Dr. Kai-Philipp Kairies working in a meetingDr. Kai-Philipp Kairies working in a meeting

This paper will explain:

Inaccurate SOC estimations have direct financial consequences, especially when batteries are deployed in energy markets and grid services. Two common financial consequences of inaccurate state of charge estimations include:

  1. Trading on wrong energy and power volumes: An over or underestimated SOC can lead to trading decisions that sell either too much or too little energy or power. This leads to suboptimal asset use and decreased revenue.  
  2. Penalties for non-compliance with market conditions: Batteries are contracted on energy and grid services markets based on their ability to deliver a certain power output for a specified time. Inaccurate SOC can result in the battery underperforming, leading to financial penalties. In the worst-case scenario, operators can even be excluded from markets.

Common Battery SOC Estimation Methods

Battery management systems usually use one of the following SOC estimation methods.

Voltage Method

The voltage method uses the open-circuit voltage (OCV) of the battery to estimate its SOC. Since the OCV of a lithium-ion battery is related to its SOC, a resting battery voltage can provide SOC information. However, the method gets inaccurate when overvoltages during operation and temperature effects occur. In addition, not all operational use cases have regular resting phases where the voltage can relax.

Unlike other battery chemistries, such as nickel manganese cobalt oxide (NMC), LFP batteries exhibit a flat voltage profile over a wide range of SOC levels, making it nearly impossible to deduce the SOC from voltage measurements alone during field operation. Figure 1 shows the open circuit voltage curves of both an NMC and an LFP cell. While the steeper course of NMC leads to a clear relationship between voltage and SOC, the flat LFP curve does not easily enable such a relationship. So, even slight inaccuracies in the measurement of the open circuit voltage can lead to significant differences in SOC estimation. On top of that, so-called hysteresis effects lead to different voltages for the same SOC, depending on the recent charge direction. This is why additional approaches are used in the field.

Open circuit voltage of NMC and LFP batteries according to Sterner et Stadler
Figure 1: Open circuit voltage of NMC and LFP batteries according to Sterner et Stadler (doi:10.1007/978-3-642-37380-0)

Coulomb Counting (Ah Counting)

Coulomb counting is based on the principle of conservation of charge and involves integrating the battery current over time to estimate the change in SOC. To estimate the actual SOC value, it requires knowledge about the initial SOC and actual remaining capacity.

So, let’s do the math. If a 100 Ah battery cell has an initial charge of 10 Ah, its SOC is 10%, as one-tenth is charged. If we now charge the battery with a current of 100 A for half an hour, the battery is charged with an additional 50 Ah up to 60 Ah, which equals a SOC of 60%.

However, it is not as simple as it sounds. The sensors are not ideal, and thus, typically errors accumulate over time. To account for these, the coulomb counting method is often recalibrated via the voltage method. After times of recalibration, operators see a jump in SOC values, which they can use to get a first impression on the occurring inaccuracies.

Unfortunately, the flat LFP voltage in mid-SOC ranges and hysteresis do not easily allow for such a recalibration – especially if the BESS is in operation. This is why, in reality, operators regularly fully charge and discharge the batteries to reach the steep voltage edges for recalibrating the SOC estimation algorithms. Some operators state that this has to be done every one or two weeks.

In the best case, these full charges and discharges can be implemented in regular trading activities. In the worst case, they lead to additional costs and suboptimal asset use. In any case, implementing these events takes time and resources.

How to Improve Battery SOC Estimation

There are typically two major things you can do to increase your SOC estimation quality: improve your hardware and/or software.

In terms of hardware, more precise voltage, temperature and current sensors can contribute to a higher accuracy. But this comes at a higher cost and needs to be done during manufacturing.

Software improvements can be made either locally on the battery management system (BMS) or in the cloud. Regarding BMS, computational power and data storage is typically limited by the used hardware. This severely limits BMS analytics to basic safety-relevant tasks like ensuring that voltage, current, and temperature stay in their intended value ranges or making basic SOC estimations. Therefore, most improvements in BMS software leads to higher hardware costs as well.

In contrast, cloud battery management system analytics can use physics-based battery models and artificial intelligence to improve the accuracy of SOC estimation for LFP batteries. This leap in precision does not just promise enhanced operational efficiency; it directly translates to increased revenue streams for the battery storage systems. Another benefit of using advanced software solutions is that they account for battery aging. Setting up good software solutions comes mainly at the cost of server rent and labor but can be scaled rapidly and cost-efficiently to a large number of systems.

Note that all software solutions still require a minimum hardware accuracy, because if you put bad quality in, you will get bad quality out. This is why the cost optimum lies somewhere in the middle with cloud-based software solutions promising the highest improvements. They enable flexible development for your specific needs and can cover various tasks, such as performance, safety and lifetime analytics.


Exact state of charge estimates are crucial for an economic BESS operation. However, the gaining market shares of LFP brings special challenges due to its flat voltage curve and hysteresis. Inaccurate SOC estimations lead to decreased revenue, but innovative software approaches can be used to overcome these challenges in many cases.

Senior Battery Expert
About the author



Jan is a Senior Battery Expert at ACCURE covering battery diagnostics and science communication. He evaluates current market developments and closely collaborates with international companies, agencies, and ministries on the topic of battery storage. His research has been published in international journals. In his free time, Jan enjoys playing sports and music.

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About ACCURE Battery Intelligence

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