below IGhz is particularly useful for RF IOT (internet of things) applications. The Internet of things (loT) describes the network of physical objects — “things” — that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet. Other LPWAN-IOT technologies include narrowband internet of things (NB-IOT) and Category Ml internet of things (Cat M1-I0T). Wireless communications for the disclosed systems may include cellular, e.g., 2G/3G/4G (etc.). Other wireless platforms based on RFID technologies include Near-Field-Communication

(NFC), which is a set of communication protocols for low-speed communications, e.g., to exchange data between electronic devices over a short distance. NFC standards are defined by the ISO/IEC (defined below), the NFC Forum and the GSMA (Global System for Mobile Communications) group. The above is not intended to limit the scope of applicable wireless technologies.

[0094] Wired connections may include connections (cables/interfaces) under RS (recommended standard)-422, also known as the TIA/EIA-422, which is a technical standard supported by the Telecommunications Industry Association (TIA) and which originated by the Electronic Industries Alliance (EIA) that specifies electrical characteristics of a digital signaling circuit. Wired connections may also include (cables/interfaces) under the RS-232 standard for serial communication transmission of data, which formally defines signals connecting between a DTE (data terminal equipment) such as a computer terminal, and a DCE (data circuit-terminating equipment or data communication equipment), such as a modem. Wired connections may also include connections (cables/interfaces) under the Modbus serial communications protocol, managed by the Modbus Organization. Modbus is a client-server protocol designed for use with its programmable logic controllers (PLCs) and which is a commonly available means of connecting industrial electronic devices. Wireless connections may also include connectors (cables/interfaces) under the PROFibus (Process Field Bus) standard managed by PROFIBUS & PROFINET International (PI). PROFibus which is a standard for fieldbus communication in automation technology, openly published as part of IEC (International Electrotechnical Commission) 61158. Wired communications may also be over a Controller Area Network (CAN) bus. A CAN is a vehicle bus standard that allow microcontrollers and devices to communicate with each other in applications without a host computer. CAN is a message-based protocol released by the International Organization for Standards (ISO). The above is not intended on limiting the scope of applicable wired technologies.

[0095] When data is transmitted over a network between end processors as identified herein, the data may be transmitted in raw form or may be processed in whole or part at any one of the end processors or an intermediate processor, e.g., at a cloud service (e g., where at least a portion of the transmission path is wireless) or other processor. The data may be parsed at any one of the processors, partially or completely processed or complied with, and may then be stitched together or maintained as separate packets of information. Each processor or controller identified herein may be, but is not limited to, a single-processor or multi-processor system of any of a wide array of possible architectures, including field programmable gate array (FPGA), central processing unit (CPU), application specific integrated circuits (ASIC), digital signal processor (DSP) or graphics processing unit (GPU) hardware arranged homogenously or heterogeneously. The memory identified herein may be but is not limited to a random access memory (RAM), read only memory (ROM), or other electronic, optical, magnetic or any other computer readable medium.

[0096] The controller may further include, in addition to a processor and nonvolatile memory, one or more input and/or output (I/O) device interface(s) that are communicatively coupled via an onboard (local) interface to communicate among other devices. The onboard interface may include, for example but not limited to, an onboard system bus, including a control bus (for inter-device communications), an address bus (for physical addressing) and a data bus (for transferring data). That is, the system bus may enable electronic communications between the processor, memory, and I/O connections. The I/O connections may also include wired connections and/or wireless connections identified herein The onboard interface may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers to enable electronic communications. The memory may execute programs, access data, or lookup charts, or a combination of each, in furtherance of its processing, all of which may be stored in advance or received during execution of its processes by other computing devices, e.g., via a cloud service or other network connection identified herein with other processors.

[0097] Embodiments can be in the form of processor-implemented processes and devices for practicing those processes, such as processor. Embodiments can also be in the form of computer code based modules, e.g., computer program code (e.g., computer program product) containing instructions embodied in tangible media (e.g., non -transitory computer readable medium), such as floppy diskettes, CD ROMs, hard drives, on processor registers as firmware, or any other non-transitory computer readable medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes a device for practicing the embodiments. Embodiments can also be in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes a device for practicing the exemplary embodiments. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.

[0098] The terminology used herein is for the purpose of describing embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof.

[0099] Those of skill in the art will appreciate that various example embodiments are shown and described herein, each having certain features in the embodiments, but the present disclosure is not thus limited. Rather, the present disclosure can be modified to incorporate any number of variations, alterations, substitutions, combinations, subcombinations, or equivalent arrangements not heretofore described, but which are commensurate with the scope of the present disclosure. Additionally, while various embodiments of the present disclosure have been described, it is to be understood that aspects of the present disclosure may include only some of the described embodiments. Accordingly, the present disclosure is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.