Digital illustration of a boost converter with real-time inductance and capacitance adjustment.

Boost Your Converter's Performance: How to Identify Inductance and Capacitance Online

Jordan Keane in Tech & Innovation December 2025 • 4 min read.

"Unlock peak efficiency and responsiveness in your boost converter with real-time inductance and capacitance identification."

In today's world, digitally controlled DC-DC converters have become incredibly important, and are now a popular research area. They are less sensitive to system noise and changing component values, and also allow for complex control methods and online updates. This makes them ideal for many modern applications.

Among the various control techniques used in digital DC-DC converters, voltage mode and current mode control are two popular approaches. Both rely on accurate converter modeling, but face a common challenge: the inductance of the inductor and the capacitance of the output capacitor can change over time due to temperature variations, aging, and load conditions. These changes directly impact the performance of the converter.

To tackle this issue, researchers have been exploring ways to identify inductance and capacitance online, allowing for real-time adjustments that maintain optimal performance. This article dives into a promising solution: a Variable Forgetting Factor Recursive Least-Squares (VFF-RLS) algorithm designed specifically for boost converters. Let's explore how this algorithm works and what makes it such an attractive option.

The Key: Online Inductance and Capacitance Identification with RLS

Digital illustration of a boost converter with real-time inductance and capacitance adjustment.

The core of this method lies in accurately identifying the inductance (L) and capacitance (C) values while the boost converter is running. By continuously monitoring and adjusting these parameters, the converter can maintain its desired performance despite any changes in operating conditions or component characteristics.

The process starts with a dynamic adjustment to the voltage reference, creating a small, controlled disturbance. Then, by measuring the input voltage, output voltage, and inductor current, the algorithm can calculate the inductance and capacitance using the Recursive Least-Squares (RLS) method. This adaptive approach allows for continuous learning and correction.

Accurate Converter Modeling: Improved discrete-time average state equation that considers inductor current ripple, enhancing accuracy in parameter identification.
Online Identification: Real-time adaptation of inductance and capacitance through RLS algorithm, enabling continuous monitoring and adjustment.
Nonlinearity Mitigation: Elimination of boost converter's nonlinearity effects on parameter identification within the models.

However, a standard RLS algorithm uses a fixed 'forgetting factor,' which determines how much weight is given to recent data versus past data. A fixed value can be a problem: a value too close to 1 results in high precision but poor tracking of changes. Decreasing the value improves tracking, but reduces accuracy in stable conditions. The VFF-RLS algorithm addresses this by dynamically adjusting the forgetting factor.

The Future of Converter Control

The VFF-RLS algorithm offers a practical and effective way to maintain optimal performance in boost converters by dynamically identifying and compensating for variations in inductance and capacitance. Its simplicity and low hardware requirements make it a compelling solution for a wide range of applications, paving the way for more robust and efficient power electronic systems.

About this Article -

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Everything You Need To Know

What is a Variable Forgetting Factor Recursive Least-Squares (VFF-RLS) algorithm and how does it work with a boost converter?

A Variable Forgetting Factor Recursive Least-Squares (VFF-RLS) algorithm is used to identify the inductance and capacitance in real-time while the boost converter is running. By continuously monitoring and adjusting these parameters, the converter maintains its desired performance despite any changes in operating conditions or component characteristics. This adaptive approach allows for continuous learning and correction.

Why is it important to identify inductance and capacitance online in a boost converter?

The ability to identify inductance and capacitance online is important because the inductance of the inductor and the capacitance of the output capacitor can change over time due to temperature variations, aging, and load conditions. These changes directly impact the performance of the converter. Without online identification, the boost converter's performance may degrade over time.

How does the Recursive Least-Squares (RLS) method work, and what is the role of the 'forgetting factor' in the Variable Forgetting Factor Recursive Least-Squares (VFF-RLS) algorithm?

The Recursive Least-Squares (RLS) method is used to calculate the inductance and capacitance by measuring the input voltage, output voltage, and inductor current. A standard RLS algorithm uses a fixed 'forgetting factor' which determines how much weight is given to recent data versus past data. The Variable Forgetting Factor Recursive Least-Squares (VFF-RLS) algorithm improves upon this by dynamically adjusting the forgetting factor.

What are voltage mode and current mode control, and how are they related to inductance and capacitance in digital DC-DC converters?

Voltage mode and current mode control are two popular approaches used in digital DC-DC converters. Both rely on accurate converter modeling, but face a common challenge: the inductance of the inductor and the capacitance of the output capacitor can change over time. The concepts of voltage mode and current mode control are important because their performance is linked to the accuracy of the inductance and capacitance values, making online identification methods valuable.

What are the main advantages of using online inductance and capacitance identification in boost converters?

The key advantages include: Accurate Converter Modeling: Improved discrete-time average state equation that considers inductor current ripple, enhancing accuracy in parameter identification. Online Identification: Real-time adaptation of inductance and capacitance through the RLS algorithm, enabling continuous monitoring and adjustment. Nonlinearity Mitigation: Elimination of boost converter's nonlinearity effects on parameter identification within the models.