Improving Aging Prediction for Electric Vehicle Operation with Combined Electrical, Thermal and Aging Model for Lithium-Ion Battery Packs Using Quantitative Cell Data

Abstract

Electric vehicles (EV) in the power grid are not necessarily an additional burden, but can also act grid relieving. This is possible by storing energy from renewable sources and shaving the volatility of generation. For EV battery owners it is important to predict the aging effects resulting from secondary battery use. For a future energy system, it is therefore important to address the topic of EV battery aging for the group of stakeholders: EV owners, EV manufacturers, energy providers and grid operators. A physico-chemically motivated, impedance-based lithium-ion battery model has recently been presented in [6] and is available as open source [5]. Additionally, the authors present a new set of quantitative measurement data for parameterization of a battery pack of Daimler’s Smart electric drive. A battery pack was disassembled to gather all information from inside the battery pack. The cells have been aged as part of an extensive aging testing. Therefore, the simulation setup enables in-situ and operando analysis of cell behavior and helps understanding aging effects in electric vehicle operation. For model parameterization, the research contributions for electrical modeling based on electrical impedance spectroscopy from [1], as well as studies on aging behavior of lithium-ion cells with graphite anode from [2] and [3] are considered and used to adapt the method presented in [4]. The model is parameterized with electrical, thermal and aging parameters for an automotive battery pack that consists of 93 high-energy lithium-ion pouch cells with NMC cathode and graphite anode with a capacity of 50 Ah. A 3D-geometrical representation of the battery pack topology including cooling allows a detailed assessment of the simulated cell temperatures. It therefore helps to generate a better understanding of the incremental battery aging effects that are caused by vehicle-to-grid (V2G) operation. Controlled charging and discharging of EVs could help the grid in terms of stability and avoid additional cost for grid expansion. At the same time, V2G services could generate additional revenues to the EV battery owners from service payment. Nevertheless, calculation of EV battery aging is one major purpose for EV battery owners before they agree to the active use of their EV battery by third parties. Sources: [1] Dissertation H. Witzenhausen, https://doi.org/10.18154/RWTH-2017-03437 [2] Dissertation A. Warnecke, https://doi.org/10.18154/RWTH-2017-09646 [3] Dissertation M. Lewerenz https://doi.org/10.18154/RWTH-2018-228663 [4] Schmalstieg et al., https://doi.org/10.1016/j.jpowsour.2014.02.012 [6] Dissertation F. Hust, “Physico-Chemically Motivated Parameterization and Modelling of Real-Time Capable Lithium-Ion Battery Models - a Case Study on the Tesla Model S Battery”, January 2019 [5] www.openbat.de

Correlation of Health Indicators on Lithium‐Ion Batteries

“…The testset is used to illustrate the comparison of chemistries. 3) Schöneberger–2019, [ 20 ] LiTec HEI40 40 Ah, automotive large‐scale pouch cells. The testset is used in the analysis of calendar and cyclic test conditions.…”