skip to main content


Title: Electric Vehicle Battery End-of-Use Recovery Management: Degradation Prediction and Decision Making
Electric vehicles (EVs) are spreading rapidly in the market due to their better responsiveness and environmental friendliness. An accurate diagnosis of EV battery status from operational data is necessary to ensure reliability, minimize maintenance costs, and improve sustainability. This paper presents a deep learning approach based on the long short-term memory network (LSTM) to estimate the state of health (SOH) and degradation of lithium-ion batteries for electric vehicles without prior knowledge of the complex degradation mechanisms. Our results are demonstrated on the open-source NASA Randomized Battery Usage Dataset with batteries aging under changing operating conditions. The randomized discharge data can better represent practical battery usage. The study provides additional end-of-use suggestions, including continued use, remanufacturing/repurposing, recycling, and disposal; for battery management dependent on the predicted battery status. The suggested replacement point is proposed to avoid a sharp degradation phase of the battery to prevent a significant loss of active material on the electrodes. This facilitates the remanufacturing/repurposing process for the replaced battery, thereby extending the battery's life for secondary use at a lower cost. The prediction model provides a tool for customers and the battery second use industry to handle their EV battery properly to get the best economy and system reliability compromise.  more » « less
Award ID(s):
2026276
NSF-PAR ID:
10356439
Author(s) / Creator(s):
;
Date Published:
Journal Name:
the ASME Manufacturing Science and Engineering Conference (MSEC)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Electric vehicles (EVs) are considered an environmentally friendly option to conventional vehicles. As the most critical module in EVs, batteries are complex electrochemical components with nonlinear behavior. On-board battery system performance is also affected by complicated operating environments. Real-time EV battery in-service status prediction is tricky but vital to enable fault diagnosis and aid in the prevention of dangerous occurrences. Data-driven models with advantages in time series analysis can be used to capture the degradation pattern from data about certain performance indicators and predict the battery states. The Transformer model is capable of capturing long-range dependencies efficiently using a multi-head attention block mechanism. This paper presents the implementation of a standard Transformer and an encoder-only Transformer neural network to predict EV battery state of health (SOH). Based on the analysis of the lithium-ion battery from NASA Prognostics Center of Excellence website's publicly accessible dataset, 28 features related to the charge and discharge measurement data are extracted. The features are screened using Pearson correlation coefficients. The results show that the filtered features can effectively improve the accuracy of the model as well as the computational efficiency. The proposed standard Transformer shows good performance in SOH prediction. 
    more » « less
  2. null (Ed.)
    The high cost and growing environmental concerns surrounding lithium-ion batteries have motivated research into extending the life of electric vehicle(EV) batteries by repurposing them for second life grid applications. The incorporation of repurposed electric vehicle batteries (REVBs) has the potential to decrease the overall cost of new battery energy storage systems (BESS) and extend the useful life of the materials. This paper focuses on maximizing daily profit that can be made from REVBs by stacking two grid services such as frequency regulation and energy arbitrage while minimizing battery capital cost by using second life EV batteries. A model for battery management with stacked frequency regulation and energy arbitrage is developed and tested using PJM market data. A mixed integer linear programming (MILP) is used to solve the optimization problem. It is found that REVBs can generate higher net profits than a new BESS. 
    more » « less
  3. Power conversion is a significant cost in second-use battery energy storage systems (2-BESS). 2-BESS is a sustainable pathway for retired batteries of electrical vehicles (EV) to provide energy storage for the grid and EV fast charging. We present and demonstrate the optimization of Lite-Sparse Hierarchical Partial Power Processing (LS-HiPPP) for battery degradation over the potential lifetime of the 2-BESS. LS-HiPPP has a significantly better performance tradeoff with lower power processing than other partial and full power processing architectures. 
    more » « less
  4. Wang, Dong (Ed.)
    Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions. 
    more » « less
  5. Lithium-ion batteries almost exclusively power today’s electric vehicles (EVs). Cutting battery costs is crucial to the promotion of EVs. This paper aims to develop potential solutions to lower the cost and improve battery performance by investigating its design variables: positive electrode porosity and thickness. The open-access lithium-ion battery design and cost model (BatPac) from the Argonne National Laboratory of the United States Department of Energy, has been used for the analyses. Six pouch battery systems with different positive materials are compared in this study (LMO, LFP, NMC 532/LMO, NMC 622, NMC 811, and NCA). Despite their higher positive active material price, nickel-rich batteries (NMC 622, NMC 811, and NCA) present a cheaper total pack cost per kilowatt-hour than other batteries. The higher thickness and lower porosity can reduce the battery cost, enhance the specific energy, lower the battery mass but increase the performance instability. The reliability of the results in this study is proven by comparing estimated and actual commercial EV battery parameters. In addition to the positive electrode thickness and porosity, six other factors that affect the battery's cost and performance have been discussed. They include energy storage, negative electrode porosity, separator thickness and porosity, and negative and positive current collector thickness. 
    more » « less