The use of Lithium-Ion Batteries (LIBs) have increased in recent years in many applications such as hybrid electrical vehicles (HEV), consumer electronic equipment, and electricity grid. The batteries undergo degradation during usage due to material aging and electrochemical processes, leading to efficiency reduction of battery-powered systems as well as catastrophic events. Several stress factors such as battery temperature, ambient temperature, and C-rate in the loading profiles influence the degradation. Therefore, predicting the health of the battery has gained attention. The service life can be extended or a system failure can be avoided by maintenance measures precisely matched to the function loss or by changing usage strategies. The State-of Health (SoH) condition of the battery can be determined by the application of lifetime models. Various health indicators such as remaining useful lifetime (RuL) and capacity fade are determined by the models based on the stress factors (utilization variables). For optimal use of the battery, it is helpful to develop an accurate lifetime model to represent the dynamic properties. However, models developed are less computationally efficient and unable to represent the non-linear degradation behavior well. The development of a precise model with correct parameterization is also costly. This is particularly true for models developed based on physical and chemical properties of the battery. In this contribution, an artificial neural network (ANN)-based state machine approach is introduced for capacity fade estimation. The degradation process is represented using three states modeling three different levels and the progression from the first state to the last. Capacity associated with each state is described using the non-linear auto regressive neural network with external input (NARX). The NARX is selected due to its ability to accurately model non linear behavior and time series data. Unlike known models, which are developed using analytical mathematical equations related to the battery properties, a combined machine learning approach is used here instead to learn the capacity behavior from historical data. Battery data sets from NASA are used for experimental verification. Based on the results, the estimated capacity fade show close proximity to actual capacity fade, with a low mean square error for different data sets. In addition, the estimated state progression follows the actual state progression.
How to Cite
Lithium-Ion Batteries, Capacity degradation, State machine, Non-linear auto regressive neural network with external input
Beganovic, N., & S¨offker, D. (2017). Remaining lifetime modeling using state-of-health estimation. Mechanical Systems and Signal Processing, 92, 107-123.
Bole, K. C., B., & Daigle, M. (2014). Adaptation of an electrochemistry-based li-ion battery model to account for deterioration observed under randomized use’, annual conference of the prognostics and health man prognostics and health management society. NASA Prognostics Data Repository, NASA AmesResearch Center, Moffett Field, CA.
Catelani, M., Ciani, L., Fantacci, R., Patrizi, G., & Picano, B. (2021). Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network. IEEE Transactions on Instrumentation and Measurement, 70, 1-11.
Chan, R. W., Yuen, J. K., Lee, E. W., & Arashpour, M. (2015). Application of nonlinear-autoregressive exogenous model to predict the hysteretic behaviour of passive control systems. Engineering Structures, 85, 1–10.
Christensen, J., & Newman, J. (2003). Effect of anode film resistance on the charge/discharge capacity of a lithium-ion battery. Journal of The Electrochemical Society, 150(11), A1416.
Daigle, M., &Kulkarni, C.S. (2013). Electrochemistry-based battery modeling for prognostics. In Annual Conference of the PHM Society.
David, R., Rothe, S., & Soeffker, D. (2021). Lane changing behavior recognition based on artificial neural network-based state machine approach. In 2021 IEEE International Intelligent Transportation Systems conference(ITSC) (p. 3444-3449).
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga ii. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Downey, A., Lui, Y.-H., Hu, C., Laflamme, S., & Hu, S. (2019). Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds. Reliability Engineering System Safety, 182, 1-12.
Gill, A. (1962). Introduction to the theory of finite-state machines. McGraw-Hill.
Horne, B., & Giles, C. (1994). An experimental comparison of recurrent neural networks. Advances in neural information processing systems, 7, 697–704. Hu, X., Zou, C., Zhang, C., & Li, Y. (2017). Technological developments in batteries: A survey of principal roles, types, and management needs. IEEE PowerandEnergyMagazine, 15(5), 20-31.
Keil, P., & Jossen, A. (2017). Impact of dynamic driving loads and regenerative braking on the aging of lithium-ion batteries in electric vehicles. Journal of The Electrochemical Society, 164, A3081-A3092.
Koegler, F., & Soeffker, D. (2020). State-based open-loop control of plant growth by means of water stress training. Agricultural Water Management, 230, 105963.
Liu, Z., Sun, G., Bu, S., Han, J., Tang, X., & Pecht, M. (2017). Particle learning framework for estimating the remaining useful life of lithium-ion batteries. IEEE Transactions on Instrumentation and Measurement, 66(2), 280-293.
Lopes, S. F., Silva, S., & Monteiro, J. L. (2012). An easy-to-use and flexible object-oriented framework for extended finite state machines. In IEEE 10th International Conference on Industrial Informatics (p. 35-40).
Micheli, G. D., Brayton, R. K., & Sangiovanni-Vincentelli, A. L. (1985). Optimal state assignment for finite state machines. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 4, 269-285.
Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015). A novel multistage support vector machine-based approach for li-ion battery remaining useful life estimation. Applied Energy, 159, 285-297.
Plett, G. L. (2004). Extended Kalman filtering for battery management systems of lipb-based HEV battery packs: Part 2. Modeling and identification. Journal of Power Sources, 134(2), 262-276.
Saha, B., & Goebel, K. (2007). Battery data set. NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA.
Saha, B., & Goebel, K. (2009). Modeling li-ion battery capacity depletion in a particle filtering framework. In Annual Conference of the PHM Society.
Siegelmann, H., Horne, B., & Giles, C. (1997). Computational capabilities of recurrent narx neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 27(2), 208-215.
Thangaraj, R., Pant, M., Abraham, A., & Snasel, V. (2012). Modified particle swarm optimization with time-varying velocity vector. International Journal of Innovative Computing, Information and Control, 8(1), 201–218.
Venturini, M. (2005). Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models. Journal of Turbomachinery, 128(3), 444-454.
Waldmann, T., Wilka, M., Kasper, M., Fleischhammer, M., & Wohlfahrt-Mehrens, M. (2014). Temperature-dependent ageing mechanisms in lithium-ion batteries a post-mortem study. Journal of Power Sources, 262, 129-135.
Wang, J., Liu, P., Hicks-Garner, J., Sherman, E., Soukiazian, S., Verbrugge, M., ... Finamore, P. (2011). Cycle-life model for graphite-lifepo 4 cells. Lancet, 196, 3942-3948.
Wilson, P., & Mantooth, H. A. (2013). Chapter 6 block diagram modeling and system analysis (P. Wilson & H. A. Mantooth, Eds.). Oxford: Newnes. doi:https://doi.org/10.1016/B978-0-12-385085-0.00006-3
Wu, J., Kong, L., Cheng, Z., Yang, Y., & Zuo, H. (2022). Rul prediction for lithium batteries using a novel ensemble learning method. Energy Reports, 8, 313-326. (2022 International Conference on the Energy Internet and Energy Interactive Technology)
Wu, Y., Li, W., Wang, Y., & Zhang, K. (2019). Remaining useful life prediction of lithium-ion batteries using neural network and bat-based particle filter. IEEE Access, 7, 54843-54854.
Xiao, Y. (2015). Model-based virtual thermal sensors for lithium-ion battery in ev applications. IEEE Transactions on Industrial Electronics, 62, 3112-3122.
Xu, X., & Chen, N. (2017). A state-space-based prognostics model for lithium-ion battery degradation. Reliability Engineering System Safety, 159, 47-57.
Zhang, H., Wu, D., Wang, Z., & Chen, Y. (2021). An ensemble method for the heterogeneous neural network to predict the remaining useful life of lithium-ion battery. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2433–2438).
Zhao, S., Zhang, C., & Wang, Y. (2022). Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network. Journal of Energy Storage, 52,104901.
Zheng, L., Zhang, L., Zhu, J., Wang, G., & Jiang, J. (2016). Co-estimation of state-of-charge, capacity, and resistance for lithium-ion batteries based on a high-fidelity electrochemical model. Applied Energy, 180, 424-434.
Alvarez Ant´on, J. C., Garc´ıa Nieto, P. J., Garc´ıa Gonzalo, E., Viera P´erez, J. C., Gonz´alez Vega, M., & Blanco Viejo, C. (2016). A new predictive model for the state-of-charge of a high-power lithium-ion cell based on a pso-optimized multivariate adaptive regression spline approach. IEEE Transactions on Vehicular Technology, 65(6), 4197-4208.
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