This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time. 1. Introduction Condition monitoring is an essential process for fault detection and diagnosis. It involves monitoring system states or parameters over an operational period, where abnormal values or significant changes would indicate a fault. Quite often direct measurements of the states are not available due to limitations in design or cost. In these cases, state and parameter estimation techniques can be used for information extraction. Condition monitoring of systems allows proper maintenance to be scheduled, which helps reduce unscheduled downtime of manufacturing equipment, as well as the cost to repair damaged systems [1, 2]. An important area for condition monitoring is energy management for hybrid electric (HEVs) and battery electric vehicles (BEVs). In general, HEVs have two power sources: a gasoline engine and an electric motor. In full hybrid vehicles, the engine and the motor can operate separately or simultaneously. The motor is used mainly during acceleration, startup, reverse mode, and in regenerative braking. A traction battery pack is used to provide power to the motor. It is recharged by a generator or during regenerative braking. The performance of an HEV is largely dependent on a balance between the gasoline engine and the electric motor, optimized with respect to fuel consumption based on vehicle conditions [3]. Many different types of control methods have been applied to balance the power and energy requirements of HEVs, including fuzzy logic [4–6], genetic algorithms [7], dynamic programming [8, 9], Pareto optimization [10], and intelligent mechanism designs [11]. These control strategies rely heavily on the availability of
References
[1]
T. L. Churchill, “Battery condition management: an important way to protect a critical asset,” IEEE Aerospace and Electronic Systems Magazine, vol. 15, no. 7, pp. 41–47, 2000.
[2]
F. V. Conte, “Battery and battery management for hybrid electric vehicles: a review,” Elektrotechnik und Informationstechnik, vol. 123, no. 10, pp. 424–431, 2006.
[3]
I. S. Kim, “Nonlinear state of charge estimator for hybrid electric vehicle battery,” IEEE Transactions on Power Electronics, vol. 23, no. 4, pp. 2027–2034, 2008.
[4]
A. Piccolo, A. Vaccaro, and D. Villacci, “Fuzzy logic based optimal power flow management in parallel hybrid electric vehicles,” Iranian Journal of Electrical and Computer Engineering, vol. 4, no. 2, p. 83, 2005.
[5]
X. Li, J. Li, L. Xu, and M. Ouyang, “Power management and economic estimation of fuel cell hybrid vehicle using fuzzy logic,” in Proceedings of the 5th IEEE Vehicle Power and Propulsion Conference (VPPC '09), pp. 1749–1754, September 2009.
[6]
P. Naderi, M. Mirsalim, M. T. Bathaee, and R. Chini, “Fuzzy controller design for parallel hybrid vehicle analysis using forward simulation,” in Proceedings of the 5th IEEE Vehicle Power and Propulsion Conference (VPPC '09), pp. 234–241, September 2009.
[7]
M. Montazeri-Gh, A. Poursamad, and B. Ghalichi, “Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles,” Journal of the Franklin Institute, vol. 343, no. 4-5, pp. 420–435, 2006.
[8]
L. V. Pérez, G. R. Bossio, D. Moitre, and G. O. García, “Optimization of power management in an hybrid electric vehicle using dynamic programming,” Mathematics and Computers in Simulation, vol. 73, no. 1–4, pp. 244–254, 2006.
[9]
B. Sampathnarayanan, L. Serrao, S. Onori, G. Rizzoni, and S. Yurkovich, “Model predictive control as an energy management strategy for hybrid electric vehicles,” in Proceedings of the ASME Dynamic Systems and Control Conference (DSCC '09), pp. 1161–1168, October 2009.
[10]
D. F. Opila, X. Wang, R. McGee, J. A. Cook, and J. W. Grizzle, “Fundamental structural limitations of an industrial energy management controller architecture for hybrid vehicles,” in Proceedings of the ASME Dynamic Systems and Control Conference (DSCC '09), pp. 213–221, October 2009.
[11]
K. D. Huang, S. C. Tzeng, T. M. Jeng, and C. C. Chen, “Integration mechanism for a parallel hybrid vehicle system,” Applied Energy, vol. 82, no. 2, pp. 133–147, 2005.
[12]
F. Codecà, S. M. Savaresi, and V. Manzoni, “The mix estimation algorithm for battery state-of-charge estimator-analysis of the sensitivity to model errors,” in Proceedings of the ASME Dynamic Systems and Control Conference (DSCC '09), pp. 97–104, October 2009.
[13]
B. S. Bhangu, P. Bentley, D. A. Stone, and C. M. Bingham, “Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles,” IEEE Transactions on Vehicular Technology, vol. 54, no. 3, pp. 783–794, 2005.
[14]
V. Marano, S. Onori, Y. Guezennec, G. Rizzoni, and N. Madella, “Lithium-ion batteries life estimation for plug-in hybrid electric vehicles,” in Proceedings of the 5th IEEE Vehicle Power and Propulsion Conference (VPPC '09), pp. 536–543, September 2009.
[15]
G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—part 1. Background,” Journal of Power Sources, vol. 134, no. 2, pp. 252–261, 2004.
[16]
G. L. 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, no. 2, pp. 262–276, 2004.
[17]
G. L. 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, no. 2, pp. 277–292, 2004.
[18]
A. Vasebi, M. Partovibakhsh, and S. M. T. Bathaee, “A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications,” Journal of Power Sources, vol. 174, no. 1, pp. 30–40, 2007.
[19]
A. Vasebi, S. M. T. Bathaee, and M. Partovibakhsh, “Predicting state of charge of lead-acid batteries for hybrid electric vehicles by extended Kalman filter,” Energy Conversion and Management, vol. 49, no. 1, pp. 75–82, 2008.
[20]
G. L. Plett, “Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs—part 1: introduction and state estimation,” Journal of Power Sources, vol. 161, no. 2, pp. 1356–1368, 2006.
[21]
G. L. Plett, “Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs—part 2: simultaneous state and parameter estimation,” Journal of Power Sources, vol. 161, no. 2, pp. 1369–1384, 2006.
[22]
A. Widodo and B. S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007.
[23]
Y. Bar-Shalom, X. Rong Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley and Sons, New York, NY, USA, 2001.
[24]
N. Nise, Control Systems Engineering, John Wiley and Sons, New York, NY, USA, 4th edition, 2004.
[25]
R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME Journal of Basic Engineering, pp. 35–45, 1960.
[26]
M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, John Wiley and Sons, New York, NY, USA, 3rd edition, 2008.
[27]
S. Habibi, R. Burton, and Y. Chinniah, “Estimation using a new variable structure filter,” in Proceedings of the American Control Conference, pp. 2937–2942, May 2002.
[28]
P. Del Moral, “Measure-valued processes and interacting particle systems. Application to nonlinear filtering problems 1,” Annals of Applied Probability, vol. 8, no. 2, pp. 438–495, 1998.
[29]
N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “Novel approach to nonlinear/non-gaussian Bayesian state estimation,” IEE Proceedings. Part F, vol. 140, no. 2, pp. 107–113, 1993.
[30]
J. MacCormick and A. Blake, “Probabilistic exclusion principle for tracking multiple objects,” in Proceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99), pp. 572–578, September 1999.
[31]
K. Kanazawa, D. Koller, and S. J. Russell, “Stochastic simulation algorithms for dynamic probabilistic networks,” in Proceedings of the 11th Annual Conference on Uncertainty in Artificial Intelligence, pp. 346–351, 1995.
[32]
B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House, Boston, Mass, USA, 2004.
[33]
I. Arasaratnam, S. Haykin, and R. J. Elliott, “Discrete-time nonlinear filtering algorithms using gauss-hermite quadrature,” Proceedings of the IEEE, vol. 95, no. 5, pp. 953–977, 2007.
[34]
I. Arasaratnam and S. Haykin, “Square-root quadrature Kalman filtering,” IEEE Transactions on Signal Processing, vol. 56, no. 6, pp. 2589–2593, 2008.
[35]
S. R. Habibi and R. Burton, “The variable structure filter,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 125, no. 3, pp. 287–293, 2003.
[36]
S. Habibi, “The smooth variable structure filter,” Proceedings of the IEEE, vol. 95, no. 5, pp. 1026–1059, 2007.
[37]
T. Okoshi, K. Yamada, T. Hirasawa, and A. Emori, “Battery condition monitoring (BCM) technologies about lead-acid batteries,” Journal of Power Sources, vol. 158, no. 2, pp. 874–878, 2006.
[38]
J. Voelcker, “Lithium batteries take to the road,” IEEE Spectrum, vol. 44, no. 9, pp. 26–31, 2007.
T. Markel, A. Brooker, T. Hendricks et al., “ADVISOR: a systems analysis tool for advanced vehicle modeling,” Journal of Power Sources, vol. 110, no. 2, pp. 255–266, 2002.
[41]
V. H. Johnson, “Battery performance models in ADVISOR,” Journal of Power Sources, vol. 110, no. 1, pp. 321–329, 2002.
[42]
V. H. Johnson, M. Zolot, and A. Pesaran, “Development and validation of a temperature-dependent resistance/capacitance battery model for ADVISOR,” in Proceedings of the 18th Electric Vehicle Symposium, Berlin, Germany, 2001.
[43]
HEV Implementing Agreement, International Energy Agency, 2008.
