Contributed by Marie Sayegh, Technical Solution Engineer at TWAICE
Battery technology stands at the forefront of the energy revolution. Battery energy storage systems (BESS) are crucial for the clean energy transition. They provide additional stability and flexibility and prepare grids to operate fully on renewable energy.
Yet, with increasing deployment, the challenges of BESS become more apparent. BESS are highly complex systems. They involve networks of battery cells, inverters, battery management systems, cables, and other hardware. While a recent study done by EPRI, PNNL, and TWAICE showed that BESS failure incident rates have dropped by 97% since 2018, availability issues and underperforming components still plague many storage operators. In this context, software plays a crucial role in right-size, managing, and maximizing storage systems and their lifetime.
By simulating battery behavior under various conditions, simulation models allow operators to predict battery performance and lifetime and optimize system designs. For utilities and energy operators looking to deploy BESS, right-sized deployments are critical to ensure they are getting the most from their investment.
Simulation models play a critical role throughout the lifecycle of a BESS project. They help to align stakeholders on realistic performance expectations during planning, and help operators determine how batteries will perform in various usage scenarios, like peak shaving.
Aging models and their application
One key subset of modeling software is battery aging models. These models simulate different degradation processes occurring within battery cells over time. Grid-scale BESS may be asked to perform a variety of functions, from peak shaving and backup power to wholesale market participation and supporting solar. Battery aging models offer valuable insights into how batteries degrade under these different operating conditions.
For asset managers, it is paramount that the battery cells hold up to all kinds of fluctuations. For residential storage applications, for example, variations in temperature are a major concern. Simulation models can provide the necessary insights. Similar to the residential storage temperature challenge, Swedish mining firm Epiroc recently applied modeling software to better understand how the batteries for its electric vehicles would perform and age while operating in extremely high temperatures underground. Using simulation software, Epiroc chose the cell that performed best. With these insights, Epiroc made sure that the batteries would still deliver high performance after several years of operation. Residential storage operators can apply the same simulation models to assess the impact of temperature fluctuations on battery aging.
Temperature fluctuations might not be a particular concern for grid-scale BESS. Usually, the containers are air-conditioned at a stable temperature. However, other parameters like depth of discharge, load profiles, and cycle numbers per day influence a BESS lifetime and performance. Battery aging models can predict the impact of all these parameters, not just temperature.
New aging models offer a further advantage. While lithium-ion remains the dominant battery storage technology, alternative chemistries are reaching commercialization just as storage operators look to overcome limitations of lithium, such as supply chain concerns. Aging models can predict how alternative batteries will respond in real-world scenarios. Comparing lithium-ion with sodium-ion batteries, engineers can see which technology fits the best in terms of battery performance and lifetime. Modeling that’s compatible with sodium-ion batteries, for example, helps operators learn more about the technology.
What’s to come
Battery aging simulation has improved significantly in recent years. One subset of simulation models, for example, investigates physicochemical degradation effects and integrates them into semi-empirical approaches. With that, it can capture complex degradation mechanisms like lithium plating. Lithium plating is a process where lithium ions deposit in a metal form on the anode surface. It can lead to reduced capacity and potentially to short circuits.
New aging models also simplify the identification of degradation issues. They summarize them into three main modes: Loss of lithium inventory, loss of active anode material, and loss of active cathode material. This categorization improves the analysis and predictions of battery performance and longevity.
Overcoming SOC estimation challenges in aging LFP batteries
In addition to providing information about degradation modes, the new generation of aging models can model open circuit voltage (OCV) over the lifetime of the battery. Especially with Lithium-Iron-Phosphate (LFP) batteries, commonly used with grid-scale batteries, the change in OCV plays a major role. OCV is the voltage at the battery’s terminals when it is at rest, so when no current is flowing. The OCV is directly linked to the State of Charge (SOC).
LFP batteries have a very flat voltage profile over a large portion of their charge range. This means that the voltage doesn’t change much when the battery is charging or discharging, especially in the middle range of the SOC. Why is this a problem? For LFP batteries it is hard to estimate SOC: Since the voltage changes very little in the mid-range of SOC, it becomes difficult to accurately determine how much charge is left in the battery using voltage alone.
With battery aging, things get even more complicated: Over time, as the battery ages, the OCV curve may shift slightly, based on the reigning degradation modes. This change further complicates SOC estimation and can lead to sudden drops in State of Charge. The change in OCV, however, is often overlooked in simulations. Over time, the accuracy of the simulation models is reduced.
New aging models can simulate how the OCV curve changes as the battery ages. In return, it leads to more accurate estimations of State of Charge and State of Health, two of the most common real-time battery indicators for BESS operators.
Accurate state estimations are an important factor when operating BESS. In the worst case, inaccurate values lead to penalties. A sudden drop in SOC reduces the amount of energy a system can provide to the grid.
Balancing trading with battery aging
Next to penalties, utilities also need to think about BESS lifetime. In trading applications, asset managers need to weigh potential revenue against the long-term impact on battery health. The standard degradation curves from cell suppliers are too generic to provide realistic insights. Usually, this aging data assumes that the battery is fully charged and discharged – something that is not happening in real life.
Simulation models assess battery aging based on real-world scenarios, for example with a depth of discharge around 80%. New aging models provide realistic insights into battery aging. With that, they help to develop trading strategies that generate revenue without excessively accelerating aging. This ensures that the system remains cost-effective over its entire lifecycle.
For a European generator of renewable energy, adapting trading algorithms to battery aging was beneficial. The utility improved its profitability by more than $1M per 10 MWh. It used the scenarios from the aging models to identify the operating conditions that maximized BESS performance and lifetime. The increase in profitability was followed by a 20% increase in battery lifetime.
What the new generation of aging models mean for BESS
The advancements in aging models extend far beyond theoretical insights. Using aging simulation, stakeholders ensure they have safe storage operations while simultaneously enhancing system performance and extending the lifetime of their energy storage investments. This proactive approach to managing battery health ensures that BESS installations function optimally, reducing downtime and maximizing return on investment.
With simulation models, engineers can make informed decisions regarding cell selection, system design, and operation, ultimately maximizing the efficiency and longevity of BESS installations. Increasingly sophisticated simulation models signify a profound shift in battery technology.
