Blended Positive Electrode Battery State Estimation

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Solution Overview

Problem

Existing methods for estimating the state of secondary batteries, particularly those with blended positive electrode materials, face challenges in accurately predicting voltage variation behavior, making it difficult to optimize the mixing ratio of these materials for specific applications.

Innovation Solution

The use of an Extended Kalman Filter (EKF) algorithm to estimate the state of a secondary battery by measuring voltage and current intervals, incorporating a circuit model that accounts for the blended positive electrode material's unique electrochemical properties, allowing for reliable prediction of state parameters such as state of charge and voltage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a blended positive electrode material is used to optimize battery performance, then the energy capacity and stability are improved, but the voltage variation behavior becomes complex and difficult to predict

Engineering Contradiction:
Improvebattery performanceVSAvoidvoltage variation prediction
Core Design Contradiction:
ReliabilityVSMeasurement precision

Solution Approach 1:

The blended positive electrode material is divided into multiple components, each with distinct voltage characteristics. The EKF algorithm separately estimates the state of charge for each component based on its unique voltage profile, allowing accurate prediction of overall voltage behavior despite the complexity of the blend.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The EKF algorithm continuously compares predicted voltage (based on estimated SOC and battery model) with actual measured voltage, using the difference (innovation) to update and refine the SOC estimates. This feedback mechanism enables accurate voltage prediction even for blended materials with complex electrochemical behavior.

Inventive Principle:
Principle #23Feedback

2Ease of manufacture

If the state of charge is estimated using conventional methods, then the estimation process is simple, but the accuracy is insufficient for blended electrode materials

Engineering Contradiction:
Improveestimation processVSAvoidstate of charge accuracy
Core Design Contradiction:
Ease of manufactureVSMeasurement precision

Solution Approach 1:

The EKF algorithm acts as an intermediary between the simple voltage measurement and the complex blended electrode material system. It uses the voltage input along with battery parameters and electrochemical models to compute accurate SOC estimates, bridging the gap between simple measurement and complex material behavior.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The algorithm dynamically adjusts estimation parameters based on operating conditions. It updates the state vector to include individual SOC values for each electrode material component, and modifies the prediction and update steps of the EKF algorithm to account for the specific voltage characteristics of each blended component.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentEP3056917B1Apparatus for estimating state of secondary battery including blended positive electrode material and method thereof
Publication Date: 2019.09.18 LG CHEM LTD
  • EP3056917B1 patent drawingFigure 1~2
  • EP3056917B1 patent drawingFigure 3~4
  • EP3056917B1 patent drawingFigure 5

AI summary

An apparatus for estimating a state of a secondary battery by using an Extended Kalman Filter is provided, in which the secondary battery includes a positive electrode including a first positive electrode material and a second positive electrode material having different operating voltage ranges from each other, a negative electrode including a negative electrode material, and a separator interposed therebetween. The apparatus includes a sensor unit which measures voltage and current of the secondary battery at time intervals, and a control unit electrically connected with the sensor unit, and estimates the state of the secondary battery including a state of charge of at least one of the first positive electrode material, the second positive electrode material, or the negative electrode material, by implementing an Extended Kalman Filter algorithm using a state equation including, as a state parameter, the state of charge of at least one of the first positive electrode material, the second positive electrode material, or the negative electrode material, and an output equation including, as an output parameter, the voltage of the secondary battery. The state equation and the output equation are derived from a circuit model which includes a first positive electrode material circuit unit and a second positive electrode material circuit unit connected in parallel with each other, and a negative electrode material circuit unit connected in series with these two circuit units.