The development of battery technology has been a critical focus in the pursuit of sustainable energy solutions. Recent findings from Modo Energy and Wenzhou University show the state of battery degradation. They also offer new ways to predict battery life.
The Author’s Perspective
The author, Shaniyaa Holness-McKenzie, said that in Great Britain, batteries are cycling more. They are delivering more energy than ever before. Some are nearing a decade of use. These factors cause degradation. It, in turn, affects the revenue of battery energy storage systems.
Modo Energy’s Estimation Method
Modo Energy estimates battery degradation using metered generation data. There is no public data on battery degradation. The company tracks the total energy exported by batteries before charging. It also records the change in maximum exported energy over time to estimate degradation.
Analysis and Findings
The analysis reveals that some batteries may have lost up to 13% of their energy capacity through degradation. On average, batteries performing 365 cycles (one cycle per day for a year) have degraded by 4.4%, which aligns with expected degradation curves from the industry. The system with the highest estimated degradation has a 12.9% drop in maximum exported energy after 526 cycles. Based on this curve, it would have degraded 11% after 365 cycles.
Depth-of-Discharge Cycles
Shaniyaa also highlights that batteries are now performing higher depth-of-discharge cycles than ever before. In 2024, batteries discharged up to 18% of their full energy capacity before charging, compared to only 8% between 2020 and 2022. The change occurred when the company shifted from providing Dynamic Containment, a low-energy, low-cycling service, to engaging in other services.
Battery Life Prediction Technology Breakthrough
A team from Wenzhou University, led by Jiawei Xiang and Dongzhen Lyu, published a groundbreaking battery life prediction technology in Cell Reports. They collaborated with Bin Zhang’s team at the University of South Carolina and Enrico Zio’s team at MINES Paris. This tech introduces a “cumulative lifetime.” It simplifies the model by combining many factors. So, it is transferable across different applications. It bridges the gap between lab research and real-world use. In large-scale transfer tests, this method improved early life predictions. Errors fell below 5%. It also enabled millisecond-level real-time predictions on portable devices. This shows great engineering practicality.
Implications for Energy Storage
Modo Energy’s findings give key insights into battery degradation in Great Britain. They may affect energy storage in the years to come. We must address battery degradation as batteries are key to a sustainable energy system. It is vital for optimizing their performance and profits. Meanwhile, research from Wenzhou University offers better ways to predict battery life. It should boost battery technology’s development and use.
Further Reading
Litharv stresses two things when discussing battery storage economics. First, select the right cells in the early product development stages. Second, the depth of discharge (DOD) should be considered. Litharv focuses on the product’s traits throughout its lifecycle. It aims to create the lowest levelized cost of electricity (LCOE) for its customers.
The levelized cost of electricity (LCOE) is the average cost of electricity per kWh over a project’s lifetime. This formula calculates it.
Where:
- I0 is the initial investment of the project,
- VR is the salvage value of the fixed assets,
- An is the operating cost in year n,
- Dn is the depreciation in year n,
- Pn is the interest payment in year n,
- Yn is the energy production in year n,
- i is the discount rate,
- n is the lifetime of the power plant in years.
Litharv’s research shows that proper cell selection and a good DOD can cut LCOE. This makes energy storage systems more economical and reliable.
References
- To access the full article and learn more about battery degradation, visit the Modo Energy platform. It discusses how cycling, depth of discharge, cell chemistry, and discharge power affect it.
2. Publication Details:
- Paper Title: Battery cumulative lifetime prognostics to bridge laboratory and real-life scenarios
- Journal: Cell Reports (SCI Q1)
- Publication Date: 2024.07
- Paper Link: https://doi.org/10.1016/j.xcrp.2024.102164
- Authors: Dongzhen Lyu (1, 5), Bin Zhang (2), Enrico Zio (3, 4), Jiawei Xiang (1)
- Affiliations:
- College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
- College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA
- MINES Paris, PSL University, 75272 Paris, France
- Energy Department, Politecnico di Milano, 20156 Milano, Italy
Access to and use of the related data and code are subject to the Creative Commons Attribution-NoDerivatives 4.0 International Public License (CC BY-ND 4.0). When using or sharing this data or code with others, please make sure to use the unified naming format “Wenzhou Randomized Battery Data” and cite the following source article:
Dongzhen Lyu et al., Battery Cumulative Lifetime Prognostics to Bridge Laboratory and Real-Life Scenarios, Cell Reports Physical Science (2024), https://doi.org/10.1016/j.xcrp.2024.102164
When sharing the data or code, the original integrity of the dataset must be maintained, and a link to directly access the original data must be provided; any modification, processing, reorganization, or repackaging is strictly prohibited. Please be sure to visit the links below for the full text of the license agreement.
X_MOL Homepage: https://www.x-mol.com/groups/DongzhenLyu
ResearchGate: https://www.researchgate.net/profile/Dongzhen-Lyu
GitHub: https://github.com/lvdongzhen/Wenzhou-Randomized-Battery-Data
OneDrive: https://1drv.ms/f/s!AnQLciP1URipksZQPfoVLhdf67Y8mg
If you have any questions that need clarification, please contact Dr. Dongzhen Lyu. Email: lvdongzhen@hrbeu.edu.cn