Battery Management System (BMS): A Comprehensive Guide to Measuring State-of-Charge and State-of-Health


By Alastair Bright, freelance writer.

In the field of Battery Management Systems (BMS), the primary objective is to ensure the safety and longevity of batteries. Achieving this goal hinges on the continuous monitoring and control of a battery’s State-of-Charge (SOC) and State-of-Health (SOH). Let’s explore the essential functions of BMS and techniques for estimating SOC and SOH in large-scale projects, including Battery Energy Storage Systems (BESS). It’s important to note that the insights shared here are applicable to any system that relies on rechargeable batteries and may require a BMS.

Considering Battery Requirements

Rechargeable batteries serve as power sources for a wide range of applications, and most of them require a BMS to guarantee the battery’s safe and enduring performance, with a particular focus on accurate SOC and SOH calculations.

Before we proceed, it’s important to understand that the methods we discuss for estimating SOC and SOH were developed in the context of BESS projects. However, these methods can be adapted for use in various systems, irrespective of their size or complexity.

Battery Energy Storage Systems (BESS)

A BESS is an electrochemical energy storage solution that centers around a rechargeable battery. This system can either store or discharge power as needed by charging and discharging the battery at optimal times.

Battery Management System (BMS)

A battery management system plays a dual role as both supervisor and caretaker of the battery. It continuously monitors and controls the condition of the battery cells while protecting them from potential risks. The development of a BMS is a multifaceted process that entails designing subsystems at both the hardware and software levels.

Selecting the Right Battery Technology

One of the initial considerations when developing a BMS is the choice of battery technology for your BESS. Battery energy storage solutions can employ various battery cell configurations, including:

  • Lithium nickel manganese cobalt oxide
  • Lead-acid
  • Nickel-cadmium
  • Sodium-sulfur
  • Zinc-bromine

Battery chemistry offers a wide array of combinations, each with its unique features and characteristics. It is essential to select a battery that aligns with your BESS specifications. For instance, the system’s operating conditions are a critical factor, as different battery chemistries have varying thermal tolerances. For example, lithium-ion batteries perform optimally within the range of 10°C to 40°C.

Charging and Discharging

Rechargeable batteries can be charged and discharged multiple times due to reversible electrochemical reactions that can restore a battery’s electric charge. Specific recommendations govern the charging and discharging of different battery types.

For instance, it’s advisable to recharge lithium-ion batteries even after a partial discharge, such as 30% of capacity, while avoiding complete energy depletion, which could damage the battery. Nickel-metal hydride (Ni-MH) batteries can be safely recharged, regardless of the capacity level, but regular recharging is necessary if they are stored or left unused for extended periods. Lead-acid batteries must be stored at full charge to prevent sulfation and capacity loss.

Charging and discharging requirements encompass voltage, current, and temperature limits. Exceeding these limits may lead to battery damage.

BMS Custom Battery Management Algorithms for Monitoring and Control

BMS is equipped with custom battery management algorithms that continuously monitor key parameters and intervene when necessary. These algorithms ensure that the battery operates within safe parameters, thereby protecting it from premature capacity loss and prolonging its lifetime.

Measuring State-of-Charge (SOC)

Calculating the state-of-charge is a fundamental function of a BMS, as it enables precise control over the charging and discharging processes, safeguarding the battery. Accurate SOC measurement is crucial for maintaining the battery’s performance over time.

Estimating State-of-Health (SOH)

State-of-health estimation is another essential function of a BMS. It helps users enhance battery performance and provides early warning of deterioration, indicating the need for battery replacement. Knowing the SOH provides valuable insights into the battery’s performance and the overall energy storage system, including efficiency and reliability.

Challenges in SOC and SOH Estimation

Unlike voltage or temperature, SOC and SOH cannot be directly measured as physical quantities. Instead, they require consideration of a range of factors and parameters to be assessed accurately. The following parameters are involved in calculating SOC:

  • Age
  • Cycle life (number of charge/discharge cycles)
  • Capacity
  • Internal resistance
  • Energy throughput
  • Temperature
  • Self-discharge rate
  • Voltage

When evaluating SOC, you should take into account the following parameters:

  • Battery chemistry
  • Voltage
  • Current
  • Capacity
  • Impedance
  • Charging/discharging rate
  • Temperature

SOC and SOH estimation is a challenging task, with no straightforward formula to assist BMS developers in identifying these characteristics. However, various scientific studies and technical articles on SOC and SOH estimation methods can be found online. Many of these methods claim to be accurate and reliable.

Estimating State-of-Charge (SOC)

Multiple approaches can be employed to determine a battery’s SOC, including direct measurements, indirect calculations, predictive techniques, and other technologies. Here are some of the most common methods:

Open Circuit Voltage (OCV) Method

This method relies on the relationship between a battery’s remaining capacity or SOC and its open-circuit voltage, which is the voltage with no current load. The stronger the dependence between voltage and SOC, the more accurate the measurements. This relationship is typically represented in a discharge curve, which can be found in battery datasheets or created through experimental measurements.

The OCV method is well-suited for determining the initial SOC of a battery characteristic. However, it may not work well for lithium-based batteries with flat discharge curves. Therefore, the OCV method is often combined with other measurement practices for more accurate SOC estimation in lithium battery management systems.

Coulomb Counting (Current Integration)

This method involves calculating coulombs or the quantity of electric charge, derived from the product of current and the time it takes for the charge to flow. Coulomb counting is a widely used method, but its accuracy depends on knowing the initial SOC as a reference point. In practice, the SOC is often reset to 100% periodically in the BMS design. Accurate current measurement is also crucial for reliable SOC estimation.

Kalman Filtering

Kalman filtering relies on measurements and analysis of a battery’s input and output data, such as current, voltage, temperature, internal resistance, and other parameters. Using this data, the Kalman filter algorithm builds an electrical model of the battery, simulates its behavior under various conditions, and estimates SOC accordingly. This approach is more complex but offers more accurate SOC estimation when combined with high-quality measurement devices and well-tuned parameters.

Model-Based Approaches

Model-based techniques employ mathematical models of the battery’s electrochemical behavior to estimate SOC. Such models are complex and require parameters such as battery capacity, internal resistance, and more. Advanced electrochemical models, like the Thevenin model or the electrical circuit model, can provide precise SOC estimates.

AI and Machine Learning

Artificial intelligence and machine learning have emerged as powerful tools for SOC estimation. These techniques leverage historical data, measurements, and relevant parameters to create predictive models for SOC estimation. Such models can adapt to various battery types and operational conditions, making them versatile and accurate. With continuous learning, machine learning models can enhance their accuracy over time.

Voltage-Based SOC Estimation

Voltage is a straightforward metric to measure and use for estimating SOC, but it is limited in its accuracy. This limitation is especially evident in lithium-ion batteries with relatively flat voltage discharge curves. Despite its limitations, voltage-based SOC estimation is a practical and cost-effective approach for many battery management systems, particularly in stationary energy storage systems where precise SOC measurement may not be critical.

Combining Methods for Improved Accuracy

Battery management systems often use a combination of these methods to improve the accuracy and robustness of SOC estimation. The hybrid approach compensates for the limitations of each individual method. The OCV method might be used as a primary estimator, while Coulomb counting or Kalman filtering can be employed to correct the initial estimate.

Estimating State-of-Health (SOH)

State-of-health estimation can be more challenging than SOC estimation since it involves predicting a battery’s long-term performance and degradation. SOH estimation is important because it provides a basis for optimizing maintenance and replacement strategies to ensure a battery’s reliability. Several methods are used to estimate SOH:

Capacity Testing: Periodically testing a battery’s capacity by fully charging and discharging it and comparing the result to its initial capacity provides a simple but effective way to estimate SOH. The decrease in capacity over time indicates a loss of SOH.

Resistance Testing: Measuring the internal resistance of a battery can offer insights into its health. An increase in resistance is an indicator of degradation. Various methods, including impedance spectroscopy and voltage response analysis, can be used to determine resistance.

Cycle Life Estimation: Manufacturers often specify the cycle life of a battery. Monitoring the number of cycles and considering how close the battery is to its expected cycle life can provide a rough estimate of SOH.

Aging Model-Based Approaches: Some advanced battery management systems use aging models that account for various factors contributing to degradation, such as temperature, current, and depth of discharge. These models provide more accurate SOH estimates by considering a broader range of parameters.

Machine Learning: Machine learning models can be trained on historical data to predict SOH based on a wide range of parameters. These models become increasingly accurate as more data becomes available and are capable of estimating SOH in real time.

Choosing the Right Estimation Method

The choice of SOC and SOH estimation methods should depend on various factors, including the type of battery, the intended application, the available instrumentation and sensors, and the complexity of the battery management system.

For applications that require high-precision SOC and SOH measurements, advanced techniques such as Kalman filtering or model-based approaches are preferred. In applications where precision isn’t critical, voltage-based methods or hybrid approaches combining multiple methods may be adequate. In real-time applications, machine learning-based approaches can provide flexibility and adaptability.

Whatever the chosen method, regular calibration and validation are essential to maintain accurate SOC and SOH estimates.


Battery management systems are critical components in the performance and longevity of batteries, especially in battery energy storage systems and other applications that rely on rechargeable batteries. Accurate measurement of state-of-charge (SOC) and state-of-health (SOH) is essential for optimizing battery operation, ensuring safety, and extending battery life. The right choice of SOC and SOH estimation methods depends on various factors and should be tailored to the specific needs of your application.

In practice, a combination of methods is often used to achieve the most accurate results. Latest advances in technology, such as machine learning and AI, are increasingly playing a role in enhancing SOC and SOH estimation capabilities. As battery technologies continue to evolve, so too will the tools and techniques for effectively managing and maintaining these vital energy storage components. Battery management system developers should stay abreast of these developments to keep their systems at the cutting edge of the field.

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