Monitoring and optimizing valve-regulated lead-acid (VRLA) batteries are essential to ensure efficient performance and safe operation. Two key indicators, the State of Charge (SOC) and the State of Health (SOH), allow for an accurate assessment of a battery’s remaining capacity and aging level, respectively. However, accurately estimating these values requires advanced models and cutting-edge algorithms. In this article, we explore how techniques such as the Extended Kalman Filter and the Particle Swarm Optimization (PSO) algorithm are used to achieve precise SOC and SOH estimations, significantly enhancing the safety, lifespan, and reliability of VRLA battery systems.

Introduction

Monitoring and optimizing valve-regulated lead-acid (VRLA) batteries are essential to ensure their performance and safety. Key indicators such as the State of Charge (SOC) and the State of Health (SOH) provide critical insights into a battery’s capacity and aging. This article delves into the estimation of SOC and SOH using advanced algorithms like the Kalman filter and Particle Swarm Optimization (PSO).

What are SOC and SOH?

The State of Charge (SOC) measures the remaining capacity of the battery, while the State of Health (SOH) assesses its aging level relative to its original maximum capacity. These parameters are essential to evaluate the operational reliability of a battery system.

Key Differences:

• SOC: Indicates the current available capacity of the battery.
• SOH: Represents the ratio of the battery’s maximum available capacity to its factory-rated capacity.

The Importance of Accurate SOC and SOH Estimation

Accurate SOC and SOH estimation not only enhances operational safety but also extends the battery’s lifespan. Since SOC accuracy depends on SOH values and vice versa, their joint estimation ensures reliable monitoring.

Benefits:

• Improves operational safety.
• Reduces measurement errors to within 5%.
• Optimizes the performance of the Battery Management System (BMS).

First-Order RC Model for SOC and SOH Estimation

The first-order RC model is widely used due to its ability to accurately represent the electrical behavior of batteries. This model incorporates parameters such as internal resistance (R0), polarization resistance (R1), and polarization capacitance (C1).

Algorithms Used: PSO and Kalman

PSO (Particle Swarm Optimization)

The PSO algorithm adjusts the battery model parameters based on the minimal variance between measured data and the model.

Kalman Filter

The Kalman filter uses input data such as voltage, current, and temperature to estimate the SOC in real time, efficiently filtering system noise.

Battery Parameter Identification

The parameter identification process involves:

1. Collecting initial data for voltage, current, and temperature.
2. Using PSO to optimize key parameters such as R0, R1, and C1.
3. Minimizing the error between model predictions and measured data as the objective function.

Parameter Deduction

Since battery parameters vary with temperature and SOC, fuzzy logic rules are applied to predict and adjust these parameters.

Key Rules:

1. Ohmic and polarization resistance follow a U-shaped trend across different SOC levels.
2. At lower temperatures, resistance increases while capacitance decreases.
3. Newly identified parameters are assigned greater weight.

SOC Estimation Using the Extended Kalman Filter

The extended Kalman filter enables precise SOC estimation through the following steps:

1. Prior spatial state estimation.
2. Calculation of the observed value.
3. Adjustment of spatial state covariance.
4. Application of the Kalman gain matrix.
5. Posterior spatial state estimation.

This approach ensures superior accuracy by filtering out system noise and errors.

SOH Estimation

SOH is estimated by observing differences in SOC changes during identical discharge conditions. This method helps determine the battery’s actual remaining capacity.

Main Steps:

1. Compare discharged SOC levels with those of a new battery.
2. Assess the maximum available capacity based on aging effects.

Conclusion

Accurate SOC and SOH estimation using advanced techniques like PSO and Kalman filters significantly improves the efficiency, safety, and lifespan of VRLA batteries. These methodologies are crucial for developing next-generation battery management systems.