Battery Impedance Estimation Using Weighted Initialization
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Solution Overview
Problem
Existing methods for estimating traction battery power capability, such as the Extended Kalman Filter (EKF), face challenges in converging to true parameter values, leading to unreliable battery impedance parameter estimates and battery power capability calculations, especially due to initial parameter value choices and temperature variations.
Innovation Solution
A controller initializes a battery impedance parameter estimation model with weighted initial values from predetermined and historical parameters, prioritizing recent historical values and adjusting based on temperature, using an EKF to converge to stable impedance parameters for accurate battery power capability calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EKF is used to estimate battery impedance parameters, then the battery power capability can be calculated, but the estimation may diverge or require excessive time to converge to true parameter values
Solution Approach 1:
The patent applies preliminary action by initializing the EKF with predetermined impedance parameter values before the estimation process begins. These predetermined values are selected based on battery state of charge and temperature, providing a reliable starting point that prevents divergence and reduces convergence time. The initialization occurs before actual operation, preparing the system in advance with optimal parameters.
2Loss of time
If EKF convergence time is reduced by better initial values, then timely battery power capability estimation is achieved, but the complexity of selecting appropriate initial values increases
Solution Approach 1:
The patent applies parameter changes by selecting predetermined impedance parameter values that vary according to battery state of charge and temperature conditions. Different parameter sets are prepared for different operating conditions, allowing the system to adapt to varying temperatures and charge states without requiring complex real-time parameter selection logic. This reduces convergence time while maintaining manageable system complexity.
3Measurement precision
If historical impedance parameter values are used to initialize EKF, then estimation accuracy improves, but the system becomes more sensitive to temperature variations
Solution Approach 1:
The patent applies local quality by using different predetermined impedance parameter values for different temperature ranges and battery states. Rather than using a single universal initialization set, the system selects parameter values locally optimized for specific temperature conditions and charge states. This ensures high accuracy for each local operating condition while reducing overall temperature sensitivity through appropriate parameter selection.
Data Source
AI summary
A vehicle is disclosed comprising a battery and a controller. Projected battery impedance parameters are calculated based on predetermined parameter values and historical parameter values generated by a battery parameter estimation model. The values may be weighted according to time data associated with the historical impedance parameter values and a temperature of the battery. Recent historical impedance parameter values may affect the projected battery impedance parameter values more than older historical impedance parameter values. The model is initialized with projected parameter values at vehicle initialization. Battery power capability is calculated using the projected parameter values for a period of time following vehicle initialization. After the period of time following vehicle initialization, battery power capability is calculated using impedance parameters generated by the model. The period of time following vehicle initialization may end when the model output has converged to a stable solution.