BLAST: Battery Lifetime Analysis and Simulation Tool Suite
Pairing NREL's battery degradation modeling with electrical and thermal performance models, the Battery Lifetime Analysis and Simulation Tool (BLAST) suite assesses battery lifespan and performance for behind-the-meter, vehicle, and stationary applications.
Research at NREL is optimizing lithium-ion (Li-ion) batteries used in electric vehicles (EVs) and stationary energy storage applications to extend the lifetime and performance of battery systems. Battery lifetime predictive modeling considers numerous variables that factor into battery degradation during use and storage, including:
- Ambient temperature, cell self-heating, and thermal management
- State-of-charge histories
- Electrical current levels
- Cycle depth and frequency
- Cell balance in packs and modules.
NREL's BLAST suite pairs predictive battery lifetime models with electrical and thermal models specific to simulate energy storage system lifetime, cell performance, or pack behavior.
BLAST-Lite is a simplified version of NREL's battery lifetime models for a variety of Li-ion battery designs, parameterized from lab data available in Python or MATLAB. BLAST-Lite can be easily implemented into larger techno-economic analysis tools and is currently used by the System Advisor Model and Renewable Energy Integration and Optimization platform. BLAST-Lite incorporates example load profiles for stationary energy storage or vehicle applications and temperature profiles for U.S. cities.
Access BLAST-Lite in NREL's GitHub repository.
NREL's BLAST suite provides insight into research or engineering problems related to the design, economics, controls, or thermal management for common use-cases of battery energy storage systems.
Stationary Energy Storage Systems
Researchers can use BLAST tools to simulate the lifetime performance of stationary energy storage applications, such as behind-the-meter residential systems, corner charging stations for EVs, and utility-scale energy storage.
BLAST tools incorporate realistic lab-based drive-cycles or simulated real-world driving patterns generated by the to anticipate EV battery lifetime. Pack-level simulations can also incorporate the effects of heat generation and thermal management on pack performance and lifetime.
Second-Life and Application Stacking
NREL's collection of battery life models, including BLAST, are able to test data from many cell chemistries, designs, and manufacturers, which allows users to estimate the value of end-of-life batteries if some information on the cell's history is known. This can inform second-life applications for batteries, such as an EV battery being used for stationary energy storage. Similarly, researchers can quantify the battery life impact of application stacking—or using the battery for multiple purposes, such as behind-the-meter storage.
Learn more about NREL's battery longevity-performance modeling in these publications.
Machine-Learning Assisted Identification of Accurate Battery Lifetime Models With Uncertainty, Journal of the Electrochemical Society (2022)
Life Prediction Model for Grid-Connected Li-Ion Battery Energy Storage System, American Control Conference (2017)
Measuring the Benefits of Public Chargers and Improving Infrastructure Deployments Using Advanced Simulation Tools, NREL Conference Paper (2015)
Will Your Battery Survive a World With Fast Chargers?, NREL Conference Paper (2015)
Analyzing the Effects of Climate and Thermal Configuration on Community Energy Storage Systems, NREL Presentation (2013)