Virtual assessment of battery state of health in electrified vehicles

A vehicle includes a traction battery and a controller programmed to operate the traction battery according to an estimated value of a battery state of health parameter. The estimated value is updated based on drive cycle parameters of the vehicle over a time interval. The state of health parameters include a battery capacity and a resistance of the traction battery.

TECHNICAL FIELD

This application is generally related to estimating state of health parameters for a traction battery in a vehicle.

BACKGROUND

Electrified vehicles include hybrid electric vehicles (HEV) and battery electric vehicles (BEV). Electrified vehicles include a traction battery to store energy to be used for propulsion and other purposes. The traction battery is generally operated using various parameters that are defined during the development phase. Over time, operating parameters of the traction battery change causing changes in performance of the traction battery.

SUMMARY

In some configurations, a vehicle includes a traction battery. The vehicle also includes a controller programmed to operate the traction battery according to an estimated value of a state of health parameter and to change the estimated value based on statistical parameters describing vehicle motion during a drive cycle and parameters describing a relationship between vehicle motion during past drive cycles and resulting current flow through the traction battery.

Some configurations may include one or more of the following features. The vehicle in which the state of health parameter is a capacity of the traction battery. The vehicle in which the state of health parameter is an internal impedance of the traction battery. The vehicle in which the statistical parameters include a mean positive velocity of the vehicle. The vehicle in which the statistical parameters include a standard deviation of acceleration of the vehicle. The vehicle in which the controller is programmed to change the estimated value based on a second set of statistical parameters describing current flow through the traction battery during a drive cycle and parameters describing a relationship between current flow through the traction battery during past drive cycles and the state of health parameter. The vehicle in which the controller is programmed to receive temperature data and change the estimated value further based on a temperature associated with the drive cycle. The vehicle in which the parameters describing the relationship are derived from a regression function such that that the estimated value is within a predetermined confidence interval of a true value of the state of health parameter.

In some configurations, a vehicle power system includes a controller programmed to operate a traction battery according to an estimated value of a state of health parameter and to change the estimated value based on statistical parameters describing vehicle motion during a drive cycle and parameters describing a relationship between vehicle motion during past drive cycles and resulting changes to the state of health parameter.

Some configurations may include one or more of the following features. The vehicle power system in which the state of health parameter is a capacity of the traction battery. The vehicle power system in which the state of health parameter is an internal impedance of the traction battery. The vehicle power system in which the statistical parameters include a mean positive velocity of the vehicle. The vehicle power system in which the statistical parameters include a standard deviation of acceleration of the vehicle. The vehicle power system in which the controller is programmed to receive temperature data and change the estimated value further based on a temperature associated with the drive cycle. The vehicle power system in which the parameters describing the relationship are derived from a regression function such that that the estimated value is within a predetermined confidence interval of a true value of the state of health parameter.

In some configurations, a method of operating a battery in a vehicle includes operating, by a controller, the battery according to an estimated value of a battery state of health parameter. The method also includes changing, by the controller, the estimated value based on statistical parameters describing vehicle motion during a drive cycle and parameters describing a relationship between vehicle motion during past drive cycles and resulting current flow through the battery.

Some configurations may include one or more of the following features. The method may include changing, by the controller, the estimated value further based on a temperature associated with the drive cycle. The method may include changing, by the controller, the estimated value further based on statistical parameters describing current flow through the battery during a drive cycle and parameters describing a relationship between current flow through the battery during past drive cycles and the state of health parameter. The method may include describing the relationship according to a regression function such that that the estimated value is within a predetermined confidence interval of a true value of the state of health parameter.

DETAILED DESCRIPTION

FIG. 1depicts an electrified vehicle112that may be referred to as a plug-in hybrid-electric vehicle (PHEV). A plug-in hybrid-electric vehicle112may comprise one or more electric machines114mechanically coupled to a hybrid transmission116. The electric machines114may be capable of operating as a motor or a generator. In addition, the hybrid transmission116is mechanically coupled to an engine118. The hybrid transmission116is also mechanically coupled to a drive shaft120that is mechanically coupled to the wheels122. The electric machines114can provide propulsion and deceleration capability when the engine118is turned on or off. The electric machines114may also act as generators and can provide fuel economy benefits by recovering energy that would normally be lost as heat in a friction braking system. The electric machines114may also reduce vehicle emissions by allowing the engine118to operate at more efficient speeds and allowing the hybrid-electric vehicle112to be operated in electric mode with the engine118off under certain conditions. An electrified vehicle112may also be a battery electric vehicle (BEV). In a BEV configuration, the engine118may not be present. In other configurations, the electrified vehicle112may be a full hybrid-electric vehicle (FHEV) without plug-in capability.

A traction battery or battery pack124stores energy that can be used by the electric machines114. The vehicle battery pack124may provide a high voltage direct current (DC) output. The traction battery124may be electrically coupled to one or more power electronics modules126. One or more contactors142may isolate the traction battery124from other components when opened and connect the traction battery124to other components when closed. The power electronics module126is also electrically coupled to the electric machines114and provides the ability to bi-directionally transfer energy between the traction battery124and the electric machines114. For example, a traction battery124may provide a DC voltage while the electric machines114may operate with a three-phase alternating current (AC) to function. The power electronics module126may convert the DC voltage to a three-phase AC current to operate the electric machines114. In a regenerative mode, the power electronics module126may convert the three-phase AC current from the electric machines114acting as generators to the DC voltage compatible with the traction battery124.

The vehicle112may include a variable-voltage converter (VVC)152electrically coupled between the traction battery124and the power electronics module126. The VVC152may be a DC/DC boost converter configured to increase or boost the voltage provided by the traction battery124. By increasing the voltage, current requirements may be decreased leading to a reduction in wiring size for the power electronics module126and the electric machines114. Further, the electric machines114may be operated with better efficiency and lower losses.

In addition to providing energy for propulsion, the traction battery124may provide energy for other vehicle electrical systems. The vehicle112may include a DC/DC converter module128that converts the high voltage DC output of the traction battery124to a low voltage DC supply that is compatible with low-voltage vehicle loads. An output of the DC/DC converter module128may be electrically coupled to an auxiliary battery130(e.g., 12V battery) for charging the auxiliary battery130. The low-voltage systems may be electrically coupled to the auxiliary battery130. One or more electrical loads146may be coupled to the high-voltage bus. The electrical loads146may have an associated controller that operates and controls the electrical loads146when appropriate. Examples of electrical loads146may be a fan, an electric heating element and/or an air-conditioning compressor.

The electrified vehicle112may be configured to recharge the traction battery124from an external power source136. The external power source136may be a connection to an electrical outlet. The external power source136may be electrically coupled to a charger or electric vehicle supply equipment (EVSE)138. The external power source136may be an electrical power distribution network or grid as provided by an electric utility company. The EVSE138may provide circuitry and controls to regulate and manage the transfer of energy between the power source136and the vehicle112. The external power source136may provide DC or AC electric power to the EVSE138. The EVSE138may have a charge connector140for plugging into a charge port134of the vehicle112. The charge port134may be any type of port configured to transfer power from the EVSE138to the vehicle112. The charge port134may be electrically coupled to a charger or on-board power conversion module132. The power conversion module132may condition the power supplied from the EVSE138to provide the proper voltage and current levels to the traction battery124. The power conversion module132may interface with the EVSE138to coordinate the delivery of power to the vehicle112. The EVSE connector140may have pins that mate with corresponding recesses of the charge port134. Alternatively, various components described as being electrically coupled or connected may transfer power using a wireless inductive coupling.

One or more wheel brakes144may be provided for decelerating the vehicle112and preventing motion of the vehicle112. The wheel brakes144may be hydraulically actuated, electrically actuated, or some combination thereof. The wheel brakes144may be a part of a brake system150. The brake system150may include other components to operate the wheel brakes144. For simplicity, the figure depicts a single connection between the brake system150and one of the wheel brakes144. A connection between the brake system150and the other wheel brakes144is implied. The brake system150may include a controller to monitor and coordinate the brake system150. The brake system150may monitor the brake components and control the wheel brakes144for vehicle deceleration. The brake system150may respond to driver commands and may also operate autonomously to implement features such as stability control. The controller of the brake system150may implement a method of applying a requested brake force when requested by another controller or sub-function.

Electronic modules in the vehicle112may communicate via one or more vehicle networks. The vehicle network may include a plurality of channels for communication. One channel of the vehicle network may be a serial bus such as a Controller Area Network (CAN). One of the channels of the vehicle network may include an Ethernet network defined by Institute of Electrical and Electronics Engineers (IEEE) 802 family of standards. Additional channels of the vehicle network may include discrete connections between modules and may include power signals from the auxiliary battery130. Different signals may be transferred over different channels of the vehicle network. For example, video signals may be transferred over a high-speed channel (e.g., Ethernet) while control signals may be transferred over CAN or discrete signals. The vehicle network may include any hardware and software components that aid in transferring signals and data between modules. The vehicle network is not shown inFIG. 1but it may be implied that the vehicle network may connect to any electronic module that is present in the vehicle112. A vehicle system controller (VSC)148may be present to coordinate the operation of the various components.

The traction battery124may be constructed from a variety of chemical formulations. Typical battery pack chemistries may be lead acid, nickel-metal hydride (NIMH) or Lithium-Ion.FIG. 2shows the traction battery pack124as a simple series configuration of N battery cells202. The traction battery124, however, may be composed of any number of individual battery cells connected in series or parallel or some combination thereof. A battery management system may have one or more controllers, such as a Battery Energy Control Module (BECM)206, that monitor and control the performance of the traction battery124. The traction battery124may include sensors to measure various pack level characteristics. The traction battery124may include one or more pack current measurement sensors208, pack voltage measurement sensors210, and pack temperature measurement sensors212. The BECM206may include circuitry to interface with the pack current sensors208, the pack voltage sensors210and the pack temperature sensors212. The BECM206may have non-volatile memory such that data may be retained when the BECM206is in an off condition. Retained data may be available upon the next key cycle.

In addition to the pack level characteristics, there may be battery cell202level characteristics that are measured and monitored. For example, the terminal voltage, current, and temperature of each cell202may be measured. A system may use one or more sensor modules204to measure the battery cell202characteristics. Depending on the capabilities, the sensor modules204may measure the characteristics of one or multiple of the battery cells202. The traction battery224may utilize up to Ncsensor modules204to measure the characteristics of all the battery cells202. Each of the sensor modules204may transfer the measurements to the BECM206for further processing and coordination. The sensor modules204may transfer signals in analog or digital form to the BECM206. In some configurations, the functionality of the sensor modules204may be incorporated internally to the BECM206. That is, the hardware of the sensor modules204may be integrated as part of the circuitry in the BECM206and the BECM206may handle the processing of raw signals. The BECM206may also include circuitry to interface with the one or more contactors142to open and close the contactors142.

It may be useful to calculate various characteristics of the battery pack. Quantities such as battery power capability, battery capacity, and battery state of charge may be useful for controlling the operation of the traction battery124as well as any electrical loads receiving power from the traction battery124. Battery power capability is a measure of the maximum amount of power the traction battery124can provide or the maximum amount of power that the traction battery124can receive. Knowing the battery power capability allows the electrical loads to be managed such that the power requested is within limits that the traction battery124can handle.

Battery capacity is a measure of a total amount of energy that may be stored in the traction battery124. The battery capacity (often represented by variable Q) may be expressed in units of Amp-hours. Values related to the battery capacity may be referred to as amp-hour values. The battery capacity of the traction battery124may decrease over the life of the traction battery124.

State of charge (SOC) gives an indication of how much charge remains in the traction battery124. The SOC may be expressed as a percentage of the total possible charge remaining in the traction battery124. When the SOC is at one hundred percent, the traction battery124may be charged to the battery capacity. The SOC value may be output to inform the driver of how much charge remains in the traction battery124, similar to a fuel gauge. The SOC may also be used to control the operation of an electric or hybrid-electric vehicle. Calculation of SOC can be accomplished by a variety of methods. One possible method of calculating SOC is to perform an integration of the traction battery current over time. This is well-known in the art as ampere-hour integration.

An energy management system or vehicle power system may operate the traction battery124to manage the state of charge of the traction battery124. The traction battery124may be charged or discharged according to a target state of charge compared to a present state of charge. For example, when the present state of charge is greater than the target state of charge, the traction battery124may be discharged. Operation of the traction battery124may be achieved by commanding a torque of the electric machines114to draw current from or provide current to the traction battery124. Operation of the traction battery124may further involve commanding operation of the engine118to provide power to the electric machines114to charge the traction battery124.

Values that are often computed for the traction battery124may be state of health (SOH) related parameters. The SOH parameters may provide an indication of the age of the traction battery124. The SOH parameters may also provide information on the state of the battery and how the battery has degraded over time. The SOH parameters may include a computed battery capacity and a battery internal impedance. The SOH parameters may indicate a change to the battery capacity and the battery internal impedance. The battery internal impedance may be represented as a resistance value. As the traction battery124ages, the battery internal impedance may change. The battery internal impedance generally increases as the battery degrades. Knowledge of the battery internal impedance and battery capacity allows for improved control of the traction battery124. Various methods are available to determine the SOH of the traction battery124. A battery age indicator may be output and displayed based on the battery capacity and/or the battery internal impedance values. For example, the battery capacity and/or battery internal impedance values may be compared to corresponding values at a beginning of battery life to determine an approximate age of the traction battery.

Battery SOH prediction algorithms generally monitor battery related quantities such as voltages and currents. For example, one factor for battery aging is the amount of current that flows through the battery. Algorithms may function by measuring the current through the battery and estimating a battery age parameter based on the accumulated current flow. Some battery SOH algorithms may attempt to measure or estimate the battery internal impedance using voltage and current measurements. However, it is possible to predict battery SOH based on drive cycle information if a relationship between driving cycles and battery SOH can be identified.

A driving cycle may be characterized by a number of quantities relating to velocity and acceleration of the vehicle. The drive cycle may be characterized by statistical parameters that describe vehicle motion during the drive cycle. The characterization may include one or more of a standard deviation of velocity, a mean positive acceleration, a minimum acceleration, a percentage of driving time under positive acceleration, a percentage of driving time under negative acceleration, a mean positive velocity, a percentage of idle time, and a number of stops per mile. The quantities may analyzed for various drive cycles to determine the impact of the variable on battery SOH. After some analysis, the mean positive velocity and the standard deviation of acceleration are the most relevant to battery SOH. Intuitively, this may be understood as higher velocity and faster accelerations may result in higher battery usage. For example, the traction battery may be subject to larger currents at higher speeds and accelerations.

The mean positive velocity and the standard deviation of acceleration may be utilized to estimate statistical variables related to the battery current. A standard deviation of the battery current and a mean absolute battery current may be derived from the drive cycle properties. The battery statistical variables may then be used to predict a change in battery SOH parameters at an arbitrary time in the future. For example, a change in battery capacity and/or internal impedance may be computed from a statistical analysis based on the battery current parameters.

FIG. 3is a plot300depicting standard deviation of acceleration and mean positive velocity for a variety of drive cycles. Each point on the plot may represent a particular drive cycle. For example, point A302may represent a mild drive cycle. Note that point A has relatively low mean positive velocity and standard deviation of acceleration. Point B304may represent an aggressive drive cycle. As the points move in the direction indicated by the line306, more aggressive battery operation may be observed. More aggressive battery operation may lead to more rapid battery aging or changing of battery SOH parameters.

FIG. 4depicts a plot400that indicates possible distribution of battery current versus vehicle acceleration. For the mild drive cycle (denoted by A), the distribution may fall within a first area402. For the aggressive drive cycle (denoted by B), the distribution may fall within a second area404. Note that the second area404contains a broader range of battery current and acceleration values than the first area402. Faster accelerations/decelerations may be indicative of a more aggressive drive cycle. In addition, faster accelerations/decelerations may result in larger magnitude battery currents as the battery may be utilized to a greater extent to satisfy the faster accelerations/decelerations.

A regression analysis may be performed to find a regression equation that relates independent variables to a dependent variable. Data for a plurality of drive cycles may be sampled or measured during the drive cycle and statistical values may be derived from the data. Velocity may be measured during the drive cycle at periodic intervals. Acceleration may be measured during the drive cycle at periodic intervals. The acceleration value may be based on an output of an acceleration sensor or may be computed as a derivative of the velocity. In addition, battery current may be measured during each of the drive cycles. The measured values may be sampled at periodic intervals. At the end of each of the drive cycles, a series of velocity, acceleration, and battery currents values may be available. A mean or average of each variable may be computed as the summation of the values over all of the time intervals divided by the number of time intervals. The standard deviation may be computed as:

σ⁡(x)=1n⁢∑i=1n⁢(xi-μ)2(1)
where μ is the mean value, xiis the value of the variable from sample i, and n is the number of samples taken.

A regression model may be defined as:
y=β0+β1x1+ . . . +βpxp+ε  (2)
where p is the number of independent variables, βiis ithcoefficient of the regression equation, and i=0, 1, . . . , p. The regression analysis may consider N data points of a drive cycles. As such, a matrix corresponding to the x elements may be constructed as:

where N is the number of data points of a drive cycle.

The parameters of the regression model (or regression coefficients) may be computed as:
{circumflex over (β)}=(XTX)−1XTy(4)
where X is a N-by-(p+1) matrix and y is a N-by-1 system response matrix. The regression coefficients calculated from a specific y matrix are used to calculate the regressed response ŷ.

Once the regression parameters are determined, the regression analysis may be expressed as:
ŷ={circumflex over (β)}x(5)
where y may be the standard deviation of battery current y1and the mean absolute battery current y2. The elements of y may be defined as:
y1=σ(ibatt)  (6)
with β1 calculated from the y matrix consisting of y1responses, and
y2=mean(|ibatt|)  (7)
with β2 calculated from the y matrix consisting of y2responses, and the vector x may be defined as:
x=[1νposσ(a)νposσ(a)νpos2σ(a)2νposσ(a)2]  (8)
where νposis the mean positive velocity and σ(a) is the standard deviation of the acceleration. Each of the x vectors has p+1 components. The x vectors computed from each data points of a drive cycle may be combined to form the X matrix of equation (3).

The regression matrix, X, may be derived from a sampling of certain data during each of the drive cycles. For a given drive cycle, vehicle data such as vehicle speed and vehicle acceleration may be sampled. In addition, battery current may be sampled. From the vehicle speed, the mean positive velocity may be computed. From the vehicle acceleration, the standard deviation of the vehicle acceleration may be computed. The mean positive velocity and standard deviation of acceleration may be utilized to define the input vector as defined in equation (8). The mean and standard deviation of the battery current may also be computed. These values may be used in the regression analysis to determine the regression coefficients according to equation (4). Once the regression matrix is known, it may be utilized in different drive cycles to estimate the absolute mean battery current and the standard deviation of the battery current according to equations (6) and (7) respectively.

The controller206may be programmed with the regression matrix so that the battery current parameters may be estimated or predicted for any drive cycle. The battery current parameters may be estimated without any present knowledge of the control strategies, vehicle control strategies and configurations. The battery current parameters are estimated only from the drive cycle data. The regression matrix may be stored or programmed into the controller206.

The above analysis derives the battery current parameters from vehicle drive cycle data. However, with some additional computation, state of health (SOH) parameters of the battery may be computed. The procedures to be defined enable the prediction of battery SOH parameters directly from the drive cycle data and temperature within a predetermined uncertainty bounds.

The uncertainty bounds are dependent upon the supervisory control strategies of the hybrid powertrain. The uncertainty bounds may be expressed as:
σ(ibatt)=f1(x)+ε1(9)
mean(|ibatt|)=f2(x)+ε2(10)
where x is defined by equation (8), and the ε terms are the uncertainty bounds.

The battery SOH may be characterized as a change in battery capacity (ΔQbatt) and/or a change in battery internal impedance during charging (ΔRint,chg) and discharging (ΔRint,disch). The change quantities may be relative to values at the beginning of life (BOL) of the battery. The change values may be expressed relative to the BOL values as follows:
ΔQbatt=Qbatt,BOL−Qbatt(11)
ΔRint,chg=Rint,chg−Rint,cg,BOL(12)
ΔRint,disch=Rint,disch−Rint,disch,BOL(13)
The battery capacity of the battery may be expected to decrease over the life of the battery. The internal impedance values may be expected to increase over the life of the battery. The values of the SOH parameters at the present time may be derived from equations (11-13).

The change in capacity and internal impedances may be related to the battery current parameters, which are, but are not limited to, the standard deviation of battery current and the mean absolute battery current. The battery current parameters may provide a measure of how the battery is used over time. The change in capacity and internal impedances may be expressed as:
ΔQbatt=g1(σ(ibatt),mean(|ibatt|)|T,tdrv,tpark)+ε3(14)
ΔRint,chg=g2(σ(ibatt),mean(|ibatt|)|T,tdrv,tpark)+ε4(15)
ΔRint,disch=g3(σ(ibatt),mean(|ibatt|)|T,tdrv,tpark)+ε5(16)
where T is a temperature associated with the drive cycle, tdrvis the elapsed time of a drive cycle, tparkis an elapsed time the vehicle is resting. Each change has an associated uncertainty bound, ε.

The functions g1, g2, and g3may be derived from another regression analysis. For example, y1=G1(x) where y is ΔQbatt, x is [σ(ibatt) mean(|ibatt|)], and G represents the regression matrix. Various values for each drive cycle may be computed using a vector similar to equation (8). The regression matrix may be derived by collecting data over a number of drive cycles and generating a vector for each as described previously. For example, battery capacity and battery current may be measured while operating the battery at a variety of operating cycles. In some cases, the battery capacity may be computed from other battery parameters such as current and voltage. Variables that may be varied between the operating cycles may be a temperature profile of the battery, a driving duration, and a parking duration. After completion of the operating cycle, the battery current parameters may be computed from the measurements during the operating cycle. The change in battery capacity may also be determined from the measured data. Note that the regression matrix may be obtained based on simulated or actual drive cycle data.

Equations (14-16) are expressed in terms of the battery current parameters by substituting equation (5) into equations (14-16). The battery current parameters may be expressed as a function of the drive cycle parameters. As a result, the change in capacity and internal impedance may also be expressed as functions of the drive cycle parameters as described by equation (8) as follows:
ΔQbatt=H1(x|T,tdrv,tpark)+εQ(17)
ΔRint,chg=H2(x|T,tdrv,tpark)+εR,chg(18)
ΔRint,disch=H3(x|T,tdrv,tpark)+εR,disch(19)

The change in battery capacity and internal impedance values may be derived from equations (17-19) directly from the drive cycle parameters. The end result is that battery aging parameters can be derived from the measured vehicle velocity. The controller may store a plurality of regression functions or matrices that correspond to various combinations of temperature, drive time, and park time. For example, the regression matrix may be selected based on a temperature associated with the recently completed drive cycle.

The resulting equations may be implemented in the controller206. The measured vehicle velocity may be sampled and stored over a predetermined time interval. The predetermined time interval may be based on a predetermined time period such as one day. After the predetermined time interval, the collected vehicle velocity samples may be processed to compute the acceleration. Further, statistical parameters of the velocity (e.g., mean positive velocity) and the acceleration (e.g., standard deviation of acceleration) may be computed. Once these values are known, the vector defined by equation (8) may be computed. In addition to monitoring the vehicle velocity, a temperature, a drive time, and a park time may be measured or received by the controller. For example, temperature may be periodically sampled over the predetermined time interval. The drive time and park time may be determined by monitoring an amount of time that the vehicle is in a drive mode and in a park mode. Park time may include time periods in which the vehicle is in an ignition-off condition. The park time may be used to filter out associated velocity values from the average and standard deviation.

The controller206may then select the appropriate function based on the temperature, the drive time, and the park time. The extracted information from the measured vehicle velocity (e.g., from equation (8)) may be input to the function to determine the change in capacity or internal impedance over the predetermined time interval. The estimated value of the state of health parameter is based on statistical parameters describing vehicle motion during a drive cycle. The statistical parameters include mean positive velocity and standard deviation of vehicle acceleration. The estimated value is further based on parameters that describe a relationship between vehicle motion during past drive cycles and resulting current flow through the battery. The estimated value may be further based on parameters describing a relationship between current flow through the battery during past drive cycles and resulting changes to the state of health parameter.

FIG. 6depicts a flowchart describing a possible sequence of operations for the described system and method. At operation600, the regression structure is formulated. Formulating the regression structure may be performed offline based on analysis of previous drive cycle data.FIG. 5depicts a flowchart describing the general procedure for formulating the regression structure. At operation502, battery life simulations may be performed and the results collected. Such simulations may be simulated by a model and/or may be derived from actual vehicle operating data. At operation504, the regression structure may be selected. For example, the vector of quantities for the matrix, X, may be formulated resulting in a vector such as equation (8). At operation506, a regression analysis may be performed as described previously. For example, collected data for the drive cycle may be processed to compute the regression matrix, and regression coefficients are calculated using the regression matrix and the system response matrix. At operation508, an assessment of the regression analysis may be performed. For example, the regression matrix and vector may be used to derive parameter values from additional drive cycles (or even previously used ones). For example, an R2value may be computed to assess the relative quality of the regression. Normal probability distribution plots may be generated and analyzed. At operation510, a check may be performed to determine if the predictions generated by the regression structure is acceptable. For example, an R2value being in a particular range may indicate a satisfactory prediction. If the prediction is unacceptable, operation512may executed to modify the regression structure and repeat the process from operation506. If the prediction result is acceptable, operation514may be executed. At operation514, the final regression structure may be determined. At operation516, confidence intervals of the SOH parameters may be computed. The regression may be configured to ensure that the a true value of the SOH parameters are within a predetermined confidence interval (e.g., 95% confidence interval).

The result of operation600may be a regression matrix or function as described above. The regression matrix may define parameters that describe the relationship between vehicle motion during past drive cycles and the resulting current flow through the traction battery. The regression matrix may further define parameters that describe the relationship between vehicle motion during past drive cycles and the resulting changes to the state of health parameters.

The regression matrix or function may be stored or programmed into the controller206and is represented by a regression matrix data store602. At operation604, vehicle velocity data is collected and stored in a velocity data store606during vehicle operation. The velocity data store606may be retained in non-volatile memory such that the data is available in subsequent ignition cycles.

At operation608, the time interval defining the drive cycle may be monitored. For example, the time interval may be defined as a duration of time from initiation of an ignition cycle to initiation of a subsequent ignition cycle of the vehicle. The time interval may be based on a predetermined distance traveled by the vehicle as determined by a monitoring received odometer values over time. The time interval may be defined as a predetermined period of time. For example, the predetermined period of time may be one hour, one day or one week. Time and distance data may be monitored periodically to determine an elapsed time or distance. At operation610, a check may be performed to determine if the time or distance interval has been achieved. If the interval is not achieved, execution may be repeated from operation604.

If the interval is achieved, operation612may be executed. At operation612, the statistical parameters may be computed using the stored velocity data606as input. The statistical parameters include the mean positive velocity and standard deviation of acceleration. At operation614, the battery SOH parameter may be computed based on the regression matrix602and the statistical values. The controller206may further receive temperature data associated with the drive cycle. The estimated value may further be changed based on the temperature data. At operation616, the traction battery may be operated according the estimated SOH values. The process may be repeated over a lifetime of the vehicle and traction battery.

The system and method described may be beneficial in that battery SOH parameters are estimated from easily obtainable drive cycle data. Further, the results are based on statistical results and may be configured to estimate the values with a predetermined amount of accuracy. Traction battery operating limits may be set according to the estimated battery SOH parameters. For example, battery capacity may be used to determine changes in SOC based on a current integration. Using an accurate battery capacity may ensure the battery SOC is accurate. In addition, the battery capacity may be used to set SOC operating windows to ensure that adequate battery power is available over the vehicle lifetime. In addition, an indicator of battery age that is based on the SOH parameters may be output and displayed to the operator.