System and method for multi-objective charge control optimization

Abstract

A vehicle includes at least one electric motor configured to convert electrical power into rotational motion, a battery system electrically connected to the at least one electric motor, and a controller coupled to the battery system. The controller includes a non-transitory computer-readable memory and a processor. The memory stores a charging control software module configured to cause the processor to execute a method of determining an optimal charging profile based at least in part on available received charging power, an ambient temperature of the battery system, charging system constraints, and at least one driver preference.

Description

Technical Field

[0001] The present invention relates to charging systems for electric and hybrid electric vehicles and, more particularly, to control logic for coordinating thermal and electrical conditions for charging operations. Background Art

[0002] Vehicles, including electric and hybrid electric vehicles, have battery storage devices for purposes such as powering electric motors, electronic devices, and other vehicle subsystems. The vehicle battery system can be charged using power sources such as charging stations, other electric vehicle battery systems, and / or the electrical grid. In current systems, batteries are charged at their maximum charge limit. This charging decision is made based solely on minimizing charging time without considering other factors. However, charging a battery at full charge current at low temperatures is inefficient and results in inefficiency losses.

[0003] Therefore, it is desirable to provide an adaptive charging strategy that can take multiple objectives into account. Summary of the invention

[0004] In an exemplary embodiment, a vehicle includes at least one electric motor configured to convert electric power into rotational motion, a battery system electrically connected to the at least one electric motor, a controller coupled to the battery system, the controller including a non-transitory computer readable memory and a processor, the memory storing a charging control software module configured to cause the processor to execute a method for determining an optimal charging profile based at least in part on available received charging power, an ambient temperature of the battery system, charging system constraints, and at least one driver preference.

[0005] In addition to one or more features described herein, at least one driver preference includes a relative ranking of a set of parameters including at least charging time, cost-energy efficiency, monetary cost, and battery life.

[0006] In addition to one or more features described herein, the charging system constraints include at least one of an expected distance to next available charge and an expected time to departure.

[0007] In addition to one or more features described herein, determining the optimal charging curve includes minimizing the cost equation: Where g(X,u,W)=α*(1-η dff )+β*Δt*(SOC t ≤SOC target )+γ*i aging , where η eff is the system efficiency, Δt is the time step used by the optimization algorithm, SOC target is the final charging target of the charging state, i agingis the aging rate of the battery, X is a set of state variables including the battery temperature T and the state of charge SOC, and may have other possible other state variables such as the lithium ion surface density, u is the ratio of the battery charge to the battery thermal (hot or cold) power, and W is the available power from the outside to the vehicle, which can be through a charger or wirelessly.

[0008] In addition to one or more of the features described herein, the cost equation includes at least one of the following: charging time is less than driver demand, the end of charge state is equal to the charge state target, the power required by the battery is less than the charging limit, the power required by the thermal system (heater or cooler) is less than the thermal system power limit, and the power required by the battery plus the power required by the thermal system is less than the total available power.

[0009] In addition to one or more features described herein, the memory stores a charge profile selection module configured to cause the controller to output a plurality of charge profiles to a user selection system and configured to cause the controller to implement a selected one of the plurality of charge profiles.

[0010] In addition to one or more features described herein, at least one driver preference includes a relative ranking of a set of parameters including at least charging time, cost-energy efficiency, monetary cost of charging, and battery life, and wherein the plurality of charging curves correspond to unique driver preference rankings.

[0011] In another exemplary embodiment, a method for controlling a vehicle charging operation includes determining an optimal charging profile based at least in part on available received charging power, an ambient temperature of a battery system, charging system constraints, and at least one driver preference using a controller that evaluates an optimization cost function, wherein the optimization cost function balances power requirements of a battery thermal system and a battery charging system.

[0012] In addition to one or more features described herein, evaluating the optimization cost function includes minimizing the cost equation: Where g(X,u,W)=α*(1-η eff )+β*Δt*(SOC t ≤SOC target )+γ*i aging , and where η eff is the system efficiency, Δt is the time step used by the optimization algorithm, SOC target is the final charging target of the charging state, i aging is the aging rate of the battery, X is a set of state variables including the battery temperature T and the state of charge SOC, u is the ratio of the battery charge to the battery thermal power, and W is the available power from the outside to the vehicle, which can be through a charger or wirelessly.

[0013] In addition to one or more of the features described herein, the cost equation includes at least one of the following: charging time is less than driver demand, the end of charge state is equal to the charge state target, the power required by the battery is less than the charging power limit, the power required by the thermal system (heater or cooler) is less than the thermal system power limit, and the power required by the battery plus the power required by the thermal system is less than the total available power.

[0014] In addition to one or more features described herein, the memory stores a charge profile selection module configured to cause the controller to output a plurality of charge profiles to a user selection system and configured to cause the controller to implement a selected one of the plurality of charge profiles.

[0015] In addition to one or more features described herein, at least one driver preference includes a relative ranking of a set of parameters including at least charging time, cost-energy efficiency, and battery life, and wherein the plurality of charging curves correspond to unique driver preference rankings.

[0016] In addition to one or more features described herein, at least one driver preference includes a relative ranking of a set of parameters including at least charging time, cost-energy efficiency, and battery life.

[0017] In addition to one or more features described herein, the charging system constraints include at least one of an expected distance to next available charge and an expected time to departure.

[0018] In yet another exemplary embodiment, a charging control module for a vehicle includes a plurality of inputs configured to receive at least a temperature sensor output and an available wall power value, a plurality of outputs configured to output at least a thermal power control signal and a charging power control signal, a processor, and a memory storing instructions configured to cause the processor to evaluate an optimization cost function to determine an optimal charging profile based at least in part on the available received charging power, an ambient temperature of a battery system, charging system constraints, and at least one driver preference, wherein the optimization cost function balances power requirements of a battery thermal system and a battery charging system, wherein evaluating the optimization cost function includes minimizing a cost equation: Where g(X,u,W)=α*(1-η eff )+β*Δt*(SOC t ≤SOC target )+γ*i aging , and where η eff is the system efficiency, Δt is the time step used by the optimization algorithm, SOC target is the final charging target of the charging state, i agingis the aging rate of the battery, X is a set of state variables including the battery temperature T and the state of charge SOC, and may have other possible other state variables such as the lithium ion surface density, u is the ratio of the battery charge to the battery thermal (hot or cold) power, and W is the available power from the outside to the vehicle, which can be through a charger or wirelessly.

[0019] In addition to one or more features described herein, a battery aging submodule is included that is configured to determine at least an aging rate of the battery based on a charging rate of the battery and a temperature of the battery.

[0020] In addition to one or more features described herein, at least one user selection system is included that is configured to display a plurality of charging curves, receive a user selection of a single charging curve from the plurality of charging curves, and implement the charging curve.

[0021] In addition to one or more features described herein, a user selection map is included that is configured to allow a user to input one or more constraints, and wherein evaluating the optimization cost function includes constraining the optimization cost function with the one or more constraints.

[0022] In addition to one or more features described herein, the one or more constraints include at least one constraint selected from the following list: charging time is less than driver demand, end of charge state is equal to charge state target, power required by the battery is less than charging limit, power required by the thermal system is less than thermal system power limit, power required by the battery plus power required by the thermal system is less than total available power.

[0023] In addition to one or more features described herein, the one or more constraints include an ordered ranking of priorities.

[0024] The above features and advantages and other features and advantages of the present disclosure will become apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, advantages and details appear, by way of example only, in the following detailed description, which refers to the accompanying drawings, in which:

[0026] Figure 1 is a vehicle that includes an electric propulsion system;

[0027] Figure 2 is used for Figure 1 A control architecture for a cold start charging process in a vehicle;

[0028] Figure 3A Is Figure 2 The charge state projection with respect to time generated by the control architecture;

[0029] Figure 3B Is Figure 2 A projection of charging current with respect to time generated by a control architecture; and

[0030] Figure 4 is used for Figure 1 Schematic diagram of a vehicle's battery charging system. DETAILED DESCRIPTION

[0031] The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or use. It should be understood that in all figures, corresponding reference numerals represent the same or corresponding parts and features. As used herein, the term module refers to a processing circuit, which may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated or group) and memory that executes one or more software or firmware programs, a combinational logic circuit and / or other suitable components that provide the described functions.

[0032] According to an exemplary embodiment, a vehicle includes control logic that allows the vehicle to optimize charging operations, including using wall power to provide battery heating when the battery is at a low temperature, or using wall power to provide cooling when the battery temperature is high. The control logic provides a centralized framework for determining optimal charging and thermal profiles based on multiple competing objectives (e.g., charging speed, charging efficiency, thermal efficiency, etc.) using a cost function. In a specific example, the control architecture can generate multiple different profiles representing different priorities of the objectives and present the different charging profiles to the vehicle operator. The vehicle operator selects the charging profile that best matches their objectives, and the vehicle executes the charging profile.

[0033] Continuing with reference to the above general systems and methods, Figure 1 An embodiment of a motor vehicle 10 is shown that includes a battery system controller 24 configured to implement a control architecture. The vehicle 10 includes a body 12 that at least partially defines an occupant compartment 14. The body 12 also supports various vehicle subsystems, including a propulsion system 16, a battery system 22, other subsystems that support the functionality of the propulsion system 16 and other vehicle components, such as a braking subsystem, a suspension system, a steering subsystem, a fuel injection subsystem, an exhaust subsystem, etc.

[0034] The vehicle 10 may be an internal combustion vehicle, an electric vehicle (EV), or a hybrid vehicle. In an embodiment, the vehicle 10 is a hybrid vehicle that includes an internal combustion engine system 18 and at least one electric motor assembly. For example, the propulsion system 16 includes a first electric motor 20 and a second electric motor 21. The motors 20 and 21 may be configured to drive wheels 23 on opposite sides of the vehicle 10. Any number of motors located in various additional locations around the vehicle 10 may be used to provide mechanical rotation for the corresponding systems and subsystems.

[0035] The battery system 22 can be electrically connected to the motors 20 and 21 and / or other components, such as vehicle electronics. The battery system 22 can be configured as a rechargeable energy storage system (RESS) and include a plurality of battery cells divided into a plurality of sections. The battery system controller 24 is included in the battery system 22 and controls the charging and discharging functions of the batteries within the battery system 22. In an alternative configuration, the battery system controller 24 can be a universal vehicle controller remote from the battery system 22 and configured to control a plurality of systems and / or subsystems. The universal vehicle controller can be located anywhere within the vehicle 10. In yet another alternative, the battery system controller 24 can be a distributed control system including a plurality of coordinated controllers throughout the vehicle 10, including controllers within the battery system 22 and controllers remote from the battery system 22.

[0036] The charging port 30 is disposed at an external location of the vehicle 10 and is electrically coupled to the battery system 22. The charging port 30 facilitates providing wall power to the battery system 22 for charging operations. As used herein, "wall power" refers to any fixed power source independent of the vehicle 10 and is not strictly limited to a power outlet provided on the wall. Wall power can be provided in a variety of forms, including 120 volts alternating current (120VAC), 240 volts alternating current (240VAC) and 480 volts direct current (480VDC), 800VDC or higher. The exemplary voltages are non-limiting in nature, and it should be understood that AC voltages may also be used. In addition, the charging port 30 may be any suitable port that allows power to be provided to the battery 22 from outside the vehicle 10, including wired power charging and wireless charging, and in some examples, charging the first vehicle from a second vehicle.

[0037] During operation of an electric vehicle or hybrid electric vehicle, the battery system 22 is discharged to provide power to the motors 20, 21 and other systems within the vehicle 10. When the vehicle 10 is not moving, electrical power may be received through a connection to an external power source, such as through the charging port 30, and the battery system controller 24 controls the receipt of power from the charging port 30 to the battery system 22.

[0038] The rate at which the battery can be charged depends on where the vehicle 10 is connected (e.g., a 120VAC home charging port, a 240VAC home charging port, or a 480V DC fast charging port) and what current the wall charger can output. In addition, at low temperatures, lithium (Li) batteries or similar types of batteries have a slower diffusion rate and therefore cannot be charged quickly. In addition, when the battery temperature is too high, the battery cannot be charged at the peak charging rate because the faster the battery is charged, the faster the temperature rises, and the charging self-heats the battery. As a result, it is desirable to keep the temperature within a window defined by an upper temperature limit and a lower temperature limit. During this charging process, wall power can be used to heat or cool the battery. However, any power diverted to heating or cooling the battery will inevitably reduce the amount and rate of power available for charging. Therefore, the controller faces an optimization problem.

[0039] Existing systems provide separate controllers or control modules for thermal control to keep the battery pack within the ideal charging temperature range and for charging control to control the power supplied to the battery pack from the wall charger. Each of these controllers is rule-based, and the control rules are set independently of and do not take into account other systems competing for power during the charging operation. This can result in inefficiencies, extended charging times, wasted power, etc.

[0040] The exemplary embodiment uses Figure 2 The control architecture 100 shown solves the optimization problem and conflicting goals of the thermal system 28 and the charging system, wherein the battery system controller 24 includes a charging control software module 102. The charging control software module 102 includes a cost function configured to determine a charging strategy based primarily on available wall charging power, ambient and battery temperature, charging system-specific constraints, and any additional information provided by the driver. For example, the additional information includes at least a priority of charging time, charging efficiency, and / or battery life. In order to balance the optimal operating conditions, the charging control software module 102 includes an optimization cost function (e.g., Equation 1 described herein) that coordinates the thermal condition of the battery 26 and the charging characteristics from the charging port 30 to achieve multiple optimal charging curves taking into account thermal power consumption and charging power availability, and allows the vehicle operator to select the optimal charging curve based on his or her preferences. The cost function balances competing priorities and constraints and determines an ideal electrical curve (e.g., current, voltage, etc.) and thermal curve (e.g., heater activation, cooler activation, etc.) to best achieve multiple charging curves that meet predetermined constraints.

[0041] The charging control software module 102 integrates thermal and charging control, replacing rule-based control of the thermal system 28 and rule-based control of the power limit of the charging system with cost function optimization. The control system 100 provides an operational output to the charging port controller 120 through the charging control signal 110. The charging control signal 110 indicates the power request from the wall charger and controls the characteristics of the received power according to the requirements of the optimal controller or control algorithm (including the optimized cost function). The charging port control 120 can be another software module within the battery system controller 24, a separate software module within another controller, or a charging port control system based on specific hardware or part of the hardware.

[0042] To facilitate use of the optimization cost function, the charge port control 120 provides an output 122 to the charge control software module 102 . The output 122 defines the available power characteristics and provides any available sensor information from the charge port 30 to the charge software control module 102 .

[0043] Similarly, the charging software control module 102 provides operational outputs to the thermal control system 130 via the thermal control signal 112. As with the charging port control 120, the thermal control 130 may be another software module within the battery system controller 24, a separate software module within another controller, or a specific hardware or partial hardware controller for the thermal system 28. The thermal control signal 112 is output to the thermal system 28 and controls the operation of the thermal system 28 according to the optimized cost function included in the charging software control module 102.

[0044] Thermal control 130 provides signals 132 that define the current temperature of thermal system 28 , expected power usage, etc., for maintaining the vehicle battery within a desired charging temperature window.

[0045] In addition, the battery system controller 24 includes a required charging / heating or cooling time prediction module 140 that uses established models to predict how long charging and heating or cooling operations will take based on the commanded power usage of each of the charging system and thermal system 28. The required heating or cooling and charging times are provided to the charging control software module 102 via signals 142, and an optimization cost function utilizes the estimated times.

[0046] In some examples, the charging control software module 102 may be interconnected with a phone app, or receive direct input from a vehicle operator, and may receive an expected departure time via input 160. In this case, the charging control software module 102 may utilize a combination of the expected departure time and the predicted charging operation time to determine when to begin the charging operation, and / or may use the departure time to determine system constraints.

[0047] In a specific example, the optimization cost equation is defined by Equation 1:

[0048] Where g(X,u,W)=α*(1-η eff )+β*Δt*(SOC t ≤SOC target )+γ*i aging .

[0049] In equation 1, η eff is the system efficiency, Δt is the time step used by the optimization algorithm, SOC target is the final charging target of the charging state, i aging is the aging rate of the battery, X is a set of state variables including the battery temperature T and the state of charge SOC, and may have other possible other state variables such as the lithium ion surface density, u is the ratio of the battery charge to the battery thermal (hot or cold) power, and W is the power available from the outside to the vehicle, such as the power available through a wall charger or through wireless charging.

[0050] In alternative examples, alternative cost functions may be used to identify the desired optimization. Alternative examples may include multiple optimization cost equations that are combined to find multiple optimal charging and thermal profiles for the customer to choose from. In other alternative examples, the optimization cost function depends on the type and form of the model used to predict system behavior.

[0051] In an alternative example, the cost equation used may be Equation 2:

[0052]

[0053] The constraints are:

[0054]

[0055] Where α is the weighting factor for temperature control, β is the weighting factor for power control of heating, T(k) is the battery temperature at the kth step, and T max is the battery temperature limit, q(k) is the ratio of the real-time charge present in the battery at step k to the full battery capacity charge, p chrg (k) is the power used for charging at step k, p heat (k) is the power used for heating in the kth step, p wall is the available wall power, i c (k) is the charging current at step k, is the maximum charging current, which depends on the ratio of q(k) at the kth step and the battery temperature T(k).

[0056] Common to all examples, including those explicitly disclosed herein and alternative examples, is the use of a cost equation that includes multiple objectives and a weighting factor corresponding to each objective.

[0057] In some examples, the cost equation may further include optional constraints that limit the potential solutions that may be defined. For example, some constraints on the cost function may include that the charging time is less than the time before departure, the end of the state of charge is equal to the state of charge target, the power required by the battery is less than the charging power limit, the power required by the thermal system (heater or cooler) is less than the thermal system power limit, and the power required by the battery plus the power required by the thermal system is less than the total available power.

[0058] Each of these constraints is imposed on the cost function by the charging control software module 102 and sets limits on acceptable solutions. In another example, the battery system controller 24 can be connected to an input screen visible to the user. The input screen can be a vehicle screen, a connected mobile device, a PC with network accessibility, or any other connected screen. The use of an input screen or similar device allows the vehicle operator to enter data, including an expected departure time, a ranked list of priorities (e.g., charging efficiency is more important than charging speed), and any other constraints on the system. The charging control software module 102 constructs constraints on the optimization cost function (Equation 1) based on the received input. For example, if the driver intends to keep the vehicle 10 stationary for a long time, the driver can indicate that cost-energy efficiency is the highest priority, battery life is the second highest priority, and charging time is the lowest priority.

[0059] In another example, the charging constraint may include an expected distance to be traveled in the next use of the vehicle 10. This may be in the form of an endpoint GPS location, a manually entered distance, or any similar means of providing an estimated distance. The controller may then generate a constraint such as "charge must be more than X when departing," where X is the minimum charge for a round trip of the estimated distance.

[0060] In some examples, the user may input optional constraints or conflicting constraint preferences. Similarly, in some examples, there may be conflicting thermal and charging constraints. In such examples, there may be multiple possible solutions to the optimization cost function. When this is the case, the controller 24 uses the charging curve selection module 104 to generate multiple charging curves and output the charging curves to the screen. The charging curve selection module 104 can be a submodule within the charging control software module 102 as shown, or can be a separate interconnected software module. An example of multiple charging curves is shown in FIG. Figure 3A and 3B It is shown in Figure 3A A set of state of charge charging curves 310 are shown, Figure 3BA set of charging current curves 320 are shown. Each charging curve 310, 320 corresponds to a given balance of priorities, resulting in a change in the constraints on the optimization cost function defined by Equation 1. Furthermore, each charging curve 310, 320 includes a different balance of priorities.

[0061] When presented with various charging curves 310, 320, the user can select the charging curve that is closest to the user's preferred operation. In some examples, the charging control software module 102 can further provide the user with a text description summarizing the conflicting optimizations, allowing less sophisticated users to make an informed decision. In some examples, the text description can be a user-defined ranked list of parameters from most important to least important. In one example, the ranked parameters include at least charging time, cost-energy efficiency, charging energy monetary cost, and battery life.

[0062] In yet another example, the charging control software module 102 may display a recommended charging curve 310, 320 based on a predetermined optimal value, such as maximizing battery life. In such an example, the user may select a recommended charging curve 310, 320, or may decide to select a different charging curve based on personal preference.

[0063] Continue to refer Figure 1-3B , Figure 4 Schematically shows the Figure 1 4 is a representation of a battery charging system 400 for a vehicle of FIG. The battery charging system performs at least two functions. The first function is to determine an optimal charging curve as described herein. The determination of the optimal charging curve, including any user selections, is performed in advance and does not occur simultaneously with the charging operation itself.

[0064] The thermal system 428 (28) predicts the battery temperature and provides the measured battery temperature rate as a battery temperature output 482 to the battery aging rate determination module 480. At the same time, the charging port 430 (30) measures the magnitude of the charging current and provides the measured charging current to the battery aging module 480 via a charging current output 484. The battery aging module 480 uses the received charging current 484 and the received battery temperature 482 to determine the battery aging rate according to known methods and provides the battery aging rate to the charging software module 402 (102) via an output 486. In some examples, the battery aging rate determination module 480 is a separate module within the controller system of the vehicle 10. In an alternative example, the battery aging rate determination module 480 is a software submodule 480 within the charging control software module 402 (102).

[0065] The charging control software module 402 also receives system state variable inputs of available wall power 450, ambient temperature around the vehicle 452, the driver's estimated departure time 454, the battery's current state of charge 456, and the battery's current temperature 458. These state variables are applied to the cost function described herein, and a charging curve is determined.

[0066] The second function uses the charging control software module 402 (102) to implement the determined charging profile 310, 320. Depending on the determined charging profile, the implementation can occur immediately after determination, or at a future time. During implementation, the charging control software module 402 (102) outputs a charging power control signal 460 to the charging port 430 (30), and the charging port provides a charging energy efficiency command 432 and a charging time efficiency command 434 to the connected wall charger. The wall charger interprets the received commands 432, 434 and provides charging power to the vehicle 10 through the charging port 30.

[0067] At the same time, the charging control software module 402 (102) provides a thermal system control signal 470 to the thermal system 428 (28). The thermal system control signal 470 provides the thermal system with operating instructions for the heating or cooling profile and causes the thermal system 428 (28) to implement the thermal profile. Throughout the charging process, when variables provided by the thermal system 428, the charging port 430, and the battery 480 are provided to the charging control software module 402, the charging control software module 402 uses control techniques to implement the determined charging profile.

[0068] The terms "one" and "an" do not indicate a limitation of quantity, but rather indicate the presence of at least one of the referenced items. The term "or" means "and / or", unless the context clearly indicates otherwise. References to "an aspect" throughout the specification mean that a particular element (e.g., feature, structure, step, or characteristic) described in conjunction with that aspect is included in at least one aspect described herein, and may or may not be present in other aspects. In addition, it should be understood that the described elements may be combined in any suitable manner in various aspects.

[0069] When an element such as a layer, film, region, or substrate is referred to as being “on” another element, it can be directly on the other element or intervening elements may also be present. In contrast, when an element is referred to as being “directly on” another element, there are no intervening elements present.

[0070] Unless otherwise indicated herein, all test standards are the most recent standards in effect as of the filing date of the present application or, if priority is claimed, the filing date of the earliest priority application in which the test standards appear.

[0071] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0072] Although the above disclosure has been described with reference to exemplary embodiments, it will be appreciated by those skilled in the art that various changes may be made and equivalents may be substituted for its elements without departing from the scope thereof. In addition, many modifications may be made to adapt specific situations or materials to the teachings of the disclosure without departing from the essential scope of the disclosure. Therefore, it is intended that the disclosure is not limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A vehicle comprising: at least one electric motor configured to convert electrical power into rotational motion; a battery system electrically connected to the at least one electric motor; A controller coupled to the battery system, the controller comprising a non-transitory computer readable memory and a processor, the memory storing a charging control software module, the charging control software module configured to cause the processor to execute a method for determining an optimal charging profile based at least in part on available received charging power, an ambient temperature of the battery system, charging system constraints, and at least one driver preference.

2. The vehicle according to claim 1, wherein: The at least one driver preference includes a relative ranking of a set of parameters including at least charging time, cost-energy efficiency, monetary cost, and battery life.

3. The vehicle according to claim 1, wherein: The charging system constraints include at least one of an expected distance to next available charge and an expected time to departure.

4. The vehicle according to claim 1, wherein: Determining the optimal charging curve involves minimizing the cost equation: Where g(X,u,W)=α*(1-η eff )+β*Δt*(SOC t ≤SOC target )+γ*i aging ,and Among them, η eff is the system efficiency, Δt is the time step used by the optimization algorithm, SOC target is the final charging target of the charging state, i aging is the aging rate of the battery, X is a set of state variables including the battery temperature T and the state of charge SOC, and may have other possible other state variables such as the lithium ion surface density, u is the ratio of the battery charge to the battery thermal (hot or cold) power, and W is the available power from the outside to the vehicle, which can be through a charger or wirelessly.

5. The vehicle according to claim 4, wherein: The cost equation also includes at least one of the following: charging time is less than driver demand, the end of charging state is equal to the charging state target, the power required by the battery is less than the charging limit, the power required by the thermal system (heater or cooler) is less than the thermal system power limit, and the power required by the battery plus the power required by the thermal system is less than the total available power.

6. The vehicle according to claim 4, wherein: The memory also stores a charging profile selection module configured to cause the controller to output a plurality of charging profiles to a user selection system and configured to cause the controller to implement a selected one of the plurality of charging profiles.

7. A method for controlling a vehicle charging operation, the method comprising: An optimal charging profile is determined based at least in part on available received charging power, an ambient temperature of the battery system, charging system constraints, and at least one driver preference using a controller that evaluates an optimization cost function, wherein the optimization cost function balances power requirements of the battery thermal system and the battery charging system.

8. The method according to claim 7, wherein: Evaluating the optimization cost function involves minimizing the cost equation: Where g(X,u,W)=α*(1-η eff )+β*Δt*(SOC t ≤SOC target )+γ*i aging ,and Among them, η eff is the system efficiency, Δt is the time step used by the optimization algorithm, SOC target is the final charging target of the charging state, i aging is the aging rate of the battery, X is a set of state variables including the battery temperature T and the state of charge SOC, u is the ratio of the battery charge to the battery thermal power, and W is the available power from the outside to the vehicle, which can be through a charger or wirelessly.

9. The method according to claim 8, wherein: The cost equation also includes at least one of the following: charging time is less than driver demand, the end of charging state is equal to the charging state target, the power required by the battery is less than the charging power limit, the power required by the thermal system (heater or cooler) is less than the thermal system power limit, and the power required by the battery plus the power required by the thermal system is less than the total available power.

10. The method according to claim 8, wherein: The memory further stores a charging profile selection module configured to cause the controller to output a plurality of charging profiles to a user selection system, and configured to cause the controller to implement a selected one of the plurality of charging profiles, and wherein the at least one driver preference includes a relative ranking of a set of parameters including at least charging time, cost-energy efficiency, and battery life, and wherein the plurality of charging profiles correspond to unique driver preference rankings.

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