What is Battery Analytics Software?
Battery analytics software platforms are used to improve battery degradation, battery manufacturing, open new markets for end-of-life batteries, and prevent safety hazards and recalls.
Storage system owners’ main objective is to generate as much profit as possible from their investment without assuming further risk. Battery analytics are useful in this situation.
Battery analytics refers to software that maximizes the battery’s performance, not just when it is in use but also when choosing the best battery cell or creating the system as a whole.
The potential for improving the in-field operation of battery storage will be the main focus of this theme.
How do you calculate a battery’s life cycle?
Throughout the course of its lifetime, a battery’s performance is significantly impacted by battery degradation, commonly known as aging. Over time, capacity deterioration and resistance building lead to a reduction in the amount of energy and power that are readily available. A KPI called state of health is frequently used to measure the entire performance as a whole (SoH).
Numerous factors influence how quickly batteries age and how healthy they are. These factors might differ substantially between battery systems. Depending on their previous treatment, two batteries containing 90% SoH could have drastically different remaining useful lifetimes. Let’s examine a few of these elements:
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- Temperature: Temperature impacts battery function. Different temperature ranges can be advantageous depending on the circumstance and cell (chemistry). As a general rule, when the battery is not in use, low temperatures prevent excessive calendric aging, whereas relatively warm temperatures, like 30°C, may be the ideal choice for strong cycling.
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- C-rate: C-rate measures the speed at which a battery is fully charged or discharged. 1C means that it takes 1 hour for a battery to go from 0-100% charge. Different battery types are employed for various use cases, according to C-rate. In general, higher C-rates have a greater effect on aging than lower C-rates.
- C-rate: C-rate measures the speed at which a battery is fully charged or discharged. 1C means that it takes 1 hour for a battery to go from 0-100% charge. Different battery types are employed for various use cases, according to C-rate. In general, higher C-rates have a greater effect on aging than lower C-rates.
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- (Average) State of Charge (SoC): Batteries that have a lot of available energy are advantageous in some use cases; however, greater average SoCs may hasten aging. On the other hand, low SOC may jeopardize the business case since that means there is less available energy. The battery management system should technically always disallow SoCs that are too low to avoid damage.
- (Average) State of Charge (SoC): Batteries that have a lot of available energy are advantageous in some use cases; however, greater average SoCs may hasten aging. On the other hand, low SOC may jeopardize the business case since that means there is less available energy. The battery management system should technically always disallow SoCs that are too low to avoid damage.
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- Depth of Discharge (DoD): DoD represents the amount of energy extracted from storage at any given time. For example, a battery discharging from 80% to 35% SoC translates to a DoD of 45%. Low DoDs are often advantageous, with five swings of 20% being less detrimental than a full cycle. The impact of 10 cycles in an arbitrage operation will therefore be greater than that of 10 cycles in ancillary services on battery degradation.
- Depth of Discharge (DoD): DoD represents the amount of energy extracted from storage at any given time. For example, a battery discharging from 80% to 35% SoC translates to a DoD of 45%. Low DoDs are often advantageous, with five swings of 20% being less detrimental than a full cycle. The impact of 10 cycles in an arbitrage operation will therefore be greater than that of 10 cycles in ancillary services on battery degradation.
Predicting DoD is much trickier in practice. Every battery type responds to each of these stressors extremely differently. Additionally, if the battery is handled too cautiously concerning the aforementioned stress factors to minimize battery degradation, a lot of potential is lost.
What does this mean for the future?
Battery analytics software platforms provide insights and solutions based on field data. The end-to-end methodology with analytics at its core is the differentiator.
The platform analytics layer executes several analytical algorithms—electrical, thermal, and aging models, as well as machine learning models—after processing and mapping the data.
The diversity of analytical methods is essential for balancing variations in data input quality and serves as the foundation for an extensive and growing range of solutions.
Battery manufacturing or energy storage companies can use battery analytics software to examine the battery system’s remaining life and health and determine if it’s fit for a second-life function or if it should go directly to recycling once it’s no longer suitable for its first-life application.
