CN107765187A - A kind of lithium battery charge state evaluation method - Google Patents

Abstract

The invention discloses a method for estimating the state of charge of a lithium battery, which includes establishing a battery equivalent circuit model and setting the number of recursions; collecting real-time voltage and current parameters of the battery; In the circuit model; update the model parameters of the equivalent circuit model by the least square method; judge whether the number of recursions meets the requirements, and output the estimated value of the state of charge of the lithium battery. The present invention combines the least square method and the Kalman filter algorithm to accurately estimate the state of charge value of the lithium battery, uses the Kalman filter algorithm to estimate the state of charge value of the lithium battery at the current moment, uses the least square method, and according to the past time The state of charge value updates the model parameters of the battery equivalent circuit model, so that the equivalent circuit model can be adjusted according to changes in the actual application conditions of the battery, improving the accuracy of the estimation of the state of charge value of the lithium battery. The invention is used for estimating the state of charge value of the lithium battery.

Description

A method for estimating state of charge of lithium battery

technical field

The invention relates to the technical field of lithium batteries, in particular to a method for estimating the state of charge of a lithium battery.

Background technique

The rapid development of modern industry has led to an increasing demand for petroleum resources, and the resulting environmental pollution has attracted attention from all sides. As a new power source for energy saving and environmental protection, lithium batteries have been used in industry, daily life and other fields. In electric vehicles industry in particular. As the energy source of electric vehicles, the management of lithium batteries is crucial to electric vehicles. In order to make full use of the energy of each power lithium battery on electric vehicles, it is required to manage lithium batteries reasonably and accurately obtain the lithium battery The state of charge (SOC) has become an important part of lithium battery management. In order to popularize electric vehicles on a larger scale, it is necessary to give full play to the power performance of the lithium battery system, improve the safety of its use, prevent overcharging or overdischarging of lithium batteries, prolong the service life of lithium batteries, optimize driving and improve the performance of electric vehicles. To ensure the performance of the lithium battery, the lithium battery management system (BMS) must accurately estimate the state of charge (SOC) of the lithium battery.

However, in the prior art, the state of charge value of the lithium battery cannot be directly measured in practical applications, mainly because the lithium battery system itself is a nonlinear system. In the prior art, there is an estimation method based on the definition of the state of charge. Since it needs to be under the condition of constant current and constant temperature, this method is difficult to meet the actual working conditions of the battery, and the rated capacity of the lithium battery is limited. The influence of discharge rate, operating environment temperature, and historical working conditions is not a constant, so this increases the difficulty of estimating the state of charge value.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method for estimating the state of charge of a lithium battery based on a Kalman filter algorithm.

The solution that the present invention solves its technical problem is:

A method for estimating the state of charge of a lithium battery, comprising the following steps:

Step A. Establishing a battery equivalent circuit model based on the Kalman filter algorithm, setting the number of recursions, and initializing the model parameters of the equivalent circuit model, the equivalent circuit model is described using state equations and measurement equations;

Step B. Collect the real-time voltage and real-time current parameters of the battery;

Step C. Input the real-time voltage and real-time current parameters of the battery at t=t _n into the equivalent circuit model of the battery, the state equation of the equivalent circuit model outputs the estimated value of the state of charge of the lithium battery at this time, and the measurement equation Output the open circuit voltage measurement value of the lithium battery at this moment;

Step D. Using the estimated value of the state of charge of the lithium battery and the measured value of the open circuit voltage at t=t _n , update the model parameters of the equivalent circuit model by the least square method;

Step E. Judging whether the number of recursions meets the requirements, if not, return to step B, and n=n+1, if yes, the estimation of the state of charge of the lithium battery is completed, and the estimated value of the state of charge of the lithium battery is output.

As a further improvement of the above scheme, in the step A, the state equation used to describe the equivalent circuit model is expression 1, X _n = φ _{n, n-1} X _n-1 + Γ _n-1 W _n-1 , where X _n is the estimated state of charge of the lithium battery at time t=t _n , φ _n,n-1 is the one-step transfer matrix, Γ _n-1 is the noise driving matrix of the state equation, W _n-1 is the state equation Noise vector; the measurement equation used to describe the equivalent circuit model is Expression 2, Z _n =H _n X _n +V _n , where Z _n is the measured value of the open circuit voltage of the lithium battery at t=t _n , and H _n is the measured matrix, the error vector of the V _n measurement equation; the model parameters of the equivalent circuit model refer to φ _{n, n-1} and H _n .

As a further improvement of the above scheme, the step D includes the following steps:

Step D1. Obtain state of charge matrix X and open circuit voltage matrix Z, where X _k = φ _{k, k-1} X _k-1 , Z _k = H _k X _k , where k = 0,1,2,3.. ...n;

Step D2. Solve the least square method estimation coefficient matrix B according to formula 3, where k=0,1,2,3...n;

Step D3. Estimate the coefficient matrix B according to the least square method, and update the model parameters φ _{n, n-1} and H _n of the equivalent circuit model.

The beneficial effects of the present invention are: the present invention combines the least square method and the Kalman filter algorithm to accurately estimate the state of charge value of the lithium battery. Least square method, and update the model parameters of the battery equivalent circuit model according to the state of charge value and open circuit voltage at the past time, so that the equivalent circuit model can be adjusted according to the change of the actual application condition of the battery, and improve the state of charge of the lithium battery. The precision of the value estimate. The invention is used for estimating the state of charge value of a lithium battery.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly describe the drawings that need to be used in the description of the embodiments. Apparently, the described drawings are only some embodiments of the present invention, not all embodiments, and those skilled in the art can obtain other designs and drawings based on these drawings without creative work.

Fig. 1 is a flow chart of the method for estimating the state of charge of the present invention.

Detailed ways

The concept, specific structure and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, features and effects of the present invention. Apparently, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative efforts belong to The protection scope of the present invention.

Referring to Fig. 1, the invention discloses a method for estimating the state of charge of a lithium battery, comprising the following steps:

Step A. Establishing a battery equivalent circuit model based on the Kalman filter algorithm, setting the number of recursions, and initializing the model parameters of the equivalent circuit model, the equivalent circuit model is described using state equations and measurement equations;

Step B. Collect the real-time voltage and real-time current parameters of the battery;

Step C. Input the real-time voltage and real-time current parameters of the battery at t=t _n into the equivalent circuit model of the battery, the state equation of the equivalent circuit model outputs the estimated value of the state of charge of the lithium battery at this time, and the measurement equation Output the open circuit voltage measurement value of the lithium battery at this moment;

Step D. Using the estimated value of the state of charge of the lithium battery and the measured value of the open circuit voltage at t=t _n , update the model parameters of the equivalent circuit model by the least square method;

Step E. Judging whether the number of recursions meets the requirements, if not, return to step B, and n=n+1, if yes, the estimation of the state of charge of the lithium battery is completed, and the estimated value of the state of charge of the lithium battery is output.

Specifically, the present invention combines the least square method and the Kalman filter algorithm to accurately estimate the state of charge value of the lithium battery. First, the Kalman filter algorithm is used to estimate the state of charge value of the lithium battery at the current moment, and then the least square method is used. And update the model parameters of the battery equivalent circuit model according to the estimated value of the state of charge and the measured value of the open circuit voltage of the lithium battery at the past time, so that the equivalent circuit model can be adjusted according to the change of the actual application condition of the battery, and the lithium battery charge can be improved. The estimation accuracy of the state of charge value can also ensure that the accuracy of the estimation of the state of charge value of the lithium battery will not shift with the change of the application condition of the lithium battery.

Further as a preferred implementation mode, in the specific implementation mode of the present invention, in the step A, the state equation used to describe the equivalent circuit model is the expression 1, X _n = φ _{n, n-1} X _n-1 + Γ _n-1 W _n-1 , where X _n is the estimated state of charge of the lithium battery at t=t _n , φ _{n, n-1} is the one-step transfer matrix, Γ _n-1 is the noise driving matrix of the state equation, W _n-1 is the noise vector of the state equation; the measurement equation used to describe the equivalent circuit model is Expression 2, Z _n =H _n X _n +V _n , where Z _n is the open circuit of the lithium battery at t=t _n Voltage measurement value, H _n is the measurement matrix, V _n is the error vector of the measurement equation; the model parameters of the equivalent circuit model refer to φ _{n, n-1} and H _n . The invention uses the Kalman filter equation to describe the equivalent circuit model of the lithium battery, uses the Kalman filter algorithm to process the observation data containing noise, and effectively improves the accuracy of the estimated state of charge of the lithium battery.

As a further preferred embodiment, the step D is used to update the model parameters of the equivalent circuit model according to the estimated value of the state of charge of the lithium battery and the measured value of the open circuit voltage at all past moments. Specifically, said step D includes the following steps:

Step D1. Obtain state of charge matrix X and open circuit voltage matrix Z, where X _k = φ _{k, k-1} X _k-1 , Z _k = H _k X _k , where k = 0,1,2,3.. ...n;

Step D2. Solve the least square method estimation coefficient matrix B according to formula 3, where k=0,1,2,3...n;

Step D3. Estimate the coefficient matrix B according to the least square method, and update the model parameters φ _{n, n-1} and H _n of the equivalent circuit model.

The preferred embodiments of the present invention have been described in detail above, but the invention is not limited to the described embodiments, and those skilled in the art can also make various equivalent modifications or replacements without violating the spirit of the present invention. These equivalent modifications or replacements are all within the scope defined by the claims of the present application.

Claims (3)

1. A lithium battery state of charge estimation method, is characterized in that, comprises the following steps: Step A. Establishing a battery equivalent circuit model based on the Kalman filter algorithm, setting the number of recursions, and initializing the model parameters of the equivalent circuit model, the equivalent circuit model is described using state equations and measurement equations; Step B. Collect the real-time voltage and real-time current parameters of the battery; Step C. Input the real-time voltage and real-time current parameters of the battery at t=t _n into the equivalent circuit model of the battery, the state equation of the equivalent circuit model outputs the estimated value of the state of charge of the lithium battery at this time, and the measurement equation Output the open circuit voltage measurement value of the lithium battery at this moment; Step D. Using the estimated value of the state of charge of the lithium battery and the measured value of the open circuit voltage at t=t _n , update the model parameters of the equivalent circuit model by the least square method; Step E. Judging whether the number of recursions meets the requirements, if not, return to step B, and n=n+1, if yes, the estimation of the state of charge of the lithium battery is completed, and the estimated value of the state of charge of the lithium battery is output. 2. A method for estimating the state of charge of a lithium battery according to claim 1, characterized in that: in the step A, the equation of state used to describe the equivalent circuit model is expression 1, X _n = φ _{n, n-1} X _n-1 +Γ _n-1 W _n-1 , where X _n is the estimated state of charge of the lithium battery at t=t _n , φ _{n, n-1} is a one-step transfer matrix, Γ _n-1 is the noise driving matrix of the state equation, W _n-1 is the noise vector of the state equation; the measurement equation used to describe the equivalent circuit model is Expression 2, Z _n =H _n X _n +V _n , where Z _n is lithium The open-circuit voltage measurement value of the battery at time t=t _n , H _n is the measurement matrix, and V _n is the error vector of the measurement equation; the model parameters of the equivalent circuit model refer to φ _{n, n-1} and H _n . 3. A method for estimating the state of charge of a lithium battery according to claim 2, wherein said step D comprises the following steps: Step D1. Obtain state of charge matrix X _k and open circuit voltage matrix Z _k , where X _k =φ _{k, k-1} X _k-1 , Z _k =H _k X _k , where k=0,1,2,3 … n; Step D2. Solve the least square method estimation coefficient matrix B according to formula 3, where k=0,1,2,3...n; Step D3. Estimate the coefficient matrix B according to the least square method, and update the model parameters φ _{n, n-1} and H _n of the equivalent circuit model.