Battery energy storage systems keep modern grids in balance, absorb surplus renewables, and unlock lucrative market services—but only when operators know exactly how much energy is really inside the system. That knowledge hinges on State of Charge (SOC).
For lithium-iron-phosphate (LFP) batteries—now powering more than 80 % of new BESS builds—SOC error margins of 10-20 % points are routine because LFP’s flat voltage profile makes precise measurements tricky. The result is real money left on the table: missed bids, derating and compliance headaches.
ACCURE’s cloud analytics correct SOC in real time without a single hardware change. To quantify the upside, ACCURE and Modo Energy analysed operating data from BESS fleets in Great Britain and Texas. The verdict: Eliminating SOC drift boosts revenue up to 11 %. Find all details below.
The Challenge with Traditional SOC Estimation
Traditional BMS rely on two primary methods to estimate State of Charge: coulomb counting (Ah-counting) and the voltage-based method. Coulomb counting uses current sensors to track charge flow in and out of the battery. However, these sensors are prone to offset and gain errors that accumulate over time, leading to a significant reduction in the accuracy of SOC estimates. Voltage-based methods are often applied to recalibrate SOC and correct this error, but this is particularly ineffective for LFP batteries.
The challenge stems from LFP's flat open circuit voltage (OCV) curve, which makes it extremely difficult to translate voltage readings into accurate SOC values. Additionally, LFP OCV curves exhibit hysteresis effects, where the voltage depends not just on the SOC but also on the direction and history of current flow.
To make matters worse, most traditional BMS do not account for battery aging, which alters capacity and internal resistance over time, compounding SOC inaccuracies. This problem is common but also complex, we cover it in much great detail in our SOC whitepaper.
Operational Implications
Inaccurate SOC estimation influences operational strategy in several significant ways. When SOC is underestimated during periods of high demand, BESS units miss out on profitable trading opportunities. This under-utilization translates directly into lost revenue.
From a regulatory standpoint, most grid services and power contracts require precise energy delivery. If a BESS fails to meet its contractual obligations due to misjudged SOC, it can incur financial penalties and long-term credibility damage.
To mitigate these risks, some operators enforce conservative depth-of-discharge limits, such as operating only within the 10–90% SOC range. While this approach does ensure compliance and safety, it significantly reduces the system’s usable capacity and revenue potential. Viewed from another perspective, these assets effectively overpaid their battery hardware CAPEX by up to 25%. Additionally, inaccurate SOC estimation can have negative effects on warranty coverage, as the BMS records higher cycling than really happened.
Economic Impact
To quantify the financial impact of improved SOC estimation, Modo Energy simulated the revenue stack of a 100 MW / 200 MWh LFP BESS under three scenarios: a multi-year forecast for Great Britain, a 2024 scenario for Great Britain, and a 2024 scenario for Texas (ERCOT). These scenarios were selected to represent both long-term and near-term market conditions in two of the most advanced and active battery storage markets globally. In all cases, increasing SOC uncertainty led to significant revenue erosion. The results of these simulations are illustrated in Figure 2.
Great Britain – Multi-Year Forecast:
Over a multi-year horizon, annual revenue per megawatt drops from £110,000 to £98,500 as SOC uncertainty rises from 0% to 20%. Even a moderate SOC error of 10% leads to an annual loss of approximately £420,000 for a 100 MW / 200 MWh system—losses that accumulate over time and scale.
Great Britain – 2024 Scenario:
In the 2024-specific scenario, revenue potential falls from £56,000 to £52,000 per megawatt when SOC uncertainty increases from 0% to 20%. This translates to £205,000 in annual lost revenue for a 100 MW / 200 MWh system operating with a 10% SOC estimation error.
Texas (ERCOT) – 2024 Scenario:
In ERCOT, where market dynamics differ, the 2024 scenario shows revenue declining from $96,000 to $91,500 per megawatt as SOC error increases to 20%. A 10% SOC error results in an annual loss of roughly $220,000 for a 100 MW / 200 MWh asset.
Implementing SOC corrections could recover up to 11% of lost revenue, particularly in high-value scenarios such as the multi-year GB forecast. Accurate SOC not only protects against degradation in market performance, but it also actively unlocks earnings otherwise lost to SoC estimation errors.
Deep Dive: Predictive Battery Analytics
ACCURE’s predictive battery analytics reduce SOC errors to about ±2%-points of the true value by combining advanced electrochemical and degradation modeling, AI-based pattern recognition, cross-fleet learning, and high-resolution historical data into a unified approach.
These integrated cloud-based models recalibrate SOC, State of Power (SoP), and maximum tradable energy, typically every five minutes, enabling accurate, near real-time insights that outperform conventional estimation techniques. As a result, operators gain the ability to reduce conservative SOC buffers without compromising safety, make better-informed and more adequate trading decisions based on reliable energy availability, and respond more effectively to shifting market conditions. Improved SOC estimation yields direct economic gains and greater operational flexibility.
Conclusion: SOC Accuracy as a Strategic Asset
LFP batteries have taken the BESS industry by storm – and are here to stay. However, their inherent challenges with SOC estimation led to inefficiencies that silently eat into the revenues of owners and operators. Cloud analytics reliably minimize these errors without the need for hardware upgrades, allowing operators to realize the full potential of their assets: Increased revenues, lower regulatory and contractual risks, and more effective energy dispatch strategies in dynamic markets.
About the Partners
Modo Energy
Modo Energy is the all-in-one data and insights platform for energy storage analysts. At Modo Energy we're on a mission to help the teams working on battery energy storage to make better decisions, faster. One of the areas where we see the greatest uncertainty for battery energy storage is revenue generation. And so we provide research, indices, and forecasts for battery energy storage revenues.
ACCURE Battery Intelligence
ACCURE Battery Intelligence helps companies reduce risk, improve performance, and maximize the business value of battery energy storage. Our predictive analytics solution makes batteries safer, more reliable, and more sustainable. By combining cutting-edge artificial intelligence with expert knowledge of batteries, we bring clarity to energy storage.
