IP Library › Granted Patent US 9,381,823
Granted Patent B2
US 9,381,823 · App. 14/334,346 · Granted Jul 5, 2016

Real-time battery estimation

Inventor: Tae-Kyung Lee (Ann Arbor, MI)
Assignee: Ford Global Technologies, LLC
B60L11/1861G01R31/3648G01R31/3662H01M10/4257H01M2010/4271H01M2220/20
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Quick Facts
Patent No.
US 9,381,823
App. No.
14/334,346
Granted
Jul 5, 2016
Kind
B2
Abstract

Vehicle systems and methods can include a traction battery and a controller to implement a state estimator configured to output battery state based on internal resistance of the traction battery and a system dynamics estimation of the traction battery using discrete battery measurements of voltage and internal resistance, and operate the traction battery according to output of the state estimator. For example, the controller can identify a system dynamics model of the traction battery using a battery input current profile and a battery output voltage profile measured within a predefined time period, transform the identified system dynamics model to a state-space model having a diagonal system matrix consisting of system Eigenvalues through the Eigendecomposition, estimate battery current limits and available power limits from the transformed system dynamics model, and operate the traction battery according to system dynamics model identified using estimated battery current limits and available power limits.

Claims (18)

1. A vehicle comprising:

a traction battery including a plurality of cells; and

at least one controller programmed to operate the traction battery according to battery current and available power limits from a state-space model derived by Eigendecomposing a system dynamics model of the traction battery having a diagonal system matrix of system Eigenvalues and identified via a battery input current profile and a battery output voltage profile measured within a predefined time period.

2. The vehicle of claim 1 wherein the system dynamics model comprises a system dynamics matrix, an input matrix and an output matrix.

3. The vehicle of claim 2 wherein the system dynamics model is updated in real time.

4. The vehicle of claim 1 wherein the system dynamics model is identified using a subspace identification algorithm.

5. The vehicle of claim 4 wherein the subspace identification algorithm uses a voltage profile manipulated by subtracting a voltage drop across a battery internal resistance from the battery output voltage profile.

6. The vehicle of claim 5 wherein a computed internal resistance is estimated using the battery input current profile during a time period and measured battery output voltage profile during the time period.

7. A method for vehicle control comprising:

identifying a system dynamics model of a traction battery using a battery input current profile and a battery output voltage profile measured within a time period;

transforming the identified system dynamics model to a state-space model having a diagonal system matrix of system Eigenvalues through Eigendecomposition;

estimating a battery current limit and an available power limit from the state space model; and

operating the traction battery according to the estimated battery current limit and available power limit.

8. The method of claim 7 wherein identifying the system dynamics model comprises identifying a system dynamics matrix, an input matrix and an output matrix.

9. The method of claim 7 wherein identifying the system dynamics model includes using a subspace identification algorithm.

10. The method of claim 9 wherein identifying the system dynamics model includes using a voltage profile manipulated by subtracting a voltage drop across a battery internal resistance from the battery output voltage profile.

11. The method of claim 10 wherein identifying the system dynamics model includes estimating the internal resistance using the battery current input profile during a time period and measured terminal voltage profile during the time period.

12. The method of claim 7 wherein identifying the system dynamics model includes identifying the system dynamics model in real time.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 17, 2014
From: LEE, TAE-KYUNG
To: FORD GLOBAL TECHNOLOGIES, LLC
Reel/Frame 033336/0912 →
Continuity (1)
Related Publication 20160016482A1 · Jan 21, 2016