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

This paper will explain:

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.

Baumhöfer battery aging study results
Figure 1: 48 cells of the same batch cycled under the same environmental and operational conditions. Data from Baumhöfer et al.: https://doi.org/10.1016/j.jpowsour.2013.08.108

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.

Limitations at the System Level

These developments pose several problems but are most pronounced in systems where batteries are connected in series. In a series connection, the weakest cell determines the total capacity. This is because the current is identical throughout the string, and the discharge or charge stops once the first cell reaches its end of discharge or charge voltage. This is far from negligible when dealing with operating voltages of around 1000 V, where, for example, approximately 300 battery cells are connected in series. In the worst case, one poor cell determines the total capacity of the 299 good cells, resulting in an immediate reduction in revenue. Additionally, with varying aging, balancing activities increase, leading to energy losses.

Diagram of battery series connection failure due to weakest cell
Figure 2: Diagram of battery series connection failure due to weakest cell

While this effect is less pronounced in parallel connections, the different internal resistances lead to varying currents, which in turn result in different rates of aging. Unfortunately, this is a highly complex issue, and the mentioned effects are just a few among many.

So what?

Just a few of the thousands of cells in a megawatt-scale energy storage system can lead to poorer performance or, worst case, a safety-critical event. Imagine a basket full of beautiful apples, where a few spoiled ones at the top diminish our appetite. Identifying and removing these spoiled apples is crucial and best done early.

Predictive battery diagnostics identify these subpar cells and prompt their replacement before the performance of the battery storage degrades or safety-critical conditions are reached. This involves analyzing vast amounts of data from the battery management system using rule-based algorithms, physicochemical battery models, and artificial intelligence.

Dive into our knowledge center to learn more about how this works.

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

ACCURE helps companies reduce risk, improve performance, and maximize the business value of battery energy storage. Our predictive analytics solution simplifies the complexity of battery data to make batteries safer, more reliable, and more sustainable. By combining cutting-edge artificial intelligence with deep expert knowledge of batteries, we bring a new level of clarity to energy storage.  Today, we support customers worldwide, helping optimize the performance and safety of their battery systems. Visit us at accure.net.