Battery Parameter Estimator Using Modified Least-Square Algorithm
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Solution Overview
Problem
Existing battery management systems face challenges in accurately estimating state of charge (SOC) and state of health (SOH) due to reliance on initial parameter assumptions, increased errors from biased current measurements, and computational expense, especially when dealing with transient conditions and aging batteries.
Innovation Solution
An electrochemical battery system that uses a processor to generate kinetic parameters for an equivalent circuit model of the battery through a modified least-square algorithm with a forgetting factor, incorporating a derivative of open cell voltage with respect to SOC and an estimated nominal capacity, which reduces the impact of initial SOC errors and converges even with inaccurate initial parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If current integration is used to determine SOC, then the method is simple to implement, but errors increase over integration time due to biased current measurements
Solution Approach 1:
The patent combines current integration with open-circuit voltage (OCV) measurements and a battery model to create a hybrid SOC estimation method. The estimator integrates current measurements while periodically correcting using OCV-SOC relationships from the battery model, thereby maintaining simplicity while reducing cumulative errors over time.
Solution Approach 2:
The patent implements a feedback mechanism where the estimated SOC is continuously compared with SOC values derived from OCV measurements and battery model predictions. This feedback loop allows the system to detect and correct drift in current integration errors, maintaining accuracy without requiring complex real-time measurements.
2Measurement precision
If OCV/SOC look-up table is used to reset SOC, then SOC accuracy is improved, but the method fails during transient conditions when voltage does not settle
Solution Approach 1:
The patent employs a dynamic estimator that adapts its behavior based on operating conditions. During transient conditions, the estimator relies more heavily on current integration and model predictions. During steady-state conditions, it incorporates OCV-based corrections. This dynamic adaptation allows accurate SOC estimation across all operating conditions without requiring voltage to fully settle.
Solution Approach 2:
The patent changes the weighting parameters of different estimation methods based on the battery's state. When the battery is in transient conditions, the estimator reduces the weight of OCV-based corrections. When stable, it increases the weight of OCV data. This parameter adjustment allows the system to leverage the strengths of each method while mitigating their weaknesses in different conditions.
3Measurement precision
If Kalman filter is used for SOC estimation, then estimation accuracy is improved, but computational expense increases
Solution Approach 1:
The patent uses a simplified estimator that achieves adequate accuracy without the full computational overhead of a Kalman filter. The estimator uses basic current integration with periodic OCV corrections and simple model-based predictions, consuming significantly less computational resources while providing sufficient accuracy for battery management applications.
Solution Approach 2:
The patent implements a partial version of advanced filtering techniques, using selective correction based on OCV measurements rather than continuous full-state Kalman filtering. This partial application of sophisticated estimation methods reduces computational expense while maintaining improved accuracy over simple current integration alone.
4Device complexity
If battery parameters are assumed known and time-invariant, then the model is simple, but accuracy decreases as battery ages and parameters vary
Solution Approach 1:
The patent performs preliminary characterization of the battery to establish initial parameter values and relationships. These pre-determined parameters and lookup tables are stored in the system and used as the basis for ongoing estimation. This preliminary action allows the simple model structure to benefit from accurate baseline data while maintaining computational efficiency.
Solution Approach 2:
The patent makes the battery model dynamic by allowing parameters to be updated based on aging indicators and operating conditions. The estimator tracks changes in battery characteristics over time and adjusts model parameters accordingly, enabling the simple model structure to adapt to battery aging and maintain accuracy throughout the battery's lifecycle.
Data Source
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AI summary
An electrochemical battery system in one embodiment includes at least one electrochemical cell, a first sensor configured to generate a current signal indicative of an amplitude of a current passing into or out of the at least one electrochemical cell, a second sensor configured to generate a voltage signal indicative of a voltage across the at least one electrochemical cell, a memory in which command instructions are stored, and a processor configured to execute the command instructions to obtain the current signal and the voltage signal, and to generate kinetic parameters for an equivalent circuit model of the at least one electrochemical cell by obtaining a derivative of an open cell voltage (Uoev), obtaining an estimated nominal capacity (Cnom) of the at least one electrochemical cell, and estimating the kinetic parameters using a modified least-square algorithm with forgetting factor.