Electric vehicle charging suggestion system and method

Abstract

The invention provides an electric vehicle charging suggestion system and method. An electric vehicle charging suggestion method is disclosed. The method may include obtaining vehicle information, driver information, and vehicle trip information associated with a vehicle. The vehicle information may include a current state of charge (SOC) of a vehicle battery. The method may also include determining a distance to be traveled by the vehicle from a current location of the vehicle based on the vehicle travel information. The method may also include determining an expected SOC required to travel the distance from the current location of the vehicle based on the vehicle information, the driver information, and the vehicle trip information. The method may additionally include transmitting a suggested notification to a user device to charge the vehicle battery when the expected SOC may be greater than the current SOC.

Description

Technical Field

[0001] The present disclosure relates to electric vehicle charging recommendation systems and methods, and more particularly to systems and methods for minimizing charging time and enabling retail or commercial vehicle drivers to complete vehicle trips without unplanned charging stops. Background Art

[0002] Electric vehicles (EVs) operate on electrical energy, and a vehicle user may periodically charge the vehicle battery to ensure uninterrupted vehicle operation. For example, when a user is driving a vehicle on a long journey, the user may charge the vehicle battery multiple times. In some cases, when battery charging is not scheduled when planning a journey, the user may have to make unplanned charging stops during the journey. This may cause inconvenience to the user.

[0003] Additionally, commercial vehicle drivers typically traverse predefined distances or clock predefined driving times during their shifts. Unplanned charging stops can impact drivers' driving schedules and can result in loss of time and resources for commercial vehicle fleet operators. Summary of the invention

[0004] The present disclosure describes an electric vehicle charging recommendation system that can provide charging recommendations to a vehicle, which can enable the vehicle to complete a vehicle trip without any unplanned charging stops. The system can provide charging recommendations when the system determines that the vehicle may not be able to complete the vehicle trip based on the current vehicle battery state of charge (SOC) level. The system can provide charging recommendations well before the vehicle battery SOC becomes critically low, thereby enabling the vehicle driver (retail or commercial vehicle driver) to minimize unplanned charging stops. The charging recommendations can include an optimal amount of energy to be charged / delivered to the vehicle battery and / or an optimal time to charge the vehicle battery.

[0005] In some aspects, the system may obtain vehicle information, driver information, and vehicle trip information from the vehicle, a user device associated with the vehicle driver, and / or one or more servers. The vehicle information may include, for example, the current battery SOC, the current vehicle geographic location, information associated with battery charging rates at different SOC levels, vehicle type, vehicle and cargo / load weight, etc. The driver information may include, for example, average driving speed, historical driving patterns, historical patterns of interruptions on vehicle trips, etc. The vehicle trip information may include, for example, trip / shift duration, trip / shift distance, information associated with road conditions, weather / season, traffic, etc. on the vehicle trip route, the location of the fleet's designated charging stations, the location of the charging station closest to the vehicle, etc.

[0006] When the vehicle is likely on a trip, the system may determine a remaining distance to be traversed by the vehicle based on the current vehicle geographic location, the average driving speed, and the remaining shift duration. In response to determining the remaining distance, the system may determine an expected battery SOC that may be required for the vehicle to traverse the remaining distance. The system may determine the expected battery SOC based on the obtained vehicle information, driver information, and vehicle trip information.

[0007] In response to determining the expected battery SOC, the system can compare the expected battery SOC to the current battery SOC. When the expected battery SOC may be greater than the current battery SOC, the system can provide charging recommendations to the vehicle. The system can also recommend an amount of energy that can be charged / delivered to the vehicle battery, which amount can be equal to the difference between the expected battery SOC and the current battery SOC.

[0008] The system may additionally suggest an optimal time to charge the vehicle battery based on the driver's rest schedule and / or information associated with battery charging rates at different SOC levels. For example, the system may suggest charging the vehicle battery when the vehicle driver is likely to be scheduled to rest. As another example, the system may suggest charging the vehicle battery when the battery SOC supports fast charging.

[0009] In a further aspect, the system may suggest to the vehicle driver the location of a charging station at which the vehicle battery may be charged. The suggested charging station may be an available charging station that may be closest to the vehicle's current geographic location and / or the driver / fleet operator's preferred charging station. In a further aspect, the system may determine whether the current battery SOC may enable (or allow) the vehicle to travel to the nearest charging station. When the system determines that the current battery SOC may not enable the vehicle to travel to the nearest charging station, the system may send a request to a mobile charging vehicle (e.g., a truck or drone) to provide charging assistance to the vehicle.

[0010] The present disclosure describes an electric vehicle charging recommendation system that provides charging recommendations to a vehicle, which can enable the vehicle to complete a planned vehicle trip without any unplanned charging interruptions. The system recommends an optimal amount of energy that can be charged in a vehicle battery, thereby saving the vehicle driver's charging time and conserving energy resources. The system also recommends an optimal time to charge the vehicle battery, which can achieve fast battery charging and efficient use of driver rest time.

[0011] These and other advantages of the present disclosure are provided in detail herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Detailed description is set forth with reference to the accompanying drawings. The use of the same reference numerals may indicate similar or identical items. Various embodiments may utilize elements and / or components other than those shown in the accompanying drawings, and some elements and / or components may not be present in various embodiments. The elements and / or components in the accompanying drawings are not necessarily drawn to scale. Throughout this disclosure, singular and plural terms may be used interchangeably, depending on the context.

[0013] Figure 1 An exemplary environment is depicted in which techniques and structures for providing the systems and methods disclosed herein may be implemented.

[0014] Figure 2 A block diagram of an exemplary electric vehicle (EV) charging recommendation system according to the present disclosure is depicted.

[0015] Figure 3 An exemplary graph of unit distance versus vehicle battery state of charge (SOC) is depicted in accordance with the present disclosure.

[0016] Figure 4 Depicted are exemplary graphs showing battery SOC of two EVs in accordance with the present disclosure.

[0017] Figure 5 An exemplary graph of battery charging power versus battery SOC is depicted in accordance with the present disclosure.

[0018] Figure 6 A flow chart of an exemplary EV charging recommendation method according to the present disclosure is depicted. DETAILED DESCRIPTION

[0019] The present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the disclosure are shown and which are not intended to be limiting.

[0020] Figure 1 An exemplary environment 100 in which techniques and structures for providing the systems and methods disclosed herein may be implemented is depicted. Environment 100 may include a vehicle 105, which may be a battery electric vehicle (BEV) or a plug-in hybrid electric vehicle (PHEV). Vehicle 105 may take the form of any passenger vehicle or commercial vehicle, such as an SUV, a car, a crossover, a van, a minivan, a bus, a truck, etc. In addition, vehicle 105 may be a manually driven vehicle and / or may be configured to operate in a partially or fully autonomous mode. Vehicle 105 may include a traction battery or battery pack ("vehicle battery", not shown) that may provide energy for vehicle propulsion. The vehicle battery may be charged by an external power source (e.g., an electric vehicle (EV) charger). Vehicle 105 may travel on a road network 110, which may be located in a city, a rural area, a suburb, etc.

[0021] Environment 100 may also include an electric vehicle charging recommendation system 115 (system 115) and one or more servers 120. System 115, server 120, and vehicle 105 may be communicatively coupled to one another via one or more networks 125. Network 125 illustrates an exemplary communication infrastructure in which the connected devices discussed in various embodiments of the present disclosure may communicate. Network 125 may be and / or include the Internet, a private network, a public network, or other configurations operating using any one or more known communication protocols, such as, for example, Transmission Control Protocol / Internet Protocol (TCP / IP), Wi-Fi, UWB based on the Institute of Electrical and Electronics Engineers (IEEE) standard 802.11, and cellular technologies such as Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), High Speed ​​Packet Access (HSPDA), Long Term Evolution (LTE), Global System for Mobile Communications (GSM), and Fifth Generation (5G), to name a few examples.

[0022] In some aspects, the server 120 may be part of a cloud-based computing infrastructure and may be associated with and / or include a telematics service delivery network (SDN) that provides services to the vehicle 105 and other vehicles that may be part of a commercial vehicle fleet. Figure 1 In this case, a vehicle fleet operator (not shown) may operate server 120. The vehicle fleet operator may store vehicle trip information associated with each vehicle in the vehicle fleet (e.g., vehicle 105) and / or vehicle trip information associated with each vehicle driver on server 120. Server 120 may be configured to transmit vehicle trip information to system 115 and / or vehicle 105 via network 125 at a predefined frequency (e.g., every 1 minute, 5 minutes, or 10 minutes). In a further aspect, server 120 may be associated with a global positioning system (GPS) and may be configured to store and provide road and traffic information associated with road network 110. Server 120 may additionally store and provide charging station locations on road network 110. In yet another aspect, server 120 may be configured as a server that stores and provides weather information in a geographic area including road network 110.

[0023] The system 115 may be configured to provide charging recommendations to a vehicle driver associated with the vehicle 105 (as well as vehicle drivers associated with other vehicles operating as part of a vehicle fleet). The vehicle driver may be a retail or commercial vehicle driver. Specifically, the system 115 may be configured to obtain one or more inputs from the vehicle 105 and the server 120, and may provide charging recommendations to the vehicle driver based on the inputs obtained. The charging recommendations may be associated with an optimal amount of energy that can be charged / delivered to the vehicle battery and / or an optimal time to charge the vehicle battery. The system 115 may obtain inputs from the vehicle 105 and the server 120, and determine charging recommendations at a predefined frequency (e.g., every 1 minute, 5 minutes, or 10 minutes) such that charging recommendations may be provided to the vehicle driver for a predetermined duration before the battery state of charge (SOC) becomes critically low.

[0024] In some aspects, the system 115 may obtain a first input (“vehicle information”) from the vehicle 105 via the network 125. The vehicle information may include, for example, the current vehicle battery SOC level, the vehicle's current geographic location, average vehicle travel speed, vehicle battery characteristics (including information associated with battery charging rates for different battery SOC levels), vehicle weight (including the weight of a load on the vehicle 105), vehicle type, vehicle historical travel or usage patterns, and the like.

[0025] The system 115 may also obtain a second input ("driver information") from a user device associated with the vehicle driver, the vehicle 105, and / or the server 120 via the network 125. The driver information may include, for example, a driver rest schedule on a vehicle trip, historical driving patterns (including the average driving speed of the vehicle driver for different vehicle types), the driver's preference for a particular EV charging station, and the like. In some aspects, the driver rest schedule may be based on a historical pattern of rest that the vehicle driver typically takes. For example, the driver rest schedule may indicate a specific time of day when the vehicle driver typically takes a rest, a specific distance that the vehicle driver can drive the vehicle before taking a rest, and the like. In other aspects, the driver rest schedule may be defined or preset by the fleet operator. In this case, the server 120 may provide the driver rest schedule to the system 115.

[0026] The system 115 may additionally obtain a third input (“Vehicle Trip Information Associated with the Vehicle 105 and / or the Vehicle Driver”) from the server 120 via the network 125. The vehicle trip information may include, for example, the total vehicle trip duration (or a preset “shift duration” for the vehicle driver), the total vehicle trip distance (or a preset “shift distance” for the vehicle driver), charging stations on the vehicle trip, locations of fleet-designated charging stations, locations of charging stations closest to the vehicle 105, fleet depot locations, source and destination locations associated with the vehicle trip, information associated with traffic, weather / season and road conditions on the vehicle trip route, information associated with the duration of planned vehicle stops on the vehicle trip, and the like. The shift duration may indicate the minimum duration that the vehicle driver may be expected to drive on the vehicle trip in a day (e.g., for 8 hours, from 8 a.m. to 4 p.m., etc.). The shift distance may indicate the minimum distance that the vehicle driver may be expected to travel on the vehicle trip in a day (e.g., 150 miles, 200 miles, etc.). The weather information may include, for example, expected ambient temperature, wind conditions, the presence or absence of snow or rain on the vehicle trip route, and the like. The road condition information may include road slope, road curvature, road inclination (eg, uphill or downhill road), and the like.

[0027] In response to obtaining the vehicle information, the driver information, and the vehicle trip information, the system 115 may be configured to estimate whether the vehicle 105 will be able to complete the vehicle trip at the current battery SOC level. In order to determine whether the vehicle 105 is able to complete the vehicle trip, the system 115 may first determine the remaining distance that the vehicle 105 is to travel on the vehicle trip based on the vehicle's current geographic location, the average driving speed of the vehicle driver on the vehicle trip (or the driving speed of the vehicle 105), and the remaining shift duration. For example, if the average driving speed is 30 miles per hour and the remaining shift duration is 3 hours, the system 115 may determine that the remaining distance that the vehicle 105 is to traverse may be 90 miles. In some aspects, the remaining distance to be traversed may be additionally based on road conditions, weather conditions, etc. (e.g., the average driving speed may be lower in bad weather conditions). In other aspects, the system 115 may determine the remaining distance that the vehicle 105 is to traverse based on the vehicle's current geographic location and the shift distance. In this case, the system 115 may obtain the vehicle trip start location from the server 120, and may determine the vehicle trip destination location by adding the shift distance (e.g., adding the geographic distance) to the vehicle trip start location. The system 115 may determine the remaining distance to be traversed by the vehicle 105 by subtracting the vehicle's current geographic location from the vehicle's trip destination location (eg, subtracting the geographic distance).

[0028] In response to determining the remaining distance to be traversed by the vehicle 105, the system 115 may determine whether the current battery SOC level will enable (or allow) the vehicle operator to traverse the remaining distance. In other words, the system 115 may "predict" whether the vehicle battery is likely to run out of charge before the vehicle 105 travels the remaining distance, or whether the vehicle battery has sufficient charge to travel the remaining distance. The system 115 may determine whether the current battery SOC level will enable (or allow) the vehicle operator to traverse the remaining distance by determining the energy efficiency patterns of the vehicle 105 and the vehicle operator. In combination Figure 2 Describe the concept of energy efficiency mode.

[0029] In response to determining that the vehicle 105 may not be able to travel the remaining distance, the system 115 can determine an optimal amount of energy that the vehicle driver can charge / transfer to the vehicle battery and / or an optimal time to charge the battery, which can enable the vehicle driver to complete the remaining distance. The system 115 can then transmit a charging recommendation notification to a user device associated with the vehicle driver and / or a vehicle human-machine interface (HMI). The recommendation notification can include the determined optimal amount of energy and optimal time to charge the battery.

[0030] In some aspects, the system 115 can determine the optimal amount of energy so that the amount of energy can be sufficient to enable the vehicle 105 to travel the remaining distance (and to travel back to the fleet depot). The system 115 may not recommend adding excess energy (which may result in increased charging time). In this way, the system 115 can enable optimization of the battery charging duration of the vehicle 105. Figure 2 Describe the details of the process used to determine the optimal amount of energy.

[0031] The system 115 may determine the best time to charge the battery based on the driver's rest schedule and / or information related to battery charging rates for different battery SOC levels. For example, the system 115 may use information associated with battery charging rates for different battery SOC levels and may suggest to the vehicle driver to charge the vehicle battery when the battery SOC level supports fast charging. This may encourage the vehicle driver to save time charging the vehicle battery.

[0032] As another example, the system 115 may suggest to the vehicle driver that the vehicle battery be charged while the driver is resting (based on the driver rest schedule). In this case, the system 115 may additionally suggest a charging station (e.g., charging station 130) at which the vehicle driver may charge the vehicle battery. The charging station 130 may be on the vehicle travel route and may be a driver preferred charging station and / or a fleet operator preferred charging station. The charging station 130 may additionally include food, entertainment, etc. facilities that may enable the vehicle driver to rest and charge the vehicle battery at the same time. In this case, the system 115 may transmit the location of the charging station 130 to the user device and / or the vehicle HMI.

[0033] In some aspects, in order to suggest a charging station 130 to the vehicle driver for charging the vehicle battery, the system 115 may first determine one or more charging stations that may be located on the vehicle's travel route where the vehicle driver can charge the vehicle battery (e.g., one or more charging stations where the driver can rest). The system 115 may also determine a charging station (e.g., charging station 130) from the one or more charging stations determined to be closest to the vehicle's current geographic location. The system 115 may then determine whether the current battery SOC level will enable (or allow) the vehicle 105 to reach the determined nearest charging station (e.g., charging station 130). When the system 115 determines that the current battery SOC level will enable (or allow) the vehicle 105 to reach the charging station 130, the system 115 may transmit the location of the charging station 130 to the user device and / or the vehicle HMI.

[0034] On the other hand, when the system 115 determines that the current battery SOC level may not enable (or allow) the vehicle 105 to reach the charging station 130, the system 115 may transmit an assistance notification / request to the server 120. In this case, in response to receiving the assistance notification, the server 120 (or the fleet operator) may send a command to a remote charging vehicle (e.g., a truck) or a charging drone to reach the location of the vehicle 105 and charge the vehicle battery. The server 120 may additionally send the real-time vehicle 105 geographic location to the remote charging vehicle / charging drone so that the charging vehicle / charging drone can track the movement of the vehicle 105 and provide charging assistance to the vehicle 105 based on the real-time vehicle 105 geographic location.

[0035] It will be appreciated by those skilled in the art from the above description that since the system 115 determines charging recommendations at a predefined frequency (e.g., every 1 minute, 5 minutes, or 10 minutes), the system 115 can predetermine well in advance that the vehicle 105 may not be able to complete the vehicle journey at the current SOC level. Therefore, the system 115 can provide charging recommendations to the vehicle driver before the battery SOC becomes extremely low. In this way, the system 115 can assist the vehicle driver in better planning battery charging on the vehicle journey. In addition, since the system 115 recommends the best time to charge the vehicle battery (e.g., during the driver's planned rest), unplanned stops for battery charging can be minimized. In addition, since the system 115 recommends the best amount of energy for charging the vehicle battery and the best time (which can be, for example, at an SOC level at which the vehicle battery can achieve fast charging), the system 115 can assist in reducing charging time.

[0036] Combine the following Figure 2 A detailed process of determining charging recommendations and providing charging recommendations to the vehicle 105 is described.

[0037] Figure 2 A block diagram of an exemplary electric vehicle (EV) charging recommendation system 200 (system 200) according to the present disclosure is depicted. Figure 2 When, you can refer to Figure 3 , Figure 4 and Figure 5 .

[0038] The system 200 can be the same as the system 115. As described herein, the system 200 can be implemented in hardware, software (e.g., firmware), or a combination thereof. The system 200 can be connected to the vehicle 202, the user device 204, and one or more servers 206 via a network 208. In some aspects, the system 200 can be part of the vehicle 202. In other aspects, the system 200 can be part of the server 206. In yet another aspect, the system 200 can be part of an external server (not shown), which may not be part of the server 206.

[0039] Vehicle 202 may be the same as vehicle 105, server 206 may be the same as server 120, and network 208 may be the same as network 125. User device 204 may be associated with a vehicle driver (not shown) of vehicle 202. User device 204 may be, for example, a mobile phone, a laptop, a computer, a tablet, a wearable device, or any other similar device having communication capabilities.

[0040] System 200 may include multiple units including, but not limited to, transceiver 210, processor 212, and memory 214. Transceiver 210 may be configured to send and receive information to and from vehicle 202, user device 204, and server 206 via network 208.

[0041] Processor 212 may be an artificial intelligence (AI) based processor and may be configured to communicate with one or more memory devices (e.g., memory 214 and / or one or more external databases ( Figure 2 214 to store programs in code and / or store data to perform various system 200 operations according to the present disclosure. The memory 214 may be a non-transitory computer-readable memory that stores EV charging suggestion program code. The memory 214 may include any one or combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), etc.), and may include any one or more non-volatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electronically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.).

[0042] The memory 214 may include a plurality of modules and databases, including but not limited to a vehicle information database 216, a vehicle trip information database 218, a vehicle driver information database 220, and an energy efficiency mode determination module 222. The energy efficiency mode determination module 222 as described herein may be stored in the form of computer executable instructions, and the processor 212 may be configured and / or programmed to execute the stored computer executable instructions for performing the functions of the system 200 according to the present disclosure.

[0043] The vehicle information database 216 may store information associated with a plurality of vehicles and / or a plurality of passenger vehicles operating as part of a vehicle fleet. For example, the vehicle information database 216 may store vehicle information associated with the vehicle 202, as described above in conjunction with Figure 1 Specifically, the transceiver 210 may receive / obtain vehicle information from the vehicle 202 at a predefined frequency (eg, every minute or every 5-10 minutes). Figure 1 An example of vehicle information is described. In response to receiving the vehicle information, the transceiver 210 may send the vehicle information to the vehicle information database 216 for storage purposes.

[0044] The vehicle trip information database 218 may store vehicle trip information associated with a plurality of vehicles and / or a plurality of passenger vehicles operating as part of a vehicle fleet, and / or vehicle trip information of associated vehicle drivers. For example, as described above, the vehicle trip information database 218 may store vehicle trip information associated with the vehicle 202. The transceiver 210 may receive / obtain vehicle trip information from the server 206 and / or the vehicle 202 (e.g., if the vehicle 202 is a passenger vehicle) at a predefined frequency. The transceiver 210 may send the received vehicle trip information to the vehicle trip information database 218 for storage purposes.

[0045] The vehicle driver information database 220 may store driver information associated with a driver who may be driving a plurality of vehicles and / or a plurality of passenger vehicles operating as part of a vehicle fleet. For example, as described above, the vehicle driver information database 220 may store driver information associated with a vehicle driver of the vehicle 202. The transceiver 210 may receive / obtain driver information from the user device 204, the vehicle 202, and / or the server 206 at a predefined frequency. The transceiver 210 may transmit the received driver information to the vehicle driver information database 220 for storage purposes.

[0046] In operation, the processor 212 may obtain vehicle information associated with the vehicle 202 from the vehicle information database 216, obtain corresponding vehicle trip information from the vehicle trip information database 218, and obtain vehicle driver information from the vehicle driver information database 220. In some aspects, the processor 212 may obtain information in response to obtaining a trigger signal from the user device 204, the vehicle 202, and / or the server 206 (via the transceiver 210). The trigger signal may indicate that the vehicle 202 may have started a vehicle trip or may start a vehicle trip within a predefined duration (e.g., within 5 minutes or 10 minutes). In response to obtaining the trigger signal, the processor 212 may obtain information from the corresponding database. In addition, when the vehicle 202 may be traversing a vehicle trip, the processor 212 may periodically obtain information from the corresponding database at a predefined frequency until the vehicle 202 completes the vehicle trip (as determined based on the geographic location of the vehicle 202 obtained from the vehicle 202 and / or the user device 204).

[0047] In response to obtaining the above information, the processor 212 may determine the remaining distance to be traversed by the vehicle 202 on the vehicle trip based on the vehicle information, the vehicle trip information, and / or the driver information. For example, the processor 212 may determine the remaining distance by multiplying the average driving / traveling speed (of the vehicle driver and / or the vehicle 202) on the vehicle trip by the remaining shift duration of the vehicle driver. As another example, the processor 212 may determine the remaining distance by subtracting the current geographic location of the vehicle 202 from the trip destination location (e.g., the trip destination entered by the vehicle driver or fleet operator on the user device 204 or the server 206). In further aspects, the processor 212 may determine the remaining distance based on planned stops along the vehicle trip (which may reduce the effective driving time from the remaining shift duration), the planned shift distance to be traveled by the vehicle driver on the vehicle trip, etc.

[0048] In further aspects, in response to obtaining the above information, the processor 212 can determine an energy efficiency mode associated with the vehicle 202 and / or the vehicle driver by using instructions stored in the energy efficiency mode determination module 222. Specifically, the processor 212 can use an artificial intelligence / machine learning (AI / ML) algorithm (which can be part of the energy efficiency mode determination module 222) to determine a unit distance that the vehicle 202 can traverse per SOC level based on the vehicle information, the driver information, and the vehicle trip information. In some aspects, the energy efficiency mode can be the same as the determined unit distance per SOC level.

[0049] Figure 3 An exemplary graph 300 showing unit distance with different battery SOC levels is depicted. The X-axis of the graph 300 shows different vehicle battery SOC levels, and the Y-axis of the graph 300 shows miles / SOC level. The processor 212 may determine the unit distance per SOC of the vehicle 202 at different SOC levels based on the vehicle 202 information, the driver information, and the vehicle trip information, as shown in the graph 300. For example, the processor 212 may determine that when the vehicle battery SOC is 50 (shown as point 305 on the graph 300), the vehicle 202 may be able to travel 2 miles per SOC. In a similar manner, the processor 212 may determine the unit distance at each SOC level (e.g., SOC levels from 0 to 100). In some aspects, the processor 212 may average the unit distance per SOC at different SOC levels to calculate a "final" unit distance per SOC for the vehicle 202.

[0050] In some aspects, the processor 212 may determine the energy efficiency mode of the vehicle 202 (as shown in the graph 300) based on the vehicle driver's historical driving patterns (e.g., over the past 1, 3, or 6 months), historical average driving speeds, historical traffic conditions over the vehicle's journey, road conditions over the vehicle's journey, environmental conditions (e.g., temperature, wind, etc.), vehicle weight (including load and / or passenger weight), etc. For example, if the vehicle weight is high (e.g., if the vehicle 202 may be carrying a heavy load) or if the road conditions indicate an uphill road, the determined unit distance per SOC may be lower. On the other hand, if the vehicle weight is low or if the road conditions indicate a downhill road, the determined unit distance per SOC may be higher. The processor 212 may send the determined energy efficiency mode to the memory 214 for storage purposes.

[0051] In response to determining the energy efficiency mode, the processor 212 may determine the expected SOC that the vehicle 202 may need to travel the remaining distance on the vehicle trip. In some aspects, the processor 212 may determine the expected SOC by dividing the remaining distance by the "final" unit distance per SOC of the vehicle 202. For example, if the remaining distance is 120 miles and the unit distance per SOC is 2 miles / SOC, the processor may determine the expected SOC to be 60 (i.e., 120 / 2). In some aspects, the processor 212 may add a predefined "buffer" SOC (e.g., 5 or 10) to the determined SOC to calculate the "final" expected SOC. The buffer SOC value (e.g., 5 or 10) may be preset by a fleet operator (if the vehicle 202 is part of a commercial vehicle fleet) or a vehicle driver (if the vehicle 202 is a passenger vehicle) and may be pre-stored in the memory 214. In this case, the processor 212 may first retrieve the pre-stored buffer SOC value from the memory and add the buffer SOC value to the determined expected SOC to calculate the final expected SOC.

[0052] In response to determining the final expected SOC, the processor 212 may determine the difference between the final expected SOC and the current vehicle battery SOC (which may be obtained as part of the vehicle information). If the difference between the final expected SOC and the current battery SOC is negative (i.e., the final expected SOC is less than the current battery SOC), the processor 212 may determine that the vehicle 202 may have sufficient battery SOC to travel the remaining distance. On the other hand, if the difference between the final expected SOC and the current battery SOC is positive (i.e., the final expected SOC is greater than the current battery SOC), the processor 212 may determine that the vehicle 202 may not have sufficient battery SOC to travel the remaining distance.

[0053] In response to determining that vehicle 202 may have sufficient battery SOC to travel the remaining distance, processor 212 may iteratively perform the process of determining the expected SOC at a predefined frequency until the vehicle trip is completed or until the determined expected SOC exceeds the current battery SOC level at any point during the vehicle trip.

[0054] On the other hand, in response to determining that the vehicle 202 may not have sufficient battery SOC to travel the remaining distance, the processor 212 may transmit a charging recommendation notification to the user device 204 or the vehicle 202 HMI (not shown) (via the transceiver 210). The recommendation notification may include an optimal amount of energy to be charged / delivered to the vehicle battery and / or an optimal time to charge the vehicle battery. In some aspects, the optimal amount of energy to be charged / delivered to the vehicle battery is the difference between the final expected SOC and the current battery SOC. For example, if the final expected SOC is 60 and the current battery SOC is 40, the optimal amount of energy may be 20. In this way, the processor 212 can advise the vehicle driver of the amount of energy that may be required to charge the remaining distance (plus the SOC buffer value), and may not recommend charging the vehicle battery 100% (which may not be required). Therefore, the processor 212 can save charging time and charging resources / energy.

[0055] As combined Figure 1 As described, the optimal time to charge the vehicle battery may be based on the driver's rest schedule. In this case, the processor 212 may be based on driver information (e.g., if the vehicle 202 is a passenger vehicle) and / or vehicle trip information (e.g., if the vehicle 202 is a commercial vehicle and the driver's rest schedule is pre-planned by a fleet operator operating the server 206), and suggest to the vehicle driver to charge the vehicle battery during the rest period. Figure 4 An exemplary scenario of a vehicle driver charging a vehicle battery during rest time is shown in FIG.

[0056] Specifically, Figure 4 Depicted are graphs showing battery SOC of two EVs in accordance with the present disclosure. Figure 4 Graph 405 in Graphs 405 depicts the SOC level of a first vehicle (e.g., vehicle 202), and graph 410 depicts the SOC level of a second vehicle (which may be similar to the first vehicle). The X-axis of graphs 405, 410 depicts distance (e.g., in miles), and the Y-axis depicts the SOC levels of batteries associated with the first and second vehicles.

[0057] In an exemplary scenario, at distance "D=0", the first vehicle may not have sufficient battery SOC to complete the remaining distance (which may be, for example, D=120 miles). For example, the battery SOC 415 of the first vehicle may drop to zero at D=90 miles. In this case, the processor 212 may determine at D=0 that the expected SOC of the first vehicle to complete the remaining distance of 120 miles may be greater than the current battery SOC at D=0. In response to the determination, the processor 212 may transmit a recommendation notification to the vehicle driver (via the user device 204 and / or the vehicle HMI) to charge the vehicle battery while the driver is resting. In an exemplary aspect, in response to receiving the recommendation notification, the driver may charge the vehicle battery while the driver is resting (which may be at D=60). When the driver charges the vehicle battery during the rest period, the battery SOC of the first vehicle may increase to the SOC level 420 shown in the graph 405. As shown in the graph 405, the SOC level 420 may enable the first vehicle to travel the remaining distance.

[0058] It will be appreciated by those of ordinary skill in the art that processor 212 can assist the vehicle driver in minimizing unplanned charging stops by suggesting that the driver charge the vehicle battery during a rest period when it is expected that the first vehicle may not be able to complete the remaining distance at the current SOC level. For example, if processor 212 does not previously identify that the first vehicle may not be able to complete the vehicle trip and suggest charging the vehicle battery during a rest period, the vehicle driver may need to make an unplanned charging stop at point 425 (i.e., at D=90). By providing charging recommendations, processor 212 can assist the driver in "converting" the unplanned charging stop at point 425 into a planned or scheduled stop (shown as point 430) when the driver is expected to take a rest. In this way, processor 212 can assist the driver in completing the vehicle trip without any unplanned charging-related interruptions.

[0059] Figure 4 Additionally depicted is a graph 410 associated with a second vehicle, which may have sufficient battery SOC (shown as SOC 435) to complete the vehicle trip. In this case, the processor 212 may not provide a charging recommendation. The driver may still charge the battery during the rest period to increase the battery SOC to level 440, which may enable the second vehicle to travel beyond the planned vehicle trip end point. For example, the second vehicle may return to a depot or point where the driver may have rested (shown as point 445) to park the second vehicle after completing the vehicle trip.

[0060] In some aspects, the energy efficiency mode may vary based on weather conditions or seasons. For example, the energy efficiency mode of the vehicle 202 may be different in different seasons or environmental conditions (such as summer, winter, spring, etc.). As described above, the processor 212 may determine the season and then determine the expected SOC associated with the determined season that the vehicle 202 may need to complete the vehicle trip. For example, the vehicle 202 may not need frequent charging in the spring; however, the vehicle 202 may need mid-trip charging in the winter or summer.

[0061] In further aspects, the optimal time to charge the vehicle battery may be based on information associated with the vehicle battery charging rate for different battery SOC levels (which may be part of the vehicle information). Figure 5 , exemplary information associated with a vehicle battery charging rate for different battery SOC levels is depicted in FIG. Figure 5 A graph 500 showing different battery charging rates in the Y-axis and different battery SOC levels in the X-axis is depicted. As shown in the graph 500, the battery charging rate may be highest in the first SOC range 505, may be high in the second SOC range 510, and may be low in the third SOC range 515. In this case, when the current battery SOC level may be within the first range 505 or the second range 510, the processor 212 may suggest to the vehicle driver to charge the vehicle battery. This may enable fast charging of the vehicle battery and thus may save the vehicle driver charging time.

[0062] In some aspects, in addition to providing recommendations for the optimal amount of energy to be charged in the vehicle battery and / or the optimal time to charge the vehicle battery, the processor 212 may also provide recommendations to the user device 204 (and / or the vehicle HMI) for the location of charging stations where the driver can charge the vehicle battery. In order to provide charging station location recommendations, the processor 212 may first identify the locations of multiple driver or fleet operator preferred charging stations that may have available chargers (in working condition) on the vehicle travel route. The processor 212 may then identify the distance of each charging station from the current vehicle 202 geographic location. In some aspects, the processor 212 may recommend to the driver the location of the nearest charging station (specifically, the charging station with the minimum distance from the current vehicle 202 geographic location) to charge the vehicle battery. In other aspects, the processor 212 may recommend the location of the charging station that may be closest to where the driver typically rests.

[0063] In yet another aspect, the processor 212 may determine that the expected vehicle battery SOC on the vehicle travel route may be within the first range 505 or the second range 510 "zone" (e.g., when the current vehicle battery SOC may be within the third range 515) to suggest a charging station location. In this case, the processor 212 may use the determined energy efficiency mode to estimate the distance that the vehicle 202 may need to travel on the vehicle trip before the vehicle battery SOC enters the first range 505 or the second range 510 (or the vehicle 202 enters the "zone" on the vehicle travel route where the vehicle battery can support a fast charge). In this regard, the processor 212 may suggest the location of a charging station that may be located in the determined zone where the vehicle battery can support a fast charge.

[0064] In another aspect, the processor 212 may determine whether the current battery SOC may enable (or allow) the vehicle 202 to reach the nearest charging station (or the charging station location suggested by the processor 212). For example, in this case, the processor 212 may use the determined energy efficiency mode to estimate the maximum distance that the vehicle 202 may travel based on the current battery SOC. The processor 212 may then compare the estimated maximum distance to the distance between the vehicle's current location and the nearest charging station location. When the estimated maximum distance may be greater than the distance between the vehicle's current location and the nearest charging station location, the processor 212 may suggest that the driver charge the vehicle battery at the nearest charging station (and transmit the nearest charging station location to the user device 204 via the transceiver 210).

[0065] On the other hand, when processor 212 determines that the maximum distance may be less than the distance between the vehicle's current location and the nearest charging station location, processor 212 may transmit an assistance notification / request to server 206. This may indicate that vehicle 202 may not be able to reach the nearest charging station and may therefore require assistance (e.g., assistance from a mobile charging vehicle, drone, etc.). Figure 1 Describes the auxiliary notification / request.

[0066] Processor 212 may additionally transmit an auxiliary notification / request to server 206 when the vehicle travel route may have no available charging stations or a low count of available charging stations, when the vehicle travel route includes an area or zone where stranded EVs may have been reported historically, or when the vehicle travel route includes an area or zone where network connectivity may be low.

[0067] In some aspects, the assistance notifications / requests provided by the processor 212 can assist fleet operators in better planning vehicle fleet battery charging and charger specifications. For example, a fleet operator can analyze historical assistance notifications / requests over a predefined duration (e.g., 3 months, 6 months, etc.) and can proactively plan to provide additional assistance to certain vehicle types, vehicle drivers, or certain vehicle travel routes based on the analysis. As an example, a fleet operator can provide or allocate portable mobile power sources to certain vehicles or vehicle drivers that may frequently require mobile charging assistance. Similarly, a fleet operator can provide batteries with greater storage capacity to vehicles or vehicle drivers that may frequently require mobile charging assistance.

[0068] Figure 6 A flow chart of an exemplary EV charging recommendation method 600 according to the present disclosure is depicted. Figures 1 to 5 ) to describe Figure 6 The following process is exemplary and is not limited to the steps described below. Furthermore, alternative embodiments may include more or fewer steps shown or described herein, and may include the steps in an order different from that described in the example embodiments below.

[0069] The method 600 starts at step 602. At step 604, the method 600 may include obtaining, by the processor 212, a first input associated with the vehicle, a second input associated with the vehicle driver, and a third input associated with the vehicle trip. Figure 1 and Figure 2 The first input may be vehicle information, the second information may be driver information, and the third input may be vehicle trip information. In some aspects, the first vehicle information may include the current vehicle battery SOC and the current geographic location of the vehicle.

[0070] At step 606, the method 600 may include determining, by the processor 212, the remaining distance to be traveled by the vehicle 202 in the vehicle trip. Specifically, the processor 212 may determine the distance to be traveled by the vehicle 202 according to the current geographic location of the vehicle based on the vehicle trip information.

[0071] At step 608, the method 600 may include determining, by the processor 212, the expected SOC required to travel the remaining distance from the vehicle's current geographic location based on the vehicle information, the driver information, and the vehicle range information, as described above in conjunction with Figure 2At step 610, method 600 may include transmitting, by processor 212 (via transceiver 210) to user device 204 (or vehicle HMI) a recommendation notification for charging the vehicle battery when the expected SOC may be greater than the current SOC. As described above, the recommendation notification may include an optimal time to charge the vehicle battery and / or an optimal amount of energy to be charged into the vehicle battery.

[0072] Method 600 ends at step 612 .

[0073] In the above disclosure, reference has been made to the accompanying drawings that form a part of the above disclosure, which illustrate specific embodiments in which the present disclosure can be practiced. It should be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure. References to "one embodiment," "embodiment," "exemplary embodiment," etc. in this specification indicate that the described embodiment may include specific features, structures, or characteristics, but each embodiment may not necessarily include the specific features, structures, or characteristics. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when features, structures, or characteristics are described in conjunction with an embodiment, whether or not explicitly described, those skilled in the art will recognize such features, structures, or characteristics in conjunction with other embodiments.

[0074] In addition, where appropriate, the functions described herein may be performed in one or more of the following: hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and programs described herein. Certain terms are used throughout the specification and claims to refer to specific system components. As will be appreciated by those skilled in the art, components may be referred to by different names. This document is not intended to distinguish between components that have different names but the same function.

[0075] It should also be understood that the word "example" as used herein is intended to be non-exclusive and non-limiting in nature. More specifically, the word "example" used herein indicates one of several examples, and it should be understood that no undue emphasis or preference is placed on the specific example described.

[0076] Computer-readable media (also referred to as processor-readable media) include any non-transitory (e.g., tangible) media that participate in providing data (e.g., instructions) that can be read by a computer (e.g., by a processor of a computer). Such media can take many forms, including but not limited to non-volatile media and volatile media. A computing device may include computer-executable instructions, where the instructions are executable by one or more computing devices (such as those listed above) and stored on a computer-readable medium.

[0077] With respect to the processes, systems, methods, heuristics, etc. described herein, it should be understood that although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes may be practiced by performing the described steps in an order different from the order described herein. It should also be understood that certain steps may be performed simultaneously, other steps may be added, or certain steps described herein may be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating various embodiments and should in no way be construed as limiting the claims.

[0078] Therefore, it should be understood that the above description is intended to be illustrative rather than restrictive. Upon reading the above description, many embodiments and applications other than the examples provided will be apparent. The scope should not be determined with reference to the above description, but should be determined with reference to the entire scope of the appended claims and equivalents to which such claims are entitled. It is anticipated and expected that the technology discussed herein will develop in the future, and the disclosed systems and methods will be incorporated into such future embodiments. In short, it should be understood that the present application is capable of modification and change.

[0079] Unless explicitly indicated to the contrary in this article, all terms used in the claims are intended to be given their ordinary meanings as understood by the skilled person described herein. Specifically, unless the claims state an explicit limitation to the contrary, the use of singular articles such as "one", "the", "said" and the like should be interpreted as describing one or more of the indicated elements. Unless otherwise specifically stated or understood in other ways within the context when used, conditional language such as, in particular, "can", "may", "can" or "may" is generally intended to express that certain embodiments may include certain features, elements and / or steps, while other embodiments may not include certain features, elements and / or steps. Therefore, such conditional language is generally not intended to imply that one or more embodiments require each feature, element and / or step in any way.

[0080] According to one embodiment, the processor is further configured to: determine an SOC range associated with fast battery charging based on the information associated with vehicle battery charging rates at different battery SOC levels; and determine a time to charge the vehicle battery based on the SOC range.

[0081] According to one embodiment, the third input includes at least one of: the location of charging stations on the vehicle's itinerary, the location of fleet-designated charging stations, the location of the charging station closest to the vehicle, traffic conditions, vehicle trip duration, vehicle trip distance, road conditions, and season or weather conditions.

[0082] According to one embodiment, the processor is further configured to: determine a second distance between the current position of the vehicle and the location of a charging station on the vehicle's itinerary; determine a third distance that the vehicle can travel based on the first input, the second input, and the third input; compare the second distance with the third distance; and when the third distance is less than the second distance, transmit an auxiliary notification to the server.

[0083] According to one embodiment, the processor is further configured to transmit the charging station location to the user device when the third distance is greater than the second distance.

[0084] According to the present invention, a non-transitory computer-readable storage medium having instructions stored thereon is provided, wherein the instructions, when executed by a processor, cause the processor to: obtain a first input associated with a vehicle, a second input associated with a vehicle driver, and a third input associated with a vehicle range, wherein the first input includes a current state of charge (SOC) of a vehicle battery; determine a first distance to be traveled by the vehicle from a current position of the vehicle based on the third input; determine an expected SOC required to travel the first distance from the current position of the vehicle based on the first input, the second input, and the third input; and when the expected SOC is greater than the current SOC, send a recommendation notification to a user device to charge the vehicle battery, wherein the recommendation notification includes a time to charge the vehicle battery or an amount of energy to be transferred to the vehicle battery.

Claims

1. A method for providing electric vehicle charging advice, the method comprising: obtaining, by a processor, a first input associated with a vehicle, a second input associated with a driver of the vehicle, and a third input associated with a range of the vehicle, wherein the first input includes a current state of charge (SOC) of a battery of the vehicle; determining, by the processor, a first distance to be traveled by the vehicle from a current location of the vehicle based on the third input; determining, by the processor, an expected SOC required to travel the first distance from the vehicle's current location based on the first input, the second input, and the third input; as well as When the expected SOC is greater than the current SOC, the processor transmits a recommendation notification to a user device to charge the vehicle battery, wherein the recommendation notification includes a time to charge the vehicle battery or an amount of energy to be transferred to the vehicle battery. The method of claim 1 , further comprising determining the first distance based on the second input.

3. The method of claim 1, further comprising: determining a difference between the expected SOC and the current SOC; determining that the difference is greater than zero; as well as The advisory notification is transmitted to the user device based on a determination that the difference is greater than zero, wherein the difference is an amount of the energy to be transferred to the vehicle battery. 4 . The method of claim 1 , wherein the second input comprises at least one of: a driver rest schedule, an average driving speed, and historical driving patterns over the vehicle trip. 5 . The method of claim 4 , further comprising determining the time to charge the vehicle battery based on the driver rest schedule.

6. The method of claim 1, wherein the first input further includes information associated with vehicle battery charge rate for different battery SOC levels, vehicle and cargo weight, vehicle type, and the vehicle's current location.

7. The method of claim 6, further comprising: determining an SOC range associated with rapid battery charging based on the information associated with vehicle battery charging rates at different battery SOC levels; as well as The time to charge the vehicle battery is determined based on the SOC range.

8. The method of claim 1, wherein the third input comprises at least one of: the location of charging stations on the vehicle's itinerary, the location of fleet-designated charging stations, the location of charging stations closest to the vehicle, traffic conditions, vehicle trip duration, vehicle trip distance, road conditions, and seasonal or weather conditions.

9. The method of claim 8, further comprising: determining a second distance between the current location of the vehicle and a location of a charging station on the vehicle's travel path; determining a third distance that the vehicle can travel based on the first input, the second input, and the third input; comparing the second distance to the third distance; as well as When the third distance is less than the second distance, an auxiliary notification is transmitted to a server. 10 . The method of claim 9 , further comprising transmitting the charging station location to the user device when the third distance is greater than the second distance.

11. A system for providing electric vehicle charging advice, the system comprising: A transceiver, the transceiver being configured to: receiving a first input associated with a vehicle, a second input associated with a driver of the vehicle, and a third input associated with a range of the vehicle, wherein the first input includes a current state of charge (SOC) of a battery of the vehicle; a processor communicatively coupled to the transceiver; and a memory for storing executable instructions, wherein the processor is configured to execute the instructions to: obtaining the first input, the second input, and the third input from the transceiver; determining a first distance to be traveled by the vehicle from a current position of the vehicle based on the third input; determining an expected SOC required to travel the first distance from the vehicle's current location based on the first input, the second input, and the third input; as well as When the expected SOC is greater than the current SOC, a recommendation notification to charge the vehicle battery is transmitted to a user device, wherein the recommendation notification includes a time to charge the vehicle battery or an amount of energy to be transferred to the vehicle battery.

12. The system of claim 11, wherein the processor is further configured to: determining a difference between the expected SOC and the current SOC; determining that the difference is greater than zero; and The advisory notification is transmitted to the user device based on a determination that the difference is greater than zero, wherein the difference is an amount of the energy to be transferred to the vehicle battery.

13. The system of claim 11, wherein the second input comprises at least one of: a driver rest schedule, an average driving speed, and historical driving patterns over the vehicle trip.

14. The system of claim 13, wherein the processor is further configured to determine the time to charge the vehicle battery based on the driver rest schedule.

15. The system of claim 11, wherein the first input further includes information associated with vehicle battery charge rate for different battery SOC levels, vehicle and cargo weight, vehicle type, and the vehicle's current location.

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