Reduced-Order Battery Model Parameter Adaptation
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Solution Overview
Problem
Existing battery management algorithms face limitations in accurately representing battery dynamics and state of charge (SOC) due to high computational complexity and inefficiency in handling electrochemical phenomena, especially when system complexity increases and prediction accuracy is required.
Innovation Solution
A reduced-order electrochemical battery model is developed, characterized by a single ordinary differential equation to emulate slow and medium electrochemical dynamics, with two model parameters (effective diffusion coefficient and internal resistance) updated based on input current and voltage profiles, allowing for real-time estimation of battery state variables and performance variables like SOC and available power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If map-based control is used to cover wide range of battery SOC and temperature variations, then the control accuracy is improved, but the device complexity and calibration requirements increase significantly
Solution Approach 1:
The patent transforms the complex map-based control into a parameter-based equivalent circuit model. By changing the parameters (R0, R1, C1, C2) of the equivalent circuit model based on SOC and temperature, the system achieves adaptive control without requiring extensive calibration maps. This parameter adaptation approach simplifies the control structure while maintaining accuracy across different operating conditions.
Solution Approach 2:
The patent segments the battery model into distinct equivalent circuit components (resistors and capacitors) that represent different electrochemical processes. This segmentation allows each component to be independently characterized and updated, reducing the overall complexity compared to a comprehensive calibration map while maintaining the ability to represent complex battery behavior.
2Productivity
If equivalent circuit model based control is used, then computational efficiency is improved, but the representation of electrochemical phenomena deteriorates
Solution Approach 1:
The patent dynamically updates the equivalent circuit model parameters (R0, R1, C1, C2) as functions of SOC and temperature, allowing the simplified circuit model to adapt its behavior to represent different electrochemical phenomena under varying operating conditions. This maintains computational efficiency while improving the physical realism of the model.
Solution Approach 2:
The patent implements parameter update mechanisms that use measured battery voltage and current data to adjust the equivalent circuit parameters in real-time. This feedback approach allows the model to continuously adapt to actual battery behavior, improving the representation of electrochemical phenomena without sacrificing computational speed.
3Measurement precision
If model complexity is increased to improve prediction accuracy, then the battery dynamics representation is improved, but the computational benefit deteriorates significantly
Solution Approach 1:
The patent uses parameter adaptation within a fixed-order equivalent circuit model to capture battery dynamics across different operating conditions. By updating parameters (R0, R1, C1, C2) based on SOC and temperature rather than increasing model order, the system maintains computational efficiency while improving prediction accuracy for battery voltage and state.
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
This approach provides a computationally efficient and accurate method for capturing battery dynamics and capacity fade over its life, enabling improved control and management of battery systems, particularly in Lithium-Ion batteries, by simplifying the model structure and reducing the need for extensive calibration.
Implementation Method 1
a single ordinary differential equation to emulate slow and medium electrochemical dynamics... estimated battery model parameters associated with battery (12) in order to accurately reflect the battery dynamic characteristics... effective diffusion coefficient
Implementation Method 2
an instantaneous voltage drops by current inputs... effective internal resistance... terminal voltage responses
Data Source
AI summary
A powertrain having a traction battery is operated according to performance variables of the battery based on state variables of a reduced-order electrochemical model of the battery. The state variables are estimated by an estimator based on the battery model. A parameter of the battery model characterizing dynamics of the state variables with respect to battery operating conditions is updated by the estimator based on the battery model.