Kalman Filter SOF Estimation for Vehicle Stop-Start Batteries
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Solution Overview
Problem
Existing methods for estimating the State of Function (SOF) of a motor vehicle battery in automatic stop/start systems are intrusive and not suitable for real-time, fine-grained monitoring due to the time-consuming nature of internal resistance measurement through discharge tests, which is prohibitive for systems requiring quick and accurate battery state assessment.
Innovation Solution
A recursive Kalman filter-based method for estimating the SOF using periodic measurements of battery voltage and current during stop and start phases, with internal resistance calculated during start-up phases and sampled at high frequency during discharge events, allowing for rapid and non-intrusive SOF estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discharge test methods are used to measure internal resistance, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent pre-charges the battery to a known state before the measurement, establishing initial conditions in advance. This allows the subsequent measurement to be performed quickly during stop phases without requiring lengthy discharge tests, as the battery state is prepared beforehand.
Solution Approach 2:
The measurement is performed periodically during stop phases of the automatic stop/start system rather than continuously or through prolonged discharge tests. This periodic approach allows brief measurement windows that are sufficient for accurate internal resistance calculation without requiring extended test durations.
2Measurement precision
If high-frequency sampling is used during stop phases, then measurement precision of SOF is improved, but use of energy increases
Solution Approach 1:
High-frequency sampling is performed only during stop phases when the engine is off and the battery is not under load, rather than continuously. This periodic high-frequency monitoring during relevant periods achieves accurate SOF estimation while minimizing overall energy consumption compared to continuous high-frequency sampling.
Solution Approach 2:
The sampling frequency is dynamically adjusted based on the engine state - high-frequency sampling during stop phases when accurate measurement is critical, and lower or no sampling during running phases. This dynamic adaptation optimizes the balance between measurement precision and energy consumption.
Data Source
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AI summary
The invention relates to a method for estimating the state-of-function (SOF) of a battery of the type that uses battery current and voltage measurements and a calculation of the internal resistance of the battery. According to the invention, the state-of-function is estimated during each stop phase (PS) controlled by the automatic stop/restart system of the heat engine, using a Kalman type recursive calculation process. The state-of-function is represented by an estimate of the voltage at the terminals of the battery during a subsequent restart phase (PRn) controlled by the automatic stop/restart system of the engine.