CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. § 119 to Indian Patent Application No. 201941034539, filed on Aug. 27, 2019, in the Indian Intellectual Property Office, the disclosure of which is incorporated by reference herein in its entirety.

1. Field

The disclosure relates to rechargeable batteries, and more particularly, to a method and a system for determining a health parameter of a battery.

2. Description of Related Art

Generally, health of rechargeable batteries deteriorates over a period of time. However, the period of time may vary for different rechargeable batteries based on factors such as charging pattern, type of chargers, current fluctuations, lifetime of the battery and the like. As the health deteriorates, charge storing capacity of the battery may decrease compared to the initial charge storing capacity. When health of the battery falls below 80%, then the battery may become unreliable and unsafe for use. This may also affect lifetime of a device in which the battery is being used. Therefore, accurate estimation of State of Health (SOH) of the battery is very crucial for understanding factors such as, remaining useful life of the battery, reliability of the battery and the like. Further, the estimated SOH helps in planning battery replacement at the right time for devices such as smartphones, Electronic Vehicles (EVs) and the like. Further, SOH may also be used for detecting internal short circuit conditions occurring in the battery. However, the existing systems do not provide an accurate estimation of the SOH of the battery when the battery is in use in the device, which may lead to scenarios such as usage of the battery even after it has reached an unreliable and unsafe state, or replacing the battery during the reliable and safe state of the battery.

A few of the related art techniques determine SOH of the battery based on State of Charge (SOC) of the battery. SOC of the battery is generally an amount of charge remaining in the battery to use at a given point. The SOC based techniques to determine SOH may not be accurate since these techniques may introduce errors that occur due to choice of initial SOC. a few other related art techniques may perform SOH estimation by monitoring the battery until it is completely charged or completely discharged. Monitoring until the batteries are completely charged or discharged to determine the SOH may consume more time and resources. Further, in practical scenarios where most of the time the batteries may be either partially charged or discharged, and in some devices like smartphones, charging and discharging cycles may not even have resting period, related art techniques may fail or provide erroneous results. Furthermore, the related art techniques may not be compatible with different computational capabilities since the batteries may be used in small devices such as smartphones to large devices such as EVs and Aircrafts.

SUMMARY

Provided are a method and apparatus for determining a health parameter of a battery.

Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the presented embodiments.

In accordance with an aspect of the disclosure, a method of determining a State of Health (SOH) of a battery includes: a method for determining State of Health (SOH) of a battery includes: detecting a charging state of the battery that is being charged by a Constant Current (CC) or a Constant Voltage (CV); adding a low frequency current signal to the CC that is charging the battery to obtain a low frequency charging current, or adding a low frequency voltage signal to the CV, that is charging the battery to obtain a low frequency charging voltage; determining one or more parameters relating to the battery during the charging state of the battery; calculating an internal impedance and Open Circuit Voltage (OCV) values based on the one or more parameters; and determining SOH of the battery based on the calculated internal impedance and the OCV values.

The calculating the internal impedance and the OCV values may include: determining a spectral magnitude and a phase of the low frequency charging current or the low frequency charging voltage by performing a frequency domain estimation; and calculating the internal impedance and the OCV values based on the spectral magnitude and the phase of the low frequency charging current or the low frequency charging voltage.

The determining the SOH of the battery may include: performing a coulomb count for collecting the OCV values at a plurality of time instances where the coulomb count increases by a predefined value, wherein the coulomb count starts from an initial OCV value, for the plurality of time instances; determining a delta OCV vector by computing a difference of each of the OCV values with the initial OCV value, and an average temperature corresponding to the OCV values; and determining the SOH of the battery based on the delta OCV vector and the average temperature using a machine learning model that is pre-trained using trial values.

The one or more parameters may include at least one of current, voltage or temperature related to the battery.

The charging state of the battery may be at least one of a partial charge or a full charge.

The low frequency current signal or the low frequency voltage signal may have a frequency in a range of 1 hertz to 10 hertz.

The low frequency current signal or the low frequency voltage signal may be a square wave including positive values.

In accordance with another aspect of the disclosure, a battery health determining system for determining State of Health (SOH) of a battery includes: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions that, when executed by the processor, causes the processor to: detect a charging state of the battery that is being charged by a Constant Current (CC) or a Constant Voltage (CV); add a low frequency current signal to the CC that is charging the battery to obtain a low frequency charging current, or add a low frequency voltage signal to the CV charging the battery to obtain a low frequency charging voltage; determine one or more parameters relating to the battery during the charging state of the battery; calculate an internal impedance and Open Circuit Voltage (OCV) values based on the one or more parameters; and determine SOH of the battery based on the calculated internal impedance and the OCV values.

The processor-executable instructions, when executed by the processor, may further cause the processor to: determine a spectral magnitude and a phase of the low frequency charging current or the low frequency charging voltage by performing a frequency domain estimation; and calculate the internal impedance and the OCV values based on the spectral magnitude and the phase of the low frequency charging current or the low frequency charging voltage.

The processor-executable instructions, when executed by the processor, may further cause the processor to: perform a coulomb count for collecting the OCV values at a plurality of time instances where the coulomb count increases by a predefined value, wherein the coulomb count starts from the initial OCV value, for the plurality of time instances; determine a delta OCV vector by computing a difference of each of the OCV values with the initial OCV value, and an average temperature corresponding to the OCV values; and determine the SOH of the battery based on the delta OCV vector and the average temperature using a machine learning model that is pre-trained using trial values.

The one or more parameters may include at least one of current, voltage or temperature related to the battery.

[0018] The charging state of the battery may be at least one of a partial charge or a full charge. [0019] The low frequency current signal or the low frequency voltage signal may have a frequency in a range of 1 hertz to 10 hertz.

The low frequency current signal or the low frequency voltage signal may be a square wave including positive values.

In accordance with another aspect of the disclosure, a non-transitory computer-readable recording medium has recorded thereon instructions executable by a processor to perform a method for determining State of Health (SOH) of a battery, the method including: detecting a charging state of the battery that is being charged by a Constant Current (CC) or a Constant Voltage (CV); adding a low frequency current signal to the CC that is charging the battery to obtain a low frequency charging current, or adding a low frequency voltage signal to the CV, that is charging the battery to obtain a low frequency charging voltage; determining one or more parameters relating to the battery during the charging state of the battery; calculating an internal impedance and Open Circuit Voltage (OCV) values based on the one or more parameters; and determining SOH of the battery based on the calculated internal impedance and the OCV values.

The calculating the internal impedance and the OCV values may include: determining a spectral magnitude and a phase of the low frequency charging current or the low frequency charging voltage by performing a frequency domain estimation; and calculating the internal impedance and the OCV values based on the spectral magnitude and the phase of the low frequency charging current or the low frequency charging voltage.

The determining the SOH of the battery may include: performing a coulomb count for collecting the OCV values at a plurality of time instances where the coulomb count increases by a predefined value, wherein the coulomb count starts from an initial OCV value, for the plurality of time instances; determining a delta OCV vector by computing a difference of each of the OCV values with the initial OCV value, and an average temperature corresponding to the OCV values; and determining the SOH of the battery based on the delta OCV vector and the average temperature using a machine learning model that is pre-trained using trial values.

The one or more parameters may include at least one of current, voltage or temperature related to the battery.

The charging state of the battery may be at least one of a partial charge or a full charge.

The low frequency current signal or the low frequency voltage signal may have a frequency in a range of 1 hertz to 10 hertz.

The low frequency current signal or the low frequency voltage signal may be a square wave including positive values.

