Method for prediction of the internal resistance of an energy storage battery, and a monitoring device for energy storage batteries

• US 7,098,665 B2
• Filed: 11/10/2003
• Issued: 08/29/2006
• Est. Priority Date: 11/13/2002
• Status: Expired due to Fees

• Current Assignee: VB Autobatterie GmbH
• Original Assignee: VB Autobatterie GmbH
• Inventors: Laig-Hoerstebrock, Helmut
• Primary Examiner(s): Tibbits, Pia
• Application Number:
  US10/705,033
• Publication Number:
  US 20040150406A1
• Time in Patent Office: 1,023 Days
• Field of Search: 324/430, 324/426, 320/132, 702/63
• US Class Current: 324/430
• CPC Class Codes: G01R 31/379 for lead-acid batteries
• CPC Class Codes: G01R 31/389 Measuring internal impedance, internal conductance or related variables

Abstract

A method for predicting the internal resistance of an energy storage battery in assumed environmental and battery state conditions includes subdividing the internal resistance into a first resistance component which represents the electrical resistance of the energy storage battery for the region of electron conduction and a second resistance component which represents the electrical resistance of the energy storage battery for the region of ion conduction. The method also includes determining the first and second resistance components to be expected for the assumed environmental and battery state conditions. Each of the first and second resistance components are determined as a function of parameters of the assumed environmental and battery state conditions. Monitoring devices or computer programs may be utilized to carry out the method.

Claims

1. A method for predicting the internal resistance of an energy storage battery in assumed environmental and battery state conditions, the method comprising:

subdividing the internal resistance into a first resistance component which represents the electrical resistance of the energy storage battery for the region of electron conduction and a second resistance component which represents the electrical resistance of the energy storage battery for the region of ion conduction;

determining a first factor for the first resistance component and a second factor for the second resistance component as a function of the type of energy storage battery, wherein the first and second factors are determined by determining parameters and internal resistances for at least two conditions which differ from one another, wherein the at least two conditions are selected from environmental and battery state conditions; and

determining the first and second resistance components to be expected for the assumed environmental and battery state conditions, wherein the first resistance component is determined from the first factor and from a battery-independent first function of parameters relating to the environmental conditions for the energy storage battery and the second resistance component is determined from the second factor and from a battery-independent second function relating to the environmental and battery state conditions for the energy storage battery.

2. The method of claim 1 further comprising:

utilizing a variable which is dependent on battery temperature as a parameter for determining the first resistance component; and

utilizing a variable which is dependent on battery temperature and on the state of charge of the energy storage battery as a parameter for determining the second resistance component.

3. The method of claim 2 further comprising:

determining at least one of the battery temperature and the environmental temperature as a temperature parameter;

determining the no-load voltage of the energy storage battery as a state of charge parameter; and

determining the internal resistance of the energy storage battery.

4. The method of claim 1 further comprising:

a) estimating the first factor;

b) determining the second factor as a function of the estimated first factor and of parameters which have been determined for first environmental and battery state conditions;

c) determining a corrected first factor for second environmental conditions and battery state conditions as a function of the previously determined second factor and of the parameters determined for the second environmental and battery state conditions; and

d) determining the second factor as a function of the corrected first factor and of the parameters determined for at least one of the first environmental conditions, the second environmental conditions, and the battery state conditions.

5. The method of claim 4 further comprising repeating steps c) and d).

6. The method of claim 1 wherein determining the first resistance component utilizes the formula

R_Met=RFact_Met(T₀)·

r(T)

where R_Met is the first resistance component, RFact_Met(T₀) is the first factor for a reference temperature T₀ and r(T) is a first function which is dependent on battery temperature T.

7. The method of claim 6 wherein the first function is defined by the equation

r(T)=l+k·

(T−

T₀)+1(T−

T₀)²

where k and l are constants.

8. The method of claim 7 wherein determining the second resistance component utilizes the formula

$R_{\mathrm{Elyt}}=\frac{1}{\mathrm{Lfact}·}$

f ⁡

( T , SOC )

where R_Elyt is the second resistance component, LFact is the second factor and f(T, SOC) is a second function which is dependent on the battery temperature T and on the state of charge SOC for the energy storage battery.

9. The method of claim 8 wherein the second function is defined by the equation

f(T,SOC)=(a+b·

SOC+c·

SOC²)·

(l +d(T−

T₀)+e (T−

T₀)²)

where a, b, c, d and e are constants and T₀ is a reference temperature.

10. The method of claim 1 further comprising:

determining an instantaneous first factor for instantaneous environmental and battery state conditions; and

calculating a corrected first factor from the instantaneous first factor and from at least one weighted previously determined first factor.

11. The method of claim 10 further comprising:

determining an instantaneous second factor for instantaneous environmental and battery state conditions; and

calculating a corrected second factor from the instantaneous second factor and from a weighted previously determined second factor.

12. The method of claim 11 further comprising:

calculating the first factor for the reference temperature utilizing the formula

${\mathrm{Rfact}}_{\mathrm{Met}}<br><br>\left(T_0\right)=\frac{R_1·<br><br>f<br><br>\left(T_1,U_{001}\right)-R_2·<br><br>f<br><br>\left(T_2,U_{002}\right)}{r<br><br>\left(T_1\right)·<br><br>f<br><br>\left(T_1,U_{001}\right)-r<br><br>\left(T_2\right)·<br><br>f<br><br>\left(T_2,U_{002}\right)}$

where Rfact_Met(T₀) is the first factor, T₀ is the reference temperature, R₁ is the internal resistance, T₁ is one of the battery temperature and the environmental temperature, SOC₁ is the state of charge parameter at the time of a first measurement for the first environmental and battery state conditions and where R₂ is the internal resistance, T₂ is the battery or environmental temperature, and SOC₂ is the state of charge parameter at the time of a second measurement for the second environmental and battery state conditions.

13. The method of claim 12 further comprising:

calculating the second factor using the formula

Lfact=l/([R₁-Rfact_Met(T₀)]·

f(T_i, SOC_i)

where Lfact is the second factor, R_i is the internal resistance, T_i is one of the battery temperature and the environmental temperature, and SOC₁ is the state of charge parameter at the time of a measurement i.

14. The method of claim 1 further comprising:

subdividing the internal resistance into a third resistance component for the region of the positive active mass of the energy storage battery and into a fourth resistance component for the region of the negative active mass of the energy storage battery; and

determining the third and fourth resistance components in using an associated factor and a function of parameters for environmental and battery state conditions.

15. The method of claim 1 further comprising predicting the state of the energy storage battery as a function of predicted internal resistance.

16. The method of claim 15 further comprising predicting at least one of the wear, the performance, and the functionality of the energy storage battery.

17. A monitoring device for an energy storage battery comprising:

a measurement device for measuring parameters for the environmental and operating state conditions of the energy storage battery; and

a computation device configured to carry out a method comprising;

subdividing the internal resistance of the energy storage battery into a first resistance component which represents the electrical resistance of the energy storage battery for the region of electron conduction and a second resistance component which represents the electrical resistance of the energy storage battery for the region of ion conduction;

determining a first factor for the first resistance component and a second factor for the second resistance component as a function of the type of energy storage battery, wherein the first and second factors are determined by determining parameters and internal resistances for at least two conditions which differ from one another, wherein the at least two conditions are selected from environmental and battery state conditions; and

determining the first and second resistance components to be expected for the assumed environmental and battery state conditions, wherein the first resistance component is determined from the first factor and from a battery-independent first function of parameters relating to the environmental conditions for the energy storage battery and the second resistance component is determined from the second factor and from a battery-independent second function relating to the environmental and battery state conditions for the energy storage battery.

18. The monitoring device of 17 wherein the measurement means are provided for measurement of at least one of the battery temperature and the environmental temperature, for determination of the internal resistance of the energy storage battery, and for measurement of the no-load voltage of the energy storage battery.

19. A computer program comprising:

computer program code that when run using a processor device is configured to carry out a method comprising;

subdividing the internal resistance of the energy storage battery into a first resistance component which represents the electrical resistance of the energy storage battery for the region of electron conduction and a second resistance component which represents the electrical resistance of the energy storage battery for the region of ion conduction;

determining a first factor for the first resistance component and a second factor for the second resistance component as a function of the type of energy storage battery, wherein the first and second factors are determined by determining parameters and internal resistances for at least two conditions which differ from one another, wherein the at least two conditions are selected from environmental and battery state conditions; and

determining the first and second resistance components to be expected for the assumed environmental and battery state conditions, wherein the first resistance component is determined from the first factor and from a battery-independent first function of parameters relating to the environmental conditions for the energy storage battery and the second resistance component is determined from the second factor and from a battery-independent second function relating to the environmental and battery state conditions for the energy storage battery.

20. The computer program of claim 19 wherein the computer program code comprises a program file which is stored on a data storage medium.

21. The computer program of claim 19 wherein the computer program code comprises a program data stream that is transmitted in a data network.