Guide to Accurate Battery SOC Estimation and Its Impact

That error margin isn't just a minor inconvenience—it’s a serious issue when batteries are used in energy markets, where precision is the name of the game for trading decisions and meeting contractual obligations. This article will explore why SOC errors are so common in LFP batteries, their potential financial consequences, and how operators can take action to mitigate these risks.

A Real-World Example of SOC Errors Impacting Revenue

One European energy storage operator learned the hard way just how unreliable BMS SOC estimation can be. Their 50 MWh battery energy storage system (BESS) consistently underestimated SOC by up to 45%. Imagine looking at your phone, seeing 65% remaining charge, and finding out later it was closer to 20%. The result? Missed trading opportunities and significant revenue loss.

The operator turned to cloud-based predictive battery analytics to resolve this issue. By benchmarking data from similar battery systems and creating corrective models, they reduced SOC errors to within ±3%. The financial impact was just as noteworthy—reliable trading returned, penalty risks fell, and profits improved.

This case study is a clear wake-up call for battery asset owners and operators. Relying solely on your BMS for accurate SOC readings is a gamble, especially with LFP batteries.

Why Are SOC Estimation Errors so Common?

To grasp the challenges of SOC estimation, it’s important to understand the two primary methods BMS use to calculate SOC:

1. Coulomb Counting (Ah-counting): This approach tracks how much charge enters or exits the battery over time. But slight errors—such as inaccurate capacity assumptions or current measurement offsets—add up quickly, leading to noticeable drift in SOC estimations.
2. Voltage Method: This method uses a battery’s open circuit voltage (OCV) to infer SOC. While it helps recalibrate drift over time, it's not foolproof—especially for LFP batteries, which have flat OCV curves and experience hysteresis, a phenomenon where voltage depends on the battery's previous charge history.

SOC errors stem from limitations inherent in these methods. For example, LFP batteries’ unique chemistry makes them particularly prone to inaccuracies. Their OCV curve has long, flat sections where small voltage estimation errors cause large SOC miscalculations. Hysteresis further complicates matters, as recent charging or discharging can alter the voltage reading regardless of the actual SOC. The reality is that this issue is common, but complex. We cover it in much greater detail in our recent white paper on SOC inaccuracies.