State of charge (SOC) is on the face of it a straightforward measurement for batteries and BESS. But in reality, it’s highly complex and under- or over-reporting still happens in the industry, leading to under-performance and losses in revenue.
The importance of SOC in BESS
The SOC is one of the most important metrics in a battery energy storage system (BESS) or battery module as it quantifies how much energy is currently stored in a battery. For example, if a battery has an 80% SOC, it has 80% of its original (full) charge left.
In BESS applications, regardless of whether the installation is residential, commercial and industrial (C&I), or utility-scale storage, accurately knowing the SOC is critical to preventing overcharging and deep discharging―both of which will degrade the cells in the BESS and shorten the BESS lifespan. Knowing how much charge a BESS has left is also vital for BESS that are involved in grid services, such as frequency regulation and arbitrage.
An inaccurate SOC has more impact on larger scale systems than small-scale and residential BESS. A lot of residential systems can get by on basic SOC monitoring, but larger projects require much more accurate SOCs. This is for a number of reasons. Larger systems have a lot more modules that need to be synchronised to avoid imbalances, meaning that accurate SOCs are needed to balance the cells. Inaccurate SOCs can worsen any existing imbalances between racks and modules in a string, and if not accurately managed, some cells will regularly become overcharged while others will be undercharged. This unexpected imbalance (if not detected) can unevenly wear the cells, reducing the capacity of the BESS, especially as some cells may not be replaced as soon as they need to be. On the other hand, cells may be replaced prematurely, which can mean that BESS operators waste money on unnecessary replacements.
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From a financial perspective, a 5% miscalculation in large systems can lead to large energy waste that can cause operators to lose a lot of money if they are involved in selling energy to the grid. 5% errors are not uncommon and can be much higher in poorly managed systems.
Overestimating can lead to a BESS asset not fully delivering the anticipated energy volume to the energy markets, leading to cost imbalances. For taking part in grid services, an accurate SOC is needed as it can affect grid operations if the right amount of charge is not delivered by systems when required (for example, if not as much charge has been stored as expected), leading to underperformance―which can be penalised by system operators.
Because of these factors, a reliable and accurate SOC measurement is critical in BESS installation for maximising both performance and revenue.
Calculating the SOC of a BESS
It can be very difficult to accurately calculate the SOC of BESS and almost impossible to directly measure it. The BMS typically calculates the SOC of most BESS installation using the current, voltage and temperature of the cells. While different methods are used to try to calculate the SOC, the cell chemistry (with differing discharge characteristics and voltage profiles), usage patterns, aging and degradation (leading to overestimation by not account for aging) C-rate, battery inhomogeneities/defects, depth of discharge, sensor measurement drift, can all affect SOC measurements and make them deviate from the true SOC.
However, if it’s not calculated with a high accuracy, any errors can compound over time, making the true SOC get further and further away from the measured SOC over time. This can lead to massive differences (from what’s expected) in profitability and available energy for grid services.
The SOC value is calculated using the algorithms within the BMS and is calculated by taking the remaining charge and dividing it by the maximum charge and multiplying by 100 to express it as a percentage. The actual measuring of the SOC is typically done via Coulomb counting or the voltage method and relies on measuring the current and voltage in the cells. Coulomb counting tracks how much charge enters and leaves the battery over time, but inaccurate capacity assumption errors can build over time. BMS that use Coulomb counting also need to be periodically recalibrated to prevent errors compounding over time.
The voltage method uses the open circuit voltage (OCV) of the cells to determine the SOC. This can help to recalibrate any drifts from the true SOC but is troublesome with LFP batteries (which most modern BESS now use) are as they have flat OCV curves and experience hysteresis― a measurable voltage gap between the charge and discharge OCV curves when they are meant to be the same. Because of the hysteresis, the recent charging and discharging values can also alter the voltage reading regardless of what the actual SOC is, so there’s a lot of potential SOC errors with LFP batteries.
For C&I and utility-scale BESS that take part in money-making and grid activities, the SOC estimations have to be a lot more accurate than residential systems and tend to use smarter methods to calculate the SOC. Current advanced and smarter methods being implemented include AI-driven methods, adaptive Coulomb counting, and Kalman filtering.
AI-driven predictive methods combine real-time voltage, temperature, and current data against historical patterns within the BESS to determine the SOC and forecast SOC trends. Adaptive Coulomb counting is an advanced version of the standard Coulomb counting method that compensates for inefficiencies in large-scale systems (such as self-discharge). Kalman filtering is an approach that combines multiple measurement methods with mathematical models to improve the accuracy of the SOC estimation. Overall, battery analytics-driven methods are becoming more commonplace as they can interpret a lot more data and account for a wider range of factors with a higher accuracy―and make correlations that simple SOC estimation models cannot―leading to more accurate SOC estimates for larger-scale BESS.
What happens when the BESS has an inaccurate SOC
Inaccurate SOCs have big implications for both energy trading and for ensuring the safety and reliability of the BESS. For energy trading, the SOC shows traders how much capacity their system has and therefore how much can be traded. If the SOC is under-reported, energy traders may lose money as many will put in lower bids because there is uncertainty that they don’t have enough energy to bid more. In the opposite scenario, energy traders may bid normally but have a lower-than-expected capacity, which will lead to them being unable to fulfil all their bid requirements and being penalised in the process.
On the safety and reliability side, an inaccurate SOC can compromise the safety of the BESS. On one hand, the wrong SOC can prevent the BMS from recognising if the cells are fully charged, which can cause the cells to overcharge―which can not only cause accelerated aging, but these cells also then become a potential fire risk through overheating and increased internal pressure. Additionally, if the SOC is lower than estimated, it can also lead to deeper discharges than the cells can handle. These deeper discharges can cause accelerated aging and reduced performance.
Additionally, frequency response services need a large power bandwidth to work with because of the unpredictable nature of localised grid loads, and any BESS taking part in these services need to make sure they have the right power levels available without compromising safety. If the SOC is not accurately known, there is the risk that the cells can overheat.
To prolong BESS life and not cause accelerated degradation, keeping the SOC between 20–80% (sometimes 10%–90%) helps to extend battery life and reduce internal stresses, so having accurate SOC measurements helps to keep within these limits. Also, if the measurements are inaccurate, staying within these as-stated values helps to prevent the BESS from overcharging and deep discharging (acting as a buffer) if the true values are significantly off. Regular audits of the BMS to recalibrate the sensors is also key for reducing SOC errors, and if there is a business case, using the latest SOC algorithms also helps to make SOC tracking more accurate.
