BATTERY INTERNAL STATE ESTIMATING APPARATUS AND BATTERY INTERNAL STATE ESTIMATING METHOD

• US 20130110429A1
• Filed: 11/12/2010
• Published: 05/02/2013
• Est. Priority Date: 03/23/2010
• Status: Active Grant

• Current Assignee: Furukawa Automotive Systems Incorporated (Furukawa Electric Company Limited), Furukawa Electric Company Limited
• Original Assignee: Furukawa Automotive Systems Incorporated (Furukawa Electric Company Limited), Furukawa Electric Company Limited
• Inventors: Sugimura, Takezo, Sato, Etsuzo, Mitsuyama, Taiji, Kovats, Antal
• Granted Patent: US 9,678,164 B2
• Time in Patent Office: Days
• US Class Current: 702/63
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: H01M 10/06 Lead-acid accumulators semi-lead accumulators H01M10/20
• CPC Class Codes: H01M 10/48 Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569
• CPC Class Codes: Y02E 60/10 Energy storage using batteries

Abstract

To learn a parameter of a simulation model of a battery efficiently. A battery internal state estimating apparatus, estimating an internal state of a battery based on a simulation model of the battery, includes a storing section (RAM10c) that stores a plurality of parameters of the simulation model, a detecting section (I/F10d) that detects a discharge current flowing from the battery to a load, a selecting section (CPU 10a) that selects a parameter to be subjected to adaptive learning based on a value of the discharge current detected by the detecting section, and an adaptive learning section (CPU 10a) that performs adapting learning on a parameter selected by the selecting section.

Claims

1. A battery internal state estimating apparatus that estimates an internal state of a battery based on a simulation model of the battery, comprising:

a storing section that stores a plurality of parameters of the simulation model;

a detecting section that detects a discharge current flowing from the battery to a load;

a selecting section that selects a parameter to be subjected to adaptive learning based on a value of the discharge current detected by the detecting section; and

an adaptive learning section that performs adapting learning on a parameter selected by the selecting section.

2. The battery internal state estimating apparatus according to claim 1, wherein

the load includes at least an electric motor for starting up an engine;

the detecting section detects an electric current at a start-up of the engine by the electric motor;

the selecting section selects a parameter corresponding to a value of electric current flowing through the electric motor; and

the adaptive learning section performs adaptive learning on a parameter selected by the selecting section.

3. The battery internal state estimating apparatus according to claim 2, wherein the simulation model has, as its constituent element, an internal resistance of the battery, a resistance value of the internal resistance being taken as the parameter,

wherein the selecting section selects the internal resistance when a peak current which flows at the start-up of the electric motor is detected.

4. The battery internal state estimating apparatus according to claim 1, wherein the simulation model has at least a constant phase element (CPE) which is an equivalent circuit of a cathode and an anode of the battery, the constant phase element being represented by an equivalent circuit in which a plurality of RC parallel units are connected in series,

wherein each RC parallel unit includes a resistance and a capacitor connected in parallel, an element value of each of the resistance and the capacitor constituting each RC parallel unit being taken as the parameter,

wherein the selecting section selects a predetermined RC parallel unit that has been determined in advance in accordance with the value of the discharge current;

and

the adaptive learning selection performs adaptive learning of the element value of each of the resistance and the capacitor which is selected by the selecting section.

5. The battery internal state estimating apparatus according to claim 1, wherein the simulation model has, as its constituent element, a voltage source, a concentration value of an electrolyte inside the battery related to a voltage of the voltage source;

wherein, in a case where the discharge current is substantially zero, the selecting section selects a concentration of the electrolyte as an object to be adaptively learned.

6. The battery internal state estimating apparatus according to claim 1, wherein the parameter selected by the adaptive learning section constitutes a state vector of an extended Kalman filter;

and

wherein the adaptive learning selection performs the adaptive learning on the state vector.

7. A battery internal state estimating method of estimating an internal state of a battery based on a simulation model of the battery, comprising:

storing a plurality of parameters of the simulation model;

detecting a discharge current flowing from the battery to a load;

selecting a parameter to be subjected to adaptive learning based on a magnitude of the discharge current detected in the detecting step; and

causing adapting learning on a parameter selected in the selecting step.

8. A battery internal state estimating apparatus for estimating an internal state of a battery based on a simulation model of the battery, comprising:

a storing section that stores a plurality of parameters of the simulation model;

a measuring section that measures a terminal voltage and a discharge current of the battery at a predetermined cycle;

an adaptive learning section that performs adaptive learning on the parameters based on a measurement result of the measuring section;

an actual measurement section that performs actual measurement of an internal resistance of the battery; and

an estimating section that estimates an SOH (State of Health) indicating a degradation state of the battery based on an actual measured value R_meas of the internal resistance obtained by the actual measurement section and a value of the at least one parameter obtained by the adaptive learning selection or a corrected value of the parameter.

9. The battery internal state estimating apparatus according to claim 8, wherein the estimating section estimates a degradation state based on the value of the at least one parameter, the value of the at least one parameter being a value of the parameter η

indicating an aging of the internal resistance of the simulation model.

10. The battery internal state estimating apparatus according to claim 8, wherein the estimating section estimates the degradation state based on the corrected value of the parameter, the corrected value of the parameter being a value obtained by multiplying a parameter R0 indicating an internal resistance of the simulation model by a parameter η

indicating an aging of the internal resistance, and correcting the obtained value based on an average current I_avrg flowing through the load and a stable open circuit voltage.

11. The battery internal state estimating apparatus according to claim 8, wherein the estimating section estimates the degradation state based on the correction value of the parameter, the correction value of the parameter being a value obtained by multiplying parameter R0 indicating an internal resistance of the simulation model by a parameter η

indicating an aging of the internal resistance, correcting the obtained value based on an average current I_avrg flowing through the load and a stable open circuit voltage, and further correcting the corrected value based on an SOC (State of Charge) indicating a state of charge of the battery, an average voltage V_avrg of the battery while the current is flowing through the load, and a voltage V_start of the battery at a state before the current flows through the load.

12. The battery internal state estimating apparatus according to claim 8, wherein the estimating section estimates a degradation state of the battery based on a value obtained by multiplying the actual measured value of the internal resistance R_meas, the parameter, and the corrected value of the parameter by predetermined constants, respectively, and summing the obtained results.

13. A battery internal state estimating method of estimating an internal state of the battery based on a simulation model of the battery, comprising:

storing a plurality of parameters in the simulation model into a memory;

measuring a terminal voltage and a discharge current of the battery at a predetermined cycle;

performing adaptive learning on the parameters based on a measurement result of said measuring step;

performing actual measurement of an internal resistance of the battery; and

estimating an SOH (State of Health) indicating a degradation state of the battery based on an actual measurement R_meas of the internal resistance obtained by said actual measuring step as well as a value of the parameters and/or a corrected value of the parameters obtained in said adaptive learning step.