Enhanced vehicle refueling

Abstract

Collect data on the turbidity of the fuel tank at each of multiple gas stations. Select a gas station based on the collected data. Move a vehicle to the selected gas station.

Description

Technical Field

[0001] The present invention relates to collecting data on the turbidity of fuel tanks at each of a plurality of gas stations. A gas station is selected based on the collected data. A vehicle is moved to the selected gas station. Background Art

[0002] Vehicles require fuel to operate. When the fuel gauge indicator is below a certain level, it can be determined to refuel the vehicle. The vehicle can then be navigated to a gas station to refuel the vehicle. However, the opportunity to refuel the vehicle may be limited to a certain time of day and / or a particular gas station (e.g., at a particular location). For refueling, various environmental conditions may affect what time and / or location is possible and / or better than other times and / or locations. Unfortunately, current vehicles lack the ability to receive and process data to determine the time and location to refuel the vehicle. Summary of the Invention

[0003] A system includes a computer programmed to: collect data on the turbidity of fuel tanks 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.

[0004] The computer can be programmed to select the gas station based on the sum of a plurality of weighting factors, the plurality of weighting factors including at least one weighting factor based on the collected turbidity data.

[0005] The computer can be programmed to weight the factors based on user input.

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

[0007] The plurality of factors can include factors based on data collected on at least one of the occupancy level of the gas station and the facilities available at the gas station.

[0008] The computer can be programmed to determine the time elapsed since the fuel filter of the vehicle was replaced and select the gas station based on the elapsed time.

[0009] The computer can be programmed to select the gas station based on a predetermined route and the distance between each of the plurality of gas stations.

[0010] The computer can be programmed to select the gas station based on the estimated fuel level of the fuel tank at each of the plurality of gas stations.

[0011] The computer can be programmed to determine the refueling time of each fuel tank at each gas station in the gas stations, and select the gas stations that have had a elapsed time greater than a time threshold since the time when the fuel tank was refueled.

[0012] The computer can be programmed to determine the refueling time based on data collected on at least one of the coolant temperature of the vehicle, the atmospheric ozone level, and the air quality.

[0013] A method includes: collecting data on the turbidity of the fuel tank at each of a plurality of gas stations; selecting a gas station based on the collected data; and moving the vehicle to the selected gas station.

[0014] The method may include selecting the gas station based on the sum of a plurality of weighting factors, the plurality of weighting factors including at least one weighting factor based on the collected turbidity data.

[0015] The method may include weighting the factors based on user input.

[0016] The method may include weighting the factors based on the user operations of the vehicle.

[0017] The method may include a plurality of factors, the plurality of factors including factors based on data collected on at least one of the occupancy level of the gas station and the facilities available at the gas station.

[0018] The method may include determining the elapsed time since the fuel filter of the vehicle was replaced, and selecting the gas station based on the elapsed time.

[0019] The method may include selecting the gas station based on a predetermined route and the distances between each of the plurality of gas stations.

[0020] The method may include selecting the gas station based on the estimated fuel level of the fuel tank at each of the plurality of gas stations.

[0021] The method may include determining the refueling time of each fuel tank at each of the gas stations, and selecting the gas stations that have had a elapsed time greater than a time threshold since the time when the fuel tank was refueled.

[0022] The method may include determining the refueling time based on data collected on at least one of the coolant temperature of the vehicle, the atmospheric ozone level, and the air quality. Brief Description of the Drawings

[0024] Figure 1 is a block diagram of an example system for refueling a vehicle.

[0025] Figure 2 An example process for determining a refueling time for a vehicle is shown.

[0026] Figure 3 An example process for determining a gas station for a vehicle is shown. DETAILED DESCRIPTION

[0027] A system includes a computer programmed to: collect data on the turbidity of fuel tanks 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.

[0028] The computer may also be programmed to select the gas station based on the sum of a plurality of weighting factors, the plurality of weighting factors including at least one weighting factor based on the collected turbidity data.

[0029] The computer may also be programmed to weight the factors based on user input. The computer may also be programmed to weight the factors based on user operations of the vehicle. The plurality of factors may include factors based on data collected on at least one of an occupancy level of the gas station and facilities available at the gas station.

[0030] The computer may also be programmed to determine the time elapsed since replacing the fuel filter of the vehicle and select the gas station based on the elapsed time.

[0031] The computer may also be programmed to select the gas station based on a predetermined route and the distance between each of the plurality of gas stations.

[0032] The computer may also be programmed to select the gas station based on the estimated fuel level of the fuel tank at each of the plurality of gas stations.

[0033] The computer may also be programmed to determine the refueling time for each fuel tank at each of the gas stations and select a gas station having an elapsed time greater than a time threshold since the time the fuel tank was refueled.

[0034] The computer may also be programmed to determine the refueling time based on data collected on at least one of a coolant temperature of the vehicle, an atmospheric ozone level, and an air quality.

[0035] A method includes: collecting data on the turbidity of fuel tanks 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.

[0036] The method may further include selecting the gas station based on the sum of a plurality of weighting factors, the plurality of weighting factors including at least one weighting factor based on the collected turbidity data.

[0037] The method may further include weighting the factors based on user input. The method may further include weighting the factors based on user operations of the vehicle. In the method, the plurality of factors may include factors based on data collected regarding at least one of the occupancy level of the gas station and the facilities available at the gas station.

[0038] The method may further include determining the time elapsed since replacing the fuel filter of the vehicle and selecting the gas station based on the elapsed time.

[0039] The method may further include selecting the gas station based on the distance between a predetermined route and each of a plurality of gas stations.

[0040] The method may further include selecting the gas station based on the estimated fuel level of the fuel tank at each of a plurality of gas stations.

[0041] The method may further include determining the refueling time for each fuel tank at each of the gas stations and selecting the gas stations for which the elapsed time since refueling the fuel tank is greater than a time threshold.

[0042] The method may further include determining the refueling time based on data collected regarding at least one of the coolant temperature of the vehicle, the atmospheric ozone level, and the air quality.

[0043] Also disclosed is a computing device programmed to perform any of the above method steps. Also disclosed is a vehicle that includes the computing device. Also disclosed is a computer program product that includes a computer-readable medium storing instructions executable by a computer processor to perform any of the above method steps.

[0044] A vehicle computer may determine the time and location to refuel the vehicle based on data collected from sensors and servers. The computer may collect data regarding, for example, environmental conditions, vehicle parameters, vehicle location, etc., and determine the time to refuel the vehicle and the location to refuel the vehicle. The computer may determine a refueling score based on environmental and vehicle data to provide a more predictable refueling opportunity and to more consistently maintain the fuel in the vehicle. The computer may 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 may reduce constraints that may limit refueling opportunities.

[0045] Figure 1 FIG. 0000140 shows an example system 100 for refueling a vehicle 101. 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 position of the vehicle 101, data about the environment around the vehicle, and data about objects outside the vehicle (such as another vehicle, etc.). The vehicle 101 position is typically provided in a conventional form, for example, geographical coordinates (such as latitude and longitude coordinates) obtained via a navigation system using the Global Positioning System (GPS). Other examples of the data 115 can include measurements of the vehicle 101 systems and components, such as the vehicle 101 speed, the vehicle 101 trajectory, etc.

[0046] As is well known, the computer 105 is typically programmed for communication on the vehicle 101 network, which can include, for example, a communication bus. Via the network, the bus, and / or other wired or wireless mechanisms (such as 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, such as controllers, actuators, sensors, etc., including the sensor 110. Alternatively or additionally, in the case where the computer 105 actually includes multiple devices, the vehicle network can be used for communication between the devices represented as the computer 105 in the present disclosure. Additionally, the computer 105 can be programmed to communicate with a network 125, which, as described below, can include various wired and / or wireless networking technologies, such as cellular, low power wired and / or wireless packet networks, etc.

[0047] The data storage device 106 can be of any known type, for example, a hard disk drive, a solid state drive, a server, or any volatile or non-volatile medium. The data storage device 106 can store the collected data 115 sent from the sensor 110.

[0048] The sensor 110 can include various devices. For example, as is well known, various controllers in the vehicle 101 can operate as the sensor 110 to provide the data 115 via the network or bus of the vehicle 101, such as data 115 related to vehicle speed, acceleration, position, subsystem, and / or component status, etc. In addition, other sensors 110 can include cameras, motion detectors, etc., that is, sensors 110 for providing data 115 for evaluating the position of a target, the path of a projected target, evaluating the position of a road lane, etc. The sensor 110 can also include short-range radar, long-range radar, Light Detection and Ranging (LIDAR), and / or ultrasonic transducers.

[0049] The collected data 115 can include a variety of data collected in the vehicle 101. Examples of collected data 115 are provided above, and additionally, the data 115 is typically collected using one or more sensors 110 and can additionally include data calculated based on 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 calculated based on such data.

[0050] 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, an internal combustion engine and / or an electric motor, etc.), transmission components, steering components (e.g., which can include one or more of a steering wheel, a steering rack, etc.), braking components, parking assist components, adaptive cruise control components, adaptive steering components, movable seats, etc.

[0051] When the computer 105 operates 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. The fully autonomous mode is defined as a mode in which each of the propulsion (typically via a powertrain including an electric motor and / or an internal combustion engine), braking, and steering of the vehicle 101 is controlled by the computer 105. The semi-autonomous mode is a mode in which at least one of the propulsion (typically via a powertrain including an electric motor and / or an internal combustion engine), braking, and steering of the vehicle 101 is at least partially controlled by the computer 105 rather than by a human operator.

[0052] The system 100 can also include a network 125 that connects to the server 130 and the data storage device 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, and such remote sites may include the data storage device 135. The network 125 represents one or more mechanisms through 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, which include wired (e.g., cable and fiber optic) 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 using multiple communication mechanisms). Exemplary communication networks include wireless communication networks that provide data communication services (e.g., using low power consumption IEEE 802.11, vehicle-to-vehicle (V2V) (such as dedicated short-range communication (DSRC)), local area network (LAN) and / or wide area network (WAN) (including the Internet), etc.

[0053] The system may include a gas station 140. The gas 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 gas station 140 may include a station computer 145. The station computer 145 can communicate with the network 125. The station computer 145 can collect data 115 about the gas station 140 and send the data 115 to the server 130 and / or the computer 105 via the network 125. The station computer 145 can be, for example, a dedicated station console, a personal computer, a laptop computer, a tablet computer, a smart phone, etc.

[0054] The computer 105 can determine multiple 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 may affect when the vehicle 101 can refuel and where the vehicle 101 can refuel. The computer 105 can determine a refueling score and a location score based on the factors. Based on the collected data 115, each factor can be a value between 0 and 1, as shown in the following equations 1 to equation 15.

[0055] The refueling score can be a value used by the computer 105 to determine when to refuel the vehicle 101. For example, the computer 105 can determine the refueling score as a function of time and determine the time when the refueling score will drop below a refueling score threshold. When the time within the time threshold of the refueling time is reached, the computer 105 can determine to refuel the vehicle 101. Alternatively or additionally, if the computer 105 determines that the current refueling score is below the threshold, the computer 105 can determine to refuel the vehicle 101. The refueling score can be a combination of the factors, such as a weighted sum of multiple factors. For example, the computer 105 can collect data 115 about at least one 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 115. The refueling score can be a weighted sum of the factors:

[0056]

[0057] where S_refuel is the refueling score, i is an index indicating one of the multiple factors, n is the total number of factors used to determine the refueling score, f i is a value of one of the factors, and k i is the predetermined weight of the factor f i of the factor fi One or more of these 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 can thus predict the refueling score S for the time period based on the time-related factors. 加油 .

[0058] The location score can be a value that computer 105 uses to determine the location where vehicle 101 is refueled. When determining to refuel vehicle 101, computer 105 can determine the location scores of multiple gas stations 140. The gas stations 140 can be determined according to a list stored in data storage device 106 and / or server 130. Computer 105 can select the gas station 140 with the highest location score, and then can move vehicle 101 to the selected gas station 140 to refuel vehicle 101. As described below, the location score can be a combination of factors, e.g., a weighted sum of multiple factors. For example, computer 105 can collect data 115 on the turbidity of the fuel in the underground fuel storage tanks at each of the multiple gas stations 140, select a gas station 140 based on the collected data 115, and move vehicle 101 to the selected gas station 140. The location score can be a weighted sum of factors:

[0059]

[0060] where S 位置 is the location score, i is an index indicating one of the multiple 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 is the predetermined weight of factor f i .

[0061] Computer 105 can determine the vehicle fuel tank factor based on data 115 collected from the fuel tank of vehicle 101. Computer 105 can collect data 115 on the fuel level in the vehicle fuel tank from vehicle fuel tank sensor 110. The vehicle fuel tank factor can 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 can be inversely proportional to the vehicle fuel level data 115 in a linear manner, or the vehicle fuel tank factor can be related to the vehicle fuel level data 115 in a non-linear manner (e.g., polynomial, exponential, factorial, etc.). Equation 3 below describes an example sigmoid function f that describes the vehicle fuel tank factor. vft .

[0062]

[0063] 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 indicating the portion of the fuel tank volume that is full of fuel), 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 increases exponentially as the vehicle fuel tank factor f vft approaches 1.

[0064] The vehicle fuel tank factor can be weighted such that when the vehicle fuel level data 115 drops below the vehicle fuel level threshold, the weighted sum exceeds the refueling score threshold. That is, the computer 105 can determine the refueling time to be no later than the time when the vehicle fuel level is predicted to drop below the vehicle fuel level threshold. For example, as shown in Example Equation 4,

[0065]

[0066] where k vft is the weight value of 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.

[0067] The computer 105 can determine the vehicle not in use factor. The computer 105 can use known techniques to determine confidence values at multiple times of the day indicating the usage of the vehicle 101. The confidence value can be the probability that the vehicle 101 will be used at a specific time of the day. The computer 105 can determine the confidence value based on, for example, the 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 specific time of the day. At the time of day when it is predicted that the vehicle 101 is not in use, the vehicle not in use factor can increase, i.e., the computer 105 can prefer the refueling time when the vehicle 101 is not in use. In addition, the computer 105 can determine the time of the previous usage of the vehicle 101, and the vehicle not in use factor can be based on the time of the previous usage of the vehicle 101. For example, the vehicle not in use factor f can be determined using the normal distribution function of Example Equation 5 below niu :

[0068]

[0069] where t is the current time between 0 and 24 in hours, t niu is the predicted time when the vehicle 101 is not in use (e.g., outside the shift of servicing the vehicle 101, outside the user's commuting schedule, etc.), and C is a value that can be based on the degree t by which the vehicle not in use factor should increase niuAnd the determined constant value.

[0070] The computer 105 can determine the off-peak usage factor. The computer 105 can determine when the roads are likely to be congested at a given time of day. The computer 105 can request traffic data 115 from the server 130 and use probability calculations such as a hidden Markov model to determine the time indicating peak usage of the roads on a given day. The computer 105 can collect data 115 on the traffic congestion level of the roads and determine the off-peak usage factor based on the traffic congestion level. The off-peak usage factor can increase during off-peak usage times of the roads on a given day and decrease during peak usage times of the roads on a given day. For example, when the time of day is a peak usage time of the day (as determined from probability calculations), the off-peak usage factor can be 0, and when the time of day is an off-peak usage time of the day, the off-peak usage factor can be 1. For example, an S-shaped function such as the following Example Equation 6 can be used to determine the off-peak usage factor f npu :

[0071] f npu = C arccot(a·p(t)-b) (6)

[0072] where a, b, and C are predetermined constants determined to bound the off-peak usage factor between 0 and 1, and p(t) is the traffic congestion probability at a specific time t.

[0073] The computer 105 can determine the fuel price factor. The computer 105 can collect data 115 indicating fuel prices at a plurality of gas stations 140 from the server 130. 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 price. For example, the computer 105 can determine the difference between the current fuel price p1 at one of the gas stations 140 and the predicted fuel price p pred and determine the fuel price factor as:

[0074]

[0075] where C is a predetermined constant.

[0076] The computer 105 can determine the 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 the precipitation data 115 increases, the wind speed data 115 increases, and the temperature data 115 is above a heat threshold or below a cold threshold. For example, the weather factor f 天气 can be the sum of the data 115:

[0077]

[0078] 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 the heat threshold and 1 when the current ambient temperature is above the heat threshold, and T LO is a Boolean value that is 0 when the current ambient temperature is above the cold threshold and 1 when the current ambient temperature is below the cold threshold.

[0079] Computer 105 can determine a station preference factor. Computer 105 can determine a plurality of preferred gas stations 140, such as company gas stations 140 approved by the fleet operator, gas stations 140 specified by the user of vehicle 101, gas stations 140 near the driving route of vehicle 101, etc. When the distance between vehicle 101 and one of the preferred stations 140 decreases, the station preference factor can increase. For example, the station preference factor f can be determined based on the following example equation 9 sp :

[0080] f sp = C exp(-ax) (9)

[0081] where a, C are predetermined constants, and x is the current distance between the location of vehicle 101 and the nearest preferred gas station 140.

[0082] Computer 105 can determine a preheat factor. Computer 105 can determine the engine coolant temperature from sensor 110. As the engine coolant warms up, the powertrain consumes less fuel, so when the engine coolant temperature is above the coolant temperature threshold, vehicle 101 can consume less fuel when moving to gas station 140. When the engine coolant temperature increases, the preheat factor can increase. For example, the preheat factor f can be determined based on the following example equation 10 预热 :

[0083]

[0084] where a, C are predetermined constants, arcsinh is the known inverse hyperbolic sine function, and T 冷却剂 is the current coolant temperature.

[0085] The computer 105 can determine an ozone alert factor. The computer 105 can receive data 115 from the server 130, and the data 115 indicates whether the current day is an ozone alert day, that is, a day when local authorities encourage users to refuel their vehicles 101 after dark to reduce ozone and smog generation. The computer 105 can determine the refueling time after the predicted sunset time on the ozone alert day. When the data 115 from the server 130 indicates that the current day is an ozone alert day, the ozone alert factor can be reduced during the time period between the predicted sunrise time and the predicted sunset time. For example, 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, the ozone alert factor can be the Boolean value 0, and when the current time is between the predicted sunset time on the current day and the predicted sunrise time on the next day, the ozone alert factor can be 1.

[0086] The computer 105 can determine an air quality factor. The air quality factor can be based on data 115 indicating the level of dust and debris that may interfere with refueling. The computer 105 can collect data 115 on 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 can be determined based on the following example equation 11 空气 :

[0087]

[0088] where a, C are predetermined constants, φ is the relative humidity, and v is the wind speed.

[0089] The computer 105 can determine a distance factor. The distance factor can be based on data 115 indicating the distance between the planned predetermined route of the vehicle 101 and each gas station 140. The distance factor can decrease as the distance increases. For example, the distance factor f 距离 can be inversely proportional to the distance x between the geographical location coordinates of the vehicle 101 and the geographical location coordinates of the gas station 140 站 , for example, as shown in example equation 12:

[0090]

[0091] where C is a predetermined constant.

[0092] The computer 105 can determine a facility factor. The facility factor can be based on data 115 indicating the facilities available at the gas station 140 (i.e., points of interest and / or amenities). Example facilities include, for example, restaurants, coffee shops, scenery, walking paths, pet areas, wireless access, etc. For facilities predetermined by the user as preferred based on, for example, historical data, user input, etc., the facility factor can increase. The computer 105 can determine the number of facilities n at the gas station 140f and determine the facility factor f according to the following exemplary equation 13 设施 :[[]]

[0093]

[0094] The computer 105 can determine the accessibility factor. The accessibility 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. These features can include, for example, whether the gas station 140 is along the direction of travel of the vehicle 101 on the route, the number of available fuel pumps at the gas station 140, etc. The accessibility factor can increase as the number of features increases. The computer 105 can identify the number N of features 特征 and determine the accessibility factor f according to the following exemplary equation 14 eac :[[]]

[0095]

[0096] The computer 105 can determine the turbidity factor of the fuel at the gas station 140. The gas station 140 can include a plurality of underground fuel storage tanks for storing liquid fuel. The fuel storage tanks can be refilled by a truck delivering fuel. When the truck refills the fuel storage tanks, the turbidity of the fuel in the fuel storage tanks can increase as the sediment in the fuel storage tanks is agitated. The sediment can be collected by the fuel filter in the vehicle 101, thereby reducing the life of the fuel filter. The computer 105 can collect data 115 from the server 130 indicating the refill time when the underground fuel storage tanks were last refilled. As the refill time increases, the sediment in the underground fuel storage tanks can settle. The turbidity factor can decrease as the refill time increases. In addition, the computer 105 can collect data 115 on the estimated fuel levels of the fuel storage tanks at each of the plurality of gas stations 140. As the amount of sediment increases relative to the remaining fuel volume, a lower estimated fuel level can increase the turbidity factor. The computer 105 can also determine the time elapsed since the fuel filter of the vehicle 101 was replaced and determine the turbidity factor based on the elapsed time. For example, according to the following exemplary equation 14, the turbidity factor f 浊度 is based on the refill time t of the fuel storage tank fill and the estimated fuel level V 存储 :[[]]

[0097]

[0098] The computer 105 can determine a congestion factor. The congestion factor can be based on data 115 indicating the occupancy of vehicles 101 at a gas station 140 and the number of available fuel pumps at the gas station 140. The computer 105 can collect data 115 indicating the occupancy of vehicles 101 and the number of available fuel pumps at each gas station 140 from a server 130. For an increasing number of vehicles 101 and a decreasing number of available fuel pumps, the congestion factor can increase. The computer 105 can determine the number n of vehicles veh and the number n of available fuel pumps 泵 , and determine a congestion factor f based on the following example equation 15 cong :

[0099]

[0100] A refueling score and a location score can be determined based on a weighted sum of factors. The computer 105 can assign weight values to each factor to control the influence of a particular factor on the corresponding score. For example, for the refueling 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 affect the refueling score more than the vehicle not in use factor. Each weight value can be a predetermined value stored in the data storage device 106 and / or the server 130. Alternatively or additionally, the computer 105 can determine each weight value based on user input and / or user operations of the vehicle 101.

[0101] For example, an initial weight value can be determined to focus on customer convenience. For example, the weight values of the vehicle fuel tank factor and the vehicle not in use factor are higher than other factors. The user can adjust the weight values based on personal preferences. For example, the user can select a higher weight value for the fuel price factor, preferably spending less money on fuel. Vehicles 101 that may require a refueling time longer than a few minutes (e.g., electric vehicles) can have a higher facility factor weight value to keep the user occupied during refueling. In areas where a daytime refueling penalty is imposed on ozone action days, the user can select a higher weight value for the ozone alert factor. If the warranty data shows that the fuel filters of certain vehicles 101 or certain geographical areas are clogged, 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 values of specific factors and / or the data 115 used to determine the factors, such as linear functions, polynomial functions, exponential functions, etc.

[0102] Figure 2An example process 200 for determining a refueling time for vehicle 101 is shown. Process 200 begins at block 205, where computer 105 actuates one or more sensors 110 to collect data 115. Computer 105 may actuate sensors 110 to collect data 115 regarding, for example, vehicle 101 location, vehicle 101 trajectory, fuel gauge level, atmospheric ozone level, weather, etc.

[0103] Next, at block 210, computer 105 determines factors based on data 115. As described above, computer 105 may determine multiple factors based on the collected data 115. For example, computer 105 may determine an ozone alert factor based on the collected data 115 regarding atmospheric ozone. In another example, computer 105 may determine a vehicle fuel tank factor based on the vehicle fuel tank level. Example factors based on data 115 are shown in equations 1 through 15 above.

[0104] Next, at 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 needs to be refueled. When the refueling score drops below a refueling score threshold, computer 105 may move vehicle 101 to be refueled. The weight of each factor may be a predetermined value or a non-constant function stored in server 130 and / or data storage device 106. Alternatively or additionally, the weight of each factor may be determined based on, for example, user input, user operations of vehicle 101, etc. For example, the weight of one of the factors may initially be a predetermined constant value stored in server 130, and computer 105 may prompt the user to provide input to optionally change the value of the weight. In another example, the weight of one of the factors may be an exponential function of the data 115 for that factor, and computer 105 may be programmed not to prompt the user for input to change the weight.

[0105] Next, at block 220, computer 105 determines the refueling time. The refueling time is the predicted time at which the refueling score will drop below the refueling score threshold. As described above, computer 105 may predict the refueling score for a future time period based on one or more time-related factors and determine the time at which the refueling score will drop below the refueling score threshold. Thus, at the refueling time, computer 105 may move vehicle 101 to gas station 140 to be refueled.

[0106] Next, in block 225, computer 105 determines whether the refueling time has arrived. When the current time is within the time threshold of the refueling time, computer 105 may determine that the refueling time has arrived. The time threshold may be a predetermined value, such as 10 minutes, and is stored in server 130 and / or data storage device 106. If the refueling time has arrived, process 200 continues in block 230. Otherwise, computer 105 remains in block 225 until the refueling time has arrived.

[0107] In block 230, computer 105 identifies gas station 140 based on the factors. As described above and shown in process 300 below, computer 105 may determine a location score for each of the plurality of gas stations 140. Computer 105 may identify gas station 140 based on the refueling score.

[0108] Next, in block 235, computer 105 moves vehicle 101 to the gas station 140 identified in block 230. Computer 105 may actuate steering device 120, propulsion device 120, and brake 120 to move vehicle 101 to gas station 140 for refueling. After block 235, process 200 ends.

[0109] Figure 3 An example process 300 for determining gas station 140 where vehicle 101 is refueled is shown. Process 300 begins at block 305, where computer 105 actuates one or more sensors 110 to collect data 115. As described above, computer 105 may collect data 115 regarding vehicle 101 and / or the plurality of gas stations 140. Computer 105 may collect data 115 from each station computer 145 at each gas station 140.

[0110] Next, in block 310, computer 105 determines a plurality of factors for each gas station 140. As described above, each factor may be based on data 115 collected by computer 105 from server 130 and / or sensor 110. For example, computer 105 may determine a distance factor that is inversely proportional to the determined distance between gas station 140 and vehicle 101 based on geographic location data 115.

[0111] Next, in block 315, computer 105 determines a location score for each gas station 140. Computer 105 may determine the location score as a weighted sum of the above factors. The weight of each factor may be a predetermined value stored in server 130 and / or data storage device 106. Alternatively or additionally, computer 105 may adjust the weight of one or more of the factors based on, for example, user input, vehicle 101 driving history, etc.

[0112] Next, in block 320, computer 105 identifies the gas station 140 with the highest location score. After identifying the gas station 140, computer 105 can move the vehicle 101 to the identified gas station 140, as described above in block 235 of process 200. After block 320, process 300 ends.

[0113] As used herein, the adverb "substantially" modifying an adjective means that a shape, structure, measurement, value, calculation, etc. may deviate from an exactly described geometric shape, distance, measurement, value, calculation, etc. due to defects in materials, processing, manufacturing, data collector measurements, calculations, processing times, communication times, etc.

[0114] Computers 105 generally each include instructions executable by one or more computing devices (such as those identified above) and for implementing the blocks or steps of the processes described above. The computer-executable instructions may be compiled or interpreted by computer programs created using a variety of programming languages and / or technologies, the variety of programming languages and / or technologies including, but not limited to, the following in single form or in combination: Java TM 、C, C++, Visual Basic, Java Script, Perl, HTML, etc. Generally, a processor (e.g., a microprocessor) receives instructions, for example, from a memory, a computer-readable medium, etc., and executes these instructions to perform one or more processes, which include one or more of the processes described herein. A variety of computer-readable media may be used to store and transmit such instructions and other data. Files in computing device 105 are generally collections of data stored on a computer-readable medium (such as a storage medium, random access memory, etc.).

[0115] A computer-readable medium includes any medium that participates in providing data (e.g., instructions) that can be read by a computer. Such media can take many forms, including but not limited to non-volatile media, volatile media, etc. Non-volatile media includes, for example, optical or magnetic disks and other persistent memories. Volatile media includes dynamic random access memory (DRAM) which typically constitutes main memory. Common forms of computer-readable media include (e.g.) floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic medium, CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with punched patterns, RAM, PROM, EPROM, FLASH EEPROM, any other memory chip or memory cartridge, or any other medium readable by a computer.

[0116] Regarding the media, processes, systems, methods, etc. described herein, it should be understood that although the steps of such processes, etc. have been described as occurring in a certain ordered sequence, such processes can be practiced with the described steps executed in an order different from the order described herein. It should also be understood that certain steps can be executed simultaneously, additional steps can be added, or certain steps described herein can be omitted. For example, in process 200, one or more steps can be omitted, or the steps can be executed in an order different from Figure 2 that shown in

[0117] In other words, the description of the systems and / or processes herein is provided for the purpose of illustrating certain embodiments and should not be construed as limiting the disclosed subject matter.

[0117] Accordingly, it should be understood that the present disclosure, including the above description and the drawings and the following claims, is intended to be illustrative and not restrictive. After reading the above description, many embodiments and applications other than the examples provided will be apparent to those skilled in the art. The scope of the present invention should not be determined with reference to the above description, but should be determined with reference to the full scope of the claims appended to the present invention and / or included in a non-provisional patent application based on the present invention and the equivalents of the rights enjoyed by such claims. It is expected and contemplated that future developments will occur in the technologies discussed herein, and 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.

[0118] Unless otherwise specified or the context otherwise requires, the article "a" modifying a noun should be understood to mean one or more. The phrase "based on" encompasses being based in part or entirely on.

Claims

1. An enhanced vehicle refueling system, comprising a computer programmed to: Collect data on refueling times indicating when the underground fuel tank was recently refilled at each of a plurality of gas stations; Calculate data on the turbidity of the underground fuel tank based on the refueling times; Select a gas station based on the collected data; And Move the vehicle to the selected gas station.

2. The system of claim 1, wherein the computer is further programmed to select the gas station based on the sum of a plurality of weighting factors, the plurality of weighting factors including at least one weighting factor based on the collected turbidity data.

3. The system of claim 2, wherein the computer is further programmed to weight the weighting factors based on user input.

4. The system of claim 2, wherein the computer is further programmed to weight the weighting factors based on user operations of the vehicle.

5. The system of claim 2, wherein the plurality of weighting factors includes a factor based on data collected on at least one of the occupancy level of the gas station and the facilities available at the gas station.

6. The system of claim 1, wherein the computer is further programmed to determine the time elapsed since the fuel filter of the vehicle was replaced and select the gas station based on the elapsed time.

7. The system of claim 1, wherein the computer is further programmed to select the gas station based on a predetermined route and the distances between each of the plurality of gas stations.

8. The system of claim 1, wherein the computer is further programmed to select the gas station based on the estimated fuel level of the fuel tank at each of the plurality of gas stations.

9. The system of claim 1, wherein the computer is further programmed to determine the refueling time of each fuel tank at each of the gas stations and select a gas station having an elapsed time greater than a time threshold since the time the fuel tank was refueled.

10. The system of claim 1, wherein the computer is further programmed to determine a refueling time based on data collected on at least one of the coolant temperature of the vehicle, the atmospheric ozone level, and the air quality.

11. An enhanced vehicle refueling method, comprising: Collect data on refueling times indicating when the fuel tank was recently refilled at each of a plurality of gas stations; Calculate data on the turbidity of the fuel tank based on the refueling times; Select a gas station based on the collected data; And Move the vehicle to the selected gas station.

12. The method of claim 11, further comprising selecting the gas station based on the sum of a plurality of weighting factors, the plurality of weighting factors including at least one weighting factor based on the collected turbidity data.

13. The method of claim 12, further comprising weighting the weighting factors based on user input.

14. The method of claim 12, further comprising weighting the weighting factors based on user operations of the vehicle.

15. The method according to claim 12, wherein the plurality of weighting factors includes a factor based on data collected regarding at least one of the occupancy level of the gas station and the facilities available at the gas station.

16. The method according to claim 11, further comprising determining the time elapsed since replacing the fuel filter of the vehicle and selecting the gas station based on the elapsed time.

17. The method according to claim 11, further comprising selecting the gas station based on the distance between a predetermined route and each of the plurality of gas stations.

18. The method according to claim 11, further comprising selecting the gas station based on the estimated fuel level of the fuel tank at each of the plurality of gas stations.

19. The method according to claim 11, further comprising determining the refueling time for each fuel tank at each of the gas stations and selecting a gas station having an elapsed time greater than a time threshold since the fuel tank was refueled.

20. The method according to claim 11, further comprising determining the refueling time based on data collected regarding at least one of the coolant temperature of the vehicle, the atmospheric ozone level, and the air quality.

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