Traction Battery Capacity Estimation With Adaptive Filter Weighting

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Solution Overview

Problem

Existing methods for estimating traction battery capacity in electric vehicles rely on fixed weights in low-pass filters, which do not accurately account for the gradual decay of battery capacity over time, leading to inconsistent and potentially inaccurate capacity estimation.

Innovation Solution

An adaptive low-pass filter method that adjusts weights based on the sample size and uncertainty of the battery's charge state and discharge history, using equations to determine the true capacity by averaging previous and new capacity values, thereby improving estimation accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If fixed weights are used in low-pass filters for capacity estimation, then the system complexity is reduced, but the measurement precision of battery capacity deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidbattery capacity estimation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent applies dynamics by transitioning from fixed weights to adaptive weights in the low-pass filter. The weight parameter is dynamically adjusted based on the charge experienced by the battery and state of charge, allowing the estimation system to adapt to changing battery conditions and improve measurement precision without excessive complexity

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter of the weight in the low-pass filter from a constant value to a variable that changes according to battery charge experience and state of charge. This parameter change enables the system to account for gradual battery capacity decay over time, improving estimation accuracy

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If adaptive weights based on charge experience are used, then the measurement precision of battery capacity improves, but the device complexity increases

Engineering Contradiction:

Improvebattery capacity estimation accuracyVSAvoidestimation system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent implements feedback by using the estimated capacity and charge experience as inputs to adjust the weight parameter. This feedback mechanism allows the system to continuously improve estimation accuracy by learning from past charge-discharge cycles while maintaining a manageable level of complexity through a structured adaptation algorithm

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If the weight parameter changes according to charge experienced, then the adaptability to battery aging improves, but the computational requirements increase

Engineering Contradiction:

Improveadaptability to battery agingVSAvoidcomputational energy consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by updating the weight parameter only during specific charging periods rather than continuously. This approach provides sufficient adaptability to battery aging while reducing computational energy consumption by performing weight adjustments only when necessary, rather than during every operational state

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12533990B2Battery capacity estimation and control

Publication Date: 2026.01.27 FORD GLOBAL TECH LLC

AI summary

An automotive power system alters a maximum discharge power of a traction battery according to an estimated capacity of the traction battery that depends on a weight parameter having a value that changes according to a charge experienced by the traction battery during periods when a switch connects the traction battery with an electric machine.