METHOD FOR ESTIMATE THE STATUS OF ELECTRIC CHARGE AND STATUS OF CONDITION OF A BATTERY AND SYSTEM THEREOF

Abstract

a method (200) and system (100) for estimating the electric charge state and the condition state of a battery (10). The first vector (x) and the second vector (Θ) are initialized. The first vector (x) is estimated and updated by a first equivalent circuit solver based on the first state space filter by assuming a fixed value of the second vector (Θ). The second vector (Θ) is estimated and updated based on the Electrochemical Model and then by a second equivalent circuit solver based on the second state space filter. The updated values ​​of the second vector (Θ) by the Electrochemical Model and the second state space filter are merged. The electric charge state is obtained from the updated values ​​of the first vector (x), and the condition state is obtained from the merged and updated values ​​of the second vector (Θ).

Description

Description of the method for estimating the state of charge and condition of a battery and the system thereof Invention Engineering Field This invention relates to estimating the electrical charge status and condition status of a battery. Background of the Invention Modern electric and hybrid vehicles typically use electrochemical cells, such as lithium-ion cells, as energy storage units. A number of such cells configured in suitable series and parallel combinations form a battery. The battery is typically connected to a Battery Management System (BMS), which is configured to monitor cell voltage, current, and temperature, as measured using appropriate sensors. One of the primary functions of a BMS is to estimate the state of charge (SOC) and state of condition (SOH) of the battery and its constituent cells. Another important function of a BMS is to ensure that the battery operates within a predetermined “Safe Operating Area”—to avoid conditions such as cell overcharging, undercharging, overheating, and so on.A cell's safe operating area generally imposes upper and lower thresholds on the cell, which regulate the voltage, temperature, and current flowing through the cell. A battery is said to be operating within its Safe Operating Area only if every cell is within its Safe Operating Area. Other important functions of the battery management system include balancing the charge between cells to extend battery life and communicating information to other controllers in the vehicle's network. Accurate state-of-charge prediction is necessary to determine the remaining energy in the battery and to determine how long the battery can be operated under current load conditions. The battery's state-of-charge also provides drivers with a good assessment of battery recharge schedules. While the battery state of charge is considered a short-term battery parameter, the state of condition is considered a long-term parameter since battery degradation occurs gradually over its lifetime. The SOH of a battery is typically characterized by the total charge storage capacity of the battery. Another parameter used to characterize the SOH is the internal impedance of the battery, which plays a major role in the available output power of the battery. Accurate health prediction improves the accuracy of SOC estimation because the accuracy of SOC estimation depends on the battery model parameters. Health prediction also provides information about battery degradation and helps to schedule battery replacement. One conventional method for estimating SOC is to integrate the current passing through the battery over time. However, this method is prone to bias due to current measurement noise and measurement offset errors. Another conventional method for estimating SOC is to utilize the known monotonic relationship between SOC and the open-circuit cell voltage of the battery. However, this method requires the battery to be in a relaxed state, with no current flowing through it for a substantial amount of time. SOC and SOH estimation methods utilize accurate equivalent models of the battery. Two broad categories of battery models are common – the first category is the Equivalent Circuit Model, which approximates the underlying chemical phenomena in the battery with an equivalent resistor-capacitor network. Examples of Equivalent Circuit models are the series resistance model, the 1RC equivalent circuit, and the 2RC equivalent circuit.The second category is Electro-Chemical Models such as the DFN (Doyle-Fuller-Newman) model and the SPM (Single Particle Model). Electro-Chemical Models are computationally intensive due to the inherent complexity of the modeling, and are therefore not commonly used in practical BMS systems. In general, Equivalent Circuit models are less accurate than Electro-Chemical models but are more suitable for implementation in practical BMSs. Thus, in this invention, a method is required to estimate the electric charge status and condition status of a battery that overcomes at least the aforementioned problems. Brief Description of the Invention In one aspect of the invention, the present invention is directed to a method for estimating the electrical charge status and condition status of a battery.The method has the steps of: initializing a first vector and a second vector based on commonly known values ​​of a voltage, a battery state of charge, an impedance and a battery charge capacity; estimating and updating the first vector by a first state space filter-based equivalent circuit solver assuming a fixed value of the second vector; estimating and updating the second vector, in whole or in part, based on an Electrochemical Model; estimating and updating the second vector, in whole or in part, by a second state space filter-based equivalent circuit solver; combining the updated values ​​of the second vector by the Electrochemical Model and the second state space filter-based equivalent circuit solver; obtaining the battery state of charge from the updated values ​​of the first vector, and obtaining the battery state of charge from the combined and updated values ​​of the second vector. In an embodiment of the invention, the first vector is a state vector comprising the voltage variables and the electrical charge state of the battery. In another embodiment of the invention, the second vector is a parameter vector consisting of the impedance elements and the electrical charge capacity of the battery. In a further embodiment of the invention, the equivalent circuit corresponding to the first equivalent circuit breaker and the second equivalent circuit breaker comprises one or more RC elements having known estimated impedances, and a voltage source representing the open circuit voltage of the cell. In a further embodiment of the invention, wherein the open circuit voltage of the cell in the equivalent circuit is a non-linear function of the electrical charge state of the cell. In another embodiment, the first state space filter is a Kalman filter. In one embodiment, the second state space filter is a Kalman filter. In one embodiment, the electrochemical model is an Electrolyte-Enhanced Single Particle Model. In another aspect, the present invention relates to a system for estimating the state of charge and state of condition of a battery. The system has a voltage sensing circuit for sensing the voltage across a battery cell; and a current sensing circuit for sensing the current passing through a battery cell. The system further has a central processing unit configured to initialize a first vector and a second vector based on generally known values ​​of the voltage, state of charge of the battery, impedance and capacity of the battery, estimate and update the first vector by a first state space filter-based equivalent circuit solver assuming a fixed value of the second vector, estimate and update the second vector, in whole or in part, based on the Electrochemical Model, estimate and update the second vector, in whole or in part, by the second state space filter-based equivalent circuit solver,combining the updated values ​​of the second vector by the Electrochemical Model and the second state space filter-based equivalent circuit solver, and obtaining the battery electric charge state from the updated values ​​of the first vector, and obtaining the battery condition state from the combined and updated values ​​of the second vector., In an embodiment of the invention, the first vector is a state vector comprising the voltage variables and the electrical charge state of the battery. In another embodiment of the invention, the second vector is a parameter vector consisting of the impedance elements and the electrical charge capacity of the battery. In a further embodiment of the invention, the equivalent circuit corresponding to the first equivalent circuit breaker and the second equivalent circuit breaker comprises one or more RC elements having known estimated impedances, and a voltage source representing the open circuit voltage of the cell. In a further embodiment of the invention, wherein the open circuit voltage of the cell in the equivalent circuit is a non-linear function of the electrical charge state of the cell. In another embodiment, the first state space filter is a Kalman filter. In one embodiment, the second state space filter is a Kalman filter. In one embodiment, the electrochemical model is an Electrolyte-Enhanced Single Particle Model. Short Description of Image Reference will be made to embodiments of the invention, examples of which may be illustrated in the accompanying drawings. These drawings are intended to be illustrative and not limiting. Although embodiments are described generally in the context of this embodiment, it should be understood that they are not intended to limit the scope of the invention to this particular embodiment. Figure 1 illustrates a system for illustrating the electrical charge status and condition status of a battery, in accordance with an embodiment of the invention. Figure 2 illustrates a method for illustrating the electrical charge status and condition status of a battery, in accordance with an embodiment of the invention. Figure 3 illustrates an equivalent circuit breaker, in accordance with an embodiment of the invention. Figure 4 illustrates a Kalman filter-based second vector estimation and an eSPM-based second vector estimation, with the first vector estimation being Kalman filter-based, in accordance with an embodiment of the invention. Figure 5 illustrates a 2RC equivalent circuit for an equivalent circuit breaker, in accordance with an embodiment of the invention. Figure 6 illustrates the internal structure of an electrochemical cell and its equivalent eSPM structure, in accordance with an embodiment of the invention. Figure 7 illustrates a capacitance plot associated with the negative electrode, considering a 2RC model cell, in accordance with an embodiment of the invention. Figure 8 illustrates a capacitance plot associated with the positive electrode, considering a 2RC model cell, in accordance with an embodiment of the invention. Complete Description of the Invention The present invention relates to a method and system for estimating the electrical charge status and condition status of a battery. More specifically, the present invention relates to a method and system for estimating the electrical charge status and condition status of a battery, wherein the battery comprises one or more electrochemical cells. Figure 1 illustrates a system (100) for estimating the state of charge and condition of a battery (10). The system comprises a battery pack (10) having one or more cell stacks, a current sensing circuit (120) for sensing the current passing through the battery cells (10), a voltage sensing circuit (110) for sensing the voltage across the battery cells (10). In one embodiment, each cell stack comprises a set of cells suitably connected in a combination of series and parallel. In one embodiment, the cell stack also comprises one or more temperature sensors for obtaining a thermal map of the battery. In the system, the current sensing circuit (120), the voltage sensing circuit (110) and the temperature sensing circuit (130) are connected to the cell stack and the central processing unit (140). In an embodiment, voltage, current, and temperature data measurements from the battery (10) are sampled periodically using relevant sensors integrated into the battery (10). A voltage sensing circuit (110) is connected to the cell terminals to sample the cell voltage and a temperature sensing circuit (130) is connected to the cell stack temperature sensor to sample the temperature data. A current sensing circuit (120) is configured to sample the current data using a current sensor on the battery (10). A central processing unit (140) is configured to estimate the state of charge (SOC) and (SOH) of the cells in the battery (10) as described below. Figure 2 illustrates the method steps involved in the method (200) for estimating the state of charge and state of condition of the battery (10). In step 2A, a first vector (x) and a second vector (Θ) are initialized, based on commonly known values ​​of the voltage, state of charge of the battery, impedance and charge capacity (Q) of the battery. In one embodiment, the first vector (x) is a state vector that includes the variables of the voltage and state of charge of the battery (10). In another embodiment, the second vector (Θ) is a parameter vector that includes the impedance and charge capacity (Q) elements of the battery. In step 28, the first vector (x) is estimated and updated by the first state-space filter-based equivalent circuit solver assuming a fixed value of the second vector (Θ). Figure 3 illustrates the first equivalent circuit solver principal to the present invention. The first equivalent circuit solver comprises one or more RC elements having known estimated impedances, and a voltage source representing the open-circuit voltage of the cell. The impedance of the RC elements is estimated by the second state-space filter-based equivalent circuit solver as further described. It is understood that the voltage across the source (Voc) is a nonlinear function of the state of charge where SOC is in turn a function of the current 'Icell' and the capacity of the charge (Q). The impedance of the equivalent circuit solver comprises a plurality of resistance and capacitance (RC) elements.The current (Icell) and terminal voltage (Vt) depend on the load connected to the cell terminals and both variables are measured using a current sensing circuit (120) and a voltage sensing circuit (110) as shown in Figure 1. As described above, the present invention categorizes the variables associated with an equivalent circuit breaker as illustrated in Figure 3, into a state vector (x) and a parameter vector (Θ). As described above, in one embodiment, the state vector (x) captures the relatively short dynamics of the cell, while the parameter vector (Θ) captures the relatively long dynamics of the cell. As an example, the state vector 'x' comprises the voltage variables (V1 to Vn) and SOC and the parameter vector comprises the impedance elements (R0, R1 to Rn, C1 to Cn) and the electrical charge capacity (Q). In step 2C, the second vector (Θ) is estimated and updated, in whole or in part, based on the Model Electrochemistry. In step 20, the second vector (Θ) is estimated and updated, in whole or in part, by a second state-space filter-based equivalent circuit solver, which captures the long-term effects of the load on the impedance. Reference is made to Figure 5 where, in the illustrated embodiment, a 2RC equivalent circuit breaker of an electrochemical cell (such as a lithium-ion cell) where the number of RC components of impedance in Figure 3 is two. The source across the voltage indicates the open circuit voltage (Voc) of the cell which is a direct indication of the amount of electrical charge contained within the cell. The open circuit voltage is represented by Voc, and is a nonlinear function of the state of electrical charge. The impedance is characterized by a DC impedance (Ro) and two AC impedance components in series. Each AC component is a parallel combination of resistance (R) and capacitance (C). Vn and Vp in Figure 5 represent the voltage drops across the respective RC components. The discharge current 'Iceii' in Figure 5 illustrates the direction of current flow when the circuit terminals are connected to a load. The relationship between voltage and current is: H = Foc(5OC) - / _ef, * - H- - K t A. S Lifii L? ίΐ p where Voc(SOC) is a previously defined function characteristic of the chemical relationships of the cell and the expressions for SOC, Vn, VP are i; · SGC - . where Q is the cell's electrical charge storage capacity. This embodiment of the present invention uses the 2RC equivalent circuit described above as the base circuit for the equivalent circuit breaker. The state vector 'x' of the first state space filter consists of SOC, Vn, Vp. The variables Vt, Iceii can be obtained using the current sensing circuit (120) and the voltage sensing circuit (110). The other parameters Ro, Rp, Rn, Cp, Cn, Q are cell parameters that mimic the internal chemical structure and are built into the parameter vector 'Θ'. The input variables such as Iceii are built into the input vector 'U'. In an embodiment, as referred to in Figure 4, the first state space filter for the equivalent circuit solver and the second state space solver for the equivalent circuit solver are Kalman Filters. The block level architecture illustrated in Figure 4 illustrates the state space filters. The first equivalent circuit solver and the second equivalent circuit solver use a Kalman filter that considers an aging model such as the known Null model, to predict the cell parameters. In an embodiment, the Kalman filter may be further embodied as a Predictor step and a Corrector step. The two Kalman filters may exchange information to provide optimal output. Reference is made to Figure 4 and Figure 6, where in one embodiment, the Electrochemical Model referred to in step 20 for estimation and updating of the second vector, is an Electrolyte Enhanced Single Particle Model (eSPM). As illustrated in Figure 6, the internal cell structure for the Electrolyte Enhanced Single Particle Model, contains a hollow negative electrode and a hollow positive electrode filled with electrolyte to facilitate the mobility of positive charge carriers (e.g., lithium ions). The electrodes are separated using a separator to prevent electron flow while allowing the mobility of positive charge carriers. An external closed electrical circuit with a load facilitates the electron flow thereby creating a current flow. Each electrode is directly connected to a high conductivity current collector for electron mobility. The eSPM equivalent cell structure considers a single spherical electrode structure with equivalent concentrations as the actual single spherical cell structure. This reduces structural complexity without significantly compromising the modeling of cell dynamics. The eSPM model also eliminates the separator, which plays a nearly passive role in ion movement. The electrode current collector is retained because it is integral to electron movement. The particle electrode impedance can be estimated by a parallel RC network as shown in Figure 5. The components Rn, Cn represent the negative electrode impedance and Rp, Cp represent the positive electrode impedance. The resistance of the two combined current collectors is related to Ro. The Electrochemical Model based on the eSPM model estimates the parameters Cn, Cp as a function of variables including SOC and electrical charge capacity (Q). This model uses the concept of internal cell diffusion that drives the flow of positive charge carriers (e.g. lithium ions) between the electrodes of a single particle. Figure 7 depicts a typical plot of Cn as a function of SOC and Figure 8 depicts a typical plot of Cp as a function of SOC. Both plots show variations as the electrical charge capacity (Q) of the cell continues to deteriorate as the cell continues to age. The update ‘Θ’ based on the eSPM model predicts the values ​​of Cn, Cp based on the input SOC and the parameter ‘Θ’. In step 2E, the updated values ​​of the second vector (Θ) by the Electrochemical Model and the second state space filter-based equivalent circuit solver as described above are combined to produce a more accurate estimate of the second vector (Θ). Finally in step 2F, the battery's electric charge state is obtained from the updated values ​​of the first vector (x), and the battery's condition state is obtained from the combined and updated values ​​of the second vector (Θ), as described above. Superiorly, the present invention provides a method for estimation of the state of charge and state of condition of a battery that benefits from the simplicity of the equivalent circuit model, while also benefiting from the accuracy of the Electrochemical model.

[035] Furthermore, the system and method of the present invention can be integrated in a battery management system for an electric or hybrid vehicle, thereby providing a system for estimation of the state of charge and state of condition of a battery, which is not only highly accurate, but also less computationally intensive. Accurate prediction of the state of charge of the battery indicates to the user when the battery should be charged and the range of the electric vehicle, and accurate prediction of the state of condition of the battery indicates to the user when the battery should be replaced. Although the present invention has been described with respect to particular embodiments, it will be apparent to those skilled in the art that various changes and modifications may be made without departing from the scope of the invention as defined in the following claims.

Claims

Claim 1. A method (200) for estimating the state of charge and state of condition of a battery (10), comprising the steps of: initializing a first vector (x) and a second vector (Θ) based on commonly known values ​​of the voltage, state of charge of the battery, impedance and capacity of the battery (10); estimating and updating the first vector (x) by a first equivalent circuit solver based on a first state space filter assuming a fixed value of the second vector (Θ); estimating and updating the second vector (Θ), in whole or in part, based on an Electrochemical Model; estimating and updating the second vector (Θ), in whole or in part, by a second equivalent circuit solver based on a second state space filter; combining the updated values ​​of the second vector (Θ) by the Electrochemical Model and the second equivalent circuit solver based on a second state space filter;and obtain the battery charge status (10) from the updated value of the first vector (x), and obtain the battery condition status (10) from the combined and updated value of the second vector (Θ).; 2. The method (200) as claimed in claim 1, wherein the first vector (x) is a state vector including the voltage and charge state variables of the battery (10).

3. The method (200) as claimed in claim 1, wherein the second vector (Θ) is a parameter vector including the impedance element and the electric charge capacity (Q) of the battery (10).

4. The method (200) as claimed in claim 1, wherein the equivalent circuit corresponding to the first equivalent circuit breaker and the second equivalent circuit breaker comprises one or more RC elements having known estimated impedances, and a voltage source representing the open circuit voltage (Voc) of the cell.

5. The method (200) as claimed in claim 4, wherein the open circuit voltage (Voc) of the cell in the equivalent circuit is a non-linear function of the electrical charge state of the cell.

6. The method (200) as claimed in claim 1, wherein the first state space filter is a Kalman filter.

7. The method (200) as claimed in claim 1, wherein the second state space filter is a Kalman filter.

8. The method (200) as claimed in claim 1, wherein the electrochemical model is an Electrolyte Reinforced Single Particle Model.

9. A system (100) for estimating the state of charge and state of condition of a battery (10), comprising: a voltage sensing circuit (110) for sensing the voltage across a battery cell (10); a current sensing circuit (120) for sensing the current passing through a battery cell (10); and a central processing unit (140) configured to initialize a first vector (x) and a second vector (Θ) based on typically known values ​​of the voltage, the state of charge of the battery (10), the impedance and the capacity (Q) of the battery (10), estimate and update the first vector (x) by a first equivalent circuit solver based on a first state space filter assuming a fixed value of the second vector, estimate and update the second vector (Θ), in whole or in part, based on an Electrochemical Model, estimate and update the second vector (Θ), in whole or in part, by a second equivalent circuit solver based on a second state space filter,combining the updated values ​​of the second vector (Θ) by the Electrochemical Model and the second state space filter-based equivalent circuit solver, and obtaining the battery electric charge state (10) from the updated values ​​of the first vector (x), and obtaining the battery condition state (10) from the combined and updated values ​​of the second vector (Θ)., 10. The system (100) as claimed in claim 9, wherein the first vector (x) is a state vector including the voltage and charge state variables of the battery (10).

11. The system (100) as claimed in claim 9, wherein the second vector (Θ) is a parameter vector including the impedance element and the electric charge capacity (Q) of the battery (10).

12. The system (100) as claimed in claim 9, wherein the equivalent circuit corresponding to the first equivalent circuit breaker and the second equivalent circuit breaker comprises one or more RC elements having known estimated impedances, and a voltage source representing the open circuit voltage (Voc) of the cell.

13. The system (100) as claimed in claim 12, wherein the open circuit voltage (Voc) of the cell in the equivalent circuit is a non-linear function of the electrical charge state of the cell.

14. The system (100) as claimed in claim 9, wherein the first state space filter is a Kalman filter.

15. Method (100) as claimed in claim 9, wherein the second state space filter is a Kalman filter.

16. System (100) as claimed in claim 9, wherein the electrochemical model is an Electrolyte Reinforced Single Particle Model.

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