Decoding Battery Aging: Understanding the Culprits and How We Model Them
- nishiparikh978
- Jul 8, 2024
- 3 min read
In the previous blog, "From Data to Insights: Understanding EV Battery State of Health" we explored the critical role of State of Health (SoH) in maximizing the performance, safety, and lifespan of your electric motorbike battery. We also discussed the challenges associated with accurately estimating SoH.
But the story doesn't end there! To effectively manage battery health, we need to understand the culprits behind degradation – the aging mechanisms themselves. This blog dives deeper into the two main types of Li-ion cell degradation – calendar aging and cycle aging – and the stress factors that accelerate them. We'll also explore various aging modeling techniques.
Types of Li-ion Cell Degradation
Calendar Aging: This insidious degradation occurs even when the battery isn't in use. It's a slow and steady decline caused by the inherent thermodynamic instability of the battery materials. Think of it like a cake slowly going stale over time, even if it's left untouched on the counter. The primary stress factors governing calendar aging are:
Temperature: Extreme temperatures, both hot and cold, can significantly accelerate calendar aging. Just like extreme heat can spoil food faster, high temperatures can degrade battery materials more rapidly.
State of Charge (SoC): Leaving a battery at a high or low SoC for extended periods can worsen calendar aging. Imagine storing your cake in a hot oven or a damp fridge - both extremes would accelerate spoilage. Similarly, keeping your EV battery at a full or very low charge for long periods can be detrimental.
Cycle Aging: This degradation is a more direct consequence of the battery's charge and discharge cycles. With each cycle, the battery undergoes physical and chemical changes that can reduce its capacity and performance. The key stress factors influencing cycle aging include:
Temperature: Similar to calendar aging, high temperatures during charging and discharging cycles can exacerbate degradation.
C-rate: This refers to the charging or discharging rate relative to the battery's capacity. Faster charging/discharging (higher C-rate) can put additional stress on the battery and accelerate cycle aging.
Depth of Discharge (DoD): The deeper you discharge a battery (higher DoD), the more stress it experiences, leading to faster cycle aging. Imagine using most of the cake batter each time you bake, compared to using only a small portion. The more you use the batter, the less remains for future baking.
Aging Modeling Techniques:
Understanding and predicting battery aging is crucial for optimizing EV performance and ensuring safety. Researchers employ various modeling techniques to simulate and forecast battery degradation under different operating conditions. Here are a few common approaches:
Electrochemical Models: These models delve into the complex electrochemical reactions occurring within the battery during charging and discharging cycles. They can be highly detailed but computationally expensive.
Empirical Models: These data-driven models are based on real-world battery data and can be more practical for implementation in BMS. They may not capture the underlying mechanisms as precisely as electrochemical models, but they can be computationally efficient and provide valuable insights.
Equivalent Circuit Models: They model the transient response of the battery using passive circuit components such as resistances, capacitances, and inductances. More complex models can also be used to simulate the internal diffusion and charge transfer processes. And based on impedance data, ageing can be incorporated using variable components.
Machine Learning Models: As the field of artificial intelligence advances, machine learning algorithms are increasingly used for battery aging prediction. These models can learn from vast datasets and identify complex relationships between various factors influencing degradation.
Modelling Approach | Amount of Data | Complexity | Accuracy |
Electrochemical | Low | High | High |
Empirical | High | Low | Low-Medium |
Equivalent Circuit | High | Medium | Medium |
Machine Learning | High | Medium-High | Medium |
The Road Ahead
By understanding the different types of aging, their stress factors, and employing sophisticated modeling techniques, we can develop strategies to mitigate degradation and extend battery lifespan. This translates to a more enjoyable riding experience for electric motorbike owners, with longer range, better performance, and ultimately, a greener future for transportation.
Stay tuned for our next blog post where we explore some health signatures to maximize the lifespan of your electric motorbike battery! In the meantime, share your thoughts on battery aging in the comments below.
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