IMPROVED VEHICLE FUELING

Abstract

A system comprising a computer (105) in a vehicle (101) configured to: collecting data (115) over a network (125) indicating the last time an underground fuel tank was refilled at each of a plurality of gas stations (140); calculate a turbidity factor inversely proportional to an elapsed time since the last time each underground fuel tank for the plurality of fueling stations (140) was refilled; selecting a gas station from the plurality of gas stations (140) based on the calculated turbidity factor; and to operate a steering, a drive and a brake to move the vehicle (101) to the selected gas station.

Description

GENERAL STATE OF THE ART

[0001] Vehicles require fuel to operate. A vehicle can be determined to refuel when a fuel gauge is below a certain level. The vehicle can then be navigated to a gas station to refuel the vehicle. However, the ability to refuel a vehicle may be limited to certain times of day and / or to specific gas stations, for example, in certain locations. Various environmental conditions may affect which times and / or locations are possible and / or better than other times and / or locations for refueling.

[0002] US 2015 / 0 316 406 A1 describes a system that uses collected vehicle data to determine the best possible local gas stations for refueling the vehicle. Various data is collected from the vehicle to determine and predict fuel consumption per distance traveled, and then selects the most conveniently located gas station from a multitude of options that is reliably accessible and utilizes the remaining fuel.

[0003] Unfortunately, current vehicles lack the ability to receive and process data to determine a time and place to refuel the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a block diagram of an exemplary system for refueling a vehicle. Fig. 2 shows an example process for determining a refueling time for the vehicle. Fig. 3 illustrates an example process for determining a gas station for the vehicle. DETAILED DESCRIPTION

[0004] A system includes a computer programmed to collect data on turbidity of a fuel tank at each of a plurality of gas stations, select a gas station based on the collected data, and move a vehicle to the selected gas station.

[0005] The computer may be further programmed to select the gas station based on a sum of a plurality of weighted factors, including at least one weighted factor based on the collected turbidity data.

[0006] The computer may be further programmed to weight the factors based on user input. The computer may be further programmed to weight the factors based on user operation of the vehicle. The plurality of factors may include factors based on collected data regarding at least one of the occupancy level of the gas stations and the amenities available at the gas stations.

[0007] The computer may be further programmed to determine a time elapsed since a fuel filter of the vehicle was replaced and to select the gas station based on the elapsed time.

[0008] The computer may be further programmed to select the gas station based on a distance between a predetermined route and each of the plurality of gas stations.

[0009] The computer may be further programmed to select the gas station based on an estimated fuel level of the fuel tank at each of the plurality of gas stations.

[0010] The computer may be further programmed to determine a time to refuel each fuel tank at each of the fueling stations and to select the fueling station at which the time elapsed since the time the fuel tank was refueled exceeds a time threshold.

[0011] The computer may be further programmed to determine the refueling time based on collected data from at least one of a vehicle coolant temperature, an atmospheric ozone level, and air quality.

[0012] A method includes collecting data on turbidity of a fuel tank at each of a plurality of gas stations, selecting a gas station based on the collected data, and moving a vehicle to the selected gas station.

[0013] The method may include selecting the gas station based on a sum of a plurality of weighted factors, including at least one weighted factor based on the collected turbidity data.

[0014] The method may further include weighting the factors based on user input. The method may further include weighting the factors based on user operation of the vehicle. In the method, the plurality of factors may include factors based on collected data regarding at least one of the occupancy level of the gas stations and the amenities available at the gas stations.

[0015] The method may further include determining an elapsed time since replacing a fuel filter of the vehicle and selecting the gas station based on the elapsed time.

[0016] The method may further include selecting the gas station based on a distance between a predetermined route and each of the plurality of gas stations.

[0017] The method may further include selecting the gas station based on an estimated fuel level of the fuel tank at each of the plurality of gas stations.

[0018] The method may further include determining a time to refuel each fuel tank at each of the refueling stations and selecting the refueling station at which the time elapsed since the time the fuel tank was refueled exceeds a time threshold.

[0019] The method may further include determining the refueling time based on collected data from at least one of a vehicle coolant temperature, an atmospheric ozone level, and air quality.

[0020] Also disclosed is a computing device programmed to perform any of the foregoing method steps. Furthermore, a vehicle including the computing device is disclosed. Furthermore, a computer program product is disclosed, comprising a computer-readable medium storing instructions executable by a computer processor to perform any of the foregoing method steps.

[0021] A vehicle computer can determine a time and place to refuel a vehicle based on data collected from sensors and a server. The computer can collect data such as environmental conditions, vehicle parameters, vehicle location, and so on, and determine a time and place to refuel the vehicle. The computer can determine a refueling score based on environmental and vehicle data to provide more predictable refueling opportunities and more consistently maintain fuel in the vehicle. The computer can determine a location rating based on environmental and vehicle data to provide more convenient locations to refuel the vehicle. By processing a variety of factors based on different environmental and vehicle data, the computer can reduce the restrictions that can limit refueling opportunities.

[0022] Fig. 1 illustrates an example system 100 for refueling a vehicle 101. A computer 105 in the vehicle 101 is programmed to receive sensed data 115 from one or more sensors 110. For example, data 115 of the vehicle 101 may include a location of the vehicle 101, data about an environment surrounding a vehicle, data about an object external to the vehicle, such as another vehicle, etc. A location of the vehicle 101 is typically provided in a conventional form, e.g., as geographic coordinates, such as longitude and latitude, obtained via a navigation system using the Global Positioning System (GPS). Other examples of data 115 may include measurements from systems and components of the vehicle 101, e.g., a speed of the vehicle 101, a trajectory of the vehicle 101, etc.

[0023] Computer 105 is generally programmed for communications on a network of vehicle 101, including, for example, a communications bus, as known. Through the network, bus, and / or other wired or wireless mechanisms (e.g., a wired or wireless local area network within vehicle 101), computer 105 may transmit messages to and / or receive messages from various devices within vehicle 101, such as controllers, actuators, sensors, etc., including sensors 110. Alternatively or additionally, in cases where computer 105 actually includes multiple devices, the vehicle network may be used for communication between devices, which are represented in this disclosure as computer 105.Furthermore, the computer 105 may be programmed to communicate with the network 125, which, as described below, may include various wired and / or wireless network technologies, e.g., cellular, Bluetooth. ® , Bluetooth ® Low Energy (BLE), wired and / or wireless packet networks, etc.

[0024] Data storage 106 may be of any known type, e.g., hard disk drives, solid-state drives, servers, or any volatile or non-volatile media. Data storage 106 may store the data 115 collected by sensors 110.

[0025] Sensors 110 may include a variety of devices. For example, numerous controllers in a vehicle 101 may operate as sensors 110 to provide data 115 via the network or bus of the vehicle 101, e.g., data 115 regarding vehicle speed, acceleration, position, subsystem and / or component status, etc. Further, other sensors 110 may include cameras, motion detectors, etc., i.e., sensors 110 to provide data 115 for assessing a location of a target, projecting a trajectory of a target, assessing a location of a roadway lane, etc. The sensors 110 may also include short-range radar, long-range radar, LIDAR, and / or ultrasonic transducers.

[0026] Collected data 115 may include a variety of data collected in a vehicle 101. Examples of collected data 115 are provided above, and furthermore, data 115 is generally collected using one or more sensors 110 and may additionally include data calculated therefrom in computer 105 and / or on server 130. In general, collected data 115 may include any data that may be collected by sensors 110 and / or calculated from such data.

[0027] The vehicle 101 may include a plurality of vehicle components 120. As used herein, each vehicle component 120 includes one or more hardware components configured to perform a mechanical function or operation—such as moving the vehicle, decelerating or stopping the vehicle, steering the vehicle, etc. Non-limiting examples of components 120 include a drivetrain component (e.g., including an internal combustion engine and / or an electric motor, etc.), a transmission component, a steering component (e.g., including one or more of a steering wheel, a steering rack, etc.), a braking component, a parking assist component, an adaptive cruise control component, a movable seat, and the like.

[0028] When the computer 105 is operating the vehicle 101, the vehicle 101 is an "autonomous" vehicle 101. For the purposes of this disclosure, the term "autonomous vehicle" is used to refer to a vehicle 101 operating in a fully autonomous mode. A fully autonomous mode is defined as a mode in which each of the propulsion (typically via a powertrain including an electric motor and / or internal combustion engine), braking, and steering of the vehicle 101 is controlled by the computer 105. A semi-autonomous mode is a mode in which at least one of the propulsion (typically via a powertrain including an electric motor and / or internal combustion engine), braking, and steering of the vehicle 101 is controlled at least partially by the computer 105 rather than by a human operator.

[0029] The system 100 may also include a network 125 connected to a server 130 and a data store 135. The computer 105 may be further programmed to communicate via the network 125 with one or more remote locations, such as the server 130, where such a remote location may include a data store 135. The network 125 represents one or more mechanisms by which a vehicle computer 105 can communicate with a remote server 130. Accordingly, the network 125 may be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber optic) and / or wireless (e.g.,Communication mechanisms (cellular, wireless, satellite, microwave, and radio frequency) and any desired network topology (or topologies if multiple communication mechanisms are used). Example communication networks include wireless communication networks (e.g., using Bluetooth). ® , Bluetooth ® Low Energy (BLE), IEEE 802.11, Vehicle-to-Vehicle (V2V), such as Dedicated Short Range Communications (DRSC), etc.), Local Area Networks (LAN) and / or Wide Area Networks (WAN), including the Internet, providing data communication services.

[0030] The system may include a gas station 140. The gas station 140 stores fuel that can be provided to the vehicles 101. The fuel may be a known energy source for the vehicle 101, e.g., gasoline, diesel, compressed natural gas, ethanol, butanol, biodiesel, jet fuel, electricity at an electric charging station, etc. The gas station 140 may include a station computer 145. The station computer 145 may communicate with the network 125. The station computer 145 may collect data 115 about the gas station 140 and send the data 115 over the network 125 to the server 130 and / or the computer 105. The station computer 145 may, for example, be a dedicated station console, a personal computer, a laptop, a tablet, a smartphone, etc.

[0031] The computer 105 may determine a variety of factors based on data 115 collected from the sensors 110 and / or the server 130. The factors may represent one or more elements that may affect when the vehicle 101 may be refueled and where the vehicle 101 may be refueled. The computer 105 may determine a refueling score and a location score based on the factors. Each factor may be a value between 0 and 1 based on the collected data 115, as shown in Equations 1-15 below.

[0032] The refueling score may be a value used by computer 105 to determine when vehicle 101 needs to be refueled. For example, computer 105 may determine the refueling score as a function of time and determine a time at which the refueling score will fall below a refueling score threshold. Upon reaching a time within a refueling time threshold, computer 105 may determine to refuel vehicle 101. Alternatively or additionally, computer 105 may determine to refuel vehicle 101 if computer 105 determines that the current refueling value is below the threshold. The refueling score may be a combination of the factors, e.g., a weighted sum of a plurality of the factors.For example, the computer 105 may collect data 115 on at least one of a vehicle's engine coolant temperature, an atmospheric ozone level, and air quality, and determine a refueling time based on the collected data 115. The refueling score may be a weighted sum of the following factors: . Srefuel=∑i=1nkifi where S refuel is the refueling score, i is an index indicating one of the plurality of factors, n is the total number of factors used to determine the refueling score, f i is the value of one of the factors and k i a given weighting for the factor f i One or more of the factors f imay change over time, e.g., the vehicle fuel tank factor may change as fuel is consumed by the vehicle 101. The computer 105 may, using known regression techniques, predict one or more of the time-dependent factors for a future period, e.g., 24 hours. The computer 105 may thus calculate the refueling score S refuel predict for the period based on the time-dependent factors.

[0033] The location score may be a value used by the computer 105 to determine when the vehicle 101 needs to be refueled. After determining to refuel the vehicle 101, the computer 105 may determine the location score for a plurality of refueling stations 140. The refueling stations 140 may be determined from a list stored in the data store 106 and / or on the server 130. The computer 105 may select the refueling station 140 with the highest location score and may then move the vehicle 101 to the selected refueling station 140 to refuel the vehicle 101. The refueling score may be a combination of the factors, e.g., a weighted sum of a plurality of the factors, as described below.For example, the computer 105 may collect data 115 regarding fuel turbidity in an underground fuel storage tank at each of a plurality of gas stations 140, select a gas station 140 based on the collected data 115, and move the vehicle 101 to the selected gas station 140. The location score may be a weighted sum of the following factors: Slocation=∑i=1nkifi where S location is the location score, i is an index indicating one of the plurality of factors, n is the total number of factors used to determine the location score, f i is the value of one of the factors and k i a given weighting for the factor f i is.

[0034] The computer 105 may determine a vehicle fuel tank factor based on data 115 collected from a fuel tank of the vehicle 101. The computer 105 may collect data 115 about a fuel level in the vehicle fuel tank from a vehicle fuel tank sensor 110. The vehicle fuel tank factor may be inversely related to the fuel level data 115, i.e., as the vehicle fuel level decreases, the vehicle fuel tank factor increases. The vehicle fuel tank factor may be inversely proportional to the vehicle fuel level data 115 in a linear manner, or the vehicle fuel tank factor may be related to the vehicle fuel level data 115 in a nonlinear manner (e.g., polynomial, exponential, factorial, etc.). The following equation 3 describes an example sigmoid function that determines the vehicle fuel tank factor f vft describes. fνft=11+exp(CV−D) where C and D are predetermined constants to adjust the factor between 0 and 1, V is a vehicle fuel level (i.e., a number between 0 and 1 indicating a fraction of the fuel tank volume filled with fuel), and exp is the well-known exponential function. As the vehicle fuel level V decreases towards 0 (i.e., an empty fuel tank), the refueling score S refuel if the vehicle fuel tank factor f vft exponentially approaches 1.

[0035] The vehicle fuel tank factor may be weighted such that the weighted sum exceeds the refueling score threshold when the vehicle fuel level data 115 falls below a vehicle fuel level threshold. That is, the computer 105 may determine that the refueling time is a time no later than a predicted time at which the vehicle fuel level will fall below the vehicle fuel level threshold. For example, as shown in Example Equation 4, kνft⋅fνft(Vthreshold)≥Srefuel,threshold where k vft is the weighting value for the vehicle fuel tank factor, V threshotld is the vehicle fuel level threshold and S refuel,threshold is the refueling point threshold.

[0036] The computer 105 may determine a vehicle-out-of-use factor. The computer 105 may, using known techniques, determine confidence values ​​for a variety of times of day that indicate a usage scenario of the vehicle 101. The confidence value may be a probability that the vehicle 101 will be in use at a particular time of day. The computer 105 may determine the confidence values ​​based, for example, on historical data 115 of the usage of the vehicle 101. The computer 105 may determine the vehicle-out-of-use factor based on confidence values ​​that indicate the vehicle 101 will not be in use at a particular time of day. The vehicle-out-of-use factor may increase for times of day when the vehicle 101 is predicted to be out of use, i.e., the computer 105 may advance a refueling time when the vehicle 101 is not in use.Further, the computer 105 may determine a time of a previous use case of the vehicle 101, and the vehicle out-of-use factor may be based on the time of the previous use case of the vehicle 101. For example, the vehicle out-of-use factor may be f. niu with a normal distribution function, the example equation 5, can be determined: fniu=exp(−(t−tniu)2C) where t is a current time in hours between 0 and 24, t niu is a predicted time at which the vehicle 101 is predicted to be not in use (e.g., outside of a work shift for a service vehicle 101, outside of a user's commuting schedule, etc.), and C is a constant value that can be determined based on how close the vehicle-not-in-use factor is to t niu should increase.

[0037] The computer 105 may determine a non-peak usage factor. The computer 105 may determine times of day when roads may be congested. The computer 105 may request traffic data 115 from the server 130 and, using probabilistic calculations such as a hidden Markov model, determine times of day that indicate peak road usage. The computer 105 may collect data 115 about a roadway congestion level and determine the non-peak usage factor based on the traffic congestion level. The non-peak usage factor may increase for times of day that do not represent peak road usage and decrease for times of day that indicate peak road usage. For example, the non-peak usage factor may be 0 if the time of day is a peak time of day (as determined from the probabilistic calculations) and 1 if the time of day is a non-peak time of day.For example, the off-peak factor f. npu can be determined using a sigmoid function, such as the exemplary equation 6: fnpu=C arccot(a⋅p(t)−b) where a, b, C are predetermined constants intended to bind the off-peak factor between 0 and 1, and p(t) is the probability of traffic congestion for a certain time t.

[0038] The computer 105 can determine a fuel price factor. The computer 105 can collect data 115 indicating fuel prices at multiple gas stations 140 from the server 130. The computer 105 can use known techniques, e.g., machine learning, historical trends, etc., to predict a fuel price. The fuel price factor can increase as data 115 indicating fuel prices decreases. For example, the computer 105 can determine a difference between a current fuel price p1 at one of the gas stations 140 and a predicted fuel price p pred and determine the fuel price factor as follows: fprice=C exp(ppred−p1p1) where C is a predetermined constant.

[0039] The computer 105 may determine a weather factor. The computer may receive data 115 from, for example, the server 130, a precipitation sensor 110, a wind speed sensor 110, a temperature sensor 110, etc. The weather factor may increase if the precipitation data 115 increases, the wind speed data 115 increases, and the temperature data 115 is either above a warm threshold or below a cold threshold. For example, the weather factor f weather a sum of the data 115: fweather=precip0precipaνg+ν0νaνg+TH1+TLO where precip0 is a current precipitation percentage, precip avg is an average precipitation percentage for a current day, v0 is a current wind speed, v avg is an average wind speed for a current day, T H1is a Boolean value that is 0 if a current ambient temperature is below the thermal threshold and 1 if the current ambient temperature is above the thermal threshold, and T LO a Boolean value that is 0 if the current ambient temperature is above the cold threshold and 1 if the current ambient temperature is below the cold threshold.

[0040] The computer 105 may determine a fueling station preference factor. The computer 105 may determine a plurality of preferred fueling stations 140, e.g., a fleet operator-approved corporate fueling station 140 specified by a user of the vehicle 101, fueling stations 140 near a route traveled by the vehicle 101, etc. The fueling station preference factor may increase as a distance between the vehicle 101 and one of the preferred stations 140 decreases. For example, the fueling station preference factor fsp based on the exemplary equation 9: fsp=C exp(−ax) where a, C are predetermined constants and x is a current distance between the location of the vehicle 101 and a nearest preferred gas station 140.

[0041] The computer 105 may determine a warm-up factor. The computer 105 may determine an engine coolant temperature from a sensor 110. As the engine coolant warms, a powertrain consumes less fuel, so the vehicle 101 may consume less fuel when traveling to the refueling station 140 when the engine coolant temperature is above a coolant temperature threshold. The warm-up factor may increase as the engine coolant temperature increases. For example, the warm-up factor f warm based on the exemplary equation 10: fwarm=C arcsinh(a⋅Tcollant) where a, C are given constants, arcsinh is the inverse hyperbolic sine function, as known, and T coolant a current coolant temperature.

[0042] Computer 105 may determine an ozone alert factor. Computer 105 may receive data 115 from server 130 indicating whether a current day is an ozone alert day, i.e., a day on which local authorities require users to refuel vehicles 101 after dark to reduce ozone and smog generation. Computer 105 may determine the refueling time after a predicted sunset time on the ozone alert day. The ozone alert factor may decrease during the time between a predicted sunrise time and the predicted sunset time if data 115 from server 130 indicates that the current day is an ozone alert day.For example, the ozone alert factor can be a Boolean value that is 0 if the current day is an ozone alert day and the current time is between the predicted sunrise time and the predicted sunset time on the ozone alert day, and 1 if the current time is between the predicted sunset time of the current day and the predicted sunrise time of the following day.

[0043] The computer 105 can determine an air quality factor. The air quality factor can be based on data 115 indicating a dust and dirt level that may interfere with refueling. The computer 105 can collect data 115 on a wind speed and humidity to determine the air quality factor. The air quality factor can decrease with increasing wind speed and decrease with decreasing humidity. For example, the air quality factor f airbased on the exemplary equation 11: fair=C exp(a⋅ϕv) where a, C are given constants, ϕ is the relative humidity and v is a wind speed.

[0044] The computer 105 may determine a distance factor. The distance factor may be based on data 115 indicating a distance between a planned predetermined route of the vehicle 101 and each gas station 140. The distance factor may decrease with increasing distance. For example, the distance factor f distance inversely proportional to a distance x station between the geolocation coordinates of the vehicle 101 and the geolocation coordinates of the gas station 140, as shown in the exemplary equation 12: fdistance=Cxstation where C is a predetermined constant.

[0045] The computer 105 can determine a facility factor. The facility factor can be based on data 115 indicating facilities, i.e., points of interest and / or amenities available at the gas station 140. Example facilities include, for example, restaurants, cafes, scenic spots, hiking trails, pet areas, Wi-Fi access, etc. The facility factor can increase for facilities that are predetermined as preferred by the user based on, for example, historical data, user input, etc. The computer 105 can determine a number of facilities n f at a petrol station 140 and the facility factor f facilities according to the exemplary equation 13: ffacilities=11+exp(−nf)

[0046] The computer 105 can determine an ease of access factor. The ease of access factor can be based on data 115 indicating features that increase access to the gas station 140 based on the route of the vehicle 101. The features can include, for example, whether the gas station 140 is located along the route of the vehicle 101, or a number of pumps available at the gas station 140, etc. The ease of access factor can increase as the number of features increases. The computer 105 can determine the number of features N features identify and the easy access factor f eac according to the exemplary equation 14: feac11+exp(−Nfeatures)

[0047] The computer 105 may determine a turbidity factor of fuel at the gas station 140. The gas station 140 may have a plurality of underground fuel storage tanks that store the liquid fuel. The fuel tanks may be refilled with fuel by trucks. As the trucks refill the fuel tanks, the turbidity of the fuel in the fuel tanks may increase as deposits in the fuel tanks are stirred. The deposits may be collected by a fuel filter in the vehicle 101, reducing the life of the fuel filter. The computer 105 may collect data 115 from the server 130 indicating a refill time at which the underground fuel tanks were last refilled. As the refill time increases, the deposits in the underground fuel tanks may settle. The turbidity factor may decrease as the refill time increases.Further, the computer 105 may collect data 115 regarding an estimated fuel level of the fuel storage tank at each of the plurality of gas stations 140. A lower estimated fuel level may increase the turbidity factor as the amount of deposits increases relative to the volume of remaining fuel. The computer 105 may further determine a time elapsed since a fuel filter of the vehicle 101 was replaced and determine the turbidity factor based on the elapsed time. For example, the turbidity factor f. turbidity based on the refill time t fill and the estimated fuel level of the fuel storage tank V storage according to the exemplary equation 14: fturbidity(1tfill)(1Vstorage)

[0048] The computer 105 can determine a congestion factor. The congestion factor can be based on data 115 indicating an occupancy of vehicles 101 at the gas station 140 and a number of available pumps at the gas station 140. The computer 105 can collect data 115 from the server 130 indicating the occupancy of vehicles 101 and the number of available pumps at each gas station 140. The congestion factor can increase for an increasing number of vehicles 101 and a decreasing number of available pumps. The computer 105 can determine a number of vehicles n veh and a number of available pumps n pump and determine the crowd factor f cong based on the example equation 15: fcong=nvehnpump

[0049] The refueling score and the location score may each be determined based on a weighted sum of the factors. The computer 105 may assign a weighting value to each factor to control the influence of the specific factor on the respective score. For example, for the refueling score, the computer 105 may assign a higher weighting value to the fuel gauge factor than to the vehicle-out-of-use factor, indicating that the fuel gauge factor may influence the refueling score more than the vehicle-out-of-use factor. Each weighting value may be a predetermined value stored in the data store 106 and / or on the server 130. Alternatively or additionally, the computer 105 may determine each weighting value based on user input and / or user operation of the vehicle 101.

[0050] For example, initial weighting values ​​can be determined to prioritize customer convenience, e.g., higher weighting values ​​for the vehicle fuel refueling factor and the vehicle-not-in-use factor than for other factors. Users can adjust the weighting values ​​based on personal preferences, e.g., a user can select a higher weighting value for the fuel price factor if they prefer to spend less money on fuel. Vehicles 101 that may require refueling times longer than a few minutes (e.g., electric vehicles) can have a higher weighting value for the setup factor to accommodate the user during refueling. In an area where daytime refueling penalties are imposed on ozone action days, the user can select a higher weighting value for the ozone alert factor.If warranty data indicates clogged fuel filters for certain vehicles 101 or in certain geographic areas, the server 130 may send an increased weighting value for the opacity factor for those vehicles 101. The weighting value may be a constant value or may be a non-constant function, e.g., a linear function, a polynomial function, an exponential function, etc., based on the value of the specific factor and / or the data 115 used to determine the factor.

[0051] Fig. 2 shows an example process 200 for determining a refueling time for the vehicle 101. The process 200 begins at a block 205, where the computer 105 actuates one or more sensors 110 to collect data 115. The computer 105 may actuate the sensors 110 to collect data 115, such as a position of the vehicle 101, a trajectory of the vehicle 101, a fuel gauge level, an atmospheric ozone level, weather, etc.

[0052] Next, in a block 210, the computer 105 determines factors based on the data 115. As described above, the computer 105 may determine a variety of factors based on the collected data 115. For example, the computer 105 may determine an ozone alert factor based on the collected atmospheric ozone data 115. In another example, the computer 105 may determine a vehicle fuel tank factor based on a vehicle fuel tank level. Example factors based on the data 115 are shown in equations 1-15 above.

[0053] Next, in a block 215, the computer 105 determines a refueling score based on a weighted sum of the factors. As described above, the refueling score indicates whether the vehicle 101 should be refueled. If the refueling score falls below a refueling score threshold, the computer 105 may move the vehicle 101 to refuel. The weight of each of the factors may be a predetermined value or a non-constant function stored on the server 130 and / or in the data store 106. Alternatively or additionally, the weight of each of the factors may be determined based on, for example, a user input, a user operation of the vehicle 101, etc.For example, the weight for one of the factors may initially be a predetermined constant value stored on server 130, and computer 105 may prompt the user to provide input to optionally change the weight value. In another example, the weight for one of the factors may be an exponential function of the data 115 used for the factor, and computer 105 may be programmed not to prompt user input to change the weight.

[0054] Next, in a block 220, the computer 105 determines a refueling time. The refueling time is the predicted time at which the refueling score will fall below the refueling score threshold. As described above, the computer 105 may predict the refueling score for a future period based on one or more time-dependent factors and determine a time at which the refueling score will fall below the refueling score threshold. Thus, at the refueling time, the computer 105 may move the vehicle 101 to a gas station 140 for refueling.

[0055] Next, in a block 225, the computer 105 determines whether the refueling time has been reached. The computer 105 may determine that the refueling time has been reached if a current time is within a time threshold of the refueling time. The time threshold may be a predetermined value, e.g., 10 minutes, and stored on the server 130 and / or in the data store 106. If the refueling time has arrived, the process 200 continues in a block 230. Otherwise, the computer 105 remains in block 225 until the refueling time is reached.

[0056] At block 230, the computer 105 identifies a gas station 140 based on the factors. As described above and shown in a process 300 below, the computer 105 may determine a location score for each of a plurality of gas stations 140. The computer 105 may identify a gas station 140 based on the fuel scores.

[0057] Next, in a block 235, the computer 105 moves the vehicle 101 to the gas station 140 identified in block 230. The computer 105 may actuate a steering 120, a drive 120, and a brake 120 to move the vehicle 101 to the gas station 140 to refuel. After block 235, the process 200 ends.

[0058] Fig. 3 illustrates an exemplary process 300 for determining a gas station 140 at which to refuel vehicle 101. Process 300 begins at a block 305, where computer 105 actuates one or more sensors 110 to collect data 115. As described above, computer 105 may collect data 115 about vehicle 101 and / or a plurality of gas stations 140. Computer 105 may collect data 115 from each gas station computer 145 at each gas station 140.

[0059] Next, in a block 310, the computer 105 determines a plurality of factors for each gas station 140. Each factor, as described above, may be based on data 115 collected by the computer 105 from the server 130 and / or the sensors 110. For example, the computer 105 may determine a distance factor that is inversely proportional to a particular distance between the gas station 140 and the vehicle 101 based on geolocation data 115.

[0060] Next, in a block 315, the computer 105 determines a location score for each gas station 140. The computer 105 may determine the location score as a weighted sum of the factors described above. The weight for each factor may be a predetermined value stored on the server 130 and / or in the data store 106. Alternatively or additionally, the computer 105 may adjust the weight for one or more of the factors based on, for example, user input, a driving history of the vehicle 101, etc.

[0061] Next, in a block 320, the computer 105 identifies the gas station 140 with the highest location score. After identifying the gas station 140, the computer 105 may move the vehicle 101 to the identified gas station 140, as described above in block 235 of the process 200. Following block 320, the method 300 ends.

[0062] As used herein, the adverbial modifier "substantially" means that a shape, structure, measurement, value, calculation, etc. may vary from an accurately described geometry, distance, measurement, value, calculation, etc. due to imperfections in materials, machining, manufacturing, data collector measurements, calculations, processing time, communication time, etc.

[0063] Computers 105 generally each include instructions executable by one or more computing devices, such as those identified above, and for performing previously described blocks or processes. Computer-executable instructions may be compiled or interpreted by computer programs created using a variety of programming languages ​​and / or techniques, including, but not limited to, either alone or in combination, Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from memory, a computer-readable medium, etc., and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media.A file in the computing device 105 is generally a collection of data stored on a computer-readable medium, such as a storage medium, random access memory, etc.

[0064] A computer-readable medium includes any medium that participates in providing data (e.g., instructions) that can be read by a computer. Such a medium can take many forms, including, but not limited to, non-volatile media, volatile media, and so on. Non-volatile media includes, for example, optical or magnetic disks and other persistent storage. Volatile media includes dynamic random access memory (DRAM), which is typically main memory.Common forms of computer-readable media include, for example, a floppy disk, a film disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, a DVD, any other optical medium, punched cards, punched tape, any other physical medium with hole patterns, a RAM, a PROM, an EPROM, a FLASH EEPROM, any other memory chip or memory cartridge, or any other medium that can be read by a computer.

[0065] With respect to the media, processes, systems, methods, etc. described herein, it is to be understood that, although the steps of such processes, etc., have been described as occurring in a particular order, such processes may be performed with the described steps performed in an order that differs from the order described herein. It is also to be understood that certain steps could be performed concurrently, other steps could be added, or certain steps described herein could be omitted. For example, in process 200, one or more of the steps could be omitted, or the steps could be performed in a different order than in Fig.2. In other words, the descriptions of systems and / or processes in this specification are provided for the purpose of illustrating particular embodiments and should not be construed in any way as limiting the disclosed subject matter.

[0066] Accordingly, it is to be understood that the present disclosure, including the foregoing description and the accompanying figures and following claims, is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided should be apparent to those skilled in the art after reading the foregoing description. The scope of the invention should be determined not with reference to the above description, but instead with reference to claims appended hereto and / or included in a non-provisional patent application based hereon, along with the full scope of equivalents to which such claims are entitled.It is anticipated and intended that future developments will occur in the art discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In summary, it should be understood that the disclosed subject matter is capable of modification and variation.

[0067] The article "a / an" when modifying a noun should be understood to mean one or more, unless otherwise stated or the context requires otherwise. The phrase "based on" includes partially or wholly based on.

Claims

[1] A system comprising a computer (105) in a vehicle (101) configured to: collecting data (115) over a network (125) indicating the last time an underground fuel tank was refilled at each of a plurality of gas stations (140); calculate a turbidity factor inversely proportional to an elapsed time since the last time each underground fuel tank for the plurality of fueling stations (140) was refilled; selecting a gas station from the plurality of gas stations (140) based on the calculated turbidity factor; and to operate a steering, a drive and a brake to move the vehicle (101) to the selected gas station. [2] The system of claim 1, wherein the computer (105) is further programmed to select the gas station based on a sum of a plurality of weighted factors, including at least one weighted factor based on the turbidity factor. [3] The system of claim 2, wherein the computer (105) is further programmed to weight the factors based on user input. [4] The system of claim 2, wherein the computer (105) is further programmed to weight the factors based on user operation of the vehicle (101). [5] The system of claim 2, wherein the plurality of factors includes factors based on collected data (115) on at least one occupancy level of the gas stations (140) and the amenities available at the gas stations (140). [6] The system of claim 1, wherein the computer (105) is further programmed to determine an elapsed time since replacing a fuel filter of the vehicle (101) and to select the gas station based on the elapsed time. [7] The system of claim 1, wherein the computer (105) is further programmed to select the gas station based on a distance between a predetermined route and each of the plurality of gas stations (140). [8] The system of claim 1, wherein the computer (105) is further programmed to select the gas station based on an estimated current fuel level of the underground fuel tank at each of the plurality of gas stations (140). [9] The system of claim 1, wherein the computer (105) is further programmed to select the gas station at which the time elapsed since the last filling of the underground fuel tank exceeds a time threshold. [10] The system of claim 1, wherein the computer (105) is further programmed to determine a refueling time of the vehicle (101) based on collected data (115) of at least one of a coolant temperature of the vehicle (101), an atmospheric ozone level, and an air quality. [11] Method comprising: Collecting data (115) at a vehicle (101) via a network (125) indicating the last time an underground fuel tank was refilled at each of a plurality of gas stations (140); calculating a turbidity factor inversely proportional to an elapsed time since each underground fuel tank was last refilled at the plurality of fueling stations (140); Selecting a gas station from the plurality of gas stations (140) based on the calculated turbidity factor; and Operating a steering, drive and brake with a vehicle computer to move the vehicle (101) to the selected gas station. [12] The method of claim 11, further comprising selecting the gas station based on a sum of a plurality of weighted factors, including at least one weighted factor based on the turbidity factor. [13] The method of claim 12, further comprising weighting the factors based on user input. [14] The method of claim 12, further comprising weighting the factors based on user operation of the vehicle (101). [15] The method of claim 12, wherein the plurality of factors includes factors based on collected data (115) on at least an occupancy level of the gas stations (140) and the amenities available at the gas stations (140). [16] The method of claim 11, further comprising determining an elapsed time since replacing a fuel filter of the vehicle (101) and selecting the gas station based on the elapsed time. [17] The method of claim 11, further comprising selecting the gas station based on a distance between a predetermined route and each of the plurality of gas stations (140). [18] The method of claim 11, further comprising selecting the gas station based on an estimated current fuel level of the underground fuel tank at each of the plurality of gas stations (140). [19] The method of claim 11, further comprising selecting the gas station at which the time elapsed since the last filling of the underground fuel tank exceeds a time threshold. [20] The method of claim 11, further comprising determining a refueling time of a vehicle (101) based on collected data (115) of at least one of a coolant temperature of the vehicle (101), an atmospheric ozone level, and an air quality.

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