Intelligent vehicles, charging systems, and control logic for V2X demand-response income and capacity optimization
Presented are charging systems for provisioning vehicle grid integration (VGI) demand-response (DR) activities, methods for making/using such systems, and vehicles with bidirectional charging and VGI DR capabilities. A method of controlling VGI operations for a host vehicle includes a resident/remote vehicle controller receiving user-specific data input by the host vehicle's operator and crowd-sourced data output by third-party vehicles deemed comparable to and located within a predefined region of the host vehicle. A predicted battery life of the battery pack resulting from the host vehicle performing VGI operations is estimated using a non-iterative, non-recursive closed-form battery life model based on the user-specific and crowd-sourced data. The controller uses the predicted battery life to determine a value proposition indicating a predicted value and battery capacity for performing VGI operations, and commands a resident subsystem of the host vehicle to execute a control operation for the VGI operation based on the value proposition.
1 . A method of controlling a vehicle grid integration (VGI) operation of a host vehicle having a rechargeable battery pack connectable to a charging station, the method comprising:
receiving, via a vehicle controller, user-specific data input by an operator of the host vehicle;
receiving, via the vehicle controller, crowd-sourced data output by multiple third-party vehicles deemed operatively comparable to the host vehicle and located within a predefined region of the host vehicle;
estimating, using a non-iterative and non-recursive closed-form battery life model based on the user-specific data and the crowd-sourced data, a predicted battery life of the rechargeable battery pack resulting from the host vehicle performing the VGI operation, the battery life model including a multiparameter hypergeometric function using battery life test data and battery cycle life data associated with the rechargeable battery pack of the host vehicle;
determining, via the vehicle controller using the predicted battery life, a value proposition indicating a predicted value and a predicted battery capacity for the host vehicle performing the VGI operation; and
commanding, via the vehicle controller, a resident subsystem of the host vehicle to execute an automated control operation for performing the VGI operation based on the value proposition, the automated control operation including a reverse power flow (RPF) transfer of power from the rechargeable battery pack of the host vehicle to a power grid through the charging station.
2 . The method of claim 1 , further comprising receiving, via the vehicle controller, utility company rate data for purchasing electrical power from the host vehicle, wherein the value proposition is further determined based on the utility company rate data.
3 . The method of claim 1 , further comprising receiving, via the vehicle controller from the host vehicle, vehicle throughput data indicative of an amount of energy available from the host vehicle, wherein the value proposition is further determined based on the vehicle throughput data.
4 . The method of claim 1 , wherein the predicted value indicated by the value proposition includes an estimated total income the operator of the host vehicle will receive by performing the VGI operation.
5 . The method of claim 1 , wherein the predicted battery capacity indicated by the value proposition includes a percent battery degradation as a function of battery life expectancy.
6 . The method of claim 1 , further comprising performing an input convergence of the user-specific data input and the crowd-sourced data by repeatedly receiving updated user-specific data and replacing the user-specific data with the updated user-specific data to converge on an average use case specific to the host vehicle.
7 . The method of claim 6 , further comprising estimating a new predicted battery life each time a set of the updated user-specific data is received using the battery life model based on a reduced portion of the crowed-sourced data and on the updated user-specific data.
8 . The method of claim 1 , wherein the user-specific data input by the operator of the host vehicle includes an indication that the host vehicle is configured to perform the VGI operation and/or the charging station to which the host vehicle is connectable is a bidirectional charger.
9 . The method of claim 1 , wherein the user-specific data input by the operator of the host vehicle includes a user-selected maximum allowable battery capacity degradation and/or a user-selected minimum allowable vehicle driving range.
10 . The method of claim 1 , wherein the multiparameter hypergeometric function executes a finite number of standard operations to combine a calendar life test data parameter and a cycle life data parameter.
11 . The method of claim 1 , wherein the resident subsystem includes an Electronic Battery Control Module (EBCM), and wherein the automated control operation further includes the EBCM preventing the host vehicle from performing the VGI operation responsive to the value proposition being below a predefined threshold.
12 . The method of claim 1 , wherein the resident subsystem includes an electronic display device mounted inside the host vehicle, and wherein the automated control operation further includes the electronic display device displaying indications of the predicted value and the predicted battery capacity degradation.
13 . The method of claim 1 , wherein the vehicle controller includes a resident vehicle controller resident to the host vehicle and a remote vehicle controller of a middleware computing service remote from the host vehicle, the resident vehicle controller receiving the user-specific data and transmitting the command signal, and the remote vehicle controller receiving the crowd-sourced data, estimating the predicted battery life, and determining the value proposition.
14 . A non-transitory, computer-readable medium storing instructions executable by one or more processors of a vehicle controller to control a vehicle grid integration (VGI) operation of a host vehicle, the host vehicle having a rechargeable battery pack connectable to a charging station, the instructions, when executed by the one or more processors, causing the vehicle controller to perform operations comprising:
receiving user-specific data input by an operator of the host vehicle;
receiving crowd-sourced data output by third-party vehicles deemed operatively comparable to the host vehicle and located within a predefined region of the host vehicle;
estimating, using a non-iterative and non-recursive closed-form battery life model based on the user-specific data and the crowd-sourced data, a predicted battery life of the rechargeable battery pack resulting from the host vehicle performing the VGI operation, the battery life model including a multiparameter hypergeometric function using battery life test data and battery cycle life data associated with the rechargeable battery pack of the host vehicle;
determining, using the predicted battery life, a value proposition indicating a predicted value and a predicted battery capacity for the host vehicle performing the VGI operation; and
commanding a resident subsystem of the host vehicle to execute an automated control operation for performing the VGI operation based on the value proposition, the automated control operation including a reverse power flow (RPF) transfer of power from the rechargeable battery pack of the host vehicle to a power grid through the charging station.
15 . A motor vehicle, comprising:
a vehicle body with a passenger compartment;
a plurality of road wheels attached to the vehicle body;
a traction motor attached to the vehicle body and operable to drive one or more of the road wheels to thereby propel the motor vehicle;
a traction battery pack attached to the vehicle body and electrically connected to the traction motor; and
a vehicle controller programmed to:
receive user-specific data input by an operator of the motor vehicle;
receive crowd-sourced data output by multiple third-party vehicles deemed operatively comparable to the motor vehicle and located within a predefined region of the motor vehicle;
estimate, using a non-iterative and non-recursive closed-form battery life model based on the user-specific data and the crowd-sourced data, a predicted battery life of the rechargeable battery pack resulting from the motor vehicle performing the VGI operation, the battery life model including a multiparameter hypergeometric function using battery life test data and battery cycle life data associated with the rechargeable battery pack of the motor vehicle;
determine, using the predicted battery life, a value proposition indicating a predicted value and a predicted battery capacity for the motor vehicle performing the VGI operation; and
command a resident subsystem of the motor vehicle to execute an automated control operation for performing the VGI operation based on the value proposition, the automated control operation including a reverse power flow (RPF) transfer of power from the rechargeable battery pack of the motor vehicle to a power grid through the charging station.
16 . The motor vehicle of claim 15 , wherein the vehicle controller is further programmed to receive a utility company rate for purchasing electrical power from the host motor vehicle, wherein the value proposition is further determined based on the utility company rate.
17 . The motor vehicle of claim 15 , wherein the vehicle controller is further programmed to receive vehicle throughput data indicative of an amount of energy available from the host motor vehicle, wherein the value proposition is further determined based on the vehicle throughput data.
18 . The motor vehicle of claim 15 , wherein the predicted value indicated by the value proposition includes an estimated total income the operator of the motor vehicle will receive by performing the VGI operation, and wherein the predicted battery capacity indicated by the value proposition includes a percent battery degradation as a function of battery life expectancy.
19 . The motor vehicle of claim 15 , wherein the battery life model includes a multiparameter hypergeometric function of a calendar life test data parameter and a cycle life data.
20 . The motor vehicle of claim 15 , wherein the vehicle controller includes a resident vehicle controller resident to the motor vehicle and a remote vehicle controller of a middleware computing service remote from the motor vehicle, the resident vehicle controller receiving the user-specific data and transmitting the command signal, and the remote vehicle controller receiving the crowd-sourced data, estimating the predicted battery life, and determining the value proposition.