Degradation Prediction and Cost Optimization of Second-Life Battery Used for Energy Arbitrage and Peak-Shaving in an Electric Grid

Rongheng Li, Ali Hassan, Nishad Gupte, Wencong Su and Xuan Zhou ()
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Rongheng Li: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Ali Hassan: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Nishad Gupte: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Wencong Su: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Xuan Zhou: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA

Energies, 2023, vol. 16, issue 17, 1-15

Abstract: With the development of the electric vehicle industry, the number of batteries that are retired from vehicles is increasing rapidly, which raises critical environmental and waste issues. Second-life batteries recycled from automobiles have eighty percent of the capacity, which is a potential solution for the electricity grid application. To utilize the second-life batteries efficiently, an accurate estimation of their performance becomes a crucial portion of the optimization of cost-effectiveness. Nonetheless, few works focus on the modeling of the applications of second-life batteries. In this work, a general methodology is presented for the performance modeling and degradation prediction of second-life batteries applied in electric grid systems. The proposed method couples an electrochemical model of the battery performance, a state of health estimation method, and a revenue maximization algorithm for the application in the electric grid. The degradation of the battery is predicted under distinct charging and discharging rates. The results show that the degradation of the batteries can be slowed down, which is achieved by connecting numbers of batteries together in parallel to provide the same amount of required power. Many works aim for optimization of the operation of fresh Battery Energy Storage Systems (BESS). However, few works focus on the second-life battery applications. In this work, we present a trade-off between the revenue of the second-life battery and the service life while utilizing the battery for distinct operational strategies, i.e., arbitrage and peak shaving against Michigan’s DTE electricity utility’s Dynamic Peak Pricing (DPP) and Time of Use (TOU) tariffs. Results from case studies show that arbitrage against the TOU tariff in summer is the best choice due to its longer battery service life under the same power requirement. With the number of retired batteries set to increase over the next 10 years, this will give insight to the retired battery owners/procurers on how to increase the profitability, while making a circular economy of EV batteries more sustainable.

Keywords: second-life battery; electricity grid application; electrochemical modeling; degradation prediction; battery operational strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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