Battery State Estimator Using Voltage-Dependent Variable Resistors
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Solution Overview
Problem
Existing battery state-of-charge (SOC) estimation models for electric vehicles are inadequate under certain operating conditions, such as low temperature or high/low SOC, due to the complexity of battery chemistry and limited computational resources, which leads to inaccuracies in voltage loss calculations during vehicle operation.
Innovation Solution
A battery circuit model with variable resistances is introduced, where ohmic, charge transfer, and diffusion resistances adjust based on voltage potential, allowing for more accurate SOC estimation by using equations that update resistances at each time step based on voltage and current balance equations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a linear circuit model with fixed parameters is used for battery SOC estimation, then computational resources are saved and the model is simple to implement, but estimation accuracy deteriorates under varying operating conditions such as low temperature or high/low SOC
Solution Approach 1:
The patent applies the dynamics principle by transforming fixed resistance parameters into voltage-dependent variable resistances. The ohmic resistance, charge transfer resistance, and diffusion resistance are no longer constant but dynamically adjust based on the battery terminal voltage, allowing the model to adapt to varying operating conditions while maintaining computational efficiency
Solution Approach 2:
The patent implements parameter changes by making the resistance values functions of voltage rather than fixed constants. The equivalent circuit model parameters (R_ohmic, R_charge_transfer, R_diffusion) are modified based on the measured terminal voltage, enabling the model to accurately represent battery behavior across different operating states without increasing computational complexity
2Reliability
If a complex circuit model with many frequency modes is used to capture full battery dynamics, then model accuracy improves, but computational power and memory requirements increase beyond available on-board resources
Solution Approach 1:
The patent applies partial action by selecting only the most significant frequency modes (three RC pairs) that capture the dominant battery dynamics. Instead of modeling all possible frequency modes, the patent focuses on the critical ones that have the greatest impact on SOC estimation accuracy, thereby reducing computational requirements while maintaining sufficient model reliability
Solution Approach 2:
The patent uses parameter changes to make the model parameters voltage-dependent, which allows a simplified model structure to capture complex nonlinear battery behavior. By allowing parameters to vary with voltage, the model achieves high reliability without requiring a complex multi-mode structure that would demand excessive computational resources
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 enables more accurate SOC estimation during vehicle operation by dynamically adjusting resistances, improving model accuracy under various conditions and reducing estimation errors, thus enhancing battery management and power prediction.
Implementation Method 1
resistances within the circuit model are variable based on voltage
Implementation Method 2
Battery state estimator with overpotential-based variable resistors
Data Source
AI summary
A battery model and equivalent circuit that includes an ohmic resistance, a first RC-pair that models a battery cell charge transfer reaction and double layer processes and a second RC-pair that models battery cell diffusion. Each of the ohmic resistance, the charge transfer reaction resistance and the diffusion resistance in the model are variable resistances, where each resistance changes in response to a change in voltage potential across the resistance.