Battery State-of-Health Aware Path Planning for a Mars Rover
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
A rover mission consists of visiting waypoints to gather scientific samples based on set requirements. However, rovers face operational uncertainties during the mission, affecting the performance of its electrical and mechanical components and overall mission success. Hence, it is critical to have a decision-making framework that is aware of the health state of the components when planning the path of the vehicle. In particular, battery degradation, and consequently the battery State of Health (SOH), can affect the optimality of decisions made by the autonomous system in the long term. This paper presents a decision-making system that incorporates information on the energy drawn from the battery (based on the velocity of the vehicle), terrain conditions, and model-based prognostic modules to assess impact on the battery state of charge (SoC). The decision-making system was formulated as a Markov Decision Process (MDP) to reach the goal destination by sending commands in a determined amount of time, while maintaining the battery SoC within the policy stated. The MDP problem was programmed using the open-source framework POMDPs.jl, which has a variety of online and offline solvers. To solve the MDP problem online, we used Monte Carlo Tree Search (MCTS). Results from simulations demonstrate the effect that battery degradation and charging plans have on decision-making.
How to Cite
##plugins.themes.bootstrap3.article.details##
Battery Health, State-of-Charge, State-of_Health, Decision Making
Balaban, E., Narasimhan, S., Daigle, M. J., Roychoudhury, I., Sweet, A., Bond, C., . . . Gorospe, G. (2020). Development
of a mobile robot test platform and methods for validation of prognostics-enabled decision making algorithms. International Journal of Prognostics and Health Management, 4(1).
Bolun Xu, A. U. G. A., Alexandre Oudalov,&Kirschen, D. S. (2018). Modeling of lithium-ion battery degradation for cell life assessment. IEEE, 9(2).
Daigle, M., & Kulkarni, C. (2013). Electrochemistry-based battery modeling for prognostics.
Egorov, M., Sunberg, Z., Balaban, E., Wheeler, T., Gupta, J., & Kochenderfer, M. (2017). Pomdps.jl: A framework for sequential decision-making under uncertainty. In A. Honkela (Ed.).
Ellery, A. (2016). Planetary rovers. Springer Berlin Heidelberg.
Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., & Ouyang, M. (2019). A review on the key issues of the lithium ion battery degradation among the whole life cycle. eTransportation.
Hogge, E., Bole, B., Vazquez, S., Kulkarni, C., Strom, T., Hill, B.,Quach, C. (2018). Verification of prognostic algorithms to predict remaining flying time for electric unmanned vehicles.
Kochenderfer, M. J., & Wheeler, T. A. (2022). Algorithms for decision-making. London, England: MIT Press.
Kocsis, L., & Szepesvári, C. (2006). Bandit-based Monte Carlo planning. In Lecture notes in computer science (p. 282-293). Springer Berlin Heidelberg.
Narasimhan, S., Balaban, E., Daigle, M., Roychoudhury, I., Sweet, A., Celaya, J., & Goebel, K. (2012, January). Autonomous decision-making for planetary rovers using diagnostic and prognostic information. IFAC Proceedings Volumes, 45(20), 289–294.
Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. C. (2021). Li-ion battery aging with hybrid physics-informed neural networks and fleet-wide data. Annual Conference of the Prognostics and Health Management Society 2021.
Puterman, M. (1990). Markov decision processes.
Quiñones-Grueiro, M., Biswas, G., Ahmed, I., Darrah, T., & Kulkarni, C. (2021). Online decision-making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios. International Journal of Prognostics and Health Management.
Rezvanizanian, S., Huang, Y., Chuan, J., & Lee, J. (2012). A mobility performance assessment on plug-in ev battery.
Rothrock, B., Kennedy, R., Cunningham, C., Papon, J., Heverly, M., & Ono, M. (2016). SPOC: Deep learning-based terrain classification for mars rover missions. In AIAA SPACE 2016. American Institute of Aeronautics and Astronautics.
Smart, M. C., Ratnakumar, B. V., Ewell, R. C., Surampudi, S., Puglia, F. J., & Gitzendanner, R. (2018). The use of lithium-ion batteries for jpl’s mars missions. Electrochimica Acta, 268, 27-40.
Swiechowski, M., Godlewski, K., Sawicki, B., & Mándziuk, J. (2022). Monte Carlo tree search: a review of recent modifications and applications. Artificial Intelligence Review, 56(3), 2497–2562.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.