Autonomous Vehicle Battery State-of-Charge Prognostics Enhanced Mission Planning
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Published
Nov 1, 2020
Bin Zhang
Liang Tang
Jonathan DeCastro
Michael Roemer
Kai Goebel
Abstract
Most mission planning algorithms are designed for healthy systems. When faults occur in a system, it is advantageous to optimize the mission plan by taking the system health condition into consideration. In this paper, a mission planning scheme is proposed to integrate real-time prognostics in a receding horizon path planning framework to accommodate the system fault. In this scheme, the state-of-charge of a battery is monitored and predicted by a particle-filtering based prognostic algorithm. The predicted state-of-charge and remaining useful life of the battery are used in the mission planning to minimize mission failure risk. A series of experiments are presented on a robotic platform, which is powered by a Lithium-ion battery, to demonstrate and verify the proposed scheme.
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Keywords
diagnosis, prognosis, fault-tolerant control, reconfigurable control, PHM
References
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Zhang, B., Khawaja, T., Patrick, R., Vachtsevanos, G., Orchard, M., & Saxena, A. (2009), Application of blind deconvolution de-noising in failure prognosis, IEEE Trans. on Instrumentation & Measurement, 58(2): 303-310.
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Saha, B., Goebel, K., Poll, S. & Christophersen, J. (2009), Prognostics methods for battery health monitoring using a Bayesian framework, IEEE Trans. on Instrumentation & Measurement, 58(2): 291-296.
Goebel, K., Saha, B., Saxena, A., Celaya J., & Christophersen J. (2008), Prognostics in battery health management, IEEE Instrumentation & Measurement Magazine, 11(4): 33-40,.
Kuwata, Y., & How, J. (2011), Cooperative distributed robust trajectory optimization using receding horizon MILP, IEEE Trans. on Control Systems Technology, 19(2): 423-431.
Toit, D. & Burdick, J. (2012), Robot motion planning in dynamic, uncertain environments, IEEE Trans. on Robotics, 28(1), 101-115.
Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002), A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. on Signal Processing, 50(2): 174-188, 2002.
Precht, A. (2000), An empirical evaluation of value at risk by scenario simulation. Journal of Derivatives, 7:1074-1240.
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Tsai, C.-C., Huang, H.-C., & Chan, C.-K. (2011), Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation, IEEE Trans. on Industrial Electronics, 58(5): 1907-1920.
Sudha, N. & Mohan, A. (2011), Hardware-efficient image-based robotic path planning in a dynamic environment and its FPGA implementation, IEEE Trans. on Industrial Electronics, 58(5): 1907-1920.
Sun, Z. & Reif, J. (2007), On robotic optimal path planning in polygonal regions with pseudo-Euclidean metrics, IEEE Trans. on Systems, Man, & Cybernetics: B, 37(4): 925-936.
Lu, Y., Huo, X., Arslan, P., & Tsiotras, P. (2011), Incremental multi-scale search algorithm for dynamic path planning with low worst-case complexity, IEEE Trans. on Systems, Man, & Cybernetics: B, 41(6): 1556-1570.
Singh, S., Simmons, R., Smith, T., Stentz, A., Verma, V., Yahja, A. & Schwehr, K. (2000). Recent progress in local and global traversability for planetary rovers, Proc. IEEE Int. Conf. Robotics & Automation, vol. 2, San Francisco, CA, 1194-1200.
Kavraki, L., Svestka, P., Latombe, J., & Overmars, M. (1996), Probabilistic roadmaps for path planning in high-dimensional configuration spaces, IEEE Trans. on Robotics & Automation, 12 (4): 566–580.
Stentz, A. (1994), Optimal and efficient path planning for partially-known environments, Int. Conf. Robotics & Automation: 3310–3317.
Carsten, J., Rankin, A., Ferguson, D., & Stentz, A., (2007), Global path planning on board the mars exploration rovers, IEEE Aerospace Conf., 1-11.
Ferguson, D. & Stentz, A. (2005), Field D*: an interpolation-based path planner and replanner, Intl. Symp. Robotics Research.
Koenig, S. & Likhachev M. (2005), Fast re-planning for navigation in unknown terrain, IEEE Trans. Robotics & Automation, 21(3), 354-363.
Chen, C., Zhang, B., Vachtsevanos, G., & Orchard M. (2011), Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filter, IEEE Transactions on Industrial Electronics, 58(9): 4353 - 4364.
Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M., & Vachtsevanos G. (2011), A probabilistic fault detection approach: application to bearing fault detection, IEEE Transactions on Industrial Electronics, 58(5): 2011 – 2018.
Chen, C., Zhang, B., & Vachtsevanos, G. (2012), Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms, IEEE Trans. on Instrumentation & Measurement, 61(2): 297-306.
Zhang, B., Khawaja, T., Patrick, R., Vachtsevanos, G., Orchard, M., & Saxena, A. (2009), Application of blind deconvolution de-noising in failure prognosis, IEEE Trans. on Instrumentation & Measurement, 58(2): 303-310.
Tang, L., Zhang, B., DeCastro, J., & Hettler, E., (2011) An integrated health and contingency management case study on an autonomous ground robot, IEEE International Conference on Control and Automation, 584-589.
Zhang, B., Tang, L., DeCastro, J., & Goebel, K. (2011), Prognostics enhanced receding horizon mission planning for field unmanned vehicles, AIAA Guidance, Navigation and Control Conference, Portland, OR.
Tang, L., Hettler, E., Zhang, B., & DeCastro, J. (2011), A testbed for real time autonomous vehicle PHM and contingency management applications, Annual Conference of the Prognostics and Health management Society, 1-11.
Tang, L., Kacprzynski, G., Goebel, K. & Vachtsevanos, G. (2010), Case studies for prognostics-enhanced automated contingency management for aircraft systems, IEEE Aerospace Conf., Big Sky, Montana.
Gebraeel, N., Elwany, A., & Pan, J. (2009), Residual life predictions in the absence of prior degradation knowledge, IEEE Trans. on Reliability, 58(1): 106-117.
Saha, B., Goebel, K., Poll, S. & Christophersen, J. (2009), Prognostics methods for battery health monitoring using a Bayesian framework, IEEE Trans. on Instrumentation & Measurement, 58(2): 291-296.
Goebel, K., Saha, B., Saxena, A., Celaya J., & Christophersen J. (2008), Prognostics in battery health management, IEEE Instrumentation & Measurement Magazine, 11(4): 33-40,.
Kuwata, Y., & How, J. (2011), Cooperative distributed robust trajectory optimization using receding horizon MILP, IEEE Trans. on Control Systems Technology, 19(2): 423-431.
Toit, D. & Burdick, J. (2012), Robot motion planning in dynamic, uncertain environments, IEEE Trans. on Robotics, 28(1), 101-115.
Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002), A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. on Signal Processing, 50(2): 174-188, 2002.
Precht, A. (2000), An empirical evaluation of value at risk by scenario simulation. Journal of Derivatives, 7:1074-1240.
Schreiner, A., Balzer, G., & Precht, A. (2010), Risk sensitivity of failure rate and maintenance expenditure: application of VaR metrics in risk management. IEEE Mediterranean Electrotechnical Conf., 1624-1629, 2010.
Section
Technical Papers