System and method for estimating the state of charge of a vehicle lithium battery

Abstract

The present disclosure provides a method and system for estimating the state of charge of a vehicle lithium battery. The method includes providing a nonlinear function of the lithium battery, a normalized lithium surface density, and an actual voltage under a current of a lithium battery with internal resistance. The method further includes determining the actual lithium surface density based on the actual voltage associated with the inverse function of the nonlinear function. The method further includes determining a variation parameter based on the actual lithium surface density relative to a first diffusion model. The method further includes determining a predicted lithium surface density based on a variation parameter relative to a second diffusion model. The method further includes determining an estimated state of charge of the lithium battery when the difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold.

Description

Technical Field

[0001] The present disclosure relates to lithium batteries, and more particularly, to systems and methods for estimating the state of charge of a vehicle lithium battery. Background Art

[0002] In the automotive industry, estimating the state of charge of a vehicle's lithium-ion battery has long been a challenging problem. For example, some state-of-charge estimates are not as reliable as desired. In addition, existing systems and methods require considerable calibration effort. Summary of the invention

[0003] Therefore, while current state of charge estimates achieve their intended purpose, a need exists for new and improved systems and methods for estimating the state of charge of a vehicle lithium battery.

[0004] One aspect of the present disclosure includes a method for estimating the state of charge of a lithium battery of a vehicle. The method includes providing a nonlinear function of the lithium battery, a normalized lithium surface density, and an actual voltage under current of the lithium battery with internal resistance. The nonlinear function can be expressed as:

[0005]

[0006] Where V is the actual voltage, is the normalized lithium surface density, I is the current, Ro hmic is the internal resistance of the battery. In this example, the method further includes determining the actual lithium surface density based on the actual voltage associated with the inverse function of the nonlinear function, the inverse function of the nonlinear function being expressed as:

[0007]

[0008] In this aspect, the method further comprises determining a variation parameter based on the actual lithium surface density relative to a first diffusion model. The first diffusion model may be expressed as:

[0009]

[0010] Where b0 is a parameter that varies with temperature and aging, k is a discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at the k time index, and n is the sum variable.

[0011] In this aspect of the present disclosure, the method further includes determining a predicted lithium surface density and an estimated state of charge based on a variation parameter relative to a second diffusion model. The second diffusion model may be expressed as:

[0012] x(k+1)=x(k)-I(k) and

[0013]

[0014] in, is the normalized lithium surface density, and SoC + is the estimated state of charge.

[0015] In this aspect, the method further includes determining an estimated state of charge of the lithium battery when a difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold.

[0016] In another example of this aspect, the step of determining the estimated state of charge of the lithium battery includes comparing the predicted lithium surface density with the actual lithium surface density in real time. In addition, the step of determining the estimated state of charge of the lithium battery also includes correcting the predicted lithium surface density in real time if the difference between the predicted lithium surface density and the actual lithium surface density is greater than a second threshold.

[0017] In another example, determining the actual lithium surface density includes applying the actual voltage to an inverse function of the non-linear function.

[0018] In another example, the step of determining the variation parameter includes applying the actual lithium surface density to a first diffusion model of the lithium battery.

[0019] In another example of this aspect, the steps of determining the predicted lithium surface density and estimating the state of charge include applying the variation parameters to a second diffusion model of the lithium battery.

[0020] In another example of this aspect, the method further includes transmitting a signal of the estimated state of charge to a receiver having a display in the vehicle, and displaying the estimated state of charge based on the signal.

[0021] Another aspect of the present disclosure provides a method for estimating the state of charge of a lithium battery of a vehicle. The method includes providing a nonlinear function of the lithium battery, a normalized lithium surface density, and an actual voltage under current of the lithium battery with internal resistance. The nonlinear function can be expressed as:

[0022]

[0023] Where V is the actual voltage, is the normalized lithium surface density, I is the current, R ohmic is the internal resistance of the battery. In this example, the method further includes determining the actual lithium surface density based on the actual voltage associated with the inverse function of the nonlinear function, the inverse function of the nonlinear function being expressed as:

[0024]

[0025] In addition, the method further includes determining a variation parameter based on an actual lithium surface density relative to a first diffusion model, the first diffusion model can be expressed as:

[0026]

[0027] Where b0 is a parameter that varies with temperature and aging, k is a discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at the k time index, and n is the sum variable.

[0028] In this aspect, the method further includes determining a predicted lithium surface density and an estimated state of charge based on the variation parameters relative to a second diffusion model, the second diffusion model may be expressed as:

[0029] x(k+1)=x(k)-I(k) and

[0030]

[0031] in, is the normalized lithium surface density, and SoC+ is the estimated state of charge.

[0032] In addition, the method further includes determining an estimated state of charge of the lithium battery when the difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold. In addition, the method includes displaying the estimated state of charge of the lithium battery on the vehicle to monitor the state of charge of the battery.

[0033] In one example of this aspect, the step of determining the estimated state of charge of the lithium battery includes comparing the predicted lithium surface density with the actual lithium surface density in real time.

[0034] In another example, the step of determining the estimated state of charge of the lithium battery further includes correcting the predicted lithium surface density in real time if a difference between the predicted lithium surface density and the actual lithium surface density is greater than a second threshold.

[0035] In another example, determining the actual lithium surface density includes applying the actual voltage to an inverse function of the non-linear function.

[0036] In another example of this aspect, the step of determining the variation parameter includes applying the actual lithium surface density to a first diffusion model of the lithium battery.

[0037] In another example, the steps of determining the predicted lithium surface density and estimating the state of charge include applying the variation parameters to a second diffusion model of the lithium battery.

[0038] In another example, the step of displaying includes transmitting a signal of the estimated state of charge to a receiver having a display on the vehicle and displaying the estimated state of charge based on the signal.

[0039] In another aspect of the present disclosure, a system for estimating the state of charge of a lithium battery of a vehicle is provided. The system includes a display disposed on the vehicle for indicating the estimated state of charge of the lithium battery. The system further includes a control unit disposed on the vehicle. In this aspect, the control unit is connected to the battery and the display. The electronic control unit is programmed to include or store a nonlinear function of the lithium battery, a normalized lithium surface density, and an actual voltage under current of the lithium battery with internal resistance. The nonlinear function can be expressed as:

[0040]

[0041] Where V is the actual voltage, is the normalized lithium surface density, I is the current, Ro hmic is the internal resistance of the battery. The control unit is programmed to determine the actual lithium surface density based on the actual voltage related to the inverse function of the nonlinear function expressed as:

[0042]

[0043] In the present example, the control unit is programmed to determine the variation parameter based on the actual lithium surface density relative to a first diffusion model represented by:

[0044]

[0045] Where b0 is a parameter that varies with temperature and aging, k is a discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at the k time index, and n is the sum variable.

[0046] Furthermore, in this example, the control unit is programmed to determine the predicted lithium surface density and the estimated state of charge based on the variation parameters relative to the second diffusion model. The second diffusion model may be expressed as:

[0047] x(k+1)=x(k)-I(k) and

[0048]

[0049] in, is the normalized lithium surface density, and SoC+ is the estimated state of charge. In this example of the present aspect, the control unit is programmed to determine the estimated state of charge of the lithium battery when the difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold.

[0050] Furthermore, the control unit is programmed to transmit a signal of an estimated state of charge of the lithium battery to a display in the vehicle. Furthermore, the display is configured to indicate the estimated state of charge based on the signal.

[0051] In one example of this aspect, the step of determining the estimated state of charge of the lithium battery includes comparing the predicted lithium surface density with the actual lithium surface density in real time.

[0052] In another example, the step of determining the estimated state of charge of the lithium battery includes correcting the predicted lithium surface density in real time if a difference between the predicted lithium surface density and the actual lithium surface density is greater than a second threshold.

[0053] In another example, determining the actual lithium surface density includes applying the actual voltage to an inverse function of the non-linear function.

[0054] In yet another example of this aspect, the step of determining the variation parameter includes applying the actual lithium surface density to a first diffusion model of the lithium battery.

[0055] In another example, the steps of determining the predicted lithium surface density and estimating the state of charge include applying the variation parameters to a second diffusion model of the lithium battery.

[0056] Further areas of applicability will become apparent from the description provided herein.It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.

[0058] Figure 1 is a schematic diagram of a system for estimating the state of charge of a lithium battery of a vehicle according to an example of the present disclosure;

[0059] Figure 2A is based on Figure 1 A nonlinear function of a lithium battery plotting the voltage versus normalized lithium surface density;

[0060] Figure 2B is based on Figure 1 Another relationship diagram of the actual lithium surface density and voltage drawn by the inverse function of the nonlinear function of the lithium battery;

[0061] Figure 3 It is a method for estimating Figure 1 A flowchart of a method for determining a charging state of a system;

[0062] Figure 4 Another method for estimating Figure 1 A flow chart of a method for illustrating a charging status of a system in FIG. DETAILED DESCRIPTION

[0063] The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application, or uses.

[0064] According to an example of the present disclosure, Figure 1 A system 10 for estimating the state of charge of a lithium battery 12 of a vehicle 14 is depicted. As shown, the system 10 includes a display 16 disposed on the vehicle 14 for indicating the estimated state of charge of the lithium battery 12. The system 10 further includes a control unit 20 disposed on the vehicle 14. As shown, the control unit 20 is connected to the battery 12 and the display 16.

[0065] Typically, the control unit 20 may include an observer 22 that may be programmed to estimate the state of charge of the vehicle's lithium battery. This estimation is based on the relationship between the battery current, the battery state of charge, and the lithium surface density. This relationship can be described by a linear model and a nonlinear function, as will be discussed in detail below.

[0066] In addition, the control unit 20 or the observer 22 of the control unit 20 estimates the state of charge by comparing the actual lithium surface density with the predicted lithium surface density through the given parameters, linear models and nonlinear functions discussed below. The control unit 20 or the observer 22 corrects the error between the actual lithium surface density and the predicted lithium surface density in real time to a first predetermined percentage error or a first threshold value that may be greater than or equal to zero. When the actual lithium surface density and the predicted lithium surface density are both within a second threshold value or a second predetermined percentage error, the state of charge is estimated. It should be understood that the control unit 20 or the observer 22 can be programmed as described herein without departing from the spirit or scope of the present disclosure.

[0067] More specifically, the control unit 20 is programmed to include or store the nonlinear function of the lithium battery 12, the normalized lithium surface density, and the actual voltage under the current of the lithium battery 12 with internal resistance. In this example, the nonlinear function can be expressed as:

[0068]

[0069] Where V is the actual voltage, is the normalized lithium surface density, I is the current, R ohmic is the internal resistance of the battery. Figure 2A It is a graph describing the relationship between voltage and normalized lithium surface density based on a nonlinear function.

[0070] In the present example, the control unit 20 is programmed to determine the actual lithium surface density based on the actual voltage associated with the inverse function of the non-linear function expressed as:

[0071]

[0072] The control unit determines the actual lithium surface density by applying the actual voltage to the inverse of the non-linear function. Figure 2B It is another relationship diagram describing the actual lithium surface density and voltage based on the inverse function of the nonlinear function.

[0073] Furthermore, the control unit 20 is programmed to determine the variation parameter based on the actual lithium surface density relative to the first diffusion model. The first diffusion model can be expressed as:

[0074]

[0075] Where b0 is a parameter that varies with temperature and aging, k is a discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at the k time index, and n is the sum variable.

[0076] In one example, the control unit determines the variation parameter b0 by applying the actual lithium surface density to the first diffusion model of the lithium battery 12. In this example, the variation parameter b0 is tracked using the first diffusion model using a recursive least squares (RLS) approximation method. In addition, the variation parameter b0 is updated in real time before the next execution to improve the estimation accuracy.

[0077] In addition, the control unit 20 is programmed to determine the predicted lithium surface density based on the change parameters of the second diffusion model relative to the lithium battery 12. The second diffusion model is a dynamic diffusion model that includes the relationship between the current, the state of charge, and the lithium surface density of the battery 12. The second diffusion model can be expressed as:

[0078] x(k+1)=x(k)-I(k) and

[0079]

[0080] Where k is the discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at time index k, is the normalized lithium surface density, and SoC+ is the estimated state of charge, and x is the state of the system. For example, the control unit 20 may determine the predicted lithium surface density by applying the variation parameter b0 to the second diffusion model of the lithium battery 12 .

[0081] In this example, the control unit 20 is programmed to determine the estimated state of charge of the lithium battery 12 when the difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold value (the first threshold value may be greater than or equal to 0). In one example, the control unit 20 determines the estimated state of charge of the lithium battery 12 by comparing the predicted lithium surface density with the actual lithium surface density in real time. Then, if the difference between the predicted lithium surface density and the actual lithium surface density is equal to or greater than a second threshold value, the control unit 20 corrects the predicted lithium surface density in real time.

[0082] In addition, the control unit 20 is programmed to transmit a signal of the estimated state of charge of the lithium battery 12 to a display 16 in the vehicle 14. In this example, the display 16 may include a receiver (not shown) for receiving the signal from the control unit 20. In addition, the display 16 is configured to indicate the estimated state of charge based on the signal. It should be understood that the display 16 may indicate the estimated state of charge in any suitable manner (e.g., an LED (light emitting diode) display) without departing from the spirit or scope of the present disclosure.

[0083] By way of an example of the present disclosure, Figure 3 Describes a basis Figure 1 A method 110 for estimating the state of charge of a lithium battery 12 of a vehicle 14 of a system 10 in FIG. As shown, the method 110 includes providing a nonlinear function of the lithium battery 12, a normalized lithium surface density, and an actual voltage under a current of the lithium battery 12 with an internal resistance in box 111. The nonlinear function can be expressed as:

[0084]

[0085] Where V is the actual voltage, is the normalized lithium surface density, I is the current, R ohmic is the internal resistance of the battery. As mentioned above, Figure 2A The relationship between voltage and normalized lithium surface density is described in terms of a nonlinear function.

[0086] In this example, the method 110 further includes determining, at block 112, an actual lithium surface density based on an actual voltage associated with an inverse function of a nonlinear function represented by:

[0087]

[0088] For example, the step of determining the actual lithium surface density includes applying the actual voltage to the inverse of the non-linear function. Figure 2B The relationship between the actual lithium surface density and voltage is described based on the inverse function of the nonlinear function.

[0089] In this aspect, the method 110 further includes determining a variation parameter based on the actual lithium surface density relative to the first diffusion model in block 114. The first diffusion model may be expressed as:

[0090]

[0091] Where b0 is a parameter that varies with temperature and aging, k is a discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at the k time index, and n is the sum variable.

[0092] For example, the step of determining the variation parameter may include applying the actual lithium surface density to the first diffusion model of the lithium battery 12. In this example, the variation parameter b0 is tracked using the first diffusion model using a recursive least squares (RLS) approximation method. In addition, the variation parameter b0 is updated in real time before the next execution to improve the estimation accuracy.

[0093] In this aspect of the disclosure, the method 110 further includes determining a predicted lithium surface density based on a change parameter relative to a second diffusion model in block 116. The second diffusion model is a dynamic diffusion model that includes a relationship between the current, the state of charge, and the lithium surface density of the battery 12. The second diffusion model can be expressed as:

[0094] and

[0095]

[0096] Where k is the discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at time index k, is the normalized lithium surface density, and SoC + is the estimated state of charge, and x is the state of the system. In the present example, the step of determining the predicted lithium surface density includes applying a variation parameter to a second diffusion model of the lithium battery 12 .

[0097] In this aspect, the method 110 further includes determining the estimated state of charge of the lithium battery 12 in box 120 when the difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold value (the first threshold value may be greater than or equal to 0). In one example, the step of determining the estimated state of charge of the lithium battery 12 includes comparing the predicted lithium surface density with the actual lithium surface density in real time. In addition, the step of determining the estimated state of charge of the lithium battery 12 also includes correcting the predicted lithium surface density in real time if the difference between the predicted lithium surface density and the actual lithium surface density is equal to or greater than a second threshold value.

[0098] In this example, the method 110 may further include transmitting a signal of the estimated state of charge of the lithium battery 12 to the display 16. In this example, the display 16 may include a receiver (not shown) for receiving the signal from the control unit 20. The method 110 also includes displaying the estimated state of charge based on the signal. It should be understood that the display may indicate the estimated state of charge in any suitable manner (e.g., an LED (light emitting diode) display) without departing from the spirit or scope of the present disclosure.

[0099] By way of an example of the present disclosure, Figure 4 Describe a basis Figure 1 A method 210 for estimating the state of charge of a lithium battery 12 of a vehicle 14 in a system. As shown, the method 210 includes providing a nonlinear function of the lithium battery 12, a normalized lithium surface density, and an actual voltage under a current of the lithium battery 12 with an internal resistance in box 211. The nonlinear function can be expressed as:

[0100]

[0101] Where V is the actual voltage, is the normalized lithium surface density, I is the current, R ohmic is the internal resistance of the battery. As mentioned above, Figure 2A The relationship between voltage and normalized lithium surface density is plotted according to a nonlinear function.

[0102] In this example, the method 210 further includes determining, at block 212, an actual lithium surface density based on an actual voltage associated with an inverse function of a nonlinear function represented by:

[0103]

[0104] For example, the step of determining the actual lithium surface density includes applying the actual voltage to the inverse of the non-linear function. Figure 2B The relationship between the actual lithium surface density and voltage is depicted according to the inverse function of the nonlinear function.

[0105] In this aspect, the method 210 further includes determining a variation parameter based on the actual lithium surface density relative to the first diffusion model in block 214. The first diffusion model may be expressed as:

[0106]

[0107] Where b0 is a parameter that varies with temperature and aging, k is a discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at the k time index, and n is the sum variable.

[0108] For example, the step of determining the variation parameter includes applying the actual lithium surface density to the first diffusion model of the lithium battery 12. In this example, the variation parameter b0 is tracked using the first diffusion model using a recursive least squares (RLS) approximation method. In addition, the variation parameter b0 is updated in real time before the next execution to improve the estimation accuracy.

[0109] In this aspect of the disclosure, the method 210 further includes determining a predicted lithium surface density based on a change parameter relative to a second diffusion model in block 216. The second diffusion model is a dynamic diffusion model that includes a relationship between the current, the state of charge, and the lithium surface density of the battery 12. The second diffusion model may be expressed as:

[0110] and

[0111]

[0112] Where k is the discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at time index k, is the normalized lithium surface density, and SoC + is the estimated state of charge, and x is the state of the system. In the present example, the step of determining the predicted lithium surface density includes applying a variation parameter to a second diffusion model of the lithium battery 12 .

[0113] In this aspect, the method 210 further includes determining the estimated state of charge of the lithium battery 12 in box 220 when the difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold value (e.g., 0.5%). In one example, the step of determining the estimated state of charge of the lithium battery 12 includes comparing the predicted lithium surface density with the actual lithium surface density in real time. In addition, the step of determining the estimated state of charge of the lithium battery 12 also includes correcting the predicted lithium surface density in real time if the difference between the predicted lithium surface density and the actual lithium surface density is equal to or greater than a second threshold value.

[0114] In this example, the method 210 may further include transmitting a signal of the estimated state of charge of the lithium battery 12 to the display 16. In this example, the display 16 may include a receiver (not shown) for receiving the signal from the control unit 20. In addition, the method 210 also includes displaying the estimated state of charge based on the signal in box 222. It should be understood that the display 16 can indicate the estimated state of charge in any suitable manner (e.g., an LED (light emitting diode) display) without departing from the spirit or scope of the present disclosure.

[0115] The description of the present disclosure is merely exemplary in nature, and changes that do not depart from the gist of the present disclosure are intended to fall within the scope of the present disclosure. Such changes are not considered to be contrary to the spirit and scope of the present disclosure.

Claims

1. A method for estimating the state of charge of a lithium battery of a vehicle, the method comprising: A nonlinear function of a lithium battery is provided, normalizing the lithium surface density and the actual voltage under the current of a lithium battery with internal resistance, the nonlinear function being expressed as: Where V is the actual voltage, is the normalized lithium surface density, I is the current, R ohmic is the internal resistance of the battery; The actual lithium surface density is determined based on the actual voltage associated with the inverse function of the nonlinear function, which is expressed as: The variation parameter is determined based on the actual lithium surface density relative to a first diffusion model, which is expressed as: Where b0 is a parameter that varies with temperature and aging, k is a discrete time index, h is a known constant, Δt is the time difference between discrete time indexes, I(k) is the measured current at the k time index, and n is the sum variable; The predicted lithium surface density and estimated state of charge are determined based on the changed parameters relative to a second diffusion model, the second diffusion model being represented by: x(k+1)=x(k)-I(k) and as well as When the difference between the predicted lithium surface density and the actual lithium surface density is less than a first threshold, an estimated state of charge of the lithium battery is determined.

2. The method according to claim 1, wherein: The step of determining the estimated state of charge of the lithium battery includes comparing the predicted lithium surface density with an actual lithium surface density in real time.

3. The method according to claim 2, wherein: The step of determining the estimated state of charge of the lithium battery includes correcting the predicted lithium surface density in real time if a difference between the predicted lithium surface density and the actual lithium surface density is greater than a second threshold.

4. The method according to claim 1, wherein: The step of determining the actual lithium surface density includes applying the actual voltage to an inverse function of the non-linear function.

5. The method according to claim 1, wherein: The step of determining the variation parameter includes applying the actual lithium surface density to the first diffusion model of the lithium battery.

6. The method according to claim 1, wherein: The step of determining the predicted lithium surface density includes applying the variation parameters to the second diffusion model of the lithium battery.

7. The method according to claim 1, further comprising: transmitting the estimated state of charge signal to a receiver having a display on the vehicle; as well as The estimated state of charge is displayed based on the signal to be monitored.

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