Battery State-of-Health Aware Path Planning for a Mars Rover



Published Oct 26, 2023
Mariana Salinas-Camus Chetan Kulkarni Marcos Orchard


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

Salinas-Camus, M., Kulkarni, C., & Orchard, M. (2023). Battery State-of-Health Aware Path Planning for a Mars Rover. Annual Conference of the PHM Society, 15(1).
Abstract 117 | PDF Downloads 32



Battery Health, State-of-Charge, State-of_Health, Decision Making

Balaban, E., Alonso, J., & Goebel, K. (2012). An approach to prognostic decision making in the aerospace domain.

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.
Technical Research Papers

Most read articles by the same author(s)

1 2 3 > >>