Battery Parameter Estimation Using Linear Regression
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Solution Overview
Problem
Existing battery parameter estimation methods require complex hardware or high-performance software to estimate battery parameters, making it difficult to implement in a single integrated circuit due to area and cost constraints, especially when dealing with secondary batteries like lithium ion batteries that require precise state-of-charge estimation to avoid over-discharge or over-charge issues.
Innovation Solution
A battery parameter management system and method that uses a simple battery equivalent model with an internal resistor, internal capacitor, and a parallel dynamic resistor and capacitor, estimating parameters using a processor that applies measured current and voltage through a control switch unit, and supplies pulse current at varying frequencies to estimate resistance, capacitance, and dynamic element parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex battery equivalent model is used to improve measurement precision, then parameter estimation accuracy is improved, but device complexity increases requiring large hardware area or expensive high-performance processors
Solution Approach 1:
The patent transforms the complex parameter estimation problem into a simple linear regression problem by changing the mathematical parameters and estimation method. Instead of using complex nonlinear models, the patent employs a linear regression approach that can be efficiently implemented with simple hardware or basic processors, thus resolving the contradiction between accuracy and complexity
Solution Approach 2:
The patent creates a simplified mathematical model (linear regression model) that copies the essential behavior of the complex battery system without requiring complex computational resources. This simplified model maintains sufficient accuracy for practical applications while dramatically reducing hardware requirements
2Device complexity
If a simple battery equivalent model is used to reduce device complexity, then hardware area or processor cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent changes the estimation approach from complex nonlinear parameter estimation to simple linear regression, achieving accurate parameter estimation with minimal computational resources. This parameter transformation allows simple hardware to achieve precision previously requiring complex systems
Solution Approach 2:
The patent enables the battery system to self-characterize by using its own operational data (voltage, current measurements during charging/discharging) to automatically determine its equivalent circuit parameters through linear regression, eliminating the need for external complex testing equipment or processors
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient estimation of battery parameters without the need for expensive processors, allowing for real-time monitoring and management of battery state, reducing the risk of over-discharge or over-charge, and simplifying the implementation in hardware or software.
Implementation Method 1
a parallel circuit including a dynamic resistor and a dynamic capacitor which exhibit a non-linear operation characteristic based on an electrochemical reaction inside the battery
Data Source
AI summary
The present invention relates to a battery parameter management system and a battery parameter estimation method which are capable of simply estimating parameters of elements forming a battery equivalent model having a simple structure. The battery parameter system includes an amperemeter, a voltmeter, a control switch unit, and a processor, and the battery parameter estimation method includes supplying a pulse current, estimating resistance of an internal resistor, estimating capacitance of an internal capacitor, and estimating parameters of dynamic elements.