In order to tackle the detrimental impacts of climate change and move towards a more sustainable future, people worldwide widely acknowledge the necessity of attaining carbon neutrality. Incorporating clean and renewable energy into the grid is a vital approach. To address their intermittent characteristics, energy storage devices play a vital role in bridging the divide between energy generation and utilization. Lithium-ion batteries are acknowledged as the favored option for energy storage due to their notable attributes, such as high power density, excellent energy efficiency, extended operational lifespan, and safety enhancements. In the face of the large number of battery packs required in these applications, BMS is an important guarantee for the efficient and safe operation of these battery packs.
The primary objective of BMS is to enhance battery safety and extend its lifespan. In addition to monitoring the battery’s SOC, this can also be done by continuously monitoring and controlling the battery’s SOH. In this article, we will focus on the important role of BMS in monitoring battery SOH and how to measure battery SOH through BMS. The solution we will present here can be adapted to any system that uses rechargeable batteries and may require a BMS.
What is the Battery SOH?
The battery’s health status (SoH) (i.e., the battery or battery pack or battery module) indicates the ongoing general condition and performance capabilities of the battery compared to the new battery.
The SoH is expressed as a percentage (%):
- 100% SoH = BoL- Beginning of Life: This signifies that the battery’s condition aligns with the manufacturer’s specifications.
- 0% SoH = EoL- End of Life: This indicates that the batteries are no longer suitable for particular applications.
As time passes, the battery’s SoH gradually decreases from 100% to 0% in a linear fashion as the battery’s performance capabilities diminish. Typically, batteries use up to 70% or 80% of their SoH for applications related to electrical mobility. This is considered the first life of the battery. Afterward, the battery embarks on a second phase of usefulness, allowing it to serve in applications pertaining to stationary energy storage systems.
What Can We Benefit from Measuring Battery SOH?
The SOH of a battery reflects its ability to store and transfer energy relative to its initial state and is a key indicator of whether there is aging. An accurate battery SOH estimation system is an important aspect of BMS because it provides knowledge about battery performance, allows for battery fault diagnosis, and helps achieve an accurate estimation of battery SOC SOH. Furthermore, there is a strong desire for predicting long-term performance degradation and estimating the remaining useful life (RUL) of the battery. This predictive capability is crucial in guiding the battery’s management, maintenance, and recycling throughout its entire lifecycle.
How to Measure Battery SOH?
Since it is so important to measure battery SOH, what is the effective way to measure it? Let’s take a look.
While SOC can be aligned with the charge in the battery, SOH is a more abstract property that is correlated with the capacity, age, and wear level of the battery. Even if the SOH battery parameters cannot be measured directly, they can be calculated by measuring the relevant physical quantities. Let’s see how this works.
Internal Resistance Measurement
Internal resistance can be a clear sign of SOH and is inversely proportional to this parameter -higher internal resistance of the battery indicates lower health status. You can determine the internal resistance by measuring both the open circuit voltage and the voltage while applying the current load. The voltage drop is indicated by the difference between these two values. Then, the internal resistance can be calculated using Ohm’s law.
Calculating the battery’s internal resistance using this method is time-consuming because OCV measurements are only possible when the battery is at rest. An alternative approach entails measuring the battery’s energy output during operation to calculate its internal resistance using Joule’s law. Alternatively, electrochemical impedance spectroscopy (EIS) can be used to obtain the resistance value from the impedance of the cell.
Internal Impedance Measurement
As a battery degrades, it experiences an increase in internal impedance, akin to the rise in electrical resistance. Therefore, impedance measurements can also be used to estimate health status. As mentioned earlier, the EIS method measures battery impedance by employing AC at varying frequencies and then identifies impedance as a frequency-dependent function.
This approach allows for precise calculation of internal impedance, thereby enabling accurate estimation of battery degradation and SOH. However, it’s worth noting that EIS, being a complex solution, may not align with the needs of every BMS and might not perfectly suit the battery’s operational conditions.
Charge/Discharge Cycle Count
There is a relationship between the health status of lithium-ion batteries and their cycle life. Therefore, counting the remaining number of charge/discharge cycles can be their simplest and most usable SOH estimator. In this case, the cycle life can be used as a reference point, but to calculate the number of cycles, the battery needs to be charged to full.
Although this method is simple, it offers limited accuracy because it does not account for critical factors like voltage and current, which can influence the battery’s state. Additionally, it overlooks the battery’s operating conditions.
SOC Estimators Working for Battery SOH
Certain well-known techniques used to measure battery SOC are also applicable to BMS SOH estimation, including:
Coulomb counting: The battery experiences a simultaneous reduction in health status and a loss of rated capacity. Thus, once the rate at which ability decays over time is known, SOH can be found.
Kalman filter: The Kalman filter relies on several cell parameters, such as internal resistance, which plays a crucial role in estimating SOH. This battery SOH algorithom can actively monitor the battery’s real-time performance and forecast its degradation and aging.
Neural networks: Neural networks have the capability to process both linear and nonlinear data. By scrutinizing the internal battery parameters, the ANN can make estimations of the SOH across different conditions.
Fuzzy logic: The fuzzy logic model can evaluate the SOH in battery by employing input data such as internal resistance, impedance, and other parameters. Implementing the fuzzy logic approach doesn’t require having complete and exhaustive data about your battery.
As with SOC estimation, SOH is typically defined by mixing several measurement practices. This approach empowers BMS developers to examine the battery’s state meticulously and attain a high level of accuracy. For example, when we integrate Coulomb counting with neural networks or combine internal resistance and impedance measurements with fuzzy logic, we can achieve superior results.
It’s important to note that some SOH estimation methods may not be suitable for particular applications. For instance, to extend the battery life in an EV, it is highly recommended not to fully charge or discharge the battery. This is why cycle counting and Coulomb counting techniques are not suitable for EV SOH assessment.
What is the Battery SOH Used for?
The goal is to offer a performance indication for the battery’s current state or to convey how much of its useful lifespan it has utilized and how much remains before necessitating replacement. In crucial scenarios like backup and emergency power plants, the SOC BMS offers insight into the battery’s ability to sustain the load when called upon. SOH’s knowledge will also help plant engineers anticipate problems, perform fault diagnosis, or plan replacements. Essentially, this function monitors changes in the battery over time.
SOH for EV Applications
In electric vehicle applications, achieving the required range of car battery state of health when necessary is of utmost importance. Therefore, EV SOH relies on comparing the current capacity with the new capacity.
SOH for HEV Applications
In HEV applications, ensuring the capability to deliver the specified power holds significant importance. Hence, evaluating the SOH includes comparing the current DC resistance (or 1 kHz impedance) with the initial DC resistance (or 1 kHz impedance).
SOH for Industrial Equipment
Batteries are used in industrial machinery and equipment. SOH battery tests can prevent unexpected downtime and optimize equipment performance.
SOH for Smart Energy Management
In-home energy systems and microgrids, SOH data enables smart energy management by directing the use of batteries based on their health, optimizing energy usage and storage.
SOH for Uninterruptible Power Supplies (UPS)
SOH assessment is essential for backup power systems that provide electricity during power outages. Ensuring the health of batteries guarantees that UPS systems can execute their intended function when required.
Over time, each battery will age and degrade, which is an inevitable process as a result of the declining rated capacity. BMS can provide managers with early warnings of deterioration and the requirement for battery replacement by offering precise battery SOH assessments.
With up to 17 years of R&D experience, MOKOEnergy is well-positioned to adopt the right technology to escort your battery SOH according to your customer’s needs. If you have any questions, feel free to reach out to us without hesitation.
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