NREL's Battery Life Predictive Model Helps Test Aging
Southern California Edison (SCE) and Next Era Energy will use the NREL's Battery Life Predictive Model to select long-lasting energy storage systems most capable of consistently balancing grid electricity demands.
by Staff
November 24, 2015
Photo courtesy of NREL
2 min to read
The National Renewable Energy Laboratory (NREL) licensed its Battery Life Predictive Model to two utility providers in the U.S.: Southern California Edison (SCE) and Next Era Energy this spring. The utilities will use the NREL model to select long-lasting energy storage systems most capable of consistently balancing grid electricity demands.
The NREL’s Battery Life Predictive Model adapts standardized laboratory battery-aging datasets to real-world scenarios. The NREL model captures separate degradation pathways that can occur depending upon the exact combination of calendar time, environment, and duty cycle.
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Based on physical degradation mechanisms, the model accurately calculates lifetime for variable temperature and charge/discharge cycling, such as those encountered in real-world vehicle and grid storage applications. The model is also applied in real-time control algorithms that co-optimize battery life and performance.
SCE is looking to incorporate MWh-class energy storage on its distribution circuits, with expected system life of more than 15 years. The storage systems help reduce costs to upgrade the grid when demand grows, and respond quickly to grid fluctuations, such as those that occur as wind and solar power vary with weather conditions.
Photo courtesy of NREL
Other recent projects have allowed the NREL team to refine and validate the methodology. Under funding from the Department of Energy's Advanced Research Projects Agency-Energy, NREL is demonstrating that Eaton Corporation can downsize its hybrid electric vehicle (HEV) battery pack by 30% while maintaining the same HEV performance and life.
NREL's Battery Life Predictive Model allows Eaton's HEV controls to manage battery performance and lifetime in real time. The Eaton project, along with previous projects with General Motors, have demonstrated that models developed from standard laboratory cell aging tests can accurately predict lifetime for a complete battery systems undergoing complex real-world cycling and variable temperature conditions.
The model has been licensed to a variety of automotive manufactures, EV service providers, and university and laboratory research groups, and there are various ways it is being applied. NREL researchers can take client's usage data and run them in the predictive model; companies can license the software code and do their own analyses; or NREL can conduct the battery aging tests in its labs, analyze the results and develop specific models for the client.
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