System for optimizing the lifespan of a battery pack in a plug-in vehicle

Abstract

System for optimizing the lifespan of a battery pack (12) in a plug-in vehicle (10), the system comprising: a large number of sensors (S X ), which are operable to measure the performance data of the battery of the battery pack (12), wherein the performance data of the battery include an open-circuit voltage and / or a charging current and / or a temperature of the battery pack (12); a global positioning system (GPS) receiver (16R) that can be operated to determine the position of the vehicle (10); a user interface (40); and a controller (50) which is connected to the user interface (40) and the GPS receiver (16R) and is programmed to monitor the degradation of the battery pack (12) over time using the battery's performance data, wherein the controller (50) is also programmed to: Determining a driving history and a battery charging history for an operator of the vehicle (10) using the measured performance data of the battery and a position signal (16) from the GPS receiver (16R), wherein the driving history and battery charging history each identify the days, hours and locations on which the operator drove the vehicle (10) and charged the battery pack (12); Determining a number of state-of-charge (SOC) data classes between a current SOC and a target SOC; Identify, from a multitude of SOC data classes, each configured to store the measured battery performance data for a predetermined SOC range, and within the specified number of SOC data classes between the current SOC and the target SOC, a SOC data class with the highest priority, which includes an evaluation against a predefined criterion that includes a lack of battery performance data and old battery performance data relative to a calibrated aging threshold, where a lack of performance data corresponds to a higher priority than old battery performance data relative to a calibrated aging threshold; automatic control of a charging process of the battery pack (12) via a charge control signal (25) until a current SOC of the battery pack (12) is within a SOC range that defines the identified SOC data class with the highest priority; and Recording the measured performance data of the battery for the identified SOC data class, thereby optimizing the lifespan of the battery pack (12).

Description

TECHNICAL AREA

[0001] The present invention relates to an adaptive system and method for optimizing battery life in a plug-in vehicle. BACKGROUND

[0002] High-voltage batteries can be used to start electric motors in a wide variety of systems. For example, the output torque from an electric motor can be used to power a drive element of a transmission in a vehicle—that is, a vehicle with a battery pack that can be recharged via a charging port or other off-board power supply. The individual cells of a battery pack gradually age and discharge over time. This can cause battery performance parameters, such as open-circuit voltage, cell resistance, and state of charge, to change from calibrated / new values. Therefore, battery discharge is typically monitored by a controller to estimate the amount of electrical energy remaining in the battery pack.Electric vehicle range estimates can be generated from the estimated electrical energy and then used for effective route planning and / or for executing automatic drive control measures.

[0003] Several factors can contribute to battery discharge and shorten battery life. For example, battery packs maintained at a high state of charge tend to discharge much faster than those maintained within a lower, more optimized state of charge range. Higher charging currents and temperatures can also shorten battery life. Battery packs of the types typically used in plug-in hybrid vehicles tend to be larger, designed for longer all-electric driving ranges, in some cases exceeding 200 miles on a full charge. However, range anxiety and other factors, such as time of day, personal driving habits, and limited knowledge of battery physics, can lead to preferred charging habits that may shorten battery life.For example, if a given operator's daily electric driving range in a vehicle with a fully charged electric range of 320 kilometers is between 48 and 80 kilometers, fully charging the battery pack every time will result in maintaining a high state of charge for the entire duration of the vehicle's ownership. This, in turn, can reduce battery lifespan and affect the accuracy of electric range estimates over time.

[0004] German patent DE 10 2015 208 758 A1 discloses a system for controlling the state of charge (SOC) in an electric vehicle battery in response to environmental and operating conditions, in order to improve the impact of these conditions on battery capacity and battery lifespan. A SOC profile is stored in memory, which includes desired and undesired SOC ranges to facilitate the management of battery capacity and battery lifespan. If new data becomes available via communication channels, the desired and undesired SOC ranges can be updated. A powertrain control module manages the charging and discharging of the battery to achieve a desired SOC in response to the ambient temperature and / or planned usage and storage time. Furthermore, target state of charge values ​​are determined for previously identified different usage patterns to enable active battery capacity management.Further state of the art is known from DE 10 2014 219 658 A1, DE 11 2014 001 111 T5 and DE 10 2012 221 708 A1. SUMMARY

[0005] The object of the invention is to provide an improved system for optimizing the service life of a battery pack in a plug-in vehicle.

[0006] To solve the problem, a system with the features of claim 1 is provided. Advantageous embodiments of the invention can be found in the dependent claims, the description, and the drawings.

[0007] This reveals a system that enables a plug-in hybrid vehicle operator to extend the lifespan of a vehicle's battery pack and improve the overall accuracy of any onboard electric range estimation. Over time, a control unit monitors and learns the operator's personal driving habits, energy consumption, and battery charging behavior.

[0008] The charging of the battery pack is automatically controlled in response to various sensor signals. This extends the battery pack's lifespan and optimizes it for a given vehicle operator by selectively charging the battery pack to a state of charge (SOC) level closer to the optimal SOC level required for maximizing battery life, and by selectively controlling the charging process to populate specific data classes according to the SOC range, as disclosed herein.

[0009] In particular, an exemplary system for use in a plug-in hybrid vehicle is disclosed herein. The system includes sensors, a global positioning system (GPS) receiver, a user interface, and a controller. The sensors can be operated together to measure the performance data of a battery in the vehicle's battery pack, where the battery performance data includes open-circuit voltage, state of charge (SOC) level, charging current, and / or temperature of the battery pack. The GPS receiver can be operated to determine the vehicle's position, which is then recorded over time to enable the controller to build and record a driving history for a given driver. The controller, which is connected to the user interface and the GPS receiver, is programmed to monitor the discharge of the battery pack over time using the measured battery performance data.

[0010] The controller is further programmed to determine the driving history and battery charging history for the operator using the measured battery performance data and a position signal from the GPS receiver. The driving history and battery charging history each identify the days, hours, and locations during which the operator drives the vehicle and charges the battery pack. The controller defines a number of state-of-charge (SOC) data classes between a current SOC and a target SOC.The controller also identifies a data class with the highest priority from a multitude of SOC data classes, each configured to store the measured battery performance data for a predetermined SOC range. Within a specific number of SOC data classes between the current SOC and the target SOC, the controller evaluates this class based on a predefined criterion: the absence of battery performance data and the prevalence of old battery performance data relative to a calibrated aging threshold. The absence of performance data corresponds to a higher priority than the prevalence of old battery performance data relative to the calibrated aging threshold. The controller automatically initiates a charging process of the battery pack via a charge control signal and also records the measured battery performance data for the identified data class with the highest priority.

[0011] This also discloses a method for optimizing the service life of a battery pack in a plug-in hybrid vehicle. In one embodiment, the method includes measuring the battery pack's performance data using a variety of sensors, including measuring the battery pack's open-circuit voltage and determining the vehicle's position using a GPS receiver. The method also includes monitoring the battery pack's discharge over time via a control system using the measured battery performance data, as well as determining a driving history and a battery charging history for the vehicle operator using the measured battery performance data and a position signal from the GPS receiver.

[0012] Additionally, the process involves the controller identifying, from a variety of SOC data classes, each configured to store measured battery performance data for a predetermined SOC range, a data class lacking battery performance data, or containing old battery performance data relative to a calibrated aging threshold. The controller then automatically initiates a battery pack charge using a charge control signal. The process includes recording the measured battery performance data for the identified data class. This allows for optimization of the battery pack's lifespan compared to systems using conventional approaches.

[0013] The above-mentioned features and other benefits of the present disclosure are easily recognizable from the following detailed description of the best ways of carrying out the disclosure in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a schematic representation of an exemplary plug-in vehicle with a rechargeable battery pack and a system for optimizing the battery pack's lifespan. Fig. Figure 2 is a schematic logic flow diagram for a controller, which is part of the system shown in Figure 2. Fig. 1, usable. Fig. Figure 3 is a flowchart describing an exemplary adaptive procedure for optimizing the lifetime of a battery pack in a plug-in vehicle, such as the exemplary vehicle shown in Fig. 1. DETAILED DESCRIPTION

[0014] Referring to the drawing, in which reference numbers are used to identify similar or identical components in the different views, illustrates Fig. Figure 1 schematically shows an exemplary plug-in vehicle 10 with a body 11, a rechargeable battery pack 12, and a controller (C) 50 programmed with battery degradation monitoring logic 30. The vehicle 10 also includes a global positioning system (GPS) receiver 16R, which is capable of receiving position data from a set of GPS satellites (not shown) and delivering a corresponding position signal (arrow 16) to the controller 50, describing the geographic coordinates of the vehicle 10 on a geocoded map, as is known in the art.

[0015] The controller 50 is programmed to record the driving and charging history of a given operator of the vehicle 10 over time and to use the recorded driving and charging histories to improve the accuracy of the battery degradation monitoring logic 30. Additionally, the controller 50 is programmed to automatically control a charging process of the battery pack 12, as described below with reference to the Fig. 2 and Fig. 3 is described such that the specific SOC data classes are filled during the anticipated gaps in the operator's schedule, thereby helping to increase the lifespan of battery pack 12.

[0016] Vehicle 10 from Fig. 1 can include an electric drivetrain (not shown) from which one or more electric motors draw electrical energy from the battery pack 12 and deliver motor torque to the drive wheels 14 via one or more front and / or rear drive axles 15F and / or 15R. The controller 50 automatically executes instructions according to a procedure 100 to increase and optimize the service life of the battery pack 12, partly informed by using information from the GPS receiver 16R contained in the GPS position signal (arrow 16) and information from the battery degradation monitoring logic 30.

[0017] The vehicle 10 can be embodied as a mobile platform whose battery pack 12 can be selectively recharged by connecting to an off-board power supply 21, such as a 120 VAC or 240 VAC wall socket or an electric charging station. The vehicle 10 can include an on-board charging module (OBCM) 18 of a type known in the field. The OBCM 18 can selectively connect to the power supply 21 via an electrical connector 22 and suitable electrical leads 23, as indicated by arrow A. The OBCM 18 converts AC power from the power supply 21 into DC power suitable for increasing the state of charge (SOC) level of the battery pack 12.In various embodiments, the vehicle 10 can be an electric vehicle or a battery-powered electric vehicle, the latter typically having an electric vehicle operating range of 40-200 miles or more with a fully charged battery pack 12 when such a battery pack 12 is new.

[0018] Vehicle 10 from Fig. 1 can also include a thermal conditioning device 17, which can be operated to heat or cool the battery pack 12 as required, which can be achieved by transmitting a thermal control signal (arrow 13) from the controller 50 as part of the method 100. The vehicle 10 is also equipped as part of the method 100 with a plurality of battery sensors (S X ) can be used to measure and / or otherwise determine a corresponding current parameter of the battery pack 12. For example, the various battery sensors S Xcan be used to directly measure or help determine a state of charge (arrow SOC) and include a temperature sensor operable to measure a battery temperature (arrow T), a voltage sensor operable to measure the battery voltage (arrow V) and / or a current sensor to determine a battery current (arrow i) of individual battery cells or groups of battery cells (not shown) of the battery pack 12, with these values ​​being transmitted or otherwise reported to the controller 50.

[0019] As is known in the prior art, the state of charge (SOC) of a battery, such as battery pack 12, can be determined by various methods, such as using an equivalent circuit to model battery pack 12 and measuring the surface charge on the various conductive plates (not shown) of battery pack 12. The controller 50 uses the collected battery performance parameters from the battery degradation monitoring logic 30 to determine or estimate the amount of electrical energy in battery pack 12 and also to estimate the remaining electric vehicle range, as is well known to those skilled in the art.

[0020] The controller 50 can automatically determine the voltage (arrow V) as the open-circuit voltage after the vehicle 10 has been stationary for a calibrated period of time, i.e., when the vehicle 10 is switched off or not running. The use of the battery degradation monitoring logic 30 can, if necessary, involve comparing a measured open-circuit voltage curve with a calibrated / new open-circuit voltage curve and estimating the amount of energy remaining in the battery pack 12 based on the differences in the open-circuit voltage curves. The estimated energy can then be used by the controller 50 to estimate the remaining electrical operating range of the vehicle 10.

[0021] The use of method 100 is intended to ensure optimal range and service life of the battery pack 12 by automatically adapting charging processes to the unique driving and charging behavior of a given operator of the vehicle 10. As such, the controller 50 can record a corresponding driving and charging history for multiple operators of the vehicle 10, for example, by storing different seat positions or steering wheel height settings for different operators. In particular, method 100 takes into account the need to collect battery information at low or high state-of-charge (SOC) levels of the battery pack 12 in order to better estimate the true electrical capacity and remaining electric range of the battery pack 12.

[0022] The use of method 100 results in an automatic setting of a normally used SOC range via the charging control signals (arrow 25) transmitted to the OBCM 18 when the battery pack 12 is plugged in and actively charging. This control measure is intended to better meet the needs of the battery degradation monitoring logic 30 by providing the most accurate possible estimates and electric range predictions, while still allowing the battery pack 12 to charge the vehicle 10 according to the driving and charging habits of a given operator.

[0023] An operator of the vehicle 10 can be provided with the option to deactivate the execution of the procedure 100 and thus control the charging process in a specific way by receiving an override signal (arrow 42) from a user interface 40, such as a mobile phone, a tablet, or a touchscreen. For example, an operator can temporarily decide to prevent active charging control for the optimization of the battery degradation monitoring logic 30 in situations where the operator anticipates a deviation from normal operating behavior, such as driving to an unexpected meeting instead of remaining parked at a charging station.The controller 50 can then automatically control the charging process by charging the battery pack 12 to a standard SOC in response to receiving the override signal (arrow 42), such as by allowing the battery pack 12 to be charged to a full SOC, so that the battery pack 12 is provided to the operator with full energy capacity.

[0024] The control 50 from Fig. 1 can be embodied as one or more distinct devices, each optionally comprising one or more microcontrollers or central processing units (P) and a memory (M), for example, read-only memory, RAM, and an electrically erasable, programmable read-only memory. The controller 50 and the interactive user interface 40 can include a calendar 52, recorded charge control targets 36, as explained below, as well as a high-speed clock, input / output circuitry, and / or any other circuitry necessary to perform the functions described herein. In various embodiments, the user interface 40 and the controller 50 can be the same device or separate devices. The controller 50 can be configured to open / run various software, including the battery degradation monitoring logic 30.

[0025] The user interface 40 and controller 50 can be digitally connected to the memory (M) and configured to retrieve and execute these software applications as known in the art. Likewise, the user interface 40 can include a liquid crystal display, a light-emitting diode display, an organic light-emitting diode display, and / or similar types of display / monitor, either existing or future. In various embodiments, the user interface 40 can be a touch-sensitive screen of a navigation or infotainment system located in a center console (not shown) of the vehicle 10 and / or a mobile phone or other portable electronic device.A capacitive or sensorimotor digitizer can be integrated and operated within the user interface 40 to detect contact from a driver as an override signal (arrow 42) and automatically convert the digitized contact into a suitable input signal that is used by the controller 50.

[0026] Regarding the battery degradation monitoring logic 30, the method 100 is intended to enable the collection of battery performance data for all required SOC ranges or regions, including those that could not otherwise be collected with the frequency required for accurate monitoring or tracking of battery degradation. The calculation and display of an estimated electric range to a driver of a vehicle with an electric powertrain, such as the exemplary vehicle 10 of Fig. 1. is an important component for minimizing range anxiety. Such range anxiety is a subliminal cause of the gaps in the battery performance data that are normally collected and provided to the battery degradation monitoring logic 30. Operators tend to feel comfortable within the range of their office, home, or other preferred charging station, or they tend to initiate charging of the battery pack 12 when the battery pack 12 has a relatively high charge level in order to avoid the possibility of the battery pack 12 discharging. Such a scenario is analogous to that of an operator of a conventional vehicle refueling a fuel tank when the fuel tank remains half full, or steadily charging a laptop computer when the state of charge remains well above 50%.However, maintaining the SOC at a high level can degrade the battery pack 12 over time, as mentioned above. Method 100 is designed to prevent such degradation while further optimizing the performance of the battery degradation monitoring logic 30.

[0027] With reference to Fig. 2, which create a logical flow through the control 50 from Fig. As shown in Figure 1, the controller 50 can be programmed with an adaptive learning module (ALM) 38 that optimizes the charging of the battery pack 12. As mentioned above, in some embodiments, the battery degradation monitoring logic 30 can use a measured open-circuit voltage (OCV) after the vehicle 10 has been stationary / switched off for a calibrated period of time, with the controller 50 comparing a measured OCV curve, represented as OCV, over time against a calibrated / new OCV curve for a calibrated / new battery pack 12, thereby estimating the amount of energy in the battery pack 12, for example, as a function of the difference between the actual and the new OCV curve. The battery degradation monitoring logic 30 can be programmed to divide the SOC of the battery pack 12 into data SOC ranges or data classes, for example 5% or 10% SOC increments.The estimated energy in the battery pack 12 can then be used to estimate the remaining electric vehicle operating range of the vehicle 10, the accuracy of the estimate depending on the presence and recency of such OCV or other battery performance data in each of the data classes.

[0028] In a logic block 32, the controller 50 can, for example, determine the respective state-of-charge (SOC) range required for optimizing the battery degradation monitoring logic 30. For instance, the range of a full state of charge for the battery pack 12 can be divided into a variety of SOC data ranges or classes, e.g., ten data classes using the example of the 10% SOC increments mentioned above. The controller 50 can also be programmed with a calibrated aging threshold so that the collected battery performance data in each of the data classes can be evaluated for "obsolescence," i.e., as being too old or not current enough to be useful. The controller 50 can therefore check each of the data classes and identify those containing minimal, missing, or outdated collected battery performance data. The controller 50 can then issue a SOC request signal (SOC arrow). R) generate a request for the collection of an OCV measurement or other battery performance data for the identified data class(es).

[0029] The controller 50 can also determine the control targets 36 for the SOC or the state of energy (SOE), as well as the required time (t). R ) and the available time (t A ) to achieve such goals. The available time (t) A ) can be determined by the control unit 50 using the operator's previous driving history, such as by knowing exactly how long the operator is at work on a typical weekday or how long the battery pack 12 has been in the offboard charging station 21 Fig. 1 remains connected for a given charging process. The adaptive learning module 38 is also equipped with a calibrated optimal state of charge (SOC). OPT) programmed for battery pack 12, for example with 50-60% SOC, which the controller 50 tries to maintain at different times than when it actively controls the charging processes to fill the respective data classes.

[0030] The controller 50 then determines the respective charging strategy to be implemented. In particular, the controller 50 determines when charging of the battery pack 12 should be initiated, when this charging should be interrupted or aborted, the level of the charging current to be used, when the charging should be complete, and the state-of-charge level to be used as a threshold for determining when charging is complete. The various measures taken by the adaptive learning module 38 are described in more detail below with reference to the procedure 100, as presented in Fig. 3, described.

[0031] The adaptive learning module 38 then outputs status signals 37, including a charging status signal (arrow STAT) indicating whether the charging processes are pending, active, or complete, as well as a charging current level (arrow i). C Optionally, a thermal control module 39 of the controller 50 or a separate control device can be used to control the operation of the thermal conditioning device 17, shown in Fig. 1. To control. The thermal control module 39 can receive the measured temperature (arrow T) and then determine, based on the measured temperature (arrow T), whether / when the heating or cooling of the battery pack 12 should be initiated. Fig. 1 before or simultaneously with the charging processes. The thermal conditioning device 17 can then send a thermal conditioning control signal (arrow T). CC) to the thermal conditioning device 17 to instruct the required heating or cooling effect, and send a status signal (arrow 139) to the adaptive learning module 38.

[0032] The thermal conditioning of the battery pack 12 can be automatically adjusted by the controller 50 in this manner to maximize battery conditioning while the vehicle 10 remains connected. Using a better optimal temperature boundary condition may result in the use of more power from the offboard power supply 21. However, this can improve the longevity of the battery pack 12. In some embodiments, the controller 50 can be programmed to reduce the charging current level for the battery pack 12 in order to maintain the state of charge (SOC) of the battery pack 12 at a given level until such thermal conditioning of the battery pack 12 is complete.

[0033] Using the adaptive learning module 38, the controller 50 can take into account any available energy recovery based on the vehicle 10's altitude during a normal operator route. Whether the operator is working, existing, driving, or charging at a higher altitude, the controller 50 can use the altitude history to schedule regenerative charging operations. These operations, as is known in the art, involve the use of one of several electric machines, i.e., motor / generator units connected to the battery pack 12 and controlled as generators. The adaptive learning module 38 thus records the energy that can be fed into the battery pack 12 through recovery and uses the altitude knowledge to enable the recording and utilization of all possible energy and to optimize battery life when it automatically schedules or controls a respective state of charge (SOC) for one of the given SOC data classes.

[0034] Monitoring normal charging behavior by the controller 50 records the locations and number of charging events that typically occur each weekday via the calendar 52 to further optimize the lifespan of the battery pack 12. The operator can, if necessary, adjust the learning process to include an additional "range buffer" based on a preferred minimum distance to minimize range anxiety. For example, the operator might feel more comfortable with an additional range buffer of 20-30 miles, ensuring the battery pack 12 always retains at least enough energy to travel that distance. A default range buffer can be built into the controller 50's default settings, and the operator can increase or decrease the range buffer as needed via the user interface 40.Alternatively, the controller 50 can receive a specific range buffer via the user interface 40 and automatically control the charging process with the specific range buffer, so that after the completion of a given charging process, the battery pack 12 has an estimated range that is equal to or greater than the range of the specific range buffer.

[0035] With reference to Fig. Figure 3 shows an embodiment of the method 100 for an exemplary charging scenario of the battery pack 12, as shown in Figure 3. Fig. 1. The method 100 is based on the control unit 50 with a previously determined driving history and a battery charging history for an operator of the vehicle 10 using the measured performance data of the battery and the position signal (arrow 16) of the GPS receiver 16R. Fig. 1. The past driving history and battery charging history identify the days, hours, and locations to which the operator drove the vehicle 10 or charged the battery pack 12. Since most operators will tend to drive / charge in a certain way on a given day, such as driving to / from work on weekdays and driving differently on weekends, and tend to repeat these patterns of behavior from week to week, the calendar 52 can be used to record actual behavior over time and to schedule charging based on these histories, according to Fig. 3, to control.

[0036] As an underlying part of the procedure 100, the controller 50 must identify, from a multitude of SOC data classes, each configured to store the measured battery performance data for a predetermined SOC range, a SOC data class that either lacks battery performance data or contains old battery performance data relative to a calibrated aging threshold. The controller 50 then automatically initiates a charging process of the battery pack 12 via the charge control signal (arrow 25). Fig. 1, until an actual SOC of the battery pack 12 is within a SOC range that defines the identified SOC data class, and records the measured performance data of the battery for the identified SOC data class.

[0037] In one embodiment, the method 100 includes step S102, in which, for a given charging process, the controller 50 determines the time required to charge the battery pack 12 to a full / 100% charge capacity. As part of step S102, the controller 50 gathers information about the current performance of the battery pack 12, for example, its current state of charge (SOC), temperature, voltage, current, etc., as well as the voltage / charging current available via the power supply 21. The method 100 proceeds to step S104 after the time required to charge the battery pack 12 has been determined.

[0038] In step S104, the controller 50 next determines whether there is sufficient time for a complete charging process of the battery pack 12 via the power supply 21. Fig. 1 is available without interruption. Procedure 100 continues with step S106 if insufficient time is available, i.e., if the operator's current schedule, which is based on calendar 52, is not met. Fig. 1 and the operator's past charging / driving behavior indicates that all available charging time must be actively used for charging. In other words, due to time constraints in the operator's schedule, charging cannot be delayed or interrupted to fill the specified SOC data classes. However, if sufficient time is available for planned charging interruptions, procedure 100 proceeds to step S108 instead.

[0039] In step S106, the controller 50 begins charging the battery pack 12 without instructing any charging delays or interruptions. The execution of step S106 is therefore the ordinary or normal use of the offboard power supply 21, whereby the operator connects the battery pack 12 to the power supply 21 and the charging process continues for the entire duration until either the available charging time elapses or a full charge is reached.

[0040] In step S108, the controller 50 determines a number of SOC "limit values" between the current SOC, which was captured in step S102, and the target SOC, shown in Fig. 2 and then proceeds to step S110. Such SOC limits can be embodied as the aforementioned SOC data classes, i.e., calibrated bands or SOC ranges from 0 to 100% SOC. As an illustrative example, if the total SOC range of 0-100% SOC is divided into ten equal SOC limits or data classes of 0-10%, 11-20%, 21-30%, etc., and the current SOC is 50%, then step S108 will determine that five remaining SOC limits or data classes remain, i.e., 51-60%, 61-70%, 71-80%, 81-90%, and 91-100%.

[0041] Step S110 involves determining, from the identified limits in step S108, the number of SOC limits that can be reached within the time available for charging, as determined in step S102. Procedure 100 continues with step S112.

[0042] Step S112 involves selecting the highest-priority SOC threshold from step S110 and then proceeding to step S114 for data collection within that threshold. Step S112 may include evaluating each SOC threshold from step S110 against a predefined criterion, such as age / obsolescence or missing data in a given SOC data class. For example, if four of the identified data classes have obsolete data and one has no data, controller 50 may prioritize collecting data in the SOC data class with no data. Of the remaining SOC data classes, controller 50 may use age to determine which data classes to collect first, starting with the oldest or most obsolete previously collected data.

[0043] In step S114, the controller 50 begins charging the battery pack to the highest priority threshold identified in step S112. The charging processes of a battery, such as battery pack 12 from Fig. 1, are known in engineering and involve instructing the closing of contactors or relays (not shown) in a circuit between the battery pack 12 and the OBCM 18. Closing the contactors connects the battery pack 12 to the offboard power supply 21 to start charging the battery pack 12. Optionally, the controller 50 can be programmed to automatically control the charging process by delaying the charging of the battery pack 12 for a predetermined duration after the battery pack 12 is connected to the offboard power supply 21. Fig. 1 was connected. Procedure 100 continues with step S116 while the charging process continues.

[0044] Step S116 involves determining whether the charging process is complete as of step S114, for example, by comparing the actual state of charge (SOC) of battery pack 12 with a target SOC determined by controller 50 at the start of the charging process. The procedure continues with step S117 if the charging process is complete. Otherwise, the procedure 100 continues with step S118.

[0045] Step S117 results in the termination of the charging process that began in step S116, e.g. by instructing the interruption of a circuit between the battery pack 12 and the off-board power supply 21.

[0046] In step S118, procedure 100 determines whether the current state of charge (SOC) of battery pack 12 is equal to or exceeds the SOC limit. For example, if the SOC data class is currently filled at 51–60%, the controller 50 determines that the current SOC exceeds the SOC limit if the current state of charge reaches 60% or more. The procedure then proceeds to step 120. Step S118 is repeated until the current SOC of battery pack 12 is equal to or greater than the SOC limit, and then proceeds to step S120.

[0047] In step S120, the controller 50 next determines whether a thermal condition is active, such as whether the thermal control module 39 heats or cools the battery pack 12 via the thermal conditioning device 17, as shown in Fig.1. If yes, then procedure 100 continues with step S122. If no, controller 50 continues with step S124 instead.

[0048] Step S122 can determine the charging current level (arrow i). C ) reduce the flow to battery pack 12 to maintain the state of charge (SOC) of battery pack 12 until thermal conditioning is complete. Step S120 is then repeated.

[0049] Step S124 involves interrupting the charging process, such as by instructing one of the relays or contactors between the power supply 21 and the battery pack 12 to open, or otherwise interrupting a charging current to the battery pack 12. The procedure 100 then continues with step S126.

[0050] Step S126 involves determining whether the charging interruption is complete. Procedure 100 then continues with step S128.

[0051] In step S128, the controller 50 next determines whether data has been collected for all SOC data classes. If so, the procedure 100 continues with step S130. If data has not been collected for all SOC data classes, the procedure 100 proceeds to step S132 instead.

[0052] In step S130, the controller 50 continues charging the battery pack 12 until charging is complete. For example, step S130 might involve activating one of the contactors or relays between the power supply 21 and the battery pack 12 to prevent charging current from flowing to the battery pack 12. Afterwards, charging can continue without interruption until it is finished.

[0053] In step S132, the controller 50 can select a different SOC limit using the criteria of aging or missing data as mentioned above and proceed to step S134.

[0054] Step S134 involves continuing the charging process of battery pack 12, e.g. by closing the relays or contactors between the power supply 21 and the battery pack 12. The procedure 100 then returns to step S116.

[0055] Through adaptive control of the charging processes, informed by the demonstrated past driving styles, energy consumption, driving distances, and battery conditioning tasks, as explained above, the procedure 100 can help populate the SOC data classes within the time allowed by the operator's schedule, as demonstrated by the operator's unique charging and driving histories, and thus help improve the lifespan of the battery pack 12. At the same time, the user interface 40 provides an operator with the ability to quickly override these automatic charging control measures, whether from within the vehicle 10 or via a mobile phone. Simultaneously, the operator can further benefit from a measurable state of health of the battery pack 12 by ensuring that the battery degradation monitoring logic 30 is continuously supplied with timely SOC data across the full SOC range, e.g., by monitoring the battery's charge level.B. by increasing the resale value of vehicle 10. That is, a potential buyer of vehicle 10, faced with two otherwise identical vehicles 10, will choose the vehicle 10 with the battery pack 12 with the longest remaining lifespan or the best health.

[0056] While the best ways of carrying out the disclosure have been described in detail, those skilled in the art in the field to which this disclosure relates will recognize various alternative designs and embodiments that fall within the scope of protection of the added claims. It is intended that all items contained in the above description and / or shown in the accompanying drawings are to be interpreted as illustrative and not as limiting.

Claims

[1] System for optimizing the lifespan of a battery pack (12) in a plug-in vehicle (10), the system comprising: a large number of sensors (S X ), which are operable to measure the performance data of the battery of the battery pack (12), wherein the performance data of the battery include an open-circuit voltage and / or a charging current and / or a temperature of the battery pack (12); a global positioning system (GPS) receiver (16R) that can be operated to determine the position of the vehicle (10); a user interface (40); and a controller (50) which is connected to the user interface (40) and the GPS receiver (16R) and is programmed to monitor the degradation of the battery pack (12) over time using the battery's performance data, wherein the controller (50) is also programmed to: Determining a driving history and a battery charging history for an operator of the vehicle (10) using the measured performance data of the battery and a position signal (16) from the GPS receiver (16R), wherein the driving history and battery charging history each identify the days, hours and locations on which the operator drove the vehicle (10) and charged the battery pack (12); Determining a number of state-of-charge (SOC) data classes between a current SOC and a target SOC; Identify, from a multitude of SOC data classes, each configured to store the measured battery performance data for a predetermined SOC range, and within the specified number of SOC data classes between the current SOC and the target SOC, a SOC data class with the highest priority, which includes an evaluation against a predefined criterion that includes a lack of battery performance data and old battery performance data relative to a calibrated aging threshold, where a lack of performance data corresponds to a higher priority than old battery performance data relative to a calibrated aging threshold; automatic control of a charging process of the battery pack (12) via a charge control signal (25) until a current SOC of the battery pack (12) is within a SOC range that defines the identified SOC data class with the highest priority; and Recording the measured performance data of the battery for the identified SOC data class, thereby optimizing the lifespan of the battery pack (12). [2] System according to claim 1, wherein the controller (50) is programmed to automatically control the charging process by delaying the charging of the battery pack (12) for a predetermined duration after the battery pack (12) has been connected to an offboard power supply (21). [3] System according to claim 1, wherein the plurality of sensors (S X ) includes a voltage sensor that is operable to detect the open-circuit voltage, and wherein the measured performance data of the battery includes the open-circuit voltage. [4] System according to claim 3, wherein the plurality of sensors (S X ) also includes a current sensor that is operable to detect the charging current and a temperature sensor that is operable to measure a temperature of the battery pack (12), wherein the measured performance data of the battery include the charging current and the temperature. [5] System according to claim 4, wherein the vehicle (10) comprises a thermal conditioning device (17) that is operable for thermal conditioning of the battery pack (12), wherein the controller (50) is programmed to automatically control the charging process by controlling the operation of the thermal conditioning device (17) in response to the temperature of the battery pack (12). [6] System according to claim 5, wherein the controller (50) is programmed to reduce a charging current level to the battery pack (12) in order to maintain the SOC of the battery pack (12) until the thermal conditioning of the battery pack (12) is complete. [7] System according to claim 1, wherein the controller (50) is programmed to receive an override signal (42) from the user interface (40) and to automatically control the charging process by charging the battery pack (12) to a standard SOC in response to the receipt of the override signal (42). [8] System according to claim 1, wherein the controller (50) uses the position signal (16) from the GPS receiver (16R) to determine an altitude of the vehicle (10) and is programmed to plan the charging process using the altitude data so that it coincides with a regenerative event of the vehicle (10). [9] System according to claim 1, wherein the controller (50) is programmed to receive a specific range buffer via the user interface (40) and to automatically control the charging process with the specific range buffer, so that after a completed charging process the battery pack (12) has an estimated range which is equal to or greater than a range of the specific range buffer.

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