SYSTEM AND METHOD FOR MULTI-CRITERIAL OPTIMIZATION OF CHARGE CONTROL

Abstract

A vehicle comprises at least one electric motor designed to convert electrical energy into rotary motion; a battery system electrically connected to the at least one electric motor; and a control unit connected to the battery system. The control unit comprises non-transient computer-readable memory and a processor. The memory contains a charging control software module designed to cause the processor to perform the following procedure: determining an optimal charging profile based at least in part on available received charging power, ambient battery system temperature, charging system limitations, and at least one driver preference.

Description

INTRODUCTION

[0001] The subject matter of the disclosure relates to charging systems for electric and hybrid electric vehicles and, in particular, to control logic for coordinating the thermal and electrical conditions for a charging process.

[0002] Vehicles, including electric and hybrid electric vehicles, are equipped with battery storage systems that power electric motors, electronic systems, and other vehicle subsystems. Vehicle battery systems can be charged from power sources such as charging stations, other electric vehicle battery systems, and / or a power grid. Current systems charge batteries to their maximum charge limit. This charging decision is made solely to minimize charging time, without consideration of other factors. However, charging the battery at full charge current at low temperatures is inefficient and can lead to efficiency losses.

[0003] Therefore, it is desirable to develop an adaptive charging strategy that can take multiple criteria into account. SUMMARY

[0004] In an exemplary embodiment, a vehicle includes at least one electric motor configured to convert electrical energy 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 non-transitory computer-readable memory and a processor, the memory including a charging control software module configured to cause the processor to perform the 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 of the features described herein, the at least one driver preference comprises a relative ranking of a number of parameters, including at least charging time, cost-energy efficiency, monetary cost, and battery lifetime.

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

[0007] In addition to one or more of the features described herein, determining the optimal charging profile includes minimizing the cost equation ∑t=0Ng(X,u,W), where g(X, u, W) =α ∗ (1 - η eff ) + β ∗ Δt ∗ (SOC t ≤ SOC target ) + γ ∗ i aging and η eff is a system efficiency, Δt is the time step used by the optimization algorithm, SOCtarget the final charging target of the state of charge is i aging is an aging rate of a battery, X is a set of state variables that includes a battery temperature T and a state of charge SOC and may have other possible state variables, e.g., lithium ion surface density, u is the ratio of battery charge to thermal (heating or cooling) power of the battery, and W is the available power supplied to the vehicle from an external source, e.g., via the 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 features: the charging time is less than the time required by the driver, the end-state of charge is equal to the target state of charge, the power required by the battery is less than the charging limits, the power required by the thermal systems (heating or cooling) 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 of the features described herein, the memory further comprises a charging profile selection module configured to cause the controller to output a plurality of charging profiles to a user selection system, and to cause the controller to implement the profile selected from the plurality of charging profiles.

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

[0011] In another exemplary embodiment of a method for controlling a vehicle charging process, the method comprises determining an optimal charging profile using a control unit that evaluates a cost optimization function, wherein the optimal charging profile is based at least in part on an available received charging power, an ambient temperature of a battery system, charging system constraints, and at least one driver preference, wherein the cost optimization function trades off a power demand of a thermal battery system and a battery charging system.

[0012] In addition to one or more of the features described herein, evaluating a cost optimization function includes minimizing the cost equation ∑t=0Ng(X,u,W), where g(X, u, W) = α ∗ (1 - η eff ) + β ∗ Δt ∗ (SOC t ≤ SOC target ) + γ ∗ i aging and η effis a system efficiency, Δt is the time step used by the optimization algorithm, SOC target the final charging target of the state of charge is i aging is an aging rate of a battery, X is a set of state variables including battery temperature T and state of charge SOC, u is a ratio of battery charge to battery thermal power, and W is the available power supplied to the vehicle from an external source, e.g., via the 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 features: the charging time is less than the time required by the driver, the end-of-charge state of charge is equal to the target state of charge, the power required by the battery is less than the charging power limit, the power required by a thermal system (heating or cooling) 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 of the features described herein, the memory further comprises a charging profile selection module configured to cause the controller to output a plurality of charging profiles to a user selection system, and to cause the controller to implement the profile selected from the plurality of charging profiles.

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

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

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

[0018] In another exemplary embodiment, a charging control module for a vehicle comprises a plurality of inputs configured to receive at least one temperature sensor output value and an available power value for the grid current from the wallbox, a plurality of outputs configured to output at least one thermal power control signal and one charging power control signal, a processor, and a memory storing instructions configured to cause the processor to evaluate a cost optimization function and thereby determine an optimal charging profile based at least in part on an available received charging power, an ambient temperature of a battery system, charging system constraints, and at least one driver preference, wherein the cost optimization function trades off a power demand of a thermal battery system and a battery charging system,where evaluating a cost optimization function involves minimizing the cost equation, ∑t=0Ng(X,u,W) where g(X, u, W) = α ∗ (1 - η eff ) + β ∗ Δt ∗ (SOC t ≤ SOC target ) + γ * i aging and η eff is a system efficiency, Δt is the time step used by the optimization algorithm, SOC target the final charging target of the state of charge is i aging is an aging rate of a battery, X is a set of state variables that includes a battery temperature T and a state of charge SOC and may have other possible state variables, e.g., lithium-ion surface density, u is the ratio of battery charge to battery thermal power, and W is the available power supplied to the vehicle from an external source, e.g., via the charger or wirelessly.

[0019] In addition to one or more of the features described herein, the method further comprises a battery aging sub-module, wherein the battery aging sub-module is configured to determine at least the aging rate of the battery based on a charge rate of the battery and a temperature of the battery.

[0020] In addition to one or more of the features described herein, the method further comprises at least one user selection system configured to display a plurality of charging profiles, receive a user selection of a single charging profile from the plurality of charging profiles, and implement the charging profile.

[0021] In addition to one or more of the features described herein, the method further comprises a user selection screen configured to enable a user to input one or more constraints, and wherein evaluating a cost optimization function comprises constraining the cost optimization function with the one or more constraints.

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

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

[0024] The foregoing features and advantages, as well as other features and advantages of the disclosure, will be readily apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Further features, advantages and details are listed only as examples in the following detailed description, which refers to the drawings in which: Fig. 1 is a vehicle with an electrically powered drive system; Fig. 2 is a control architecture for a cold start charging process in the vehicle of Fig. 1; Fig. 3A is a projection of the state of charge with respect to time, generated by the control architecture Fig. 2; Fig. 3B is a projection of the charging current with respect to time, generated by the control architecture from Fig. 2; and Fig. 4 is a schematic diagram of a battery charging system for the vehicle of Fig. 1. DETAILED DESCRIPTION

[0026] The following description is merely exemplary and is not intended to limit the present disclosure, its application, or uses. It should be understood that throughout the drawings, corresponding reference characters designate like 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 (common, dedicated, or group), and memory executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.

[0027] According to an exemplary embodiment, a vehicle includes control logic that enables the vehicle to optimize a charging process, including providing battery heating using grid power from a wallbox when the battery temperature is low or cooling using grid power from a wallbox when the battery temperature is high. The control logic provides a centralized framework for determining an optimal charging and thermal profile based on multiple competing criteria (e.g., charging speed, charging efficiency, thermal efficiency, etc.) using a cost function. In a specific example, the control architecture may generate multiple different profiles representing different priorities of the criteria and present the different charging profiles to a vehicle operator.The driver selects the charging profile that best meets his criteria and the vehicle implements the charging profile.

[0028] With further reference to the general system and method described above, Fig. 1 illustrates an embodiment of a motor vehicle 10 with a battery system control unit 24 configured to implement the control architecture. The vehicle 10 includes a vehicle body 12 that at least partially defines a passenger compartment 14. The vehicle body 12 also supports various subsystems of the vehicle, including a propulsion system 16, a battery system 22, other subsystems for supporting the functions 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, and others.

[0029] The vehicle 10 may be an internal combustion engine vehicle, an electric vehicle (EV), or a hybrid vehicle. In one embodiment, the vehicle 10 is a hybrid vehicle that includes an internal combustion engine system 18 and at least one electric motor unit. The drive system 16 includes, for example, 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 mounted at various additional locations on the vehicle 10 may be used to provide mechanical rotation for the corresponding systems and subsystems.

[0030] The battery system 22 may be electrically connected to the motors 20 and 21 and / or other components, such as the vehicle's electronics. The battery system 22 may be configured as a rechargeable energy storage system (RESS) and include multiple cells divided into parts. A battery system control unit 24 is included in the battery system 22 and controls the charging and discharging functions of the batteries in the battery system 22. In alternative configurations, the battery system control unit 24 may be a general vehicle control unit separate from the battery system 22 and configured to control multiple systems and / or subsystems. The general vehicle control unit may be located anywhere in the vehicle 10.In further alternatives, the battery system controller 24 may be a distributed controller with multiple coordinating controllers throughout the vehicle 10, including controllers within the battery system 22 and controllers external to the battery system 22.

[0031] A charging port 30 is provided on the exterior of the vehicle 10 and is electrically connected to the battery system 22. The charging port 30 enables the battery system 22 to be supplied with utility power from a wall box for a charging session. As used herein, the term "utility power from a wall box" refers to any stationary power source independent of the vehicle 10 and is not limited exclusively to outlets in a wall. The power supply can be in various forms, such as 120 volts alternating current (120 VAC), 240 volts alternating current (240 VAC), and 480 volts direct current (480 VDC), 800 VDC, or higher. The voltages mentioned are non-limiting examples, and it is understood that other voltages may be used.Furthermore, the charging port 30 may be any suitable port that allows the battery 22 to be supplied with power from outside the vehicle 10, including wired and wireless charging, and in some examples, charging a first vehicle from a second vehicle.

[0032] During operation of an electric vehicle or a hybrid electric vehicle, the battery system 22 is discharged to supply power to the motors 20, 21 and other systems in the vehicle 10. When the vehicle 10 is not moving, electrical power may be drawn via a connection to an external power source, such as the charging port 30, and the battery system control unit 24 controls the delivery of power from the charging port 30 to the battery system 22.

[0033] The speed at which the battery can be charged depends on where the vehicle 10 is connected (e.g., a 120 VAC home charging station, a 240 VAC home charging station, or a 480 VDC fast charging station) and the power output of the wallbox. In addition, a lithium (Li) battery or a similar battery type has a slower diffusion rate when cold and therefore cannot be charged as quickly. Furthermore, if the battery temperature is too high, the battery cannot be charged at the maximum charging rate because the battery itself heats up during charging; the faster the temperature rises, the faster the battery charges. Therefore, it is desirable to keep the temperature within a window defined by an upper and lower temperature limit. During charging, the battery can be heated or cooled using the utility power from the wallbox.However, the current diverted to heat or cool the battery inevitably reduces the available charging power and speed. The control unit therefore faces an optimization problem.

[0034] Existing systems have separate controllers or control modules for thermal control, which keeps a battery pack within an ideal charging temperature range, and for charging control, which manages the power supply from the wallbox to the battery pack. Each of these controllers is rule-based, with control rules set independently and without regard to the other system(s) competing for energy during a charging session. This can lead to inefficiencies that translate into longer charging times, wasted energy, and so on.

[0035] The exemplary embodiments solve the optimization problem and the conflicting objectives of the thermal system 28 and the charging systems by using a control architecture 100 which is Fig. 2, wherein the battery system control unit 24 comprises a charging control software module 102. The charging control module 102 comprises a cost function configured to determine a charging strategy based primarily on the available wallbox charging power, the ambient and battery temperature, the specific limitations of the charging system, and additional information provided by the driver. The additional information includes, for example, prioritization of at least one of a charging time, a charging efficiency, and / or a battery lifetime. To weigh the optimal operating conditions, the charging control module 102 comprises a cost optimization function (e.g.EQN 1, as described herein), which coordinates the thermal conditions of the batteries 26 and the charging characteristics of the charging port 30 to achieve multiple optimal charging profiles, considering both thermal energy expenditure and charging power availability, and allowing the vehicle operator to select the optimal charging profile based on their preferences. The cost function balances competing priorities and constraints and determines an ideal electrical profile (e.g., current, voltages, etc.) and an ideal thermal profile (e.g., activation of heating and cooling, etc.) to best achieve multiple charging profiles that meet the specified constraints.

[0036] The charging control software module 102 centralizes the heat and charge controls, replacing the rule-based control of the heat systems 28 and the rule- and power limit-based control of the charging system with cost function optimization. The control system 100 provides operating power to a charging port controller 120 via charging control signals 110. The charging control signals 110 specify the power requirements of the wallbox and control the characteristics of the received energy according to the requirements of an optimal control unit or control algorithm, including the cost optimization function. The charging port controller 120 may be another software module within the battery system control unit 24, an independent software module within another control unit, or a fully or partially hardware-based charging port control system.

[0037] To facilitate the use of the cost optimization function, the charging port controller 120 provides an output value 122 to the charging control software module 102. The output value 122 defines the available performance characteristics and provides the charging software control module 102 with all available sensor information from the charging port 30.

[0038] Similarly, the charging software control module 102 provides operating power to a thermal control system 130 via a thermal control signal 112. As with the charging port controller 120, the thermal controller 130 may be another software module within the battery system controller 24, an independent software module within another controller, or a fully or partially hardware-based controller for the thermal systems 28. A thermal control signal 112 is output to the thermal system 28 and controls the operation of the thermal system 28 according to the cost optimization function included in the charging software control module 102.

[0039] The thermal controller 130 provides a signal 132 that defines the current temperature, expected power consumption, etc., of the thermal systems 28 used to maintain the vehicle battery within a desired charging temperature window.

[0040] In addition, the battery system control unit 24 includes a required charging, heating, or cooling time prediction module 140, which uses proven models to predict how long a charging, heating, or cooling operation will take based on the requested power consumption of the charging systems and the thermal systems 28. The required heating or cooling and charging times are provided to the charging control software module 102 via a signal 142, and the estimated times are used by the cost optimization function.

[0041] In some examples, the charging control software module 102 may be connected to a phone app or receive direct input from the vehicle operator, and may receive an expected departure time via input 160. In such cases, the charging control software module 102 may use the combination of the expected departure time and the predicted charging time to determine when to start charging and / or may use the departure time in determining system constraints.

[0042] In a concrete example, the cost optimization equation is defined by EQN 1: ∑t=0Ng(X,u,W), where g(X,u,W)=α*(1−ηeff)+β*Δt*(SOCt≤SOCtarget)+γ*iaging

[0043] In EQN 1. η eff a system efficiency, Δt the time step used by the optimization algorithm, SOC target the final charging target of the state of charge, i agingthe aging rate of a battery, X is a set of state variables, including battery temperature T and state of charge SOC, and possibly other state variables, e.g., the surface density of lithium ions, u is a ratio of battery charge to thermal (heating or cooling) power of the battery, and W is the available power supplied to the vehicle from an external source, e.g., via the wallbox or wireless charging.

[0044] In alternative examples, other cost functions can also be used to determine the desired optimization. The alternative examples can contain multiple cost optimization functions, which together result in several optimal loading and thermal profiles from which the customer can choose. In other alternative examples, the cost optimization function depends on the type and form of the models used to predict system behavior.

[0045] In an alternative example, the cost equation used can be Equation 2: J=∑k=1N((1−q(k))+α*max(T(k)−Tmax,0)+β* with the restrictions of (α>>0,β>0q(k)≤10≤pchrg(k)≤pwall,|pheat(k)|≤pwallpchrg(k)+|pheat(k)|≤pwallic(k)≤icmax(q(k),T(k))

[0046] Where α is the weighting factor for the temperature control and β is the weighting factor for the power control used for heating, T(k) is the battery temperature in the k-th step, T max is the battery temperature limit, q(k) is the ratio of real-time charges present in a battery to the charges with full battery capacity in the k-th step, p chrg (k) is the power used for charging in the k-th step, p heat (k) is the power used for heating in the k-th step, p wall is the available mains power from the wallbox, i c (k) is the charging current in the k-th step, and icmax(q(k),T(k)) is the maximum charging current, which depends on the ratio q(k) and the battery temperature T(k) in the k-th step.

[0047] All examples, including those explicitly disclosed here and alternative examples, involve the use of cost equations that include multiple objectives and weighting factors for each objective.

[0048] The cost equation may, in some examples, also include optional constraints that limit the possible solutions. For example, some constraints on the cost function might include that a charging time must be less than the departure time, that the end-of-charge state must match the target charge state, that the power required by the battery must be less than the charging power threshold, that the power required by the thermal system (heater or cooler) must be less than the thermal system power threshold, and that the power required by the battery plus the power required by the thermal system must be less than the total available power.

[0049] Each of these constraints is applied to the cost function by the charging control software module 102, and limits are set for the acceptable solutions. In further examples, the battery system controller 24 may be connected to a user-visible input screen. The input screen may be a vehicle display, a connected mobile device, a PC with internet access, or another connected display. Through an input screen or similar device, the vehicle operator may enter data, including an estimated departure time, a ranked list of priorities (e.g., that charging efficiency is more important than charging speed), and other constraints on the system. The charging control software module 102 defines constraints for the cost optimization function (EQN.1) based on the inputs received.For example, if the driver intends to leave the vehicle 10 stationary for an extended period of time, he may indicate that cost-energy efficiency is the highest priority, battery life is the second highest priority, and charging time is the lowest priority.

[0050] In another example, the load constraint may include an estimated distance to be traveled during the next use of the vehicle 10. This may be in the form of a global positioning system endpoint location, a manually entered distance, or a similar way of providing the estimated distance. The controller may then impose conditions such as "the load must exceed X upon departure," where X is the minimum load for a round-trip trip over the estimated distance.

[0051] In some examples, the user may enter optional constraints or conflicting constraint preferences. Likewise, in some examples, there may be conflicts between thermal and charging constraints. In such examples, there may be multiple possible solutions to the cost optimization function. If this is the case, the control unit 24 uses a charging profile selection module 104 to create multiple charging profiles and output the charging profiles to the screen. The charging profile selection module 104 may, as shown, be a sub-module within the charging control software module 102 or a separate, connected software module. An example of multiple charging profiles is shown in Fig. 3A and Fig. 3B, where Fig. 3A a series of state of charge profiles 310 and Fig. 3B shows a series of charging current profiles 320. Each charging profile 310, 320 corresponds to a specific trade-off of priorities, resulting in variations in the constraints of the cost optimization function defined by EQN 1. Furthermore, each charging profile 310, 320 includes a unique trade-off of priorities.

[0052] The user can select the charging profile 310, 320 displayed that best matches their preferred application. In some examples, the charging control software module 102 can also provide the user with a text description summarizing the competing optimization criteria, allowing a less experienced user to make an informed decision. In some examples, the text description can be a user-defined ranked list of parameters in order of greatest importance to least importance. In one example, the ranked parameters include, at a minimum, charging time, cost-energy efficiency, monetary cost of charging energy, and battery lifetime.

[0053] In another example, the charging control software module 102 may display a recommended charging profile 310, 320 based on a predetermined optimum, e.g., maximizing battery life. In such an example, the user may select the recommended charging profile 310, 320 or choose a different charging profile based on personal preference.

[0054] With further reference to Fig. 1-3B, shows Fig. 4 schematically shows a battery charging system 400 for the vehicle of Fig. 1. The battery charging system performs at least two functions. The first function is to determine the optimal charging profile, as described herein. The determination of the optimal charging profile, including user selection, occurs in advance and not simultaneously with the actual charging process.

[0055] The thermal system 428 (28) predicts the battery temperature and sends the measured battery temperature rate as a battery temperature output value 482 to a module 480 for determining the battery aging rate. Simultaneously, the charging port 430 (30) measures the magnitude of the charging current and sends a measured charging current to the battery aging module 480 via a charging current output value 484. The battery aging module 480 uses the received charging current 484 and the received battery temperature 482 to determine a battery aging rate according to known methodologies and forwards the battery aging rate to the charging software module 402 (102) via an output value 486. In some examples, the battery aging rate determining module 480 is a separate module within the control systems of the vehicle 10. In alternative examples, the battery aging rate determining module 480 is a software sub-module 480 within the charging control software module 402 (102).

[0056] The charging control software module 402 also receives inputs to system state variables such as the available grid voltage 450, the ambient vehicle temperature 452, the driver's estimated departure time 454, the current battery state of charge 456, and the current battery temperature 458. These state variables are applied to the cost function described herein, and a charging profile is determined.

[0057] The second function implements the determined charging profile 310, 320 with the aid of the charging control software module 402 (102). The implementation can occur immediately after the determination or at a later time, depending on the determined charging profile. During the 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 sends a charging energy efficiency command 432 and a charging time efficiency command 434 to a connected wallbox. The wallbox interprets the received commands 432, 434 and supplies the vehicle 10 with charging power via the charging port 30.

[0058] At the same time, the charge control module 402 (102) sends a thermal system control signal 470 to the thermal systems 428 (28). The thermal system control signal 470 sends operating instructions for the heating or cooling profile to the thermal systems and causes the thermal systems 428 (28) to implement the thermal profile. Throughout the charging process, while variables provided by the thermal systems 428, the charging port 430, and the battery 480 are communicated to the charge control module 402, the charge control module 402 uses control techniques to enforce the selected charging profile.

[0059] The terms "a" and "an" do not imply a quantity limitation, but rather the presence of at least one of the mentioned items. The term "or" means "and / or" unless the context clearly indicates otherwise. Whenever this specification refers to "an aspect," it means that a particular element (e.g., a feature, structure, step, or property) described in connection with the aspect is included in at least one of the aspects described herein and may or may not be present in other aspects. It is understood that the described elements in the various aspects may be combined in any suitable way.

[0060] When an element such as a layer, overlay, region, or substrate is described as being "on" another element, it may be directly on top of the other element, or there may be intervening elements. In contrast, when an element is described as being "directly on" another element, no intervening elements are present.

[0061] Unless otherwise indicated herein, all examination standards are the most recent standard in effect as of the date of filing of this application or, if priority is claimed, as of the date of filing of the earliest priority application in which the examination standard appears.

[0062] Unless otherwise defined, 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.

[0063] While 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 equivalent elements may be substituted without departing from the scope of the disclosure. Furthermore, many changes may be made to adapt a particular situation or material to the teachings of the disclosure without departing from its essential scope. Therefore, the present disclosure is not intended to be limited to the particular embodiments disclosed, but is intended to include all embodiments falling within its scope.

Claims

[1] Vehicle comprising: at least one electric motor designed to convert electrical energy into rotary motion; a battery system electrically connected to the at least one electric motor; a control unit connected to the battery system, the control unit comprising non-transitory computer-readable memory and a processor, and the memory including a charging control software module configured to cause the processor to perform the 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 of claim 1, wherein the at least one driver preference comprises a relative ranking of a number of parameters, including at least charging time, cost-energy efficiency, monetary cost, and battery life. [3] The vehicle of claim 1, wherein the charging system constraints include at least one of the factors 'estimated distance to the next available charging point' and 'estimated time to departure'. [4] The vehicle of claim 1, wherein determining the optimal charging profile comprises minimizing the cost equation ∑t=0Ng(X,u,W) where g(X, u, W) = α * (1 - η eff ) + β * Δt * (SOC t ≤ SOC target ) + γ * i aging and where η eff is a system efficiency, Δt is the time step used by the optimization algorithm, SOC target the final charging target of the state of charge is i agingis an aging rate of a battery, X is a set of state variables that includes a battery temperature T and a state of charge SOC and may have other possible state variables, e.g., lithium-ion surface density, u is a ratio of battery charge to thermal (heating or cooling) power of the battery, and W is the available power supplied to the vehicle from an external source, e.g., via the charger or wirelessly. [5] The vehicle of claim 4, wherein the cost equation further comprises at least one of the following features: the charging time is shorter than the time required by the driver, the end-of-charge state corresponds to the target charge state, the power required by the battery is less than the charging limits, the power required by the thermal systems (heating or cooling) 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 of claim 4, wherein the memory further comprises a charging profile selection module configured to cause the controller to output a plurality of charging profiles to a user selection system, and the controller to implement the profile selected from the plurality of charging profiles. [7] A method for controlling a vehicle charging process, the method comprising: Determining an optimal charging profile using a controller that evaluates a cost optimization function based at least in part on an available received charging power, an ambient temperature of a battery system, charging system constraints, and at least one driver preference, wherein the cost optimization function trades off a power demand of a battery thermal system and a battery charging system. [8] The method of claim 7, wherein evaluating a cost optimization function comprises minimizing the cost equation ∑t=0Ng(X,u,W) where g(X, u, W) = α * (1 - η eff ) + β * Δt * (SOC t ≤ SOC target ) + γ ∗ i aging ; and where η eff is a system efficiency, Δt is the time step used by the optimization algorithm, SOC targetthe final charging target of the state of charge is i aging is an aging rate of a battery, X is a set of state variables including battery temperature T and state of charge SOC, u is a ratio of battery charge to battery thermal power, and W is the available power supplied to the vehicle from an external source, e.g., via the charger or wirelessly. [9] The method of claim 8, wherein the cost equation further comprises at least one of the following features: the charging time is shorter than the time required by the driver, the end-of-charge state corresponds to the target charge state, the power required by the battery is less than a charging limit, the power required by a thermal system (heating or cooling) is less than the power limit of the thermal system, 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 of claim 8, wherein the memory further includes a charging profile selection module configured to cause the controller to output a plurality of charging profiles to a user selection system, and the controller to implement a selected profile from the plurality of charging profiles, and wherein the at least one driver preference comprises a relative ranking of a series of parameters including at least charging time, cost-energy efficiency, and battery life, and wherein the plurality of charging profiles corresponds to a unique driver preference ranking.

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