A. James Clark School of Engineering

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Author: search.filters.author.He, Wei

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    Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability
    (MDPI, 2013-09-09) Williard, Nicholas; He, Wei; Hendricks, Christopher; Pecht, Michael
    On 16 January 2013, all Boeing 787 Dreamliners were indefinitely grounded due to lithium-ion battery failures that had occurred in two planes. Subsequent investigations into the battery failures released through the National Transportation Safety Board (NTSB) factual report, the March 15th Boeing press conference in Japan, and the NTSB hearings in Washington D.C., never identified the root causes of the failures—a major concern for ensuring safety and meeting reliability expectations. This paper discusses the challenges to lithium-ion battery qualification, reliability assessment, and safety in light of the Boeing 787 battery failures. New assessment methods and control techniques that can improve battery reliability and safety in avionic systems are then presented.
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    BATTERY STATE OF CHARGE ESTIMATION BASED ON DATA-DRIVEN MODELS WITH MOVING WINDOW FILTERS AND PHYSICS-BASED MODELS WITH EFFICIENT SOLID-PHASE DIFFUSION PDES SOLVED BY THE OPTIMIZED PROJECTION METHOD
    (2018) He, Wei; Pecht, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    State of charge (SOC) estimation is one of the most important functions of battery management systems (BMSs), which is defined as the percentage of the remaining charge inside the battery to its maximum capacity. SOC indicates when the battery needs to be recharged. It is necessary for many battery management applications, for example, charge/discharge control, remaining useful time/ driving range predictions, and battery power capability estimations. Inaccurate SOC estimations can lead to user dissatisfaction, mission failures, and premature battery failures. This thesis focuses on the development of advanced battery models and algorithms for SOC estimations. Two SOC estimation approaches are investigated, including electrochemical models and data-driven models. Electrochemical models have intrinsic advantages for SOC estimation since it can relate battery internal physical parameters, e.g. lithium concentrations, to SOC. However, the computational complexity of the electrochemical model is the major obstacle for its application in a real-time BMS. To address this problem, an efficient solution for the solid phase diffusion equations in the electrochemical model is developed based on projection with optimized basis functions. The developed method generates 20 times fewer equations compared with finite difference-based methods, without losing accuracy. The results also show that the developed method is three times more efficient compared with the conventional projection-based method. Then, a novel moving window filter (MWF) algorithm is developed to infer SOC based on the electrochemical model. MWF converges to true values nearly 15 times faster compared with unscented Kalman filter in experimental test cases. This work also develops a data-driven SOC estimation approach. Traditional data-driven approaches, e.g. neural network, have generalization problems. For example, the model over-fits to training data and generate erroneous results in the testing data. This thesis investigates algorithms to improve the generalization capability of the data-driven model. An algorithm is developed to select optimal neural network structure and training data inputs. Then, a hybrid approach is developed by combining the neural network and MWF to provide stable SOC estimations. The results show that the SOC estimation error can be reduced from 8% to less 4% compared with the original neural network approach.