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Data-Driven Battery Lifetime Prediction and Confidence Estimation for Heavy-Duty Trucks
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4965-1077
2018 (English)In: IEEE Transactions on Reliability, ISSN 0018-9529, E-ISSN 1558-1721, Vol. 67, no 2, p. 623-639Article in journal (Refereed) Published
Abstract [en]

Maintenance planning is important in the automotive industry as it allows fleet owners or regular customers to avoid unexpected failures of the components. One cause of unplanned stops of heavy-duty trucks is failure in the lead-acid starter battery. High availability of the vehicles can be achieved by changing the battery frequently, but such an approach is expensive both due to the frequent visits to a workshop and also due to the component cost. Here, a data-driven method based on random survival forest (RSF) is proposed for predicting the reliability of the batteries. The dataset available for the study, covering more than 50 000 trucks, has two important properties. First, it does not contain measurements related directly to the battery health; second, there are no time series of measurements for every vehicle. In this paper, the RSF method is used to predict the reliability function for a particular vehicle using data from the fleet of vehicles given that only one set of measurements per vehicle is available. A theory for confidence bands for the RSF method is developed, which is an extension of an existing technique for variance estimation in the random forest method. Adding confidence bands to the RSF method gives an opportunity for an engineer to evaluate the confidence of the model prediction. Some aspects of the confidence bands are considered: their asymptotic behavior and usefulness in model selection. A problem of including time-related variables is addressed in this paper with the argument that why it is a good choice not to add them into the model. Metrics for performance evaluation are suggested, which show that the model can be used to schedule and optimize the cost of the battery replacement. The approach is illustrated extensively using the real-life truck data case study.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 67, no 2, p. 623-639
Keywords [en]
Battery lifetime prognostics; data-driven prediction; flexible maintenance; infinitesimal jackknife (IJ) confidence bands; reliability
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-149358DOI: 10.1109/TR.2018.2803798ISI: 000433911000015OAI: oai:DiVA.org:liu-149358DiVA, id: diva2:1229803
Note

Funding Agencies|Scania CV; FFI (Vehicle Strategic Research and Innovation)

Available from: 2018-07-02 Created: 2018-07-02 Last updated: 2020-01-24
In thesis
1. Machine Learning Models for Predictive Maintenance
Open this publication in new window or tab >>Machine Learning Models for Predictive Maintenance
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The amount of goods produced and transported around the world each year increases and heavy-duty trucks are an important link in the logistic chain. To guarantee reliable delivery a high degree of availability is required, i.e., avoid standing by the road unable to continue the transport mission. Unplanned stops by the road do not only cost due to the delay in delivery, but can also lead to damaged cargo. Vehicle downtime can be reduced by replacing components based on statistics of previous failures. However, such an approach is both expensive due to the required frequent visits to a workshop and inefficient as many components from the vehicles in the fleet are still operational. A prognostic method, allowing for vehicle individualized maintenance plans, therefore poses a significant potential in the automotive field. The prognostic method estimates component degradation and remaining useful life based on recorded data and how the vehicle has been operated.

Lead-acid batteries is a part of the electrical power system in a heavy-duty truck, primarily responsible for powering the starter motor but also powering auxiliary units, e.g., cabin heating and kitchen equipment, which makes the battery a vital component for vehicle availability. Developing physical models of battery degradation is a difficult process which requires access to battery health sensing that is not available in the given study as well a detailed knowledge of battery chemistry.

An alternative approach, considered in this work, is data-driven methods based on large amounts of logged data describing vehicle operation conditions. In the use-case studied, recorded data is not closely related to battery health which makes battery prognostic challenging. Data is collected during infrequent and non-equidistant visits to a workshop and there are complex dependencies between variables in the data. The main aim of this work has been to develop a framework and methods for estimating lifetime of lead-acid batteries using data-driven methods for condition-based maintenance. The methodology is general and can be applicable for prognostics of other components.

A main contribution of the thesis is development of machine learning models for predictive maintenance, estimating conditional reliability functions, using Random Survival Forests (RSF) and recurrent neural networks (RNN). An important property of the data is that for a specific vehicle there may be multiple data readouts, but also one single data readout which makes predictive modeling challenging and dealing with this situation is discussed for both RSF and neural networks models. Data quality is important when building data-driven models, and here the data is imbalanced since there are few battery failures relative to the number of vehicles. Further, the data includes many uninformative variables and among those that are informative, there are complex dependencies and correlation. Methods for selecting which data features to use in the model in this situation is also a key contribution. When a point estimation of the conditional reliability functions is available, it is of interest to know how uncertain the estimate is as it allows to take quality of the prediction into account when deciding on maintenance actions. A theory for estimating the variance of the RSF predictor is another contribution in the thesis. To conclude, the results show that Long Short-Term Memory networks, which is a type of RNN, is the most suitable for the vehicle operational data and give the best performance among methods evaluated in the thesis.

Abstract [sv]

Mängden gods som produceras och transporteras världen runt ökar och tunga fordon är en viktig del i logistikkedjan. För att garantera pålitliga leveranser krävs hög tillgänglighet hos fordonen genom att bland annat undvika oplanerade stopp längs vägen. Tid då fordonet ej är tillgängligt kan reduceras genom att byta ut komponenter baserat på statistik från tidigare fel. En sådan ansats kan dock vara dyr på grund av för täta besök på verkstäder samt att många komponenter fungerar avsevärt längre beroende på hur hårt komponenten använts. En prognostikmetod för individualiserade underhållsplaner har därför en stor potential i fordonsfältet. Prognostikmetoden uppskattar komponenters degradation och tillgänglig livstid baserat på registrerade data och hur fordonet har använts.

Blysyrabatterier är en del av det elektriska kraftsystemet i en lastbil, primärt ansvariga för att kraftsätta startmotor, men också för att ge kraft åt hjälpsystem som kabinvärme och köksutrustning, vilket betyder att batteriet är en viktig komponent för fordonets tillgänglighet. Att utveckla fysikaliska modeller för batteridegradation är svårt och kräver tillgång till mätdata direkt kopplat till batteriets hälsa, något som inte är tillgängligt i det här arbetet. En alternativ ansats, som utforskas här, är datadrivna metoder baserade på stora mängder inspelade data som beskriver hur fordonet använts. I studien är insamlad data ej direkt relaterad till batterihälsa vilket gör prognostikproblemet utmanande.

Ett huvudbidrag är utveckling av maskininlärningsmodeller för prediktivt underhåll baserad på Random Survival Forests (RSF) och Recurrent Neural Networks (RNN). En viktig egenskap hos insamlade data är att för specifika fordon så kan det finnas flera, eller endast enstaka, datautläsningar vilket också gör prediktiv modellering svårt. Metoder för att hantera detta för modeller baserade på RSF och neuronnät behandlas. Datakvalitet är viktigt vid utveckling av datadrivna modeller. Insamlade data är obalanserade eftersom det är få batterier som felar i relation till antalet fordon. Vidare, insamlade data inkluderar många oinformativa variabler och bland de informativa så finns komplexa beroenden och korrelationer. Metoder för att välja väl valda variabler att bygga modeller på för den här situationen är utmanande och ett huvudbidrag i arbetet. En central fråga är hur säker en punktskattning är och hur den osäkerheten kan vägas in när prediktiva underhållsplaner bestäms, speciellt när modellen baseras på så osäkra data och så ostrukturerade modeller som här. Ett viktigt bidrag är metodik för att estimera prediktionsvarians för RSF-modeller. Slutligen, ett huvudresultat för användarfallet är att LSTM-nät, ett typ av RNN, är den modellstruktur som ger bäst prestanda för prognostik av blysyrabatterier med det data som använts i avhandlingen.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. p. 297
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2040
National Category
Transport Systems and Logistics Computer Sciences Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-162649 (URN)10.3384/diss.diva-162649 (DOI)9789179299231 (ISBN)
Public defence
2020-03-06, Ada Lovelace, Building B, Campus US, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2020-01-31Bibliographically approved

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