Enhanced vehicle refueling

Abstract

Data is collected regarding at least one of a coolant temperature of a vehicle, an atmospheric ozone level, and an air quality. A refueling time is determined based on the collected data. The vehicle is moved to a refueling station based on the refueling time.

Description

TECHNICAL FIELD

[0001] The present invention relates to collecting data regarding at least one of coolant temperature, atmospheric ozone level, and air quality of a vehicle. A refueling time is determined based on the collected data. The vehicle is moved to a refueling station based on the refueling time. BACKGROUND

[0002] A vehicle requires fuel to operate. When a fuel gauge indicator is below a certain level, it can be determined to refuel the vehicle. The vehicle can then be navigated to a refueling station to refuel the vehicle. However, opportunities to refuel the vehicle can be limited to a certain time of day and / or a particular refueling station (e.g., at a particular location). Various environmental conditions can affect what time and / or location is possible and / or better than others for refueling. Unfortunately, current vehicles lack the ability to receive and process data to determine a time and location to refuel the vehicle. SUMMARY

[0003] A system includes a computer programmed to collect data regarding at least one of coolant temperature, atmospheric ozone level, and air quality of a vehicle; determine a refueling time based on the collected data; and move the vehicle to a refueling station based on the refueling time.

[0004] The computer can be programmed to collect data regarding a time of use of the vehicle and determine the refueling time based on the time of use.

[0005] The computer can be programmed to determine the refueling time based on a weighted sum of factors, the factors based on the collected data.

[0006] The computer can be programmed to weight the factors based on user input.

[0007] The computer can be programmed to weight the factors based on user operation of the vehicle.

[0008] The user operation can include data regarding a time of previous use of the vehicle, and a factor includes a factor based on the time of previous use of the vehicle.

[0009] The computer can be programmed to identify a refueling station from a stored list, and determine the refueling time based on a location of the identified refueling station.

[0010] The computer can be programmed to determine that a day is an ozone alert day, and determine the refueling time to be after a predicted sunset time of the ozone alert day.

[0011] The computer can be programmed to collect data regarding a coolant temperature of the vehicle, an atmospheric ozone level, and an air quality, and determine the refueling time based on the collected data.

[0012] The computer can be programmed to collect data regarding a traffic congestion level of a road and determine the refueling time based on the traffic congestion level.

[0013] A method includes collecting data regarding at least one of a coolant temperature of a vehicle, an atmospheric ozone level, and an air quality; determining a refueling time based on the collected data; and moving the vehicle to a refueling station based on the refueling time.

[0014] The method can include collecting data regarding a usage time of the vehicle and determining the refueling time based on the usage time.

[0015] The method can include determining the refueling time based on a weighted sum of a plurality of factors, the plurality of factors based on the collected data.

[0016] The method can include weighting the factors based on user input.

[0017] The method can include weighting the factors based on user operation of the vehicle.

[0018] The user operation can include data regarding a time of a previous use of the vehicle, and a factor includes a factor based on the time of the previous use of the vehicle.

[0019] The method can include identifying a refueling station from a stored list, and determining the refueling time based on a location of the identified refueling station.

[0020] The method can include determining that a current day is an ozone alert day, and determining the refueling time after a predicted sunset time of the ozone alert day.

[0021] The method can include collecting data regarding a coolant temperature of the vehicle, an atmospheric ozone level, and an air quality, and determining the refueling time based on the collected data.

[0022] The method can include collecting data regarding a traffic congestion level of a road and determining the refueling time based on the traffic congestion level. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a block diagram of an example system for refueling a vehicle.

[0024] Figure 2 An example process of determining a refueling time for a vehicle is shown.

[0025] Figure 3 An example process for determining a gas station for a vehicle is shown. DETAILED DESCRIPTION

[0026] A system includes a computer programmed to: collect data regarding at least one of a coolant temperature of a vehicle, an atmospheric ozone level, and an air quality; determine a refueling time based on the collected data; and move the vehicle to a gas station based on the refueling time.

[0027] The computer can also be programmed to collect data regarding a usage time of the vehicle and determine the refueling time based on the usage time.

[0028] The computer can also be programmed to determine the refueling time based on a weighted sum of factors based on the collected data. The computer can also be programmed to weight the factors based on user input. The computer can also be programmed to weight the factors based on user operation of the vehicle.

[0029] The user operation can include data regarding a time of a previous use of the vehicle, and the factors can include factors based on the time of the previous use of the vehicle.

[0030] The computer can also be programmed to identify a gas station from a stored list and determine the refueling time based on a location of the identified gas station.

[0031] The computer can also be programmed to determine that a current day is an ozone alert day and determine the refueling time to be after a predicted sunset time for the ozone alert day.

[0032] The computer can also be programmed to collect data regarding all of the coolant temperature of the vehicle, the atmospheric ozone level, and the air quality and determine the refueling time based on the collected data.

[0033] The computer can also be programmed to collect data regarding a traffic congestion level of a road and determine the refueling time based on the traffic congestion level.

[0034] A method includes: collecting data regarding at least one of a coolant temperature of a vehicle, an atmospheric ozone level, and an air quality; determining a refueling time based on the collected data; and moving the vehicle to a gas station based on the refueling time.

[0035] The method can also include collecting data regarding a usage time of the vehicle and determining the refueling time based on the usage time.

[0036] The method can further include determining the refueling time based on a weighted sum of a plurality of factors, the plurality of factors based on the collected data. The method can further include weighting the factors based on user input. The method can further include weighting the factors based on user operation of the vehicle.

[0037] In the method, the user operation can include data regarding time of previous use of the vehicle, and the factors can include a factor based on time of previous use of the vehicle.

[0038] The method can further include identifying a gas station from a stored list and determining the refueling time based on a location of the identified gas station.

[0039] The method can further include determining that the day is an ozone alert day, and determining the refueling time after a predicted sunset time for the ozone alert day.

[0040] The method can further include collecting data regarding all of coolant temperature of the vehicle, atmospheric ozone levels, and air quality, and determining the refueling time based on the collected data.

[0041] The method can further include collecting data regarding traffic congestion levels of roads and determining the refueling time based on traffic congestion levels.

[0042] A computing device programmed to perform any of the above method steps is also disclosed. A vehicle including the computing device is also disclosed. A computer program product including a computer readable medium storing instructions executable by a computer processor to perform any of the above method steps is also disclosed.

[0043] A vehicle computer can determine a time and location to refuel a vehicle based on data collected from sensors and servers. The computer can collect data regarding, for example, environmental conditions, vehicle parameters, vehicle location, etc., and determine a time to refuel the vehicle and a location 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 score based on environmental and vehicle data to provide a more convenient location to refuel the vehicle. By processing a plurality of factors based on various environmental and vehicle data, the computer can reduce constraints that can limit refueling opportunities.

[0044] Figure 1An example system 100 for refueling a vehicle 101 is shown. A computer 105 in the vehicle 101 is programmed to receive collected data 115 from one or more sensors 110. For example, the vehicle 101 data 115 can include the location of the vehicle 101, data about the environment surrounding the vehicle, data about objects outside the vehicle, such as another vehicle, etc. The vehicle 101 location is typically provided in conventional form, e.g., geographic coordinates such as latitude and longitude coordinates obtained via a navigation system using a global positioning system (GPS). Other examples of data 115 can include measurements of vehicle 101 systems and components, e.g., vehicle 101 speed, vehicle 101 trajectory, etc.

[0045] As is known, the computer 105 is typically programmed for communication on a vehicle 101 network, e.g., including a communication bus. Via the network, bus, and / or other wired or wireless mechanisms, e.g., a wired or wireless local area network in the vehicle 101, the computer 105 can send messages to and / or receive messages from various devices in the vehicle 101, e.g., controllers, actuators, sensors, etc., including the sensors 110. Alternatively or additionally, where the computer 105 actually comprises multiple devices, the vehicle network can be used for communication between devices represented in this disclosure as the computer 105. In addition, the computer 105 can be programmed for communication with a network 125, which as described below can include various wired and / or wireless networking technologies, e.g., cellular, Low power consumption (BLE), wired and / or wireless packet networks, etc.

[0046] The data storage 106 can be of any known type, e.g., a hard drive, solid state drive, server, or any volatile or non-volatile medium. The data storage 106 can store collected data 115 sent from the sensors 110.

[0047] The sensors 110 can include various devices. For example, as is known, various controllers in the vehicle 101 can operate as sensors 110 to provide data 115 via the network or bus of the vehicle 101, e.g., data 115 related to vehicle speed, acceleration, location, subsystem and / or component status, etc. In addition, other sensors 110 can include cameras, motion detectors, etc., i.e., sensors 110 for providing data 115 for evaluating the location of a target, projecting a path of a target, evaluating the location of a road lane, etc. The sensors 110 can also include short-range radar, long-range radar, laser radar (LIDAR), and / or ultrasonic transducers.

[0048] The collected data 115 can include a variety of data collected in the vehicle 101. Examples of collecting data 115 are provided above, and additionally, the data 115 is generally collected using one or more sensors 110, and can additionally include data computed from the data 115 in the computer 105 and / or at the server 130. Generally, the collected data 115 can include any data that can be collected by the sensors 110 and / or computed from these data.

[0049] The vehicle 101 can include a plurality of vehicle components 120. As used herein, each vehicle component 120 includes one or more hardware components adapted to perform a mechanical function or operation, such as moving the vehicle, slowing or stopping the vehicle, steering the vehicle, etc. Non-limiting examples of components 120 include propulsion components (which include, for example, internal combustion engines and / or electric motors, etc.), transmission components, steering components (which can include, for example, one or more of steering wheels, steering racks, etc.), braking components, park assist components, adaptive cruise control components, adaptive steering components, movable seats, etc.

[0050] The vehicle 101 is an “autonomous” vehicle 101 when the computer 105 is operating the vehicle 101. For 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 propulsion (typically via a powertrain including electric motors and / or internal combustion engines), 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 propulsion (typically via a powertrain including electric motors and / or internal combustion engines), braking, and steering of the vehicle 101 is controlled at least partially by the computer 105 rather than by a human operator.

[0051] The system 100 can also include a network 125 connected to the server 130 and the data store 135. The computer 105 can also be programmed to communicate with one or more remote sites, such as the server 130, via the network 125, such remote sites can include the data store 135. The network 125 represents one or more mechanisms by which the vehicle computer 105 can communicate with the remote server 130. Thus, the network 125 can be one or more of a variety of wired or wireless communication mechanisms, including wired (e.g., cable and fiber) and / or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms in any desired combination with any desired network topology (or multiple network topologies when multiple communication mechanisms are used). Exemplary communication networks include wireless communication networks that provide data communication services (e.g., using 3G, 4G, 5G, or other cellular communication protocols) and / or data communication services over the Internet (e.g., using Wi-Fi, Bluetooth, or other wireless protocols). Low energy consumption (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) (such as dedicated short-range communications (DSRC)), etc.), local area networks (LANs), and / or wide area networks (WANs) (including the Internet).

[0052] The system can include a fueling station 140. The fueling station 140 stores fuel that can be provided to the vehicle 101. The fuel can be a known energy source for the vehicle 101, such as gasoline, diesel, compressed natural gas, ethanol, butanol, biodiesel, jet fuel, electricity at a charging station, etc. The fueling station 140 can include a station computer 145. The station computer 145 can be in communication with the network 125. The station computer 145 can collect data 115 about the fueling station 140 and transmit the data 115 to the server 130 and / or the computer 105 through the network 125. The station computer 145 can be, for example, a dedicated station console, a personal computer, a laptop computer, a tablet, a smartphone, etc.

[0053] The computer 105 can determine a plurality of factors based on the data 115 collected by the sensors 110 and / or from the server 130. The factors can represent one or more elements that can affect when the vehicle 101 can be fueled and where the vehicle 101 can be fueled. The computer 105 can determine a fueling score and a location score based on the factors. Each factor can be a value between 0 and 1 based on the collected data 115, as shown in Equations 1-15 below.

[0054] The fueling score can be a value that the computer 105 uses to determine when to fuel the vehicle 101. For example, the computer 105 can determine the fueling score as a function of time and determine a time at which the fueling score will fall below a fueling score threshold. The computer 105 can determine to fuel the vehicle 101 when the time is within a time threshold of the fueling time. Alternatively or additionally, the computer 105 can determine to fuel the vehicle 101 if the computer 105 determines that the current fueling score is below a threshold. The fueling score can be a combination of the factors, such as a weighted sum of a plurality of factors. For example, the computer 105 can collect data 115 about at least one of a coolant temperature of the vehicle, an atmospheric ozone level, and an air quality, and determine a fueling time based on the collected data 115. The fueling score can be a weighted sum of the factors:

[0055]

[0056] where S 加油 is the fueling score, i is an index indicating one of a plurality of factors, n is a total number of factors used to determine the fueling score, f i is a value of one of the factors, and k i is a factor f iThe predetermined weight of the factor f i One or more of the time-dependent factors may change over time, for example, the vehicle fuel tank factor may change as vehicle 101 consumes fuel. Computer 105 may use known regression techniques to predict one or more of the time-dependent factors for a future time period (e.g., 24 hours). Computer 105 may thus predict the refueling score S for the time period based on the time-dependent factors. 加油 .

[0057] The location score may be a value that the computer 105 uses to determine a location for refueling the vehicle 101. In determining to refuel the vehicle 101, the computer 105 may determine the location scores of a plurality of gas stations 140. The gas stations 140 may be determined based on a list stored in the data storage device 106 and / or the server 130. The computer 105 may select the gas station 140 with the highest location score and may then move the vehicle 101 to the selected gas station 140 to refuel the vehicle 101. As described below, the location score may be a combination of factors, e.g., a weighted sum of multiple factors. For example, the computer 105 may collect data 115 regarding the turbidity of fuel 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 factors:

[0058]

[0059] Among them S 位置 is the position score, i is an index indicating one of the multiple factors, n is the total number of factors used to determine the position score, and f i is the value of one of the factors, and k i is the factor f i The predetermined weight.

[0060] The computer 105 may determine a vehicle fuel tank factor based on data 115 collected from the fuel tank of the vehicle 101. The computer 105 may collect data 115 regarding the fuel level in the vehicle fuel tank from the vehicle fuel tank sensor 110. The vehicle fuel tank factor may be negatively correlated with 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.). Equation 3 below describes an example sigmoid function f that describes the vehicle fuel tank factor. vft .

[0061]

[0062] where C and D are predetermined constants suitable for factors between 0 and 1, V is the vehicle fuel level (i.e., a number between 0 and 1 indicates a fraction of the full fuel tank volume), and exp is the known exponential function. As the vehicle fuel level V decreases towards 0 (i.e., an empty fuel tank), the refueling score S 加油 As the vehicle fuel tank factor f vft increases exponentially towards 1.

[0063] The vehicle fuel tank factor can be weighted such that the weighted sum exceeds a refueling score threshold when the vehicle fuel level data 115 falls below a vehicle fuel level threshold. That is, the computer 105 can determine a refueling time to be 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,

[0064]

[0065] where k vft is a weight value for the vehicle fuel tank factor, V 阈值 is the vehicle fuel level threshold, and S 加油，阈值 is the refueling score threshold.

[0066] The computer 105 can determine a vehicle not in use factor. The computer 105 can determine a confidence value at a plurality of times of the day that indicates usage of the vehicle 101 using known techniques. The confidence value can be a probability that the vehicle 101 will be in use at a particular time of the day. The computer 105 can determine the confidence value based on, for example, usage history data 115 of the vehicle 101. The computer 105 can determine the vehicle not in use factor based on the confidence value indicating that the vehicle 101 will not be in use at a particular time of the day. The vehicle not in use factor can increase when the vehicle 101 is predicted to not be in use at a time of the day, i.e., the computer 105 can prefer a refueling time when the vehicle 101 is not in use. Further, the computer 105 can determine a time of previous usage of the vehicle 101, and the vehicle not in use factor can be based on the time of previous usage of the vehicle 101. For example, the vehicle not in use factor f niu :

[0067]

[0068] where t is the current time in hours between 0 and 24, t niu is a predicted time at which the vehicle 101 is predicted to not be in use (e.g., outside of a shift servicing the vehicle 101, outside of a user’s commute schedule, etc.), and C is a degree to which the vehicle not in use factor should increase based on tniu And a determined constant value.

[0069] The computer 105 can determine a non-peak usage factor. The computer 105 can determine when during the day the road is likely to be congested. The computer 105 can request traffic data 115 from the server 130 and use probabilistic calculations such as a hidden Markov model to determine times that indicate peak usage of the road during the day. The computer 105 can collect data 115 about traffic congestion levels of the road and determine a non-peak usage factor based on the traffic congestion levels. The non-peak usage factor can increase at times during the day when the road is not used at peak and can decrease at times during the day when the road is used at peak. For example, the non-peak usage factor can be 0 when the time of day is a time of day when the road is used at peak (as determined from the probabilistic calculations) and can be 1 when the time of day is a time of day when the road is not used at peak. The non-peak usage factor f npu :

[0070] f npu = C arccot(a-p(t)-b) (6)

[0071] where a, b, C are predetermined constants determined to bound the non-peak usage factor between 0 and 1 and p(t) is the probability of traffic congestion at a particular time t.

[0072] The computer 105 can determine a fuel price factor. The computer 105 can collect data 115 from the server 130 indicating fuel prices at a plurality of gas stations 140. The computer 105 can use known techniques (e.g., machine learning, historical trends, etc.) to predict fuel prices. The fuel price factor can increase with data 115 indicating a decrease in fuel prices. For example, the computer 105 can determine a difference between a current fuel price pi at one of the gas stations 140 and a predicted fuel price p pred :

[0073]

[0074] where C is a predetermined constant.

[0075] The computer 105 can determine a weather factor. The computer can receive data 115 from, for example, the server 130, precipitation sensors 110, wind speed sensors 110, temperature sensors 110, etc. The weather factor can increase when precipitation data 115 increases, wind speed data 115 increases, and temperature data 115 is above a warm threshold or below a cold threshold. For example, the weather factor f 天气 may be a sum of the data 115:

[0076]

[0077] where precip0 is the current precipitation percentage, precip avg is the average precipitation percentage for the day, v0 is the current wind speed, v avg is the average wind speed for the day, T HI is a Boolean value that is 0 when the current ambient temperature is below a warm threshold and 1 when the current ambient temperature is above the warm threshold, and T LO is a Boolean value that is 0 when the current ambient temperature is above a cold threshold and 1 when the current ambient temperature is below the cold threshold.

[0078] The computer 105 can determine a station preference factor. The computer 105 can determine a plurality of preferred fueling stations 140, such as company fueling stations 140 approved by a fleet operator, company fueling stations 140 designated by a user of the vehicle 101, fueling stations 140 near a route of travel of the vehicle 101, and the like. The station preference factor can increase as the distance between the vehicle 101 and one of the preferred stations 140 decreases. For example, the station preference factor f sp may be determined based on the following example equation 9:

[0079] f sp = C exp(-ax) (9)

[0080] where a, C are predetermined constants, and x is the current distance between the location of the vehicle 101 and the nearest preferred fueling station 140.

[0081] The computer 105 can determine a warm-up factor. The computer 105 can determine an engine coolant temperature from the sensors 110. As the engine coolant warms, the powertrain consumes less fuel, so the vehicle 101 can consume less fuel when moving to a fueling station 140 when the engine coolant temperature is above a coolant temperature threshold. The warm-up factor can increase as the engine coolant temperature increases. For example, the warm-up factor f 预热 may be determined based on the following example equation 10:

[0082] f 预热 = C arcsinh(a-T 冷却剂 ) (10)

[0083] where a, C are predetermined constants, arcsinh is the known inverse hyperbolic sine function, and T 冷却剂 is the current coolant temperature.

[0084] The computer 105 can determine an ozone alert factor. The computer 105 can receive data 115 from the server 130 indicating whether the current day is an ozone alert day, i.e., a day on which local authorities encourage users to refuel the vehicle 101 after dark to reduce ozone and smog production. The computer 105 can determine the refueling time after the predicted sunset time on the ozone alert day. The ozone alert factor can decrease during the time between the predicted sunrise time and the predicted sunset time when the data 115 from the server 130 indicates that the current day is an ozone alert day. For example, the ozone alert factor can be a Boolean value of 0 when 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 the ozone alert factor can be 1 when the current time is between the predicted sunset time on the current day and the predicted sunrise time on the next day.

[0085] The computer 105 can determine an air quality factor. The air quality factor can be based on data 115 indicating a level of dust and debris that can interfere with refueling. The computer 105 can collect data 115 of wind speed and humidity to determine the air quality factor. The air quality factor can decrease as the wind speed increases, and can decrease as the humidity decreases. For example, the air quality factor f 空气 :

[0086]

[0087] where a, C are predetermined constants, φ is the relative humidity, and v is the wind speed.

[0088] The computer 105 can determine a distance factor. The distance factor can be based on data 115 indicating a planned predetermined route of the vehicle 101 and a distance between each refueling station 140. The distance factor can decrease as the distance increases. For example, the distance factor f 距离 may be inversely proportional to the distance x 站 between the geographic location coordinates of the vehicle 101 and the geographic location coordinates of the refueling station 140, e.g., as shown in example equation 12:

[0089]

[0090] where C is a predetermined constant.

[0091] 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 refueling station 140. Example facilities include, for example, restaurants, coffee shops, scenery, walking paths, pet areas, wireless 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 nf and determine a facility factor f according to the following example equation 13 设施 :

[0092]

[0093] The computer 105 can determine an accessibility factor. The accessibility factor can be based on data 115 indicative of features that increase access to the fueling station 140 based on the route of the vehicle 101. These features can include, for example, whether the fueling station 140 is along a direction of travel of the vehicle 101 on a route, a number of fuel pumps available at the fueling station 140, etc. The accessibility factor can increase with an increase in the number of features. The computer 105 can identify a number of features N 特征 and determine the accessibility factor f according to the following example equation 14 eac :

[0094]

[0095] The computer 105 can determine a turbidity factor for fuel at the fueling station 140. The fueling station 140 can include a plurality of underground fuel storage tanks that store liquid fuel. The fuel storage tanks can be refilled by a truck that delivers fuel. When the truck refills the fuel storage tanks, the turbidity of the fuel in the fuel storage tanks can increase as sediment in the fuel storage tanks is agitated. The sediment can be collected by a fuel filter in the vehicle 101, thereby reducing a life of the fuel filter. The computer 105 can collect data 115 from the server 130 indicative of a refill time when the underground fuel storage tank was most recently refilled. As the refill time increases, sediment can settle in the underground fuel storage tank. The turbidity factor can decrease as the refill time increases. In addition, the computer 105 can collect data 115 regarding an estimated fuel level of the fuel storage tank at each of the plurality of fueling stations 140. As the amount of sediment increases relative to the amount of fuel remaining, a lower estimated fuel level can increase the turbidity factor. The computer 105 can also 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 浊度 based on a refill time t of the fuel storage tank fill and an estimated fuel level V 存储 :

[0096]

[0097] The computer 105 can determine a congestion factor. The congestion factor can be based on data 115 indicating the occupancy of vehicles 101 at the fueling stations 140 and the number of available fuel pumps at the fueling stations 140. The computer 105 can collect data 115 from the server 130 indicating the occupancy of vehicles 101 and the number of available fuel pumps at each fueling station 140. The congestion factor can increase for an increasing number of vehicles 101 and a decreasing number of available fuel pumps. The computer 105 can determine the number of vehicles n veh and the number of available fuel pumps n 泵 and determine the congestion factor f cong based on the following example equation 15:

[0098]

[0099] The fueling score and the location score can be determined based on a weighted sum of the factors. The computer 105 can assign a weight value to each factor to control the influence of a particular factor on the respective score. For example, for the fueling score, the computer 105 can assign a higher weight value to the fuel gauge factor than to the vehicle not in use factor, indicating that the fuel gauge factor can influence the fueling score more than the vehicle not in use factor. Each weight value can be a predetermined value stored in the data storage 106 and / or the server 130. Alternatively or additionally, the computer 105 can determine each weight value based on user input and / or user operation of the vehicle 101.

[0100] For example, initial weight values can be determined to focus on customer convenience, e.g., the vehicle fuel tank factor and the vehicle not in use factor have higher weight values than other factors. A user can adjust the weight values based on personal preferences, e.g., a user can select a higher weight value on the fuel price factor, preferring to spend less money on fuel. Vehicles 101 that can require longer than a few minutes to refuel, e.g., electric vehicles, can have a higher facility factor weight value to keep the user occupied during refueling. In areas that implement a daytime refueling penalty on smog action days, a user can select a higher weight value on the ozone alert factor. If warranty data shows that fuel filters clog for certain vehicles 101 or certain geographic areas, the server 130 can send an increased weight value for the turbidity factor for those vehicles 101. The weight values can be constant values, or can be non-constant functions based on the value of a particular factor and / or the data 115 used to determine the factor, e.g., a linear function, a polynomial function, an exponential function, etc.

[0101] Figure 2An example process 200 for determining a refueling time for vehicle 101 is shown. Process 200 begins in block 205, in which computer 105 actuates one or more sensors 110 to collect data 115. Computer 105 can actuate sensors 110 to collect data 115 regarding, for example, vehicle 101 location, vehicle 101 trajectory, fuel gauge level, atmospheric ozone level, weather, etc.

[0102] Next, in block 210, computer 105 determines factors based on data 115. As described above, computer 105 can determine a plurality of factors based on the collected data 115. For example, computer 105 can determine an ozone alert factor based on collected data 115 regarding atmospheric ozone. In another example, computer 105 can determine a vehicle fuel tank factor based on vehicle fuel tank level. Example factors based on data 115 are shown in Equations 1-15 above.

[0103] Next, in block 215, computer 105 determines a refueling score based on a weighted sum of the factors. As described above, the refueling score indicates whether vehicle 101 should be refueled. When the refueling score falls below a refueling score threshold, computer 105 can cause vehicle 101 to move to be refueled. The weight of each factor can be a predetermined value or a non-constant function stored in server 130 and / or data storage 106. Alternatively or additionally, the weight of each factor can be determined based on, for example, user input, user operation of vehicle 101, etc. For example, the weight of one of the factors can initially be a predetermined constant value stored in server 130, and computer 105 can prompt a user to provide input to optionally change the value of the weight. In another example, the weight of one of the factors can be an exponential function of data 115 for that factor, and computer 105 can be programmed to not prompt a user input to change the weight.

[0104] Next, in block 220, computer 105 determines a refueling time. The refueling time is a predicted time at which the refueling score will fall below the refueling score threshold. As described above, computer 105 can predict the refueling score for a future time 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. Accordingly, at the refueling time, computer 105 can move vehicle 101 to a gas station 140 to be refueled.

[0105] Next, in block 225, the computer 105 determines whether the refueling time has arrived. The computer 105 can determine that the refueling time has arrived when the current time is within a time threshold of the refueling time. The time threshold can be a predetermined value, such as 10 minutes, and stored in the server 130 and / or the data storage 106. If the refueling time has arrived, the process 200 continues in block 230. Otherwise, the computer 105 remains in block 225 until the refueling time has arrived.

[0106] In block 230, the computer 105 identifies a gas station 140 based on the factors. As described above and shown in the process 300 below, the computer 105 can determine a location score for each of the plurality of gas stations 140. The computer 105 can identify the gas station 140 based on the refueling score.

[0107] Next, in block 235, the computer 105 moves the vehicle 101 to the gas station 140 identified in block 230. The computer 105 can actuate the steering device 120, the propulsion device 120, and the brakes 120 to move the vehicle 101 to the gas station 140 for refueling. After block 235, the process 200 ends.

[0108] Figure 3 An example process 300 for determining a gas station 140 at which to refuel the vehicle 101 is shown. The process 300 begins in block 305, in which the computer 105 actuates one or more sensors 110 to collect data 115. As described above, the computer 105 can collect data 115 about the vehicle 101 and / or the plurality of gas stations 140. The computer 105 can collect data 115 from each station computer 145 at each gas station 140.

[0109] Next, in block 310, the computer 105 determines a plurality of factors for each gas station 140. As described above, each factor can be based on data 115 collected by the computer 105 from the server 130 and / or the sensors 110. For example, the computer 105 can determine a distance factor that is inversely proportional to a determined distance between the gas station 140 and the vehicle 101 based on geographic location data 115.

[0110] Next, in block 315, the computer 105 determines a location score for each gas station 140. The computer 105 can determine the location score as a weighted sum of the factors described above. The weight of each factor can be a predetermined value stored in the server 130 and / or the data storage 106. Alternatively or additionally, the computer 105 can adjust the weight of one or more of the factors based on, for example, user input, a driving history of the vehicle 101, etc.

[0111] Next, in block 320, the computer 105 identifies the gas station 140 having the highest location score. After identifying the gas station 140, the computer 105 can move the vehicle 101 to the identified gas station 140, as described above in block 235 of the process 200. After block 320, the process 300 ends.

[0112] As used herein, the adverb modifying adjective "substantially" means shapes, structures, measurements, values, calculations, etc. can deviate from exact geometric shapes, distances, measurements, values, calculations, etc. described because of imperfections in materials, processing, manufacturing, data gatherer measurements, calculations, processing time, communication time, etc.

[0113] The computers 105 generally each include instructions executable by one or more computing devices such as those identified above and for carrying out blocks or steps of the processes described above. Computer-executable instructions can be compiled or interpreted from computer programs created using a variety of programming languages and / or technologies, including, without limitation, and either alone or in combination, a single one of: Java TM , C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer-readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data can be stored and transmitted using a variety of computer-readable media. A file in the computing devices 105 is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc.

[0114] Computer-readable media include any media that participate in providing data (e.g., instructions) that is readable by a computer. Such media can take many forms, including but not limited to, non-volatile media, volatile media, etc. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes a main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH- EPROM, any other memory chip or cartridge, or any other medium from which a computer can read.

[0115] 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 according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It is further understood that certain steps could be performed simultaneously, that other steps could be added, or that described steps could be omitted. For example, in process 200, one or more steps could be omitted, or the steps could be performed in a different order than that shown in Figure 2 The description of systems and / or processes herein is intended to be illustrative, and not restrictive. It is understood that the disclosed subject matter is not limited to the examples described above, but rather the scope of the disclosed subject matter is to be determined entirely by the claims, the legal equivalents thereof, and the factual contents recited

[0116] Accordingly, it is to be understood that the present disclosure, including the above description and the accompanying drawings and the following claims, is intended to be illustrative, and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full range of equivalents to which such claims are entitled. It is anticipated and intended, for example, that future developments will occur in the technologies discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In general, it is to be understood that the disclosed subject matter is capable of modification and change.

[0117] Unless otherwise stated, or as is clear from the context, the use of any of the verbs "comprise", "comprises", "comprising", "include", "includes", "including", "contain", "contains", or "containing", is open-ended and does not exclude additional steps, processes, components, features, or elements.

Claims

1. A system for a vehicle comprising a computer programmed to: collect data regarding at least one of coolant temperature of the vehicle, atmospheric ozone level, and air quality; determine a refueling time based on the collected data regarding: at least one of atmospheric ozone level and air quality in combination with coolant temperature of the vehicle; or at least one of atmospheric ozone level and air quality; and move the vehicle to a refueling station based on the refueling time.

2. The system of claim 1, wherein the computer is further programmed to collect data regarding usage time of the vehicle and determine the refueling time based on the usage time.

3. The system of claim 1, wherein the computer is further programmed to determine the refueling time based on a weighted sum of a plurality of factors, the plurality of factors based on the collected data.

4. The system of claim 3, wherein the computer is further programmed to weight the factors based on user input.

5. The system of claim 3, wherein the computer is further programmed to weight the factors based on user operation of the vehicle.

6. The system of claim 5, wherein the user operation comprises data regarding time of previous usage of the vehicle, and the factors comprise a factor based on the time of the previous usage of the vehicle.

7. The system of claim 1, wherein the computer is further programmed to identify a refueling station from a stored list, and determine the refueling time based on a location of the identified refueling station.

8. The system of claim 1, wherein the computer is further programmed to determine that a current day is an ozone alert day, and determine the refueling time after a predicted sunset time of the ozone alert day.

9. The system of claim 1, wherein the computer is further programmed to collect data regarding all of the coolant temperature of the vehicle, the atmospheric ozone level, and the air quality, and determine the refueling time based on the collected data.

10. The system of claim 1, wherein the computer is further programmed to collect data regarding traffic congestion level of a road, and determine the refueling time based on the traffic congestion level.

11. A method for a vehicle comprising: collecting data regarding at least one of coolant temperature of the vehicle, atmospheric ozone level, and air quality; determining a refueling time based on the collected data regarding: at least one of atmospheric ozone level and air quality in combination with coolant temperature of the vehicle; or at least one of atmospheric ozone level and air quality; and moving the vehicle to a refueling station based on the refueling time.

12. The method of claim 11, further comprising collecting data regarding usage time of the vehicle and determining the refueling time based on the usage time.

13. The method of claim 11, further comprising determining the refueling time based on a weighted sum of a plurality of factors, the plurality of factors based on the collected data. ​ 14. The method of claim 13, further comprising weighting the factors based on user input.

15. The method of claim 13, further comprising weighting the factors based on user operation of the vehicle.

16. The method of claim 15, wherein the user operation comprises data regarding a time of a previous use of the vehicle, and the factors comprise a factor based on the time of the previous use of the vehicle.

17. The method of claim 11, further comprising identifying a gas station from a stored list and determining the refueling time based on a location of the identified gas station.

18. The method of claim 11, further comprising determining that a current day is an ozone alert day, and determining the refueling time after a predicted sunset time of the ozone alert day.

19. The method of claim 11, further comprising collecting data regarding all of the coolant temperature of a vehicle, the atmospheric ozone level, and the air quality, and determining the refueling time based on the collected data.

20. The method of claim 11, further comprising collecting data regarding a traffic congestion level of a road, and determining the refueling time based on the traffic congestion level.

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