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Precise and reliable SOC estimation can make the difference between hitting revenue targets or taking a financial loss. In this white paper, discover how predictive battery analytics can boost SOC accuracy and revenue.

🔋 SoC estimation errors in LFP BESS can reduce revenues by up to 11% Over 80% of new battery energy storage systems (BESS) now use LFP chemistry. It’s robust and stable — but its flat voltage curve makes precise State of Charge (SoC) estimation difficult. And inaccurate SoC readings are more than just a technical flaw. They translate into: 📉 Missed trading opportunities ⚠️ Limited operational flexibility 💸 Reduced usable capacity due to conservative buffers In collaboration with Modo Energy, we quantified the financial impact across Great Britan and Texas (ERCOT) markets. Even a 10% SoC error in a 100 MW / 200 MWh BESS can lead to: - £420,000 in annual revenue loss (GB, multi-year forecast) - $220,000 in ERCOT (2024) 👉 Predictive battery analytics can reduce SoC error margins to within ±2% — unlocking more reliable operations and higher returns, with no hardware changes required. 📊 Full analysis here 👇: https://hubs.ly/Q03ssMb10 #EnergyStorage #BESS #LFP #BatteryAnalytics #StateOfCharge #GridIntelligence #RevenueStack

🔋 SoC estimation errors in LFP BESS can reduce revenues by up to 11% Over 80% of new battery energy storage systems (BESS) now use LFP chemistry. It’s robust and stable — but its flat voltage curve makes precise State of Charge (SoC) estimation difficult. And inaccurate SoC readings are more than just a technical flaw. They translate into: 📉 Missed trading opportunities ⚠️ Limited operational flexibility 💸 Reduced usable capacity due to conservative buffers In collaboration with Modo Energy, we quantified the financial impact across Great Britan and Texas (ERCOT) markets. Even a 10% SoC error in a 100 MW / 200 MWh BESS can lead to: - £420,000 in annual revenue loss (GB, multi-year forecast) - $220,000 in ERCOT (2024) 👉 Predictive battery analytics can reduce SoC error margins to within ±2% — unlocking more reliable operations and higher returns, with no hardware changes required. 📊 Full analysis here 👇: https://hubs.ly/Q03ssMb10 #EnergyStorage #BESS #LFP #BatteryAnalytics #StateOfCharge #GridIntelligence #RevenueStack

Discover how to get sharper SOC accuracy and increase your BESS earnings

SOC estimation errors can range from 15-40% but it’s possible to get these within 2% of the true value leading to increased revenues, lower risks, and more...

Get best-in-class benchmarks for BESS operations. In the first ACCURE Energy Storage System Health and Performance Report, learn from real-life field data what’s actually happening globally and how to improve battery performance.

See how sharper SOC accuracy lifted BESS earnings by up to 11% in three different scenarios.

SOC estimation errors can eat into revenues. BESS operators need accurate state of charge for their LFP batteries in real time.

See how sharper SOC accuracy lifted BESS earnings by up to 11% in three different scenarios.

SOC estimation errors can eat into revenues. BESS operators need accurate state of charge for their LFP batteries in real time.

You can't trade on guesswork. Read Gore Street Capital's story.

Most asset managers don’t know how much SOC inaccuracy is costing them. Gore Street Capital found out and identified $110,000+ a year in untapped revenue. No hardware changes needed. Read how ACCURE’s SOC Correction can deliver a 5%+ uplift in trading revenue at a single ERCOT site.

🔋 LFP Battery Hysteresis: Why the Same Voltage Can Mean 27% or 62% SOC Estimating State of Charge (SOC) in LFP batteries is anything but trivial — especially when relying on voltage readings. Two key reasons: 1. The Hysteresis Effect 2. The Flatness of the OCV Curve Let’s talk about hysteresis. In LFP chemistry, open-circuit voltage (OCV) doesn’t follow a single curve. Instead, it depends on recent battery history: whether the cell was previously charged or discharged. That leads to different OCV curves — and the true OCV sits between the charge OCV and discharge OCV. 👉 As a result, 3.3 V might mean: - 27% SOC after charging - 62% SOC after discharging ...for the same cell, at the same moment. This divergence isn’t caused by overpotentials like internal resistance — it’s driven by thermodynamic and structural factors inherent to the LFP material. And it’s a major challenge for real-world battery management. 🚫 Traditional BMS struggle with this. They lack the computational capacity and model complexity to capture hysteresis behavior in real time. ✅ Advanced battery models with hysteresis sub-models can solve this challenge. Leveraging predictive analytics, a voltage reading like 3.3 V can be translated into a precise SOC value — e.g. 44%, based on the battery’s charge and discharge history, rather than a vague range like 27% to 62%. The impact? - Accurate SOC estimation - Optimized battery system operation - More precise trading decisions and maximized revenue potential for BESS, with fewer penalties - Improved range prediction accuracy for EVs 📘 Want to know more about SoC estimation in LFP batteries? Read our whitepaper – link in the comments. The better we understand the physics inside the battery, the more value we can extract from it. #BatteryAnalytics #LFP #SOC #EnergyStorage #BMS #PredictiveAnalytics

Gore Street Capital: $110k+ more trading revenue. Zero new hardware.

$110,000+ in identified revenue opportunity. No new hardware. Just more accurate SOC, delivered through your existing EMS. That’s what Gore Street Capital discovered with ACCURE’s SOC Correction at their ERCOT site. Discover more.

Discover how to get sharper SOC accuracy and increase your BESS earnings

Accurately estimating your state of charge (SOC) can be difficult due to errors from 15-40%. These inefficiencies can eat into your revenues. But, you can leverage SOC accuracy as a strategic asset.

🔋 LFP Battery Hysteresis: Why the Same Voltage Can Mean 27% or 62% SOC Estimating State of Charge (SOC) in LFP batteries is anything but trivial — especially when relying on voltage readings. Two key reasons: 1. The Hysteresis Effect 2. The Flatness of the OCV Curve Let’s talk about hysteresis. In LFP chemistry, open-circuit voltage (OCV) doesn’t follow a single curve. Instead, it depends on recent battery history: whether the cell was previously charged or discharged. That leads to different OCV curves — and the true OCV sits between the charge OCV and discharge OCV. 👉 As a result, 3.3 V might mean: - 27% SOC after charging - 62% SOC after discharging ...for the same cell, at the same moment. This divergence isn’t caused by overpotentials like internal resistance — it’s driven by thermodynamic and structural factors inherent to the LFP material. And it’s a major challenge for real-world battery management. 🚫 Traditional BMS struggle with this. They lack the computational capacity and model complexity to capture hysteresis behavior in real time. ✅ Advanced battery models with hysteresis sub-models can solve this challenge. Leveraging predictive analytics, a voltage reading like 3.3 V can be translated into a precise SOC value — e.g. 44%, based on the battery’s charge and discharge history, rather than a vague range like 27% to 62%. The impact? - Accurate SOC estimation - Optimized battery system operation - More precise trading decisions and maximized revenue potential for BESS, with fewer penalties - Improved range prediction accuracy for EVs 📘 Want to know more about SoC estimation in LFP batteries? Read our whitepaper – link in the comments. The better we understand the physics inside the battery, the more value we can extract from it. #BatteryAnalytics #LFP #SOC #EnergyStorage #BMS #PredictiveAnalytics

See how sharper SOC accuracy lifted BESS earnings by up to 11% in three different scenarios.

SOC estimation errors can eat into revenues. BESS operators need accurate state of charge for their LFP batteries in real time.

See how sharper SOC accuracy lifted BESS earnings by up to 11% in three different scenarios.

SOC estimation errors can eat into revenues. BESS operators need accurate state of charge for their LFP batteries in real time.

Hi %FIRSTNAME%,Getting an accurate state of charge (SOC) can be tricky, and errors often cause shutdowns or missed opportunities. Companies like %COMPANYNAME% use ACCURE’s SOC Correction to keep errors below ±2%, helping their assets run more reliably and efficiently. Would you like to explore if this could help your deployments too?

How NTR protects BESS performance from commissioning

BESS performance issues are easier to prevent than fix. NTR chose ACCURE to protect performance from commissioning through operations.

How asset managers of Finland’s largest BESS protect value with ACCURE

NTR’s BESS in Finland began operations with verified system condition, continuous monitoring, and contractual oversight already in place. See how.

Discover how to get sharper SOC accuracy and increase your BESS earnings

Accurately estimating your state of charge (SOC) can be difficult due to errors from 15-40%. These inefficiencies can eat into your revenues. But, you can leverage SOC accuracy as a strategic asset.

Discover how to get sharper SOC accuracy and increase your BESS earnings

SOC estimation errors can range from 15-40% but it’s possible to get these within 2% of the true value leading to increased revenues, lower risks, and more...

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