Nonlinear Resistance Battery Model for Low-Temperature State Estimation
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Solution Overview
Problem
Existing battery state estimation systems struggle to accurately predict battery power capability at low temperatures, leading to potential engine cranking failures or propulsion system disablement due to over- or under-prediction of power demands.
Innovation Solution
The implementation of an equivalent circuit model (ECM) with nonlinear resistance elements to accurately model battery behavior, allowing for real-time estimation of battery state of charge (SOC) and power capability, which adapts to temperature changes and aging, using parameters derived from the Butler-Volmer model and piecewise polynomial approximations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a linear average resistance model is used in the equivalent circuit model, then the device complexity is reduced and computational efficiency is improved, but the measurement precision of battery state estimation deteriorates at low temperatures
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a static linear resistance model to a dynamic nonlinear resistance model that adapts to changing operating conditions. The nonlinear resistance element's parameters are continuously updated based on real-time measurements of terminal voltage and current, allowing the model to accurately represent battery behavior across varying temperatures and states of charge without requiring complex computational resources.
Solution Approach 2:
The patent implements parameter changes by modifying the resistance element in the equivalent circuit model from a fixed linear value to a variable nonlinear parameter. The resistance value is dynamically adjusted based on the relationship between terminal voltage and current measurements, enabling the model to capture temperature-dependent and state-of-charge-dependent battery characteristics while maintaining computational efficiency for real-time applications.
2Measurement precision
If a nonlinear resistance element is added to the equivalent circuit model to improve low-temperature accuracy, then the measurement precision of battery state estimation is improved, but the device complexity increases
Solution Approach 1:
The nonlinear resistance element introduces dynamic adaptation to the equivalent circuit model, allowing it to automatically adjust its characteristics based on operating conditions. This dynamic behavior enables accurate representation of low-temperature battery physics without requiring multiple separate models or complex parameter sets, as the single nonlinear element adapts its effective resistance based on real-time voltage and current measurements.
Solution Approach 2:
The nonlinear resistance element performs self-service by automatically adjusting its parameters based on the battery's actual operating state. Through continuous monitoring of terminal voltage and current, the element self-calibrates to represent the appropriate resistance characteristics for the current temperature and state of charge, eliminating the need for external complex modeling or frequent manual parameter updates.
3Reliability
If the battery state estimation system uses a more complex model to account for temperature effects, then the reliability of power capability prediction is improved, but the productivity of real-time battery management is reduced
Solution Approach 1:
The patent applies parameter changes by implementing a nonlinear resistance model where the resistance value is dynamically adjusted based on temperature and state of charge parameters. This allows the system to capture complex temperature-dependent battery behavior through parameter variation rather than through complex model structure, maintaining real-time computational speed while improving prediction reliability across the full operating temperature range.
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 enhances the accuracy of battery state estimation, enabling more consistent vehicle performance and increased power utilization across a wider range of temperatures, allowing for improved drivability and efficient battery management.
Implementation Method 1
parameters associated with the nonlinear element may be determined based on a Butler-Volmer model describing the kinetics of charge transfer in the battery system
Data Source
AI summary
Systems and methods are disclosed for estimating a state of a battery system such as a current-limited state of power and/or a voltage-limited state of power using a battery system model incorporating a nonlinear resistance element. Parameters of elements included in a battery cell model associated with a nonlinear resistance of a battery cell may be directly parameterized and used in connection with state estimation methods. By accounting for the nonlinear effect, embodiments of the disclosed systems and methods may increase available battery power utilized in connection with battery system control and/or management decisions over a larger window of operating conditions.