Cell Aging Variation in Large Battery Storage Systems

Calendar vs. Cycle Battery Aging

Like humans, battery cells exhibit varying aging behavior and associated life expectancy. Battery aging is divided into calendar aging and cyclic aging. To maintain the human analogy, calendar aging corresponds to the constant degradation each of us experiences. Even when we sleep, we wake up a day older. Likewise, batteries age when not in operation, losing capacity and performance. The main factors influencing calendar aging are state of charge and temperature, with higher temperatures and state of charge leading to accelerated aging. The relationship is nonlinear and especially strong for high states of charge and temperatures.

Regarding cycle aging, we can again draw parallels with ourselves. Just as stress, intense and prolonged work, and insufficient sleep contribute to faster aging in humans, similar conditions in batteries, such as charge throughput, cycle depths, and charging currents, have a negative impact. A simplified rule applies here: higher values significantly harm the battery compared to lower values.

Differences in Aging Across System Cells

When we talk about a megawatt or gigawatt battery storage, we are not referring to a single large battery but to thousands of small battery cells, initially organized into modules, then packs, racks, and eventually entire containers. These containers are, in turn, installed together for large-scale storage projects.

It is almost impossible to expose each cell to the same environmental and operating conditions. This is due, on the one hand, to the system design and, on the other hand, to the operation. For instance, battery cells inside a module are exposed to higher temperatures than those at the edges, and modules at the top of a container may be subjected to warmer rising air than modules at the bottom if the air circulation is not maintained correctly or if the cooling system is not functioning perfectly.

During operation, not all strands are used the same. For example, power distribution algorithms improve efficiency by using just some containers. Over time, they may exhibit different capacities and performance capabilities.

It is clear that even if all batteries were identical, they would age differently due to varying environmental and operational conditions. This effect is further intensified by the fact that battery cells, like humans, are not identical. Similar to genetic predispositions or circumstances that develop over the course of life, batteries also exhibit slight variations due to production and operational factors, which become more pronounced during operation.

To illustrate this, the study conducted by Baumhöfer et al. is beneficial. In this study, 48 batteries of the same type and delivered on the same day were aged under identical conditions in the laboratory, clearly demonstrating the mentioned effects.

While the cells are initially quite close to each other, the capacity spread between the best and worst cells becomes visibly pronounced after several hundred cycles. Consequently, there can be a considerable number of cycles between reaching the typically assumed end-of-life of 80% of the initial capacity. In reality, the divergence in health conditions would have been even more significant if different temperatures, states of charge, and loads had been introduced.