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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 30231

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Offline Parameter Identification and State-of-Charge Estimation for Healthy and Aged Electric Vehicle Batteries Based on the Combined Model
Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.
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[1] S. Campanari, G. Manzolini, and F. Garcia de la Iglesia, “Energy analysis of electric vehicles using batteries or fuel cells through well-to-wheel driving cycle simulations,” J. Power Sources, vol. 186, no. 2, pp. 464–477, 2009.
[2] G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background,” J. Power Sources, vol. 134, no. 2, pp. 252–261, 2004.
[3] D. Anderson, “An evaluation of current and future costs for lithium-ion batteries for use in electrified vehicle powertrains,” Chem. …, no. May, p. 48, 2009.
[4] A. Andrea, Battery Management Systems for Large Lithium-Ion Battery Packs. 2010.
[5] M. Conte, F. V. Conte, I. D. Bloom, K. Morita, T. Ikeya, and J. R. Belt, “Ageing testing procedures on lithium batteries in an international collaboration context,” World Electr. Veh. J., vol. 4, pp. 335–346, 2011.
[6] R. Ahmed, J. Gazzarri, S. Onori, S. Habibi, R. Jackey, K. Rzemien, J. Tjong, and J. LeSage, “Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications,” SAE Int. J. Altern. Powertrains, vol. 4, no. 2, pp. 2015–01–0252, Apr. 2015.
[7] J. Remmlinger, M. Buchholz, M. Meiler, P. Bernreuter, and K. Dietmayer, “State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation,” J. Power Sources, vol. 196, no. 12, pp. 5357–5363, 2011.
[8] V. Pop, H. J. Bergveld, P. P. L. Regtien, J. H. G. Op het Veld, D. Danilov, and P. H. L. Notten, “Battery Aging and Its Influence on the Electromotive Force,” J. Electrochem. Soc., vol. 154, no. 8, p. A744, 2007.
[9] C. Guenther, B. Schott, W. Hennings, P. Waldowski, and M. A. Danzer, “Model-based investigation of electric vehicle battery aging by means of vehicle-to-grid scenario simulations,” J. Power Sources, vol. 239, pp. 604–610, 2013.
[10] A. T. Stamps, C. E. Holland, R. E. White, and E. P. Gatzke, “Analysis of capacity fade in a lithium ion battery,” J. Power Sources, vol. 150, no. December 2004, pp. 229–239, 2005.
[11] E. Wood, M. Alexander, and T. H. Bradley, “Investigation of battery end-of-life conditions for plug-in hybrid electric vehicles,” J. Power Sources, vol. 196, no. 11, pp. 5147–5154, 2011.
[12] J. C. B. Saha, K. Goebel, S.Poll, “An integrated approach to battery health monitoring using Bayesian regression and state estimation,” Ieee, no. November, pp. 646–653, 2007.
[13] R. Ahmed, M. El Sayed, I. Arasaratnam, J. Tjong, and S. Habibi, “Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries. Part I: Parameterization Model Development for Healthy Batteryies,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 2, no. 3, pp. 659–677, 2014.
[14] J. R. Belt, “Battery Test Manual For Plug-In Hybrid Electric Vehicles,” Dec. 2010.
[15] G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification,” J. Power Sources, vol. 134, no. 2, pp. 262–276, 2004.
[16] J. C. Forman, S. J. Moura, J. L. Stein, and H. K. Fathy, “Genetic identification and fisher identifiability analysis of the Doyle-Fuller-Newman model from experimental cycling of a LiFePO 4 cell,” J. Power Sources, vol. 210, pp. 263–275, 2012.
[17] G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3. State and parameter estimation,” J. Power Sources, vol. 134, no. 2, pp. 277–292, 2004.
[18] S. Habibi, “The Smooth Variable Structure Filter,” Proc. IEEE, vol. 95, no. 5, 2007.
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