Variable selection for heavy-duty vehicle battery failure prognostics using random survival forests

Prognostics and health management is a useful tool for more flexible maintenance planning and increased system reliability. The application in this study is lead-acid battery failure prognosis for heavy-duty trucks which is important to avoid unplanned stops by the road. There are large amounts of data available, logged from trucks in operation. However, datais not closely related to battery health which makes battery prognostic challenging. When developing a data-driven prognostics model and the number of available variables is large,variable selection is an important task, since including non-informative variables in the model have a negative impact on prognosis performance. Two features of the dataset has been identified, 1) few informative variables, and 2) highly correlated variables in the dataset. The main contribution is a novel method for identifying important variables, taking these two properties into account, using Random Survival Forests to estimate prognostics models. The result of the proposed method is compared to existing variable selection methods,and applied to a real-world automotive dataset. Prognostic models with all and reduced set of variables are generated and differences between the model predictions are discussed, and favorable properties of the proposed approach are highlighted

Data-Driven Health Management of Electrical Vehicle Battery Systems

2018PDFTech ReportDTRT13-G-UTC37Electric vehicle chargingElectric vehiclesEnergy storage systemsHybrid vehiclesIntelligent transportation systemsIntelligent vehiclesLithium batteriesdynamic systemsbattery health managementsafetyhighwaysvehicles and equipmentUnited StatesMidwest Transportation CenterWang, PingfengKrishnan, KrishnaTwomey, JanetBai, GuangxingWichita State UniversityMidwest Transportation CenterUS Transportation CollectionThe objectives of this research were to conduct theoretical and experimental investigations to develop a new battery health management paradigm based on a novel, self-cognizant dynamic system (SCDS) approach to predict and prevent failures of safety-critical battery systems (e.g., lithium plating and thermal runaway) for electric vehicles (EVs) and hybrid electric vehicles (HEVs) and develop an onboard diagnostics tool and alarm system for early awareness of these potential impending failures. This research developed a technique that can adaptively recognize the dynamic characteristics of an operating battery system over time without relying on expensive, time-consuming battery tests for the prediction and prevention of safety-critical battery system failures. Battery failure prognostics employing the proposed SCDS-based health management paradigm can not only account for normal battery capacity fading over time but also identify abnormal safety-critical failures that usually happen in a relatively shorter time period