Electric vehicle energy storage system discharge time estimation and management

Abstract

In an embodiment, a method is provided that includes obtaining sensor data of a vehicle having an energy storage system configured to power a dwelling under certain conditions; obtaining residence data regarding a plurality of features related to a power demand of the residence; and calculating, via a processor, a discharge time of the energy storage system based on the sensor data of the vehicle and the plurality of characteristics of the dwelling.

Description

[0001] TECHNICAL FIELD The present invention relates generally to electric vehicles and, more particularly, to methods and systems for estimating and managing the discharge time of an energy storage system of an electric vehicle. Background Art

[0002] Today, some electric vehicles have the ability to provide power while the vehicle's energy storage system is discharging, such as to provide power to a user's home. However, prior art may have difficulty estimating and managing the discharge time of an electric vehicle's energy storage system.

[0003] Therefore, it is desirable to provide improved methods and systems for estimating and managing discharge time of energy storage systems of electric vehicles.Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. Summary of the invention

[0004] According to an exemplary embodiment, a method is provided, the method comprising: obtaining sensor data of a vehicle having an energy storage system, the energy storage system being configured to provide power to a residence under certain conditions; obtaining residence data regarding a plurality of characteristics related to power demand of the residence; and calculating, via a processor, a discharge time of the energy storage system based on the sensor data of the vehicle and the plurality of characteristics of the residence.

[0005] Also in the exemplary embodiment, when the vehicle is plugged into the residence, the sensor data of the vehicle includes the parasitic load of the vehicle; and the discharge time is also calculated based on the parasitic load and multiple characteristics of the residence.

[0006] Also in the exemplary embodiment, the plurality of characteristics includes physical dimensions of the residence; and the discharge time is calculated using the physical dimensions of the residence.

[0007] Also in the exemplary embodiment, the plurality of characteristics includes a geographic location of the residence; and the discharge time is calculated using the geographic location of the residence.

[0008] Also in the exemplary embodiment, the plurality of characteristics includes a current time of year and a current time of day; and the discharge time is calculated using the current time of year and the current time of day.

[0009] Also in the exemplary embodiment, the plurality of features includes a list of appliances in the residence; and the discharge time is calculated using the list of appliances in the residence.

[0010] Also in the exemplary embodiment, the method further includes obtaining user input from a user of the residence regarding one or more of the plurality of characteristics; and calculating, via the processor, the updated discharge time using the user input.

[0011] Also in the exemplary embodiment, the method further includes obtaining usage data regarding actual usage of power by the residence; and calculating, via the processor, the updated discharge time using the usage data.

[0012] Also in the exemplary embodiment, the method further includes calculating, via the processor, a discharge time updated over time using the plurality of features as inputs in a machine learning algorithm.

[0013] Also in the exemplary embodiment, the method further includes obtaining a high power consumption curve associated with a first group of users whose power is greater than a predetermined threshold; obtaining a low power consumption curve associated with a second group of users whose power consumption is less than a predetermined threshold; and automatically controlling multiple electrical appliances by selectively switching one or more of the multiple electrical appliances in the residence to an energy-saving mode based on instructions provided by the processor, based on actual power consumption of the residence and one or more comparisons of the low power consumption curve, the high power consumption curve, or the low power consumption curve and the high power consumption curve.

[0014] In another exemplary embodiment, a system is provided that includes one or more sensors and a processor. The one or more sensors are configured to obtain sensor data about a vehicle having an energy storage system that is configured to provide power to a residence under certain conditions. The processor is coupled to the one or more sensors and is configured to at least facilitate obtaining residence data about a plurality of characteristics related to power demand of the residence; and based on the sensor data of the vehicle and the plurality of characteristics of the residence, calculate a discharge time of the energy storage system.

[0015] Also in the exemplary embodiment, when the vehicle is plugged into the residence, the sensor data of the vehicle includes a parasitic load of the vehicle; and the processor is configured to at least facilitate calculating a discharge time also based on the parasitic load and a plurality of characteristics of the residence.

[0016] Also in the exemplary embodiment, the plurality of characteristics includes physical dimensions of the residence; and the processor is configured to at least facilitate using the physical dimensions of the residence to calculate the discharge time.

[0017] Also in the exemplary embodiment, the plurality of characteristics includes a geographic location of the residence; and the processor is configured to at least facilitate using the geographic location of the residence to calculate the discharge time.

[0018] Also in the exemplary embodiment, the plurality of features includes a current time of year and a current time of day; and the processor is configured to at least facilitate calculating the discharge time using the current time of year and the current time of day.

[0019] Also in the exemplary embodiment, the processor is further configured to at least facilitate obtaining user input from a user of the residence regarding one or more of the plurality of characteristics; and calculating the updated discharge time using the user input.

[0020] Also in the exemplary embodiment, the processor is further configured to facilitate at least obtaining usage data regarding actual usage of power by the residence; and calculating an updated discharge time using the usage data.

[0021] Also in the exemplary embodiment, the processor is further configured to at least facilitate using the plurality of features as inputs in a machine learning algorithm to calculate a discharge time updated over time.

[0022] Also in the exemplary embodiment, the processor is further configured to at least facilitate obtaining a high power consumption curve associated with a first group of users having power greater than a predetermined threshold; obtaining a low power consumption curve associated with a second group of users having power consumption less than a predetermined threshold; and automatically controlling multiple electrical appliances by selectively switching one or more of the multiple electrical appliances in the residence to an energy-saving mode based on a comparison of actual power consumption of the residence with one or more of the low power consumption curve, the high power consumption curve, or the low power consumption curve and the high power consumption curve.

[0023] In another exemplary embodiment, a system is provided, the system comprising one or more sensors and a processor. The one or more sensors are configured to obtain sensor data about a high-voltage storage device, the high-voltage storage device being configured to provide power to a residence under certain conditions. The processor is coupled to the one or more sensors and is configured to at least facilitate obtaining residential data about a plurality of characteristics related to the power demand of the residence, the plurality of characteristics including the physical size of the residence, the geographic location of the residence, the current time of the year, the current time of the day, and a list of electrical appliances in the residence; and based on the sensor data of the high-voltage storage device and the plurality of characteristics of the residence, including the physical size of the residence, the geographic location, the current time of the year, the current time of the day, and a list of electrical appliances in the residence, calculating a discharge time of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present disclosure will be described below in conjunction with the following drawings, wherein like numerals represent like elements, and wherein:

[0025] Figure 1 is a functional block diagram of a system for estimating and managing the discharge time of an energy storage system of an electric vehicle according to an exemplary embodiment;

[0026] Figure 2 is a flow chart of a process for estimating and managing the discharge time of an energy storage system of an electric vehicle according to an exemplary embodiment and may be combined with Figure 1and Figure 3-Figure 6 Depicted is a schematic diagram of an exemplary embodiment of the present invention. Figure 2 Flowchart of the various steps of the process. DETAILED DESCRIPTION

[0027] The following detailed description is merely exemplary in nature and is not intended to limit the present disclosure or its application and uses. Furthermore, there is no intention to be bound by any theory presented in the preceding background or the following detailed description.

[0028] Figure 1 A system 10 is shown according to an exemplary embodiment. Figure 1 As depicted in , in various embodiments, the system 10 includes a vehicle 100 and a residence 102 that are configured to be coupled together via respective connection devices 109, 110 for power or current flow therebetween. Figure 1 As depicted in , in various embodiments, the system 10 may also include one or more remote servers 104 and / or other third-party sites 106 that are coupled to each other and / or to the vehicle 100 and / or residence 102 via one or more wireless communication networks 108 .

[0029] like Figure 1 As depicted in , in various embodiments, the vehicle 100 includes a rechargeable energy storage system (RESS) 114 (eg, a high voltage vehicle battery pack). Figure 1 As depicted in , when the vehicle 100 is connected to the residence 102 via the corresponding connection devices 109, 110, power or current can flow in different directions between the residence 102 and the vehicle 100 depending on the situation. For example, in a typical situation where the residence 102 has sufficient power and the RESS 114 needs to be charged, the RESS 114 is charged using the power system 152 of the residence 102. In contrast, in certain other situations where the RESS 114 has the power required by the residence 102 (e.g., in the case of a power outage and / or power shortage in the residence 102), the RESS 114 releases power, and the power is provided to the power system 152 of the residence 102 for use within the residence 102.

[0030] In various embodiments, when power is supplied from the RESS 114 of the vehicle 100 to the residence 102, the Figure 2-Figure 610 and further described below in conjunction therewith, the system 10 estimates and manages the discharge time (e.g., the time at which the current level drops below a predetermined threshold) of the RESS 114. As also described in further detail below, in various embodiments, the estimation and management of the discharge time is performed by one or more of the vehicle 100, the residence 102, and / or the remote server 104 using information obtained therefrom and / or from a third party site 106 (e.g., a utility company that provides power services to the residence 102).

[0031] In various embodiments, the vehicle 100 includes an electric vehicle. In some embodiments, the vehicle 100 also includes a car. The vehicle 100 can be any of a variety of different types of cars, such as, for example, a sedan, a van, a truck, or a sports utility vehicle (SUV), and in some embodiments can be a two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), a four-wheel drive (4WD) or an all-wheel drive (AWD), and / or various other types of vehicles. In some embodiments, the vehicle 100 may also include a motorcycle or other means of transportation, such as an airplane, a spacecraft, a ship, etc., and / or one or more other types of mobile platforms (e.g., a robot and / or other mobile platforms). In some embodiments, the vehicle 100 may also include a high energy storage device (mobile or stationary), which includes an energy storage system (ESS). For example, in some embodiments, the vehicle 100 may alternatively include or contain a stationary battery pack, such as on the wall of the residence 102, and so on.

[0032] In various embodiments, the vehicle includes a body 118 that substantially surrounds the other components of the vehicle 100. The vehicle 100 also includes a plurality of wheels 120. In one embodiment, the vehicle 100 includes four wheels 120, although this may vary in other embodiments (e.g., for trucks and certain other vehicles). The propulsion system 116 (e.g., drive system) drives the wheels 120, for example, via a shaft 122. In various embodiments, the propulsion system 116 is powered in whole or in part via the RESS 114.

[0033] As described above, in various embodiments, the RESS 114 includes a high voltage vehicle battery pack and / or other energy storage system that provides power to the vehicle 100. Also as described above, in various embodiments, in certain circumstances (e.g., where the RESS 114 needs to be charged and the power system 152 of the residence 102 has sufficient power to meet its current needs), when the vehicle 100 is plugged into the power system 152 of the residence 102 via the corresponding connection devices 109, 110, the RESS 114 is charged via the power system 152 of the residence 102. In addition, as also described above, in various embodiments, in certain other circumstances (e.g., where the RESS 114 has power and the power system 152 of the residence 102 does not have sufficient power to meet its current needs, such as in the event of a power outage or other power shortage), when the vehicle 100 is plugged into the power system 152 of the residence 102 via the corresponding connection devices 109, 110, the RESS 114 releases power, thereby providing power to the residence 102.

[0034] exist Figure 1 , the control system 112 is coupled to the RESS 114. In various embodiments, the control system 112 controls the charging and discharging of the RESS 114, including the estimation and management of the discharge time of the RESS 114 when it is supplying power to the residence 102. Also in various embodiments, the control system 112 is coupled to the propulsion system 116, as well as other components of the vehicle 100, and also controls their operation and functions.

[0035] like Figure 1 As depicted in , in various embodiments, the control system 112 includes one or more sensors 124 , a transceiver 126 , and a controller 128 .

[0036] In various embodiments, the one or more sensors 124 obtain sensor data to facilitate estimation and management of the discharge time of the RESS 114. In certain embodiments, the one or more sensors 124 obtain sensor data regarding power and / or other currents associated with the RESS 114, as well as other potential sensor data. Also in certain embodiments, the sensors 124 may also include and / or be utilized in conjunction with one or more positioning systems, such as a global positioning system (GPS).

[0037] Also in various embodiments, the transceiver 126 provides communication between the vehicle 100 and the residence 102, the remote server 104, and / or the third party site 106 to facilitate the estimation and management of the discharge time of the RESS 114, as well as various other possible communications. In various embodiments, the transceiver 126 facilitates communication between the vehicle 100 and these and / or other entities via one or more wireless communication networks 108.

[0038] In various embodiments, the controller 128 is coupled to one or more sensors 124 and receives sensor data therefrom. In various embodiments, the controller 128 is further coupled to the RESS 114 and controls its estimation and management of discharge time, as well as other functions of the vehicle 100. In various embodiments, the controller 128 is also coupled to the transceiver 126 and controls communications via the transceiver 126, including for estimation and management of discharge time, as well as other functions of the vehicle 100. In various embodiments, the controller 128 generates and receives data from the RESS 114 according to the RESS 114. Figure 2-Figure 6 Process 200 depicted in and described in further detail below in conjunction therewith provides such functionality.

[0039] In various embodiments, the controller 128 comprises a computer system (also referred to herein as computer system 128 ) and includes a processor 130 , a memory 132 , an interface 134 , a storage device 136 , and a computer bus 138 .

[0040] In various embodiments, the controller 128 (and in some embodiments, the control system 112 itself) is disposed within the body 118 of the vehicle 100. In some embodiments, the controller 128 and / or the control system 112 and / or one or more components thereof may be disposed external to the body 118, such as on a remote server, in the cloud, or other device that remotely performs image processing.

[0041] In the depicted embodiment, the computer system of controller 128 includes a processor 130, a memory 132, an interface 134, a storage device 136, and a bus 138. Processor 130 performs computational and control functions of controller 128 and may include any type of processor or multiple processors, a single integrated circuit such as a microprocessor, or any suitable number of integrated circuit devices and / or circuit boards that work in conjunction to implement the functionality of a processing unit. During operation, processor 130 executes one or more programs 140 contained within memory 132 and, therefore, controls the general operation of controller 128 and the computer system of controller 128, typically in performing the processes described herein, such as Figure 2-Figure 6 and process 200 further described below in conjunction with them.

[0042] The memory 132 can be any type of suitable memory, including various types of non-transitory computer-readable storage media. In some examples, the memory 132 is located and / or co-located on the same computer chip as the processor 130. In the depicted embodiment, the memory 132 stores the above-described program 140 and stored values ​​142 (e.g., neural networks, machine learning models, lookup tables, thresholds, and / or other values ​​related to the estimation and management of the discharge time of the RESS 114).

[0043] The interface 134 allows, for example, communication with the computer system of the controller 128 from a system drive and / or another computer system, and can be implemented using any suitable method and apparatus. In one embodiment, the interface 134 obtains various data from one or more sensors 124 and other possible data sources. The interface 134 may include one or more network interfaces to communicate with other systems or components. The interface 134 may also include one or more network interfaces for communicating with a technician, and / or one or more storage interfaces for connecting to a storage device (such as a storage device 136). In addition, in some embodiments, the interface 134 is also configured to communicate with the cloud, and, for example, communicate with a backend (e.g., such as a remote server 104, a third-party site 106, and / or other information sources).

[0044] Storage device 136 may be any suitable type of storage device, including various types of direct access storage and / or other memory devices. In an exemplary embodiment, storage device 136 includes a program product from which memory 132 may receive a program 140 that performs one or more embodiments of one or more processes of the present disclosure, such as Figure 2-Figure 6 The process 200 steps and Figure 4 In another exemplary embodiment, the program product may be directly stored in and / or accessed by memory 132 and / or disk (e.g., disk 144), such as described below.

[0045] The bus 138 is used to transfer programs, data, status and other information or signals between the various components of the computer system of the controller 128. The bus 138 can be any suitable physical or logical means of connecting the computer system and components. This includes, but is not limited to, direct hardwired connections, fiber optics, infrared and wireless bus technologies. During operation, the program 140 is stored in the memory 132 and executed by the processor 130.

[0046] It should be understood that although the exemplary embodiment is described in the context of a fully functional computer system, those skilled in the art will recognize that the mechanisms of the present disclosure can be distributed as a program product together with one or more types of non-transitory computer-readable signal-bearing media for storing the program and its instructions and performing its distribution, such as a non-transitory computer-readable medium that carries the program and contains computer instructions stored therein for causing a computer processor (such as processor 130) to execute and implement the program.

[0047] Continue to refer Figure 1 In various embodiments, residence 102 may include any of a house, a residence, an office, and a variety of other buildings, structures, equipment, and / or other systems (and for convenience, collectively referred to herein as "residence" 102). Figure 2 As depicted in FIG. 1 , residence 102 includes the above-described connection device 110 for connecting to vehicle 100 , as well as the above-described power system 152 , plus one or more appliances 154 and a control system 156 , in addition to various other possible components.

[0048] In various embodiments, when the vehicle 100 is connected to the residence 102 via corresponding connection devices 109 , 110 in various situations, in addition to providing power to and / or receiving power from the RESS 114 , the power system 152 also provides power to various appliances 154 of the residence 102 .

[0049] In various embodiments, appliances 154 include any number of different potential appliances that rely in whole or in part on electricity to operate. For example, in various embodiments, appliances 154 may include washers and dryers, dishwashers, refrigerators and freezers, air conditioning and heating systems, ovens, stoves and / or other cooking devices, televisions, gaming systems and / or other entertainment appliances, Internet connections and / or other communications devices, and / or any number of other different types of appliances and / or related systems and / or devices (collectively referred to herein as "appliances" for convenience).

[0050] In various embodiments, under normal circumstances (e.g., where sufficient power is available), the power system 152 provides power for operation of the appliances 154. Additionally, also in various embodiments, also under normal circumstances (e.g., where sufficient power is available), when the vehicle 100 is plugged into the residence 102, the power system 152 provides power for charging the RESS 114. Also in various embodiments, under certain other circumstances (e.g., where sufficient power is not available at the residence 102, such as during a power outage or other power shortage, and the vehicle 100 is connected to the residence 102), the power system 152 receives power during the discharge of the RESS 114 for powering the appliances.

[0051] In various embodiments, the control system 156 is coupled to the power system 152 and the electrical appliances 154 and controls their operation. In addition, in some embodiments, the control system 156 also controls the operation of the electrical appliances 154 according to the following examples: Figure 2-Figure 6 The steps of process 200 depicted in and described in further detail below in conjunction therewith, control (in whole or in part) the estimation and control of the discharge time of the RESS 114 of the vehicle 100. It should be appreciated that the vehicle 100 (and other components of the system 10) may estimate the time to discharge level as well as other functions.

[0052] like Figure 1 As depicted in , in various embodiments, the control system 156 includes one or more sensors 158 , a processor 160 , a transceiver 162 , and a memory 164 .

[0053] In various embodiments, one or more sensors 158 are similar to the sensors 124 of the vehicle 100 described above, and provide sensor data (such as power sensor data and / or current sensor data) that is used to estimate and manage the discharge time of the RESS 114 (as well as other possible uses).

[0054] Also in various embodiments, processor 160 is similar to processor 130 as described above and controls operation of power system 152 and appliances 154 for residence 102 in addition to controlling estimation and management of discharge time of RESS 114 (as well as various other possible functions) in various embodiments.

[0055] Additionally, in various embodiments, transceiver 162 is similar to transceiver 126 as described above and facilitates communications between residence 102 and vehicle 100, remote server 104, and / or third party site 106, including for controlling, estimating, and managing the discharge time of RESS 114 in various embodiments (as well as various other possible communications).

[0056] Also in various embodiments, the memory 164, programs 166, and stored values ​​168 are also similar to the memory 132, programs 140, and stored values ​​142, respectively, of the vehicle 100 as described above, and in various embodiments facilitate estimating and managing the discharge time of the RESS 114 (as well as various other possible functions). It should be understood that the control system 156 of the residence 102 may also include one or more other components similar to the controller 128 of the vehicle 100, as well as various other components.

[0057] In various embodiments, the remote server 104 may also control (in whole or in part) the estimation and management of the discharge time of the RESS 114, as well as various other possible functions. Figure 1 As depicted in , in various embodiments, the remote server 104 includes a transceiver 172, a processor 174, and a memory 176 including one or more programs 178 and stored values ​​180, as well as various other possible components.

[0058] Also in various embodiments, transceiver 172 is similar to transceiver 126 described above and facilitates communications between remote server 104 and vehicle 100, residence 102 and / or third party site 106, including for controlling estimation and management of discharge time of RESS 114 in various embodiments (as well as various other possible communications).

[0059] Furthermore, in various embodiments, processor 174 is similar to processor 130 as described above, and controls operation of remote server 104 in addition to controlling estimating and managing the discharge time of RESS 114 (as well as various other possible functions) in various embodiments.

[0060] Also in various embodiments, the memory 176, programs 178, and stored values ​​180 are also similar to the memory 132, programs 140, and stored values ​​142, respectively, of the vehicle 100 as described above, and in various embodiments facilitate estimating and managing the discharge time of the RESS 114 (as well as various other possible functions). It should be understood that the remote server 104 may also include one or more other components similar to the controller 128 of the vehicle 100, as well as various other components.

[0061] In various embodiments, third-party site 106 corresponds to a utility company that provides electricity service to residence 102. Also in various embodiments, third-party site 106 provides information about electricity usage of residence 102 and / or one or more similar residences to residence 102, vehicle 100, and / or remote server 104 (e.g., in various embodiments, via Figure 1Also in various embodiments, information from the third party site 106 (and / or from other providers) may also be sent to the cloud and then passed to the vehicle 100, residence 102, or remote server 104.

[0062] Figure 2 is a flow chart of a process 200 for estimating and managing the discharge time of a RESS of a vehicle, including for providing backup energy to a residence or other structure or system, according to an exemplary embodiment. Figure 1 The system 10 implements the process 200. Also according to various embodiments, this article also refers to Figure 3-Figure 6 Describing process 200, Figure 3-Figure 6 Exemplary implementations of the various steps of process 200 are depicted.

[0063] like Figure 2 , process 200 begins at step 202. In certain embodiments, process 200 begins when residence 102 requires power from RESS 114 while vehicle 100 is connected to residence 102. In one embodiment, steps of process 200 are performed continuously while vehicle 100 is connected to residence 102 and / or while residence 102 requires power from RESS 114 while vehicle 100 is connected to residence 102.

[0064] In various embodiments, vehicle data is obtained at step 204. In various embodiments, vehicle data is obtained from one or more sensors 124 of the vehicle 100 for Figure 1 Estimation and management of discharge time of the RESS 114 . Also in various embodiments, the vehicle data includes sensor data regarding the state of charge associated with the RESS 114 , among other possible data.

[0065] Also in various embodiments, the residence data is obtained at step 206. In various embodiments, the residence data is obtained in whole or in part from one or more sensors 158 of the residence 102, and / or from appliances 154 and / or control systems 156 of the residence 102, and / or from remote servers 104 and / or third-party sites 106 for use in Figure 1Estimation and management of discharge times of the RESS 114. Also in various embodiments, the residence data includes, among other possible data, sensor data regarding the current state of current or power associated with the power system 152, power demand of appliances 154, and / or other characteristics of the residence 102 related to the power demand of the residence 102 based on the physical characteristics of the residence 102, its location and contents, and / or current conditions related to the residence 102 (e.g., including the geographic region of the residence 102, the current season of the year and time of day, the square footage of the residence 102, the identification of the appliances 154, the target discharge level of the residence 102, etc.). In various embodiments, at least Figure 1 One or more of the processors of the system to facilitate the collection of sensor data via the sensor (e.g., providing querying of such information and / or its collection, and / or providing Figure 1 one or more transceivers and / or sensors providing instructions, etc.).

[0066] In addition, in some embodiments, user input may also be obtained at step 208. In some embodiments, the user input is obtained from manual input by a user of the vehicle 100 and / or the residence 102, and the manual input may involve one or more of the information described above in conjunction with steps 204 and / or 206, and / or may be used to Figure 1 In various embodiments, at least one Figure 1 One or more of the processors to facilitate the collection of user input (e.g., providing instructions for display or messages to the user to request user input, etc.).

[0067] In various embodiments, an initial time estimate is performed for the discharge time of the RESS 114 (step 210). In certain embodiments, the initial time estimate is performed when the vehicle 100 is first plugged into the residence 102. However, this may vary in other embodiments. In various embodiments, the discharge time may be calculated when the RESS 114 is in a plugged in or unplugged state relative to the residence 102.

[0068] refer to Figure 3 , according to an exemplary embodiment, a method for Figure 2 210 of process 200. Figure 3 As depicted in , in various embodiments, step 210 begins at step 302 (eg, when an initial estimate of the discharge time of the RESS 114 is requested).

[0069] In various embodiments, a latching current is determined (step 304). Specifically, in various embodiments, the latching current includes the total discharge load (I total_Dis_load), and is determined to be (A) from electric vehicle supply equipment (EVSE) (e.g., corresponding to Figure 1 The sum of the current (B) of the power system 152 of the vehicle 100 and the current associated with the parasitic loads of the vehicle 100. In various embodiments, using Figure 1 The sensor data of sensors 158, 124 are used to determine the current from the EVSE and the parasitic load of the vehicle 100. In some embodiments, firstly, Figure 1 The sensor obtains the corresponding power value, and then passes Figure 1 One or more of the processors convert it into a corresponding current value.

[0070] Specifically, in some embodiments, parasitic loads relate to electrical loads required for operation of the vehicle 100 when the vehicle 100 is plugged into the residence 102 (e.g., for cabin conditioning, etc.). In some embodiments, the parasitic loads are measured as power and converted to current. In some other embodiments, the parasitic loads are measured via current sensors, such as on the vehicle bus.

[0071] Also in certain embodiments, the current from the EVSE corresponds to the EVSE power achieved and is referred to as (e.g., Figure 1 The power capability of the station (or associated with the power system 152) of the residence 102. In some embodiments, this is determined based on the size of the station's inverter. Also in some embodiments, the power can be converted to current by accessing the voltage on the charger in the residence 102.

[0072] In various embodiments, the measure of the current produced by the sum of the realized EVSE power and the parasitic loads of the vehicle 100 is referred to as the total discharge load (I total_Dis_load ), and represents the instantaneous total current leaving the high voltage RESS 114 to support the discharge operation.

[0073] Also in various embodiments, power is latched for the remainder of the discharge cycle (step 306). In some embodiments, this is performed after a calibrable delay for circuit loading. Specifically, in some embodiments, there is a ramp-up of current provided from the EVSE (e.g., power system 152) to the vehicle 100 (also referred to as an "electric vehicle" or "EV"). Additionally, in various embodiments, there is a ramp-up of current on parasitic loads. In some embodiments, the control system 112 (e.g., Figure 1 The processor 130 will wait a calibrated amount of time and then latch the current. In various embodiments, this initial current is used for the remainder of the discharge cycle. total_Dis_load_0In addition, in some embodiments, the initial latch current of each discharge cycle is updated based on the location (e.g., the location determined by a global positioning system (GPS) of or associated with the sensor 124) and is used to estimate the uninserted discharge time. Also in some embodiments, the updated initial latch current I total_Dis_load_0 It will also be updated with a moving average during the active discharge time of the RESS 114 .

[0074] In various embodiments, the power associated with the residence 102 is determined (step 308). In various embodiments, various characteristics of the residence 102 (e.g., characteristics retrieved from a computer memory) are obtained and utilized in determining the power associated with the residence. In certain embodiments, the characteristics include the following (and other possible characteristics): a usage area associated with the residence 102 (e.g., a geographic area), a current usage season (e.g., a current season of the year), a current usage time (e.g., a current time of day), a size of the residence 102 (e.g., square footage of the residence 102), and a list of major appliances 154 utilized in the residence 102.

[0075] In some embodiments, the house features may initially include a set of default house features. In some other embodiments, the house features may be obtained via location data (e.g., from a GPS system) and / or via user input (e.g., via a mobile application on a user's smartphone or other electronic device).

[0076] In certain embodiments, the amount of power is estimated based on premises characteristics, such as using various lookup tables and / or other data stored in computer memory that correlate premises characteristics with expected power utilization in conjunction therewith.

[0077] For example, in some embodiments, the power associated with a usage area may be represented as follows:

[0078] P RoU =P(area of ​​use (eg, via GPS), square footage of home (eg, in feet), time of year (eg, Julian date / time)).

[0079] Furthermore, in some embodiments, the power (P RoU ) can also be derived from historical similar electric utility data for a residence that matches the user data.

[0080] By way of additional example, the power associated with house size or square footage may be expressed as follows:

[0081] P Sqh= P (square footage of the home, average usage of major appliances, updated power usage of appliances from user input).

[0082] Additionally, in some embodiments, the power associated with a square foot (P Sqh ) can be specific user-selected options and metrics entered by the user.

[0083] In some embodiments, the initial estimated residential latch power (eg, time zero) may be expressed as follows:

[0084] I home_est_latch_0 =(γP Rou +(1-γ)P sqh ) / V home , where γ<1 (ie, where “γ” is a calibratable value less than 1).

[0085] Also in some embodiments, the initial conditions are averaged or blended together (step 310). Specifically, in some embodiments, the power or current values ​​of steps 306 (i.e., obtained via vehicle 100) and 308 (i.e., based on characteristics of residence 102, e.g., based on default and / or static parameters) are blended and averaged together during step 310. In some embodiments, the blending and averaging of these values ​​can be represented as follows:

[0086] I avg_dis_Latch _0=β*I home_est_latch_0 +(1-β)*I total_Dis_load _0, where β<1.

[0087] Also in certain embodiments, a moving average filter is applied to update the calculated value (step 312 ). Specifically, in various embodiments, a moving average “default” power value is updated and stored in computer memory for use in determining the unplugged charging time of the RESS 114 .

[0088] In certain embodiments, during step 312, the latched average current for each discharge cycle is updated based on the geographic location ["x"] (e.g., obtained via GPS and / or other positioning systems) and used to estimate the uninserted discharge time of the RESS 114. In various embodiments, this may be expressed as follows: avg_dis_Latch [x,n]=αI home_est__latch_0 [x,n]+(1-α)I avg_dis_Latch [x,n-1].

[0089] Also in various embodiments, the initial latch current update value is also updated with a moving average during the effective discharge time. In various embodiments, this can be expressed as follows:

[0090] Iplug_in [n] = αI avg_dis_Latch +(1-α)×I plug_in [n-1], where α<1 (ie, where "α" is a calibratable value less than 1).

[0091] Also in various embodiments, one or more discharge time values ​​are calculated (step 314 ). Specifically, in some embodiments, the discharge time may be calculated based on a discharge level (DCL) or an out of energy level (OOE) of the RESS 114 .

[0092] For example, in one embodiment using discharge level (DCL), the charging time may be expressed as follows:

[0093] (Remaining energy (Whr) - DCL energy (Whr)) / Latched power (W) = basic discharge time (ie, to discharge level).

[0094] By way of additional example, in another embodiment using the out of energy level (OOE), the charging time may be expressed as follows:

[0095] (Remaining Energy (Whr) - OOE Energy (Whr)) / Latched Power (W) = Basic Discharge Time (ie, to Energy Depletion Level).

[0096] In various embodiments, step 210 then terminates at 316 .

[0097] Refer to Figure 2 , in various embodiments, the off-board information is then used to determine an updated value (step 212 ).

[0098] refer to Figure 4 , according to an exemplary embodiment, a method for Figure 2 212 of process 200. Figure 4 As depicted in , in various embodiments, step 212 begins at step 402 (eg, when off-board information is obtained after the initial determination of step 210 ).

[0099] In various embodiments, average usage data is obtained (step 404). Specifically, in various embodiments, during step 404, data regarding actual power usage of residence 102 is obtained. In various embodiments, actual power usage is obtained from a time period that is most recent to the current time period (such as the previous week, the previous month, etc.). In certain embodiments, actual power usage is obtained from a previous year (e.g., from the immediately previous year) corresponding to a time in the same year and / or season as the current time period. In various embodiments, the actual power usage is obtained via a time period determined by the user. Figure 1Average power usage of residence 102 relative to these prior time periods is obtained and reported based on information provided by a third-party site 106 (e.g., an electric company providing power to residence 102), and / or in some embodiments through monitoring and / or record keeping from residence 102 and / or remote server 104. In some embodiments, such average data may also be obtained for other homes that are similar to residence 102 (e.g., in terms of geography, size, number of appliances, etc.).

[0100] Also in some embodiments, metric data regarding actual power usage is obtained (step 406). Specifically, in some embodiments, actual power readings are obtained from a power meter associated with residence 102 (e.g., via sensor 158 and / or power system 152), and / or monitored via remote server 104 and / or provided via third party site 106. In some embodiments, the metric data corresponds to average power usage of residence 102 during the current time period.

[0101] Furthermore, in some embodiments, additional information about the power outage is obtained (step 408). Specifically, in some embodiments, the time of the power outage is obtained (e.g., from residence 102 and / or third-party site 106) and recorded in computer memory, and a profile associated with the power outage (e.g., regarding the time of the power outage, the type of power outage, and / or its estimated duration) is transmitted (e.g., in some embodiments, to vehicle 100 via remote server 104).

[0102] Also in various embodiments, a summation of power is performed (step 410). Specifically, in some embodiments, the amount of power required during the power outage is summed or integrated until the time when the power outage (e.g., blackout) is considered complete. In some embodiments, the summation of power can be expressed as follows:

[0103] DCL_time_offboard=(remaining energy (Whr)-DCL energy (Whr)) / total power.

[0104] In various embodiments, this operation is performed whenever the following conditions are met:

[0105] (Remaining Energy (Whr) - DCL Energy (Whr)) - (Total Power * Time_Increment) > 0.

[0106] Also in various embodiments, an updated time estimate is performed (step 412). Specifically, in various embodiments, an updated time estimate of the discharge time is updated based on a blend of the off-board power demand of the residence 102 (e.g., corresponding to steps 404-410) in combination with a previously calculated power value associated with the vehicle 100 (e.g., including the charge of the RESS 114 and the parasitic loads of the vehicle 100).

[0107] In various embodiments, step 212 then ends at 414 .

[0108] Refer to Figure 2 , in various embodiments, a machine learning update is performed (step 214).

[0109] Now go to Figure 5 , according to an exemplary embodiment, a method for Figure 2 214 of process 200.

[0110] like Figure 5 As depicted in , in various embodiments, input values ​​are obtained (step 502). In various embodiments, the input values ​​of step 502 include, among other possible values, previously described residential characteristics, such as usage area, usage season, usage time, square footage of the residence 102, and a list of major appliances (and / or information about their usage), as well as target discharge levels and / or energy depletion levels, such as described above. In some embodiments, the input values ​​may be initially obtained via user input and / or from other sources (e.g., remote server 104 and / or third party site 106) in one or more initial iterations, and may be subsequently learned and / or stored in computer memory for subsequent iterations.

[0111] In various embodiments, input values ​​are provided to a machine learning algorithm (eg, including a neural network model) at step 504. In various embodiments, output values ​​are obtained at step 506 and include an updated value of the discharge time of the RESS 114 based on the machine learning.

[0112] In some embodiments, the machine learning algorithm comprises a recursive least squares algorithm that continuously learns through errors and predictions using inputs to accurately predict discharge times. In some embodiments, the machine learning algorithm utilizes a logarithmic minimization function.

[0113] Specifically, Figure 5 As depicted in FIG. 5 , in various embodiments, the machine learning algorithm combines input values ​​510 (ie, from step 502 ) (eg, X 1 , X 2 , ..., X m ) combined with the updated hypothesis 512 and the weighting coefficient 514 (e.g., W 0 , W 1 , W 2 , ..., W m) to utilize bias unit 508 to generate a net input function 516. Also in various embodiments, net input function 516 is utilized to generate activation function 518, which is then used in conjunction with unit step function 520 to generate predicted class labels 522. In various embodiments, predicted class labels 522 are utilized along with true data 524 to calculate errors 526 (e.g., Err-J(w)), and optimization is performed at 528 to find new weights (e.g., weight coefficients 514) for the machine learning algorithm.

[0114] As described above, in various embodiments, the machine learning algorithm ultimately produces output values ​​associated with step 506. Additionally, also in various embodiments, the output values ​​include (A) predicted time to discharge level and (B) predicted time to energy depletion.

[0115] Refer to Figure 2 , in various embodiments, a usage-based implementation is performed (step 216).

[0116] Now go to Figure 6 , according to an exemplary embodiment, a method for Figure 2 216 of process 200.

[0117] like Figure 6 As depicted in , in various embodiments, step 216 begins at step 602 , for example after a power outage has occurred.

[0118] In various embodiments, information about the outage is obtained (step 604). In various embodiments, this information includes, among other possible information, the time of the outage, power usage before the outage (e.g., just seconds or minutes before the outage), time to discharge level, area of ​​use, and profiles of one or more residences similar to residence 102.

[0119] In various embodiments, a high power consumption curve is generated (step 606). Specifically, in some embodiments, based on the energy usage profile of the high power usage consumer (e.g., for consumers that utilize greater than average power for the premises, such as compared to other usage in other premises of similar size and characteristics), the high power consumption curve is generated (step 606). Figure 1 One or more processors and / or from Figure 1 A third party site 106 (e.g., a utility company) generates a high power consumption curve. In various embodiments, the high power consumption curve is associated with a first group of users whose power consumption is greater than a predetermined threshold. In various embodiments, the high power consumption curve is obtained by an original equipment manufacturer (OEM) or one or more operator entities; however, this may vary in other embodiments.

[0120] In various embodiments, a low power consumption curve is generated (step 608). Specifically, in some embodiments, based on the energy usage profile of the low power usage consumer (e.g., for a consumer that utilizes less than average power for the premises, such as compared to other usage in other premises of similar size and characteristics), the low power consumption curve is generated (step 608). Figure 1 One or more processors and / or from Figure 1 A third party site 106 (e.g., a utility company) generates a low power consumption curve. In various embodiments, the low power consumption curve is associated with a second group of users whose power consumption is less than a predetermined threshold. In various embodiments, the low power consumption curve is obtained by an original equipment manufacturer (OEM) or one or more operator entities; however, this may vary in other embodiments.

[0121] Also in various embodiments, the discharge time is displayed (step 610). In various embodiments, based on the different consumption curves of steps 606 and 608, a display of different potential discharge times of the RESS 114 is provided to the current user. Specifically, in various embodiments, Figure 1 One or more of the processors of the vehicle 100 provide instructions for providing a display to the current user of the vehicle 100 regarding the different discharge times that can be obtained via the low power consumption curve relative to the high power consumption curve (and, for example, with a calculated additional length of the discharge time that can be obtained from the low power consumption compared to the high power consumption, etc.). In some embodiments, the display is provided on a display of a mobile application of a user's smartphone or other electronic device. In some other embodiments, the display may also be provided on a display screen of the vehicle 100.

[0122] Also in various embodiments, smart appliance management is performed (step 612). In various embodiments, multiple appliances 154 in residence 102 are automatically controlled by selectively switching one or more of appliances 154 to energy-saving mode based on a comparison of the actual power consumption of the residence with one or more of the low power consumption curve, the high power consumption curve, or the low power consumption curve and the high power consumption curve, via Figure 1 One or more processors to perform intelligent appliance management.

[0123] Specifically, in some embodiments, when the power consumption of the residence 102 approaches the high power consumption profile, the processor 160 of the control system 156 of the residence 102 (e.g., as part of the residential energy management system of the residence 102) provides for smart appliances (e.g., Figure 1One or more of the appliances 154 that receive the communication from the processor 160 and act accordingly) provide instructions to switch to an energy efficiency mode (e.g., where one or more lights are turned off or dimmed, the settings of the air conditioning or heating unit are adjusted to save energy or power, etc.). In addition, in some embodiments, the user can provide manual commands for such an energy efficiency mode after viewing the display of step 610, etc.

[0124] In various embodiments, the reduced energy usage is reflected in the updated calculations (step 614). In various embodiments, the reduced energy usage from the smart appliance management is reflected in the updated power values ​​and discharge time values, including being reflected in the moving average filter. Specifically, in some embodiments, the update can be represented as follows:

[0125]

[0126] In various embodiments, step 216 then terminates at step 616 .

[0127] Refer to Figure 2 In various embodiments, one or more additional actions may also be performed (step 218). For example, in some embodiments, one or more recommendations may also be provided to the user (e.g., based on Figure 1 for instructions provided by one or more of the processors of the residence 102), for recommended actions from a user (e.g., for manually shutting down and / or adjusting settings of one or more appliances 154 of the residence 102), and the like.

[0128] In various embodiments, process 200 then terminates at step 220 .

[0129] Thus, methods, systems, and vehicles are provided for estimating and managing the discharge time of an electric vehicle's RESS used to provide backup power to a residence. In various embodiments, various characteristics of the residence and current conditions associated therewith are utilized in calculating the discharge time value. Further, in various embodiments, the discharge time is maintained, for example, by switching smart appliances to a low energy mode when appropriate based on calculations and determinations performed according to the system 10 and process 200, as depicted in the figure and described above.

[0130] It should be understood that the systems, vehicles, and methods may vary from those depicted in the figures and described herein. Figure 1 100 vehicles and Figure 1 The various components of the vehicle 100 may vary in different embodiments. Similarly, it should be understood that the steps of the process 200 and the manner in which they are implemented may vary. Figure 2-Figure 6 Those depicted in, and / or the various steps of process 200 may occur simultaneously and / or in parallel with Figure 2-Figure 6occur in an order different from that depicted and / or described above.

[0131] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that there are a large number of variations. It should also be understood that the exemplary embodiment or multiple exemplary embodiments are merely examples and are not intended to limit the scope, applicability or configuration of the present disclosure in any way. On the contrary, the foregoing detailed description will provide a convenient roadmap for implementing the exemplary embodiment or multiple exemplary embodiments for those skilled in the art. It should be understood that various changes may be made to the functions and arrangements of the elements without departing from the scope of the present disclosure as set forth in the attached claims and their legal equivalents.

Claims

1. A method comprising: obtaining sensor data of a vehicle having an energy storage system configured to provide power to a residence under certain conditions; obtaining residence data regarding a plurality of characteristics related to power demand of the residence; as well as A discharge time for the energy storage system is calculated via a processor based on the sensor data of the vehicle and the plurality of characteristics of the residence.

2. The method according to claim 1, wherein: The sensor data of the vehicle includes a parasitic load of the vehicle when the vehicle is plugged into the residence; and The discharge time is also calculated based on the parasitic load and the plurality of characteristics of the residence.

3. The method according to claim 1, wherein: The plurality of characteristics include physical dimensions of the residence; and The discharge time is calculated using the physical dimensions of the residence.

4. The method according to claim 1, wherein: The plurality of characteristics include a geographic location of the residence; and The discharge time is calculated using the geographic location of the residence.

5. The method according to claim 1, wherein: The plurality of characteristics include a current time of year and a current time of day; and The discharge time is calculated using the current time of year and the current time of day.

6. The method according to claim 1, wherein: The plurality of features includes a list of electrical appliances in the residence; and The discharge time is calculated using a list of the appliances in the residence.

7. The method according to claim 1, further comprising: obtaining usage data regarding actual usage of power by the residence; as well as An updated discharge time is calculated, via the processor, using the usage data.

8. The method according to claim 1, further comprising: The processor calculates, using the plurality of features as input into a machine learning algorithm, a discharge time updated over time.

9. The method according to claim 1, further comprising: obtaining a high power consumption curve associated with a first group of users having power greater than a predetermined threshold; obtaining a low power consumption curve associated with a second group of users whose power consumption is less than the predetermined threshold; as well as According to instructions provided by the processor, based on the comparison of the actual power consumption of the residence with the low power consumption curve, the high power consumption curve, or one or more of the low power consumption curve and the high power consumption curve, the multiple electrical appliances in the residence are automatically controlled by selectively switching one or more of the multiple electrical appliances in the residence to an energy-saving mode.

10. A system comprising: one or more sensors configured to obtain sensor data regarding a high voltage storage device configured to provide power to a residence under certain conditions; as well as a processor coupled to the one or more sensors and configured to at least facilitate: obtaining residence data regarding a plurality of characteristics related to power requirements of the residence, the plurality of characteristics including a physical size of the residence, a geographic location of the residence, a current time of year, a current time of day, and a list of appliances in the residence; as well as A discharge time for an energy storage system is calculated based on the sensor data of the high voltage storage device and the plurality of characteristics of the residence, including the physical dimensions of the residence, the geographic location, the current time of year, the current time of day, and a list of the appliances in the residence.

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