Understanding Your Battery State of Charge

The global IoT battery market size is expected to hit around USD 23.83 billion by 2032. Knowing your battery’s state of charge (SoC) is crucial for optimizing its performance and longevity. The SoC indicates the amount of electrical charge stored in the battery relative to its maximum capacity, providing essential insights for effective energy management in IoT devices.

In this article, we’ll explore the intricacies of SoC, including various estimation methods, factors affecting accuracy, and the importance of SoC monitoring in battery-powered applications. By mastering SoC monitoring, you can enhance your battery’s performance and extend its lifespan.

Let’s dive into the methods for estimating SoC, understand the factors that influence its accuracy, and learn why regular monitoring is vital for maintaining your battery’s health and efficiency. Uncover the secrets of your battery’s true state of charge and take control of your energy management for maximum efficiency.

What is Battery State of Charge (SoC)?

The battery state of charge (SoC) is the ratio of the available electrical charge in the battery to its maximum capacity, expressed as a percentage. A fully charged battery is at 100%, while a fully discharged battery is at 0%. Accurate SoC monitoring is crucial for optimizing battery performance, ensuring safe operation, and maximizing battery lifespan.

Importance of SoC Monitoring in IoT Development

Monitoring SoC is essential for effective energy management, especially in the development of IoT devices. By knowing the SoC, you can decide when to charge or discharge the battery to maintain optimal performance and longevity.

Accurate SoC information helps prevent overcharging and over discharging, which can degrade battery health. It also allows for better planning and prevents unexpected power losses or interruptions, which is critical for the reliability and efficiency of IoT systems. 

Understanding and monitoring your battery’s SoC is vital for maintaining efficiency and prolonging its life, ensuring that IoT devices operate consistently and effectively.

Voltage Method for SoC Estimation

One of the simplest methods for estimating the battery state of charge (SoC) is the voltage method, which relies on measuring the open circuit voltage (OCV) of the battery.

The OCV is the voltage of the battery when it is not under load and has had time to stabilize. This voltage is directly related to the SoC, as the electrochemical reactions within the battery affect the terminal voltage.

Open Circuit Voltage (OCV)

The OCV-SoC relationship can provide a straightforward way to estimate the battery’s SoC. By measuring the open circuit voltage, you can refer to a predetermined voltage-SoC characteristic curve to determine the current state of charge. 

Limitations of Voltage-Based SoC Estimation

However, the voltage method has several limitations. The voltage-SoC relationship can be affected by factors like battery chemistry, temperature, and surface charge, making it challenging to obtain accurate SoC readings, especially for batteries with flat discharge curves like lithium-ion.

To get reliable voltage-based SoC readings, the battery needs to be at rest for at least 4 hours, which is impractical for many applications. 

Coulomb Counting for SoC Estimation

Another method for battery SoC estimation is coulomb counting, which involves measuring the current flowing in and out of the battery and integrating it over time to determine the remaining capacity. 

This method is based on Coulomb’s law and is commonly used in laptops, medical technology, and other portable devices. Coulomb counting provides a more direct way of estimating SoC, as it directly measures the charge transferred, rather than relying on indirect voltage measurements.

Principle of Coulomb Counting

The principle of coulomb counting is to track the amount of charge that has been discharged from or charged into the battery over time. By integrating the battery current over time, the state of charge can be calculated as the ratio of the remaining capacity to the rated capacity of the battery cell.

This method is particularly useful for lithium-ion batteries that have high coulombic efficiency and low self-discharge rates.

Advantages and Disadvantages

The main advantage of the coulomb counting method is that it can provide accurate SoC readings, especially for lithium-ion batteries.

However, coulomb counting is not without its limitations. Losses during charge and discharge can reduce the total energy delivered, and the available capacity at the end may be less than what was put in.

Periodic calibration is also recommended to ensure the “digital battery” matches the “chemical battery.”

Advantages of Coulomb Counting	Disadvantages of Coulomb Counting
Provides direct measurement of charge transfer Can deliver accurate SoC readings, especially for lithium-ion batteries Useful for portable devices and energy storage systems	Losses during charge and discharge can reduce the total energy delivered Available capacity at the end may be less than what was put in Requires periodic calibration to match “digital battery” and “chemical battery”

Battery State of Charge Estimation Methods

In addition to the voltage and coulomb counting methods, there are other advanced techniques for estimating battery SoC.

Impedance spectroscopy, for example, uses the battery’s complex impedance to measure SoC independently of voltage, allowing for accurate readings even with steady parasitic loads.

This method is particularly useful when the battery is continuously charged and discharged.

Impedance Spectroscopy

Impedance spectroscopy is a powerful technique that leverages the battery’s complex impedance to measure SoC without relying solely on voltage measurements.

This approach is effective even in the presence of steady parasitic loads, making it valuable for applications where the battery is frequently charged and discharged.

Kalman Filtering Techniques

Another approach is the use of Kalman filtering techniques, which combine battery models with voltage, current, and temperature measurements to provide real-time SoC estimates.

These advanced algorithms can effectively account for various factors that influence SoC, such as battery chemistry, aging, and environmental conditions, resulting in more reliable SoC information for effective battery management.

Model-Based Approaches

Model-based methods, such as those using electrochemical-thermal models, can also be employed to estimate the SoC value more accurately, especially as the battery ages. 

These sophisticated models incorporate a deep understanding of the battery’s electrochemical processes and physical characteristics to provide SoC estimations, even as the state of the battery and its performance evolves over its lifetime.

These advanced techniques help overcome the limitations of simpler voltage-based and coulomb counting methods, providing more reliable SoC information for effective battery management in a wide range of applications, from electric vehicles to energy storage systems.

Factors Affecting SoC Estimation Accuracy

Accurate SoC estimation is critical for effective battery management, but it can be influenced by several key factors.

Understanding these factors is essential for obtaining reliable and accurate SoC information to optimize battery performance and lifespan.

• Temperature Effects: Temperature is one of the most significant factors that can impact SoC estimation accuracy. The battery’s electrochemical reactions are highly sensitive to temperature, which can directly affect the voltage-SoC relationship.
  
  For example, colder temperatures can increase the specific gravity of the electrolyte, leading to higher voltage readings and potentially inaccurate SoC estimates.

• Battery Aging: As batteries age, their maximum capacity decreases over time, which can affect the accuracy of SoC calculations.
  
  As the battery’s capacity diminishes and it can no longer reach full charge, the range of voltages associated with different SoC levels may change, necessitating adjustments to SoC estimation algorithms to maintain accurate readings.

• Self-Discharge: Self-discharge, which occurs even when the battery is not in use, can also impact SoC estimation, especially for batteries with high self-discharge rates. This gradual loss of stored energy can lead to inaccuracies in the SoC calculations, particularly if the self-discharge rate is not properly accounted for.

To overcome these challenges and obtain reliable SoC information, advanced modeling and filtering techniques, such as the use of extended Kalman filters or electrochemical-thermal models, are often employed.

These methods can help compensate for the effects of temperature, aging, and self-discharge, ensuring that your battery management system (BMS) provides accurate SoC readings to optimize battery performance and lifespan.

Conclusion

Understanding and accurately monitoring your battery’s state of charge (SoC) is essential for optimizing performance and extending battery life, particularly in IoT applications. 

Accurate SoC monitoring is critical for effective energy management, preventing overcharging and over discharging, and ensuring reliable operation of IoT devices. Addressing factors like temperature, aging, and self-discharge is key to maintaining accurate SoC readings.

Contact Flatirons today to take your IoT development projects to the next level with precise energy management solutions.