Claims:CLAIMS
We Claim:
1. A battery management system for SOC and SOH estimation, comprising:
a battery parameter unit configured to obtain real time values of voltage and current measured during charging and discharging operation modes of a battery;
a memory unit configured to store multiple past calculated SOC values and multiple ideal SOC values;
an adaptive processing unit configured to adjust the multiple past calculated SOC values with the real time values of voltage and current using the multiple ideal SOC values;
an SOC estimating unit configured to receive and allocate the adjusted multiple past calculated SOC values as input to an extended kalman filter for estimating an accurate SOC value; and
an SOH estimating unit configured to receive the estimated SOC value from the SOC estimating unit for estimating an accurate SOH value using a capacity fade algorithm.
2. The battery management system for SOC and SOH estimation as claimed in claim 1, wherein the real time values of voltage and current are measured using a voltage measuring unit and a current measuring unit which are electrically coupled to the battery.
3. The battery management system for SOC and SOH estimation as claimed in claim 2, wherein the battery management system comprises a control unit which instructs the voltage measuring unit and the current measuring unit to measure the real time values of voltage and current at a pre-set time period, and wherein the pre-set time period is entered by a user.
4. The battery management system for SOC and SOH estimation as claimed in claim 3, wherein the memory unit stores the multiple ideal values which are either calculated by the control unit based on the battery configuration or entered by the user.
5. The battery management system for SOC and SOH estimation as claimed in claim 1, wherein the memory unit stores the multiple past calculated SOC values which are calculated from past values of the voltage and current measured during charging and discharging operation modes of the battery.
6. The battery management system for SOC and SOH estimation as claimed in claim 1, wherein the battery is a lithium ion battery or similar other batteries thereof.
7. The battery management system for SOC and SOH estimation as claimed in claim 1, wherein the battery is a lithium iron phosphate battery.
8. The battery management system for SOC and SOH estimation as claimed in claim 1, wherein the adaptive processing unit adjusts the multiple past calculated SOC values using an OCV-SOV graph or a coulomb counter method.
9. The battery management system for SOC and SOH estimation as claimed in claim 1, wherein the capacity fade algorithm is implemented by the SOH estimating unit by combining an ordinary least squares method, a total least squares method and an approximate weighted total least squares method.
10. A method for estimating SOC and SOH of a battery using a battery management system, comprising:
obtaining real time values of voltage and current measured during charging and discharging operation modes of a battery, by using a battery parameter unit;
storing multiple past calculated SOC values and multiple ideal SOC values, by using a memory unit;
adjusting the multiple past calculated SOC values with the real time values of voltage and current using the multiple ideal SOC values, by using an adaptive processing unit;
estimating an accurate SOC value by receiving and allocating the adjusted multiple past calculated SOC values as input to an extended kalman filter, by using an SOC estimating unit; and
estimating an accurate SOH value based on the estimated SOC value using a capacity fade algorithm, by using an SOH estimating unit. , Description:DESCRIPTION:
Field of Invention:
[0001] The present invention relates to the technical field of batteries, and in particular relates to a system and method for estimating state of charge (SOC) and state of health (SOH) with accurate and reliable values under various environmental conditions.
Background of the invention:
[0002] In general, batteries can be repeatedly charged and discharged and are used as a source of power in various fields such as portable electronics and electric and hybrid-electric vehicles. Among various batteries, rechargeable lithium-ion batteries are widely used because of their high specific energy compared to other electrochemical energy storage devices. However, improper charging of lithium ion batteries may result in sub-optimal power output, shortened battery lifespan, and damage to the batteries’ cells. Various systems have evolved for the estimation of SOC and SOH parameters of the lithium-ion batteries for their stable operation. However, such systems lack accurate measurements of SOC and SOH parameters.

[0003] An LFP cell is a battery cell using lithium iron phosphate (LixFePO4) as an active material of the positive electrode. The LFP cell has a long life advantage. The problem aggravated for the LFP batteries is they have a very flat OCV-SOC correlation curve. The current SOC estimation models are unable to take care of all of the complications.

[0004] An accurate state of charge (SOC) estimation of the LFP battery is one of the most important functions for the battery management system. The SOC estimation may be performed by using an extended Kalman filter (EKF). The EKF technique is an adaptive estimator and has emerged as one of the practical solutions, but complicated and needs heavy computing resources.

[0005] The state of health (SOH) of a battery represents the percentage of the current capacity of the battery to the factory capacity after the battery has been used for a period of time. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods provide accurate measurements.

[0006] Hence, there is a need for an enhanced, accurate and reliable system and method for estimating SOC and SOH values of an LFP battery, which mitigates the problems discussed above. An improved system is in need that estimates SOC and SOH of an LFP battery with more reliable and accurate values.
Objectives of the invention:
[0007] The primary objective of the invention is to provide a system and method for estimating SOC and SOH of a battery with more accurate and reliable values.

[0008] The other objective of the invention is to provide a system that estimates the SOC and SOH values of a lithium ion battery.

[0009] The other objective of the invention is to provide a system that estimates the SOC and SOH values under various environmental conditions.

[0010] The other objective of the invention is to provide a system that estimates accurate values of SOC and SOH values using simple computing techniques.

Summary of the Invention:
[0011] The invention proposes a system and method for SOC and SOH estimation of batteries. The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key/critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0012] According to an aspect of the invention, a battery management system for SOC and SOH estimation comprises of a battery parameter unit, a control unit, a memory unit, an adaptive processing unit, an SOC estimating unit and an SOH estimating unit. The battery parameter unit is configured to obtain real time values of voltage and current measured during charging and discharging operation modes of a battery. The control unit instructs the voltage measuring unit and the current measuring unit to measure the real time values of voltage and current at a pre-set time period. The pre-set time period is entered by a user.

[0013] The memory unit is configured to store multiple past calculated SOC values and multiple ideal SOC values. The memory unit stores the multiple past calculated SOC values which are calculated from past values of the voltage and current measured during charging and discharging operation modes of the battery. Further, the memory unit stores the multiple ideal values which are either calculated by the control unit based on the battery configuration or entered by the user.

[0014] The adaptive processing unit is configured to adjust the multiple past calculated SOC values with the real time values of voltage and current using the multiple ideal SOC values. The adaptive processing unit adjusts the multiple past calculated SOC values using an OCV-SOV graph or a coulomb counter method.

[0015] The SOC estimating unit is configured to receive and allocate the adjusted multiple past calculated SOC values as input to an extended kalman filter for estimating an accurate SOC value. The SOH estimating unit is configured to receive the estimated SOC value from the SOC estimating unit for estimating an accurate SOH value using a capacity fade algorithm. This is implemented by the SOH estimating unit by combining an ordinary least squares method, a total least squares method and an approximate weighted total least squares method.
Description of Drawings:
[0016] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate an embodiment of the invention, and, together with the description, serve to explain the principles of the invention.

[0017] FIG. 1 illustrates a block diagram of a battery management system in connection with a battery, in accordance to an exemplary embodiment of the invention.

[0018] FIG. 2 illustrates an SOH estimating unit, in accordance to an exemplary embodiment of the invention.

[0019] FIG. 3 illustrates a method for estimating SOC and SOH of a battery using the battery management system, in accordance to an exemplary embodiment of the invention.
Detailed description of Drawings:
[0020] An exemplary embodiment of the present invention will be described in reference to the accompanying drawings. Wherever possible, same or similar reference numerals are used in the drawings and the description to refer to the same or like parts or steps.

[0021] The present invention discloses a system and method for estimating the SOC and SOH of lithium ion battery with more reliable and accurate values. Moreover, the SOC and SOH parameters are estimated under various environmental conditions. The SOH is estimated with more accuracy i.e., approximately 95 to 99%.

[0022] According to an exemplary embodiment of the invention, a block diagram 100 of a battery management system in connection with a battery is disclosed. The block diagram 100 comprises of a battery management system 102, a voltage measuring unit 116, a current measuring unit 118 and a battery 120.

[0023] In an embodiment, the battery management system 102 comprises of a battery parameter unit 104, a control unit 106, a memory unit 108, an adaptive processing unit 110, an SOC estimating unit 112 and an SOH estimating unit 114. The battery parameter unit 104 is configured to obtain real time values of voltage and current measured during charging and discharging operation modes of the battery 120. The real time values of the voltage and current are measured using the voltage measuring unit 116 and the current measuring unit 118 which are electrically coupled to the battery 120 and the control unit 106. The voltage measuring unit 116 and the current measuring unit 118 measures the voltage and current applied between the positive electrode and the negative electrode of the battery 120.

[0024] In an embodiment, the battery management system 100 is packed together with the battery 120. The battery 120 may be a lithium ion battery which may further comprise one or more lithium ion cells. The lithium ion battery may comprise any one of a variety of different battery chemistries and not confined to compositions such as lithium manganese oxide (LMO), lithium iron phosphate (LFP), lithium nickel manganese cobalt oxide (NMC), lithium nickel cobalt aluminum oxide (NCA), lithium titanate (LTO), and lithium cobalt oxide (LCO). In an embodiment, the battery 120 may preferably be a lithium iron phosphate (LFP) battery 120.

[0025] In an embodiment, the control unit 106 may instruct the voltage measuring unit 116 and the current measuring unit 118 to measure the real time values of the voltage and current at a pre-set time period. The pre-set time period may be entered by a user. The pre-set time period may comprise multiple timings during which the voltage and current are to be measured.

[0026] In an embodiment, the memory unit 108 is configured to store multiple past calculated SOC values and multiple ideal SOC values. The multiple past calculated SOC values are calculated from past values of the voltage and current. The past values of the voltage and current are measured during charging and discharging operation modes of the battery 120 at a certain time period selected from the pre-set time period. The multiple ideal values are either calculated by the control unit 106 based on the battery configuration or entered by the user.

[0027] In an embodiment, the adaptive processing unit 110 is configured to adjust the multiple past calculated SOC values with the real time values of voltage and current using the multiple ideal SOC values. The multiple past calculated SOC values are adjusted using an OCV-SOV graph or a coulomb counter method.

[0028] The OCV-SOV graph is obtained by an open circuit voltage measurement which measures the open circuit voltage of the LFP battery 120 and establishes the relationship between OCV and SOC of LFP battery 120. Each battery system has its own OCV curve. Under certain temperature, the SOC has a fixed relationship with OCV. At the same time, OCV will also be affected by battery aging, so OCV can be used as a basis for the diagnosis of SOH in lithium battery. The coulomb counter method measures the current of the battery and integrates the current over time to estimate SOC.

[0029] In an embodiment, the SOC estimating unit 112 is configured to receive the adjusted multiple past calculated SOC values from the adaptive processing unit 110 and thereby allocate the adjusted multiple past calculated SOC values as input to an extended kalman filter for estimating an accurate SOC value.

[0030] The extended Kalman filter (EKF) predicts internal and immeasurable states. One of these states is the SOC of the battery cell. In a second step, the EKF corrects the internal states under consideration of the estimated accuracies of the measurement and the model prediction. Thus, it provides a corrected estimation of the SOC.

[0031] In an embodiment, an SOH estimating unit 114 is configured to receive the estimated SOC value from the SOC estimating unit 112 for estimating an accurate SOH value using a capacity fade algorithm. The SOH of a battery is a measure that describes how much the battery has degraded in health over the course of its life and is often evaluated by the battery’s internal resistance or its ability to deliver a given amount of charge. The physical qualities associated with the SOH are capacity fade and impedance growth. The capacity fade reduces energy storage capability. The impedance growth reduces capability to deliver power.

[0032] According to another exemplary embodiment of the invention, Fig. 2 illustrates an SOH estimating unit 114. The SOH estimating unit 114 implements the capacity fade algorithm which uses combination of an ordinary least squares method, a total least squares method and an approximate weighted total least squares method.

[0033] The ordinary least squares minimize the sum of squares of the differences between the observed responses in the given dataset. The total least squares consider noise on both axes which makes the regression more precise and provides optimum fitting, especially for relatively large deviations on the SOC estimates.

[0034] The approximate weighted total least squares is an optimization method for on-board capacity estimation, and is examined in combination with different methods. The approximate weighted total least squares in combination with the extended kalman filter for state of charge estimation, is able to estimate total cell capacity within three-sigma error bounds of ±1.6 % and is able to track the battery’s capacity fade.

[0035] The theorem implemented for SOH estimation is as given below:
i) Consider the SOC equation: z[k2] = z[k1]- delta T* (sum(n(k)i(k))/Q
ii) Rearrange its terms to get:
Q (z[k2] – z[k1]) = -Delta T sum(n(k) i(k))
Y= Q * X
Xi = change of SOC
Yi = accumulation of charge
Xi = Z[k2, i] – Z[k1,i]
Yi = -Delta T * sum(n(k) i(k))

Q cap that minimizes the least squares (AWTLS) cost function



Jacobian for AWTLS cost function is written as

Rewrite the cost function:

Where,

Roots of quartic equation,

Initialized by setting:
X0 = 1 , Y0 = Qnom variance = uncertainty Qnom versus Q
Therefore,

Roots can be found by Ferrari method.
Rewrite Cost function as:

Hessian:

Q corrected = Q cap /K
H corrected = H/ k^2

[0036] According to another exemplary embodiment, a method 300 for estimating SOC and SOH of a battery using the battery management system is disclosed. The method 300 begins with obtaining real time values of voltage and current measured during charging and discharging operation modes of a battery, as depicted at step 302, by using a battery parameter unit. Thereafter, the method 300 discloses storing of multiple past calculated SOC values and multiple ideal SOC values, as depicted at step 304, by using a memory unit. Subsequently, the method 300 discloses adjusting of the multiple past calculated SOC values with the real time values of voltage and current using the multiple ideal SOC values, as depicted at step 306, by using an adaptive processing unit.

[0037] Later, the method 300 discloses estimating of an accurate SOC value by receiving and allocating the adjusted multiple past calculated SOC values as input to an extended kalman filter, as depicted at step 308, by using an SOC estimating unit. Thereafter, the method 300 discloses estimating of an accurate SOH value based on the estimated SOC value using a capacity fade algorithm, as depicted at step 310, by using an SOH estimating unit.

[0038] The proposed battery management system and method for estimating SOC and SOH of a battery provides more accurate and reliable values. The battery management system estimates the SOC and SOH values of the lithium ion battery. The battery management system estimates the SOC and SOH values under various environmental conditions. Further, the battery management system estimates accurate values of SOC and SOH using simple computing techniques.

[0039] It will readily be apparent that numerous modifications and alterations can be made to the processes described in the foregoing examples without departing from the principles underlying the invention, and all such modifications and alterations are intended to be embraced by this application.