Design and Implementation of a UKF-based SOC Estimator for LiMnO2 Batteries Used on Electric Vehicles

• Autorzy: Dai H. , Wei X. , Sun Z.

Warianty tytułu

PL

Projekt i zastosowanie estymatora stanu naładowania baterii LiMnO2 używanych w pojazdach elektrycznych

• Języki publikacji: EN

Abstrakty

EN

To ensure safe and reliable battery operations, an accurate battery state of charge (SOC) estimation is critical for the battery systems used in electric vehicles and hybrid electric vehicles because of the arduous operation conditions. This paper presents a SOC estimator designed based on the unscented Kalman filter (UKF), which is very popular in the state estimation in non-linear systems. The dynamic characteristics of the battery are modeled with an equivalent circuit, which is composed of two capacitors, three resistors and a voltage source to simulate the equilibrium open circuit voltage (OCV). To relieve the computation requirement of the original UKF, an efficient implementation using a Cholesky factorization is investigated, and thereby a SR-UKF based SOC estimator is proposed. Experiment results shows that the model proposed can track the dynamic behavior of the battery very well and the UKF-based SOC estimator has a good performance in the state estimation, and a comparison with EKFbased estimator also shows that a better accuracy can be got by the proposed UKF- based estimator.

PL

Artykuł prezentuje system kontroli SOC (state of charge – stan naładowania) baterii używanych w pojazdach elektrycznych. System bazuje na filtrze Kalmana typu UKF. Własności dynamiczne baterii modelowane są przy pomocy odpowiedniego obwodu elektrycznego zastępczego. System może śledzić właściwości dynamiczne baterii i badać jej stan naładowania.

Słowa kluczowe

EN

electric vehicles; unscented Kalman filter; state of charge; battery modeling

PL

pojazdy elektryczne; stan naładowania; właściwości dynamiczne; filtr Kalmana

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 1b
• Strony: 57--63
• Opis fizyczny: Bibliogr. 27 poz., rys., wykr.

Twórcy

autor

Dai H.

autor

Wei X.

autor

Sun Z.

• School of Automotive Studies, Tongji University, Shanghai 201804, China,

Bibliografia

• [1] S. Piller, M. Perrin, A. Jossen. “Methods for state-of-charge determination and their applications”. Journal of Power Sources, vol. 96, pp113-120, 2001.
• [2] G. O. Patillon and J. N. d’ Alché-Buc, “Neural network adaptive modeling of battery discharge behavior.” Artificial Neural Networks ICANN’ 97 7th Int. Conf., vol. 1327, pp 1095- 1100, 1997.
• [3] S. R. Bhatikar, R. L. Mahajan, K. Wipke, and V. Johnson. (1999, Aug.) “Neural network based energy storage system modeling for hybrid electric vehicles”. Nat. Renewable Energy Lab., Golden, CO. [Online] www.Ctts.Nrel.Gov/analysis/reading_room.
• [4] Chan CC, Lo EWC, Shen W. “The available capacity computation model based on artificial neural network for leadacid batteries in electric vehicles”. Journal of Power Sources, vol. 87, pp201-204, 2000.
• [5] Shen WX, Chan CC, Lo EWC, Chau KT. “A new batteyr available caoacity indicator for electric vehicles using neural network”. Energy Conversion and Management, vol. 43, pp 817-826, 2002.
• [6] Shen WX. “State of available capacity estimation for lead-acid batteries in electric vehicles using neural network”. Energy Conversion and Management, vol. 48, pp433-442, 2007.
• [7] Shen WX, Chau KT, Chan CC. “Neural network-based residual capacity indicator for nickel-metal hydride batteries in electric vehicles”. IEEE Transaction on Vehicular Technology, vol. 54, pp1705-1712, 2005.
• [8] Morita Y, Yamamoto S, Lee SH, Mizuno N. “On-line detection of state-of-charge in lead acid battery using radial basis function neural network”. Asia Journal of Control, vol. 8, pp268- 273, 2006.
• [9] Cheng B, Bai ZF, Cao BG. “State of charge estimation based on evolutionary neural network”. Energy Conversion and Management, vol. 49, pp2788-2794, 2008.
• [10] Cheng B. Zhou YL, Zhang JX, Wang JP, Cao BG. “Ni-MH batteries state-of-charge prediction based on immune evolutionary network”. Energy Conversion and Management, vol. 50, pp3078-3086, 2009.
• [11] Salkind AJ, Fennie C, Singh P, Atwater T, Reisne DE. “Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology”. Journal of Power Sources, vol 80, pp293-300, 1999.
• [12] Chau KT, Wu KC, Chan CC. “A new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system”. Energy Conversion and Management, vol. 44, pp2059-2071, 2003.
• [13] Malkhandi S. “Fuzzy logic- based learning system and estimation of state of charge of lead-acid battery”. Engineering Applications of Artificial Intelligence, vol. 19, pp479-485, 2006.
• [14] Hansen T, Wang CJ. “Support vector based battery state of charge estimator”. Journal of Power Sources, vol.141, pp351- 358, 2005.
• [15] Shi QS, Zhang CH, Cui NX. “Estimation of battery state-ofcharge using v-support vector regression algorithm”. International Journal of Automotive Technology, vol. 9, pp 759- 764, 2008.
• [16] Hu XS, Sun FC. “Fuzzy clustering based multi-model support vector regression state of charge estimator for lithium-ion battery of electric vehicle”. International Conference on Intelligence Human-Machine Systems and Cybernetics, pp392- 396, 2009.
• [17] G. Plett. “Extended kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 2. Modeling and identification”. Journal of Power Sources, vol. 134, pp262- 276, 2004.
• [18] G. Plett. “Extended kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 3. State and parameter estimation”. Journal of Power Sources, vol. 134, pp277-292, 2004.
• [19] Dai H.F., Wei X.Z., Sun Z.C. “Model Based SOC Estimation for High-power Li-ion Battery Packs Used on FCHVs”. High Technology Letters, vol. 13, pp322-326, 2007.
• [20] Dai H.F., Wei X.Z., Sun Z.C. “Estimate state of charge of power lithium-ion batteries used on fuel cell hybrid vehicle with method based on extended Kalman filtering”. Chinese Journal of Mechanical Engineering, vol. 43, pp92-95, 2007.
• [21] Plett GL. “Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs- Part 1: introduction and state estimation, Part 2: simultaneous state and parameter estimation”. Journal of Power Sources, vol. 161, pp1356-1384, 2006.
• [22] Santhanagopalan S, White RE. “State of charge estimation using an unscented filter for high power lithium ion cells”. International Journal of Energy Research, vol. 34, pp52-163, 2010.
• [23] Han JY, Kim DC, Sunwoo M. “State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter”. Journal of Power Sources, vol. 188, pp606-612, 2009.
• [24] Chen Quanshi, Lin Chengtao, “Summarization of Studies on Performance models of batteries for electric vehicle”. Automobile Technology, vol. 3, pp1-5, 2005.
• [25] Wei Xuezhe, Zou Guangnan, Sun Zechang, “Modeling and parameter estimation of Li-ion battery in a fuel cell vehicle”. Chinese Journal of Power Sources, vol. 28, pp605-608, 2004.
• [26] Wan EA, Van Der Merwe R. “The unscented Kalman filter for nonlinear estimation”. IEEE Symposium on Adaptive Systems for Signal Processing, Communications and Control, pp153- 158, 2000.
• [27] Van Der Merwe R. “Sigma-point Kalman filters for probabilistic inference in dynamic state-space models”. Dissertation for the Degree of Doctor of Philosophy. Oregon Health & Science University. Portland, USA, 2004.