Eric B. Mingullion, Product Manager Storage at 3E, recently released a new white paper, Battery Degradation & Digital Twins: Increasing Accuracy, Reducing Risks.
Addressing the State of Health (SoH) of Battery Energy Storage Systems (BESS), the paper explains how BESS help with power intermittancy issues, improve overall grid resilience, and are important in energy trading. There are several key takeaway points.
SoH definition and key parameters: the ratio between the actual capacity and the nameplate capacity of a battery system. SoH decreases over the battery's lifetime. Several components factor into the End of Life (EoL) of the BESS: battery components differentiated by manufacturer, high and low temperatures, C-rate (current relative to nominal current), Depth of Discharge (DoD) (percentage of the battery’s rated capacity), and resting State of Charge (SoC) which measures the charge of an idle battery (not in operation).
Estimation methods and accuracy: Two main estimation methods, experimental techniques and model-based techniques, assess the SoH of the BESS, but other methods can be implemented:
Experimental techniques provide accuracy for calibrating the BESS and establish a theoretical foundation for model-based techniques. They are performed in a laboratory setting under highly controlled conditions.
Model-based techniques range from black box models (unaware of physical component behaviors) to grey box models that combine electrochemical and equivalent circuit models with operational data.
Linear cycle-based estimation only considers the number of cycles a battery has performed.
Fixed SoH at 100% with some BESS reporting consistently a 100% battery SoH regardless of cycles and length of use.
Accuracy factors affecting SoH estimations are: diverse cell suppliers and integrators, variability in cell manufacturing, operating conditions, model uncertainties, and data quality and availability.
Consequences of inaccurate estimations:
The safety risk of thermal runaway is a worst-case scenario that impacts manufacturing environments, financials, and worker safety. BESS over-discharges or charging at low temperatures escalate into thermal runaway. At the EoL, the thermal runaway risk increases.
When less energy is produced, there is less market participation. Loss of revenue and capacity market contract infringements lead to financial penalties.
Loss of accuracy in the SoC calculations stems from an invalid parameter of the SoH. A Battery Management System (BMS) operates unsafely due to an SoC estimation error, and SoC inaccuracies lead to trading algorithm errors impacting revenue.
Reversible capacity losses due to mismatches result in BESS component connections with differing SoH, creating inaccessible cells.
BESS portfolios complicate issues with multiple battery cell manufacturers, integrators, chemistry, and operational conditions. Limited SoH visibility makes reports harder to generate and interpret, challenges the validation of warranty claims, and increases trading risks.
Strategies to address SoH challenges vary along three main types: corrective, preventive, and predictive.
Corrective strategies include replacing battery modules, updating BMS/EMS hardware, and repairing auxiliary systems to ensure proper ventilation and cooling.
Preventative strategies are scheduled tasks or organizational workflows that track the BESS SoH. Regular maintenance, capacity testing (fully charging and then discharging batteries to measure SoH), and communication across user and manufacturer channels comprise preventative strategy implementations.
Predictive strategies use modelling, forecasting, and detection. Modelling degradation due to a digital twin considering thermal, electrical, and chemical processes, maps how all the components interact, and provides a cost-effective SoH estimation. Forecasting SoH begins with a good business plan and estimates EoL. Early anomaly detection identifies BESS components' irregular behavior at an early stage, preventing accelerated degradation and enhancing system longevity.
Accurate modelling and data harmonization prevent SoH risks. The design, commissioning, and operations of the BESS require modeling. During the operational phase, an Asset Performance Management platform ensures optimal performance and increased Return on Investment (ROI). This is where SynaptiQ BESS Asset Performance Management comes in, providing deep battery insights that detect energy losses early, maximize uptime, and drive higher returns across the lifecycle of BESS assets. A dedicated physics-based model that leads the ground layer of a simulation model allows in-depth analysis of expected versus actual performance. Digital twin physics-based technologies should include cell data sheet information, operational data from BESS components, and electrochemical data combinations recorded for estimation algorithms. 3E is actively involved in a research project to enhance digital twin models and refine SoH predictions where innovation is crucial.
SoH is a crucial parameter to BESS, ensuring safety, performance, and profitability. Asset managers can expand battery lifespans through predictive analytics, digital twins, and data-driven insights. SoH tracking and predictive maintenance move a stable and sustainable energy future. Long-term BESS degradation affects the market. Portfolio harmonization and data quality allow accurate assessments of SoH. In the ever-changing Energy Industry environment, frequent business plan updates, including strategies to conquer SoH challenges, remain at the forefront of communication to ensure financial stability. Transparency and operational efficiency remain the key targets for top BESS SoH.