Health-aware control design based on remaining useful life estimation for autonomous racing vehicle

Pour, Fatemeh Karimi; Theilliol, Didier; Puig, Vicenç; Cembraño, Gabriela

doi:10.1016/j.isatra.2020.03.032

Cited by 20 publications

(9 citation statements)

References 41 publications

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“…Bayesian estimation requires the use of posterior prediction distributions for predictive inference, predicting the distribution of new, unobserved data points (Zhang et al, 2020b;Karimi Pour et al, 2021). Instead of a fixed point as a prediction, return the distribution of possible points (Wang et al, 2020d;Downey et al, 2019).…”

Section: Stochastic Process Methodsmentioning

confidence: 99%

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

et al. 2021

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Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.

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Section: Stochastic Process Methodsmentioning

confidence: 99%

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

et al. 2021

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“…( 2) is a common consideration in all studies. As indicated in Table I, most studies exclusively examined static weight distribution between the axles for reduced-scale cars [35]- [40][44]- [46], primarily due to their exceptionally low CG relative to the ground. Conversely, systems designed for full-scale vehicles often incorporate weight redistribution resulting from longitudinal acceleration, as depicted in (3).…”

Section: Xumentioning

confidence: 99%

“…Related Publications Absence of longitudinal forces [31], [51], [79] Drive/brake force/acceleration as system input variables [32], [33], [35], [36], [44], [46]- [50], [53], [55]- [57], [59], [62]- [67], [69], [71], [73], [76]- [78], [80], [84], [89], [90] Drive/brake force distribution among axles/tires [38]- [41], [44], [45], [50],…”

Section: Longitudinal Modelmentioning

confidence: 99%

A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

Zhang,

Sun,

Wang

et al. 2024

IEEE Trans. Intell. Veh.

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This paper presents the first survey of vehicle dynamics modeling methods for autonomous racing. Previous surveys have covered dynamics models for standard autonomous vehicles or, alternatively, concentrated on planning and control methods in autonomous racing with vehicle dynamics models briefly mentioned. However, previous surveys overlook the importance of vehicle dynamics under challenging conditions of top speeds and non-steady state driving, which are unique characteristics in autonomous racing. Recognizing the vital role of vehicle dynamics modeling in an autonomous racecar's prediction, planning, and control modules, this survey seeks to ascertain to what degree the nominal full-scale racecar dynamics can be streamlined without sacrificing accuracy for simplicity. Furthermore, this survey provides essential guidance for organizers of virtual autonomous races, helping them choose vehicle dynamics models that meet the required level of precision. This paper begins with a review of previous surveys on vehicle dynamics modeling, highlighting their limitations in the context of autonomous racing. Following this, it investigates the existing dynamics models for autonomous racing vehicles, along with a comprehensive examination of the existing physical/virtual testing platforms. The paper concludes by discussing emerging trends and offering perspectives in the field of vehicle dynamics modeling for autonomous racing, paving the way for groundbreaking research and innovations in autonomous racing.

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“…When such systems operate in closed loop, the degradation speed invariably depends upon the control input as well as reference trajectories. An effective assessment and extension of the remaining useful life of complex systems is a standing challenge that seeks novel solutions at the cross-over of PHM domain as well as automatic control (Pour, Theilliol, Puig, & Cembrano, 2021;Jha, Weber, Theilliol, Ponsart, & Maquin, 2019). In the aerospace domain, very few investigations have been done in the past on these lines (Ray, Wu, Dai, Carpino, & LORENZO, 1993;Holmes, Tangirala, & Ray, 1997).…”

Section: Introductionmentioning

confidence: 99%

Prognostics Aware Control Design for Extended Remaining Useful Life

Thuillier,

Jha,

Le Martelot

et al. 2024

IJPHM

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As most of the safety critical industrial systems remain sensitive to functional degradation and operate under closed loop, it becomes imperative to take into account the state of health within the control design process. To that end, an effective assessment and extension of the Remaining Useful Life (RUL) of complex systems is a standing challenge that seeks novel solutions at the cross-over of Prognostics and Health Management (PHM) domain as well as automatic control. This paper considers a dynamical system subjected to functional degradation presents a novel control design strategy. Wherein the assessment of state of health of the system is taken into account leading to effective prediction of the RUL as well as its extension. To that end, the degradation model is considered unknown but input-dependent. The control design is formulated as an optimization problem wherein a suitable comprise is reached between the performance and desired RUL of the system. The main contribution of the paper remains in proposal of set-point modulation based approach wherein the control input at a given present time stage is modulated in such way that futuristic health of the system over a long time horizon is extended whilst assuring acceptable performance. The effectiveness of the proposed strategy is assessed in simulation using a numerical example as well as liquid propellant rocket engine case.

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Health-aware control design based on remaining useful life estimation for autonomous racing vehicle

Cited by 20 publications

References 41 publications

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

Prognostics Aware Control Design for Extended Remaining Useful Life

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