System for indicating battery age

Abstract

Vehicle (12), comprising: a traction battery (24), and a controller (76) programmed to output a traction battery age estimated on the basis of a ratio of a reference battery capacity value, which has a predetermined probability of being smaller than an actual battery capacity value, to a beginning-of-life battery capacity value (beginning-of-life ampere-hour value), wherein the reference battery capacity value is derived from a mean and standard deviation of a plurality of historical estimated traction battery capacity values ​​(24).

Description

TECHNICAL AREA

[0001] This application generally concerns a system for indicating the age of a traction battery. BACKGROUND

[0002] Hybrid and electric vehicles include a high-voltage traction battery to provide stored electrical energy for propulsion and other vehicle functions. The traction battery's performance can change over time. For example, the maximum amount of energy that can be stored by the traction battery generally decreases over time. To better manage the traction battery, it is useful to estimate its capacity. Additionally, it can be helpful to estimate the approximate age of the traction battery to detect when it is nearing the end of its lifespan.

[0003] US 2005 / 0151513A1 describes an energy and battery management system for vehicles. The system comprises a high-voltage direct current bus, a first battery, a second battery, at least one bidirectional DC / DC converter, and a control unit. The control unit monitors the state of the high-voltage bus, the first battery, and / or the second battery and transfers energy between at least two of the elements—the high-voltage bus, the first battery, and the second battery—using the bidirectional DC / DC converter. The system is deployed in a process.

[0004] US Patent 8,332,342 B1 describes a mathematical model for describing battery behavior during individual discharge cycles and over the battery's lifetime. The model's form was based on the battery's internal processes and validated using experimental data. The effects of temperature and load current were also incorporated into the model. Subsequently, the model was used within a particle filtering framework to predict the remaining service life for individual discharge cycles and the cycle duration.

[0005] US 2015 / 0046109A1 describes a method for efficiently determining the replacement time for each battery module that is part of a battery system, based on information about the battery system's operating results, without reducing the battery system's operating rate. First, capacity-voltage profile data of the battery module are compiled according to its manufacturing condition and degradation state, capacity-voltage profile data at the time of shipment, and the most recent capacity-voltage profile data to estimate the battery module's degree of degradation. Then, a future usage pattern is estimated based on the battery module's past charge / discharge results. Finally, the remaining operating life is calculated based on the degree of degradation, the battery module's usage pattern, and the characteristic degradation data.Finally, based on the remaining operating time calculated for the battery module's lifespan, the time for replacing the individual battery module is specified.

[0006] Prior to the aforementioned state of the art, the objective was to provide an improved system for indicating battery age. None of the aforementioned publications discloses, as a technical solution, a reference battery capacity value that has a predetermined probability and is smaller than an actual battery capacity value as described in claim 1. The dependent claims constitute advantageous embodiments thereof. SUMMARY

[0007] In some embodiments, a vehicle includes a traction battery. The vehicle also includes a controller programmed to output a traction battery age based on the ratio of a reference ampere-hour value (which has a predetermined probability of being lower than an actual ampere-hour value) to a beginning-of-life ampere-hour value, the reference ampere-hour value being derived from the mean and standard deviation of a plurality of historical traction battery ampere-hour values. Other embodiments may include appropriate computer systems, devices, and computer programs recorded on one or more computer storage devices, each of which is designed to perform the operations of the method.

[0008] Some configurations may include one or more of the following features: The vehicle in which the historical ampere-hour values ​​are normally distributed. The vehicle in which the predetermined probability is a value between 0.5 and 1.0 inclusive. The vehicle may include an instrument cluster designed to display the traction battery age. The vehicle in which the traction battery age assumes one of a plurality of predetermined discrete states according to a ratio less than corresponding predetermined thresholds. The vehicle in which the predetermined discrete states include a new state, a mid-life state, and an end-of-life state. The vehicle in which the control system is further programmed to operate the traction battery based on the traction battery age.The vehicle, in which the historical ampere-hour values ​​comprise a recent predetermined number of estimated ampere-hour values. An implementation of the described configurations may include hardware, a method or process, or computer software on a computer-accessible medium.

[0009] In some embodiments, a vehicle includes an instrument cluster designed to display the age status of a traction battery. The vehicle also includes a control unit programmed to output the age status based on the ratio of a reference ampere-hour value (which has a predetermined probability of being lower than an actual ampere-hour value) to a beginning-of-life ampere-hour value, the reference ampere-hour value being derived from a mean and standard deviation of a plurality of historical ampere-hour values. Other embodiments may include appropriate computer systems, devices, and computer programs recorded on one or more computer storage devices, each designed to perform the operations of the method.

[0010] Some configurations may include one or more of the following features: The vehicle in which the age state comprises a new state, a mid-life state, and an end-of-life state. The vehicle in which the instrument cluster is further designed to display the new state in a first color, the mid-life state in a second color, and the end-of-life state in a third color. The vehicle in which the control system is further programmed to operate the traction battery according to its age state. The vehicle in which the set of historical ampere-hour values ​​includes a recent predetermined set of estimated traction battery ampere-hour values. The vehicle in which the set of historical ampere-hour values ​​is normally distributed.An implementation of the described configurations may include hardware, a method or process, or computer software on a medium accessible to a computer.

[0011] In some embodiments, a method comprises outputting, by a controller, the age of a traction battery based on the ratio of a reference ampere-hour value (which has a predetermined probability of being lower than an actual ampere-hour value) to a beginning-of-life ampere-hour value, wherein the reference ampere-hour value is derived from a mean and standard deviation of a plurality of historical ampere-hour values. The method also comprises operating, by the controller, the traction battery according to its age. Other embodiments may include appropriate computer systems, devices, and computer programs recorded on one or more computer storage devices, each of which is designed to perform the operations of the method.

[0012] Some embodiments may include one or more of the following features: The method in which the traction battery age assumes one of a variety of predefined discrete states. The method in which the predefined discrete states are based on the ratio being less than corresponding predefined thresholds. The method in which the historical ampere-hour values ​​comprise a recent predefined set of estimated ampere-hour values. The method may include a display of the traction battery age by means of a combination instrument. An implementation of the described embodiments may comprise hardware, a method or process, or computer software on a medium accessible to a computer. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a diagram of a hybrid vehicle, showing typical powertrain and energy storage components. Fig. Figure 2 is a diagram of a possible battery pack arrangement comprising multiple cells and monitored and controlled by a battery energy control module. Fig. Figure 3 is a diagram of possible probability density functions for a battery capacity at two different times. Fig. Figure 4 is a flowchart of a possible sequence of operations for estimating the age of a traction battery. DETAILED DESCRIPTION

[0013] Embodiments of the present disclosure are described here. It is understood, however, that the disclosed embodiments are merely examples and that other embodiments may take different and alternative forms. The figures are not necessarily to scale; some features may have been enlarged or reduced to show details of certain components. The specific structural and functional details disclosed here are therefore not to be interpreted as a limitation, but merely as a representative basis for teaching a person skilled in the art to use the present invention in various ways.As an average person skilled in the art will understand, various features illustrated and described with reference to any one of the figures can be combined with features illustrated in one or more other figures to create embodiments not explicitly illustrated or described. The illustrated combinations of features provide representative embodiments for typical applications. However, various combinations and modifications of the features consistent with the teachings of this disclosure may be desirable for certain applications or implementations.

[0014] Fig. Figure 1 illustrates a typical plug-in hybrid electric vehicle (PHEV). A typical plug-in hybrid electric vehicle 12 may include one or more electric machines 14 mechanically coupled to a hybrid transmission 16. The electric machines 14 may be capable of operating as a motor or as a generator. Furthermore, the hybrid transmission 16 is mechanically coupled to a power machine 18. The hybrid transmission 16 is also mechanically coupled to a drive shaft 20, which is mechanically coupled to the wheels 22. The electric machines 14 can provide propulsion and deceleration capabilities when the power machine 18 is switched on or off. The electric machines 14 also operate as generators and can provide fuel-saving benefits by recovering energy that would normally be lost as heat in a friction braking system.The electric machines 14 can also reduce vehicle emissions by enabling the power machine 18 to operate at more efficient speeds and by enabling the hybrid electric vehicle 12 to operate in an electric mode under certain conditions with the power machine 18 switched off.

[0015] A traction battery or battery pack 24 stores energy that can be used by the electric machines 14. A vehicle battery pack 24 typically provides a high-voltage DC output. The traction battery 24 can be electrically coupled to one or more power electronics modules. One or more contactors 42 can disconnect the traction battery 24 from other components when open and connect it to other components when closed. The power electronics module 26 can also be electrically coupled to the electric machines 14, providing the ability to transfer energy bidirectionally between the traction battery 24 and the electric machines 14. For example, a traction battery 24 can supply a DC voltage while the electric machines 14 operate on three-phase AC current.The power electronics module 26 can convert the DC voltage into three-phase AC current to operate the electric machines 14. In a regenerative mode, the power electronics module 26 can convert the three-phase AC current from the electric machines 14, which are operating as generators, into DC voltage suitable for the traction battery 24. This description applies equally to a purely electric vehicle. For a purely electric vehicle, the hybrid transmission 16 can be a transmission connected to an electric machine 14, and the power unit 18 may not be present.

[0016] In addition to supplying energy for propulsion, the traction battery 24 can supply energy to other electrical systems of the vehicle. A vehicle 12 can include a DC / DC converter module 28, which converts the high-voltage DC output of the traction battery 24 into a low-voltage DC supply suitable for low-voltage vehicle consumers. An output of the DC / DC converter module 28 can be electrically coupled to an auxiliary battery 30 (e.g., a 12 V battery). The low-voltage systems can be electrically coupled to the auxiliary battery. Other high-voltage consumers 46, such as compressors and electric heating devices, can be coupled to the high-voltage output of the traction battery 24.

[0017] The vehicle 12 can be an electric vehicle or a rechargeable hybrid vehicle in which the traction battery 24 can be recharged using an external power source 36. The external power source 36 can be connected to a power outlet. The external power source 36 can be electrically coupled to a charging device or an electric vehicle supply unit (EVSE) 38. The external power source 36 can be an electricity supply network or a power grid provided by an electricity utility. The EVSE 38 can include circuits and controls to regulate and manage the transfer of energy between the power source 36 and the vehicle 12. The external power source 36 can supply the EVSE 38 with direct current (DC) or alternating current (AC) electrical energy. The EVSE 38 can have a charging connector 40 for insertion into a charging port 34 of the vehicle 12.The charging port 34 can be any type of connector designed to transfer energy from the EVSE 38 to the vehicle 12. The charging port 34 can be electrically coupled to a charging device or an on-board energy conversion module 32. The energy conversion module 32 can condition the energy supplied by the EVSE 38 to provide the traction battery 24 with the appropriate voltage and current levels. The energy conversion module 32 can be coupled to the EVSE 38 to coordinate the supply of energy to the vehicle 12. The EVSE connector 40 can have pins that fit into corresponding recesses in the charging port 34. Alternatively, various components described as electrically coupled or connected can transfer energy using wireless inductive coupling.

[0018] One or more wheel brakes 44 can be provided for slowing down the vehicle 12 and preventing it from moving. The wheel brakes 44 can be hydraulically actuated, electrically actuated, or by a combination of both. The wheel brakes 44 can be part of a braking system 50.

[0019] The braking system 50 may include other components for operating the wheel brakes 44. For simplicity, the figure illustrates a single connection between the braking system 50 and one of the wheel brakes 44. A connection between the braking system 50 and the other wheel brakes 44 is implied. The braking system connections may be hydraulic and / or electrical. The braking system 50 may include a controller for monitoring and coordinating the operation of the wheel brakes 44. The braking system 50 can monitor the brake components and control the wheel brakes 44 to decelerate the vehicle. The braking system 50 can respond to commands from the driver and may also operate independently to implement features such as stability control. The controller of the braking system 50 may implement a procedure for applying a requested braking force when requested by another controller or sub-function.

[0020] One or more electrical loads 46 can be connected to the high-voltage bus. The electrical loads 46 can have an associated control unit that operates and controls them. Examples of electrical loads 46 include a heating module or an air conditioning module.

[0021] The vehicle 12 may include an instrument cluster 52. The instrument cluster 52 may include a display screen, which may be a touchscreen. The instrument cluster 52 may include light-generating devices, such as lamps, lights, and light-emitting diodes (LEDs). The instrument cluster 52 may include descriptive text or messages illuminated by the light-generating devices. The instrument cluster 52 may be mounted in a position easily visible to a driver and / or occupants of the vehicle 12. The instrument cluster 52 may include gauges and indicators for various operating parameters of the vehicle 12 (e.g., vehicle speed, engine speed, fuel level). The instrument cluster 52 may be designed to display information in analog or digital form.The instrument cluster 52 may include one or more message displays designed to communicate messages to the driver. The instrument cluster 52 may be configured to display information regarding the traction battery 24.

[0022] Electronic modules in the vehicle 12 can communicate via one or more vehicle networks. The vehicle network can include a variety of communication channels. One channel of the vehicle network can be a serial bus, such as a CAN (Controller Area Network). One of the channels of the vehicle network can include an Ethernet network, defined by the 802 family of standards of the Institute of Electrical and Electronics Engineers (IEEE). Additional channels of the vehicle network can include discrete connections between modules and can include power signals from the auxiliary battery 30. Different signals can be transmitted over different channels of the vehicle network. For example, video signals can be transmitted over a high-speed channel (e.g., Ethernet), while control signals can be transmitted over CAN or discrete signals.The vehicle network can include any hardware and software components that support the transmission of signals and data between modules. The vehicle network is not in . Fig. Figure 1 shows the vehicle network, but it can be implied that the vehicle network can be connected to any electronic module present in the vehicle. A vehicle system controller (VSC) may be present to coordinate the operation of the various components.

[0023] A 24-volt traction battery can be composed of a variety of chemical formulations. Typical battery pack chemistries include lead-acid, nickel-metal hydride (NMH), or lithium-ion. Fig. Figure 2 shows a typical traction battery pack 24 in a simple series configuration of N battery cells 72. However, other battery packs 24 can be constructed from any number of individual battery cells connected in series, parallel, or a combination thereof. A battery management system can include one or more controllers, such as a battery energy control module (BECM) 76, which monitor and control the work performance of the traction battery 24. The battery pack 24 can include sensors to measure various characteristics at the pack level. The battery pack 24 can include one or more pack current sensors 78, pack voltage sensors 80, and pack temperature sensors 82. The BECM 76 can include a circuit for coupling with the pack current sensors 78, the pack voltage sensors 80, and the pack temperature sensors 82.The BECM 76 can have non-volatile memory, allowing data to be retained when the BECM 76 is powered off. Retained data can be available during the next key cycle.

[0024] In addition to the pack-level characteristics, characteristics of the battery cell 72 level can be present, measured, and monitored. For example, the terminal voltage, current, and temperature of each cell 72 can be measured. A system can use one or more sensor modules 74 to measure the characteristics of the battery cells 72. Depending on the capabilities, the sensor modules 74 can measure the characteristics of one or more of the battery cells 72. The battery pack 24 can use up to Nc sensor modules 74 to measure the characteristics of all battery cells 72. Each of the sensor modules 74 can transmit the measured values ​​to the BECM 76 for further processing and coordination. The sensor modules 74 can transmit signals to the BECM 76 in analog or digital form. In some configurations, the functionality of the sensor modules 74 can be integrated into the BECM 76.This means that the hardware of the sensor modules 74 can be integrated as part of the circuit in the BECM 76, and the BECM 76 can handle the processing of raw signals. The BECM 76 can also include circuitry for coupling with one or more contactors 42 to open and close the contactors 42.

[0025] It can be useful to calculate various characteristics of the battery pack. Quantities such as battery performance, battery capacity, and battery charge level can be useful for controlling the operation of the traction battery 24, as well as any electrical loads that receive power from the traction battery 24. Battery performance is a measure of the maximum amount of power that the traction battery 24 can deliver or the maximum amount of power that the traction battery 24 can receive. Knowing the battery performance allows the electrical loads to be managed in such a way that the requested power remains within the limits that the traction battery 24 can handle.

[0026] Battery capacity is a measure of the total amount of energy that can be stored in the traction battery 24. Battery capacity can be expressed in units of ampere-hours. Values ​​relating to battery capacity can be referred to as ampere-hour values. The battery capacity of the traction battery 24 may decrease over its service life.

[0027] The state of charge (SOC) indicates how much charge remains in the traction battery 24. The state of charge can be expressed as a percentage of the total charge remaining in the traction battery 24. The state of charge can be displayed to inform the driver how much charge remains in the traction battery 24, similar to the fuel gauge. The state of charge can also be used to control the operation of an electric or hybrid electric vehicle. A variety of methods can be used to calculate the state of charge. One possible method is to integrate the traction battery current over time. This is generally known in the art as ampere-hour integration.

[0028] An energy management system can operate the traction battery 24 to manage its state of charge. The traction battery 24 can be charged and discharged according to a target state of charge compared to its current state of charge. For example, if the current state of charge is greater than the target state of charge, the traction battery 24 can be discharged. Operation of the traction battery 24 can be achieved by instructing the electric machines 14 to draw current from or supply current to the traction battery 24. Operation of the traction battery 24 can further include instructing the power machine 18 to supply power to the electric machines 14 to charge the traction battery 24.

[0029] Over the lifetime of the traction battery 24, its capacity may decrease. This can be referred to as aging of the traction battery 24. To control the vehicle 12 appropriately, it is useful to know the capacity as the traction battery 24 ages. By estimating the capacity, vehicle control strategies can be adjusted to maintain acceptable vehicle performance. For example, state-of-charge range limits (e.g., minimum, low, high, and maximum) can define the normal state-of-charge operating range.If the battery capacity deteriorates, the state-of-charge range limits can be adjusted to ensure that (i) sufficient energy is available between the low and high state-of-charge range limits, (ii) sufficient power is available to start the engine in order to meet emission standards, and (iii) battery overcharge protection remains stable against sensor measurement inaccuracies or errors.

[0030] Several self-learning algorithms are available for estimating battery capacity. However, the determined capacity value may be a non-monotentially varying parameter due to changes in environmental conditions and measurement errors. Since the estimated battery capacity value can fluctuate, the directly learned capacity value of the self-learning algorithms may be unsuitable for use as an age indicator for the traction battery. A better age indicator would be one that changes monotonically and does not vary in the presence of changing vehicle conditions and measurement errors. The system and method disclosed here provide an improved estimate of the traction battery's age in the presence of these changes and measurement errors.The age estimation of the traction battery 24 can be used to set battery control strategies to improve work performance throughout the vehicle's lifetime.

[0031] The BECM 76 can be programmed to estimate the traction battery capacity during vehicle 12 operation. The battery capacity learning strategy can be any algorithm or strategy known in the prior art. For example, battery charge capacity can be estimated as the battery current throughput divided by the difference in state-of-charge (SOC) values. This approach relies on the knowledge of two separate state-of-charge values ​​obtained independently of the battery capacity. The battery capacity can be calculated as follows: Q=∫TiTfi dtSOCi−SOCf=ThroughputSOCi−SOCf where SOC i and SOC fthe charge state values ​​at time T i or T f are and i is the current flowing to or from the battery. The battery current throughput can be defined as the integral of the battery current over a time period. In an implementation in a controller 76, the integral can be replaced by summing current values ​​multiplied by the sampling time.

[0032] The state-of-charge values ​​can be based on measured voltages sampled during two key-on / key-off cycles. For a lithium-ion battery, it is generally known that the terminal voltage is approximately the battery's open-circuit voltage (i.e., V0). tThe state of charge (Voc) is equal after the battery has been at rest for a sufficient period of time. The terminal voltage can be measured upon power-up, and the state of charge can be derived from the open-circuit voltage. A relationship between the state of charge and the open-circuit voltage can be established using test data or manufacturer's data. The throughput can be calculated for each ignition cycle and stored in non-volatile memory for use in the next ignition cycle. Other methods for determining battery capacity may be equally applicable to the methods and systems described here.

[0033] The BECM 76 can be programmed to generate a parameter representing the age of the traction battery 24. This parameter can be based on learned battery capacity values. When a new battery capacity value is available, the BECM 76 can perform a statistical algorithm to further analyze the battery capacity values ​​and output a battery age parameter. The statistical procedure can include calculating the mean and standard deviation of the estimated battery capacity values ​​from current and previous vehicle operation. The statistical algorithm can also include a safety analysis to determine whether the battery capacity is within a predefined safety range.

[0034] A sliding window can be selected. The sliding window can include a fixed number n of estimated battery capacity values. The estimated capacity values ​​can be the most recent number n of historical ampere-hour values ​​determined by the BECM 76 and can be stored in non-volatile memory of the BECM 76. The running mean can be the mean of the n historical values ​​of the sliding window. The running standard deviation can be the standard deviation of the n historical values ​​of the sliding window. The running mean can be expected to decrease as the battery ages and its capacity deteriorates. The running mean and running standard deviation can be used to determine a probability (expressed as a percentage) that the actual battery capacity (the actual ampere-hour value) lies within a selected range.

[0035] A normal distribution function can be used to describe a variety of random variables. The normal distribution can be characterized by a mean µ and a standard deviation σ. The normal distribution can be normalized to obtain a standard value, also known as a Z-score. The Z-score for a given random variable x is given as follows: Z=x−μσ where x is the random variable, µ is the mean of the random variable, and σ is the standard deviation of the random variable.

[0036] The normal distribution can be used to characterize the distribution of battery capacity values ​​in traction battery 24. In this case, the random variable can be the battery capacity represented by the historical values. The Z-value for the random variable representing the battery capacity can be specified as follows: z=C−μσ where C is the battery capacity, µ is the mean battery capacity, and σ is the standard deviation of the battery capacity. The Z-value represents the deviation of the capacity from the mean. Using this distribution, the probability that the actual battery capacity is greater than a reference battery capacity can be calculated. A reference capacity value χ can be defined such that there is a given probability that the actual battery capacity is greater than the reference capacity value, expressed as P(C ≥ χ). The reference capacity value can be in units of ampere-hours and can be referred to as a reference ampere-hour value.

[0037] The random variable can first be converted into Z-values. Converting the reference capacity limit into a Z-value yields: Z=χ−μσ

[0038] The probability that the actual battery capacity is greater than the reference capacity value can be expressed as Z-values. That is, the probability can be represented as P(z ≥ Z). The probability can be determined using lookup tables for standardized Z-values.

[0039] The battery capacity value can be modeled using a normal distribution, as in Fig. Figure 3 illustrates this. The initial capacity of the traction battery 24 can be modeled as a first probability density function 100, which has a first mean 102. A first reference capacity value 104 can be described as the reference capacity value that the actual battery capacity exceeds with a given probability at a time when the first probability density function 100 describes the state of the traction battery 24. As the battery ages, its capacity deteriorates and can be described by a second probability density function 106, which has a second mean 108 that is smaller than the first mean 102. The second probability density function 106 can represent the state of the traction battery 24 at a second time point after the first state described by the first probability density function 100.The second reference capacity value, 110, can be described as the reference capacity value that the actual battery capacity exceeds with a given probability at the second time point. The calculation and use of the reference capacity values ​​are described here.

[0040] It may be desirable to calculate the reference capacity value for which the probability of the actual battery capacity exceeding the reference capacity value has a predetermined value. It can be useful to determine when P(z ≥ Z) has a predetermined probability. For example, the predetermined probability can be set to a statistical confidence level of 0.9. This would mean that there is a 90% chance that the actual battery capacity exceeds the reference capacity value. The predetermined probability can be calibrated to a selectable value during a calibration procedure or system setup. The predetermined probability can be designed to lie within a predetermined range, such as between 0.5 and 1.0 inclusive. Furthermore, knowledge of the statistical confidence level can determine the value of Z.The value of Z can be derived from lookup tables for standardized Z-values. A statistical confidence level of 0.9 for P(z ≥ Z) results in a Z-value of -1.282 (-1.282) using the standard tables. Other statistical confidence levels can be derived in the same way.

[0041] Knowing the Z-value for the desired statistical confidence allows the reference capacity value from equation (4) to be calculated as follows: χ=μk+Zσk

[0042] This value defines the reference capacity at which the actual battery capacity is expected to be greater with a probability defined by the chosen statistical confidence level. With reference to Fig. 3. The first reference capacity value can be determined as χ1 = µ1 + Zσ1. The second reference capacity value can be determined as χ2 = µ2 + Zσ2. To achieve a statistical confidence level of 0.9, the Z-value of -1.282 (-1.282) can be used. The mean and standard deviation can be calculated as described here.

[0043] The statistical methods used provide a useful indication of the battery's age. Knowing that the actual battery capacity exceeds the reference capacity with a specified probability provides useful data for operating the vehicle. Using statistical methods provides a certain degree of confidence that the actual battery capacity is within an acceptable range.

[0044] The Z-value represents the distance of the reference capacity limit from the mean value. From the Z-value, the probability that the distribution is larger than the given value can be determined. During normal battery operation, the reference capacity value is generally smaller than the running mean value. In this application, the Z-value can generally be a negative value.

[0045] The probability that the actual battery capacity is greater than the reference capacity value can be determined as the area under the curve (e.g., 100, 106) from the reference capacity value (e.g., 104, 110) to the rightmost point. The rightmost point can be the section of the curve that approaches zero as the capacity increases to infinity. The statistical certainty that the actual battery capacity exceeds the reference capacity value can be expressed as a number P(z ≥ Z).

[0046] The mean and standard deviation can be calculated using a FIFO (first-in, first-out) buffer. When a battery capacity is estimated by the controller 76, the ampere-hour values ​​can be stored in a buffer. The buffer can represent historical ampere-hour values. When a new battery capacity estimate is determined, the oldest estimate in the buffer can be removed. The mean and standard deviation can then be calculated using the updated capacity values ​​in the buffer. This calculation method may require the last n capacity values ​​to be stored in non-volatile memory of the BECM 76 to retain the values ​​between firing cycles. The mean and standard deviation can be calculated as follows: μ=1n∑i=1nCi σ=1n∑i=1n(Ci−μ)2 where C iThe i-th capacity value is where n represents the number of capacity values ​​in the buffer.

[0047] The mean and standard deviation can also be calculated using the previously calculated mean and standard deviation values. The mean can be calculated as follows: μk=n−1nμk−1+1nCest where C est The estimated capacity from the current cycle is µ k-1 where is the average capacity from a previous execution cycle and n is a sample size. The standard deviation can be calculated as follows: σk=n−1nσk−12+1n(Cest−μk)2 where µ k and σ k The mean capacity and standard deviation are those that have been updated in the current cycle, and σ k-1 represents the standard deviation that was updated in the previous execution cycle.

[0048] The mean and standard deviation can be used to calculate the reference capacity value according to equation (5). This gives the reference capacity value at which the actual battery capacity is expected to be greater with the given probability.

[0049] As a battery ages, its capacity is expected to decrease. Estimates of the mean battery capacity are also expected to decline over time. Consequently, the reference capacity value can also decrease, as shown. A capacity conservation function can be expressed as follows: Capacity conservation = χχ0100 where χ oA capacity value at the beginning of life (beginning-of-life ampere-hour value) for an unaged cell. For example, a value equal to the initial reference capacity value of 104 can be used as the beginning-of-life capacity value. The capacity conservation value can represent a percentage of the capacity retained from the initial state of the traction battery 24. A corresponding decay function can be expressed as follows: Capacity drop = 100 - Capacity maintenance

[0050] The capacity drop value can represent a percentage of the capacity lost from the initial state of the traction battery 24.

[0051] For a new 24-volt traction battery, the capacity retention value can start at 100, since the numerator and denominator should be approximately equal. As the 24-volt traction battery ages, the numerator may decrease, thus lowering the capacity retention percentage. Alternatively, the capacity decay value may increase as the traction battery ages. The capacity decay value or the capacity retention value can be used as an indicator of battery age.

[0052] The BECM 76 can be programmed to output a value indicating the age of the traction battery 24 to other modules, including the instrument cluster 52. The BECM 76 can also be programmed to generate and output the age as one of a variety of predefined discrete values ​​for each battery life stage.

[0053] The instrument cluster 52 can be configured to display the age value digitally or analogously as a percentage. In some configurations, the instrument cluster 52 can be configured to display the age value as a bar graph. The instrument cluster 52 can be configured to display the capacity degradation value or the capacity maintenance value. In some configurations, the age of the traction battery 24 can be assigned to one of a variety of predefined discrete states. If the capacity maintenance value is greater than a first predefined threshold, the traction battery 24 can be classified as being in a new condition. If the capacity maintenance value is less than a second predefined threshold, which is less than the first predefined threshold, the traction battery 24 can be classified as being in a worn or end-of-life condition.If the capacity maintenance value is between the first and second thresholds, the traction battery 24 can be classified as being in a mid-life state. In some configurations, the instrument cluster 52 may be designed to display an output indicating the life status of the traction battery 24. The instrument cluster 52 may display a message indicating the battery's age as New, Mid-Life, or End-of-Life, depending on the age value. The instrument cluster 52 may display a message or symbol in different colors for each life state. Green may represent a New state, yellow may represent a Mid-Life state, and red may represent an End-of-Life state. The battery life indication can help the vehicle owner understand upcoming vehicle maintenance needs.

[0054] Fig.Figure 4 illustrates a flowchart describing a possible sequence of operations for battery age estimation. In Operation 200, a battery ampere-hour value can be estimated using any available method. In Operation 202, a check can be performed to determine if a new battery ampere-hour value is available. If a new battery ampere-hour value is not available, execution can return to Operation 200. If a new battery ampere-hour value is available, Operation 204 can be performed to update the historical ampere-hour values. For example, a FIFO (first-in, first-out) buffer can be updated using the recently calculated battery ampere-hour value.

[0055] Operation 206 allows the calculation of statistical values. The mean and standard deviation can be calculated based on historical ampere-hour values. Operation 208 allows the calculation of a reference ampere-hour value, which corresponds to the reference capacity value. The reference ampere-hour value can be the capacity value for which there is a predefined probability that it is lower than the actual ampere-hour value. Operation 210 allows the calculation of the maintenance capacity and / or the associated capacity decay value. A ratio of the reference ampere-hour value to the beginning-of-life ampere-hour value can be calculated. Operation 212 allows the calculation of a value indicating the battery age, which can then be output for other modules. For example, a discrete life stage can be determined according to predefined thresholds.During operation 214, the battery age can be displayed via the instrument cluster 52. During operation 216, the traction battery 24 can be operated according to its age.

[0056] Battery age calculation can provide a good estimate of the traction battery's age. Using statistical values ​​in the calculation allows for the use of more samples, thus improving the battery age estimate. Previous methods based on current battery capacity are susceptible to fluctuations and are not monotonically non-increasing, as the actual battery capacity is likely to be.

[0057] The processes, methods, or algorithms disclosed herein can be delivered to or implemented by a processing device, controller, or computer, which may include any existing programmable electronic control unit or a dedicated electronic control unit. Likewise, the processes, methods, or algorithms can be stored as data or instructions executable by a controller or computer in many forms, including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information modifiably stored on writable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software-executable object.Alternatively, the processes, procedures or algorithms can be embodied wholly or partially using suitable hardware components, such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and firmware components.

[0058] Although exemplary embodiments have been described above, it is not intended that these embodiments describe all possible forms encompassed by the claims. The words used in the description are descriptive and not limiting, and it is understood that various modifications can be made without departing from the inventive concept and the scope of the disclosure. As described above, the features of different embodiments can be combined to form further embodiments of the invention that may not be explicitly described or illustrated.Although various embodiments could be described in such a way as to offer advantages with respect to one or more desired characteristics or to be preferred over other embodiments or implementations of the prior art, a person skilled in the art recognizes that one or more features or characteristics may be compromised in order to achieve desired properties of the overall system, which depend on the specific application and implementation. These properties may include, but are not limited to, cost, robustness, durability, life-cycle costs, marketability, appearance, packaging, size, ease of maintenance, weight, manufacturability, ease of assembly, etc.Therefore, embodiments that are described as less desirable than other embodiments or implementations of the prior art with respect to one or more properties are not outside the scope of disclosure and may be desirable for certain applications.

[0059] It is further described as follows: A. Vehicle, including: a traction battery, and a controller programmed to output a traction battery age based on the ratio of a reference ampere-hour value, which has a predetermined probability of being smaller than an actual ampere-hour value, to a beginning-of-life ampere-hour value, wherein the reference ampere-hour value is derived from a mean and standard deviation of a multitude of historical traction battery ampere-hour values. B. Vehicle to A, where the historical ampere-hour values ​​are normally distributed. C. Vehicle to A, where the given probability is a value between 0.5 inclusive and 1.0 inclusive. D. Vehicle according to A, which further includes a combination instrument designed to display the traction battery age. E. Vehicle according to D, wherein the traction battery age assumes one of a plurality of predetermined discrete states according to the ratio that is smaller than corresponding predetermined thresholds. F. Vehicle according to E, wherein the specified discrete states include a new state, a midlife state and an end-of-life state. G. Vehicle according to A, wherein the control is further programmed to operate the traction battery on the basis of the traction battery age. H. Vehicle to A, wherein the historical ampere-hour values ​​comprise a most recent predetermined number of estimated ampere-hour values. I. Vehicle, comprising: a combination instrument designed to display the age status of a traction battery, and a controller programmed to output the age state based on a ratio of a reference ampere-hour value, which has a predetermined probability of being smaller than an actual ampere-hour value, to an ampere-hour value at the beginning of life (beginning-of-life ampere-hour value), wherein the reference ampere-hour value is derived from a mean and standard deviation of a multitude of historical ampere-hour values. J. Vehicle according to I, wherein the age condition includes a new condition, a midlife condition and an end-of-life condition. K. Vehicle according to J, wherein the instrument cluster is further designed to display the new state in a first color, the midlife state in a second color and the end-of-life state in a third color. L. Vehicle according to I, wherein the control is further programmed to operate the traction battery according to its age. M. Vehicle according to I, wherein the multitude of historical ampere-hour values ​​includes a most recent predetermined number of estimated ampere-hour values ​​of the traction battery. N. Vehicle according to I, where the multitude of historical ampere-hour values ​​is normally distributed. O. Procedure, comprehensive: Output, by means of a controller, of the age of a traction battery based on a ratio of a reference ampere-hour value, which has a predetermined probability of being less than an actual ampere-hour value, to a beginning-of-life ampere-hour value, wherein the reference ampere-hour value is derived from a mean and standard deviation of a multitude of historical ampere-hour values, and operate, by means of the controller, the traction battery according to its age. P. Procedure according to O, where the age assumes one of a multitude of predefined discrete states. Q. Method according to P, wherein the given discrete states are based on the ratio being smaller than corresponding given thresholds. R. Method according to O, wherein the historical ampere-hour values ​​comprise a recent predetermined number of estimated ampere-hour values. S. Procedure according to O, which further includes a display, by means of a combination instrument, of the age of the traction battery.

Claims

[1] Vehicle (12) comprising: a traction battery (24), and a controller (76) programmed to output a traction battery age estimated based on a ratio of a reference battery capacity value having a predetermined probability of being less than an actual battery capacity value to a beginning-of-life ampere-hour battery capacity value, the reference battery capacity value being derived from a mean and a standard deviation of a plurality of historical estimated battery capacity values ​​of the traction battery (24). [2] The vehicle (12) of claim 1, wherein the historical battery capacity values ​​are normally distributed. [3] The vehicle (12) of claim 1, wherein the predetermined probability is a value between 0.5 and 1.0 inclusive. [4] The vehicle (12) of claim 1, further comprising an instrument cluster (52) configured to display traction battery age. [5] The vehicle (12) of claim 4, wherein the traction battery age assumes one of a plurality of predetermined discrete states according to the ratio being less than corresponding predetermined thresholds. [6] The vehicle (12) of claim 5, wherein the predetermined discrete states include a new state, a mid-life state, and an end-of-life state. [7] The vehicle (12) of claim 1, wherein the controller (76) is further programmed to operate the traction battery based on the traction battery age. [8] The vehicle (12) of claim 1, wherein the historical battery capacity values ​​comprise a most recent predetermined number of estimated battery capacity values.

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