Browsing by Author "Gong, You"

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    A novel hybrid electrochemical equivalent circuit model for online battery management systems
    (Elsevier, 2024-10-01) Cai, Chengxi; Gong, You; Fotouhi, Abbas; Auger, Daniel J.
    Accurate battery modeling and parameter identification play pivotal roles in ensuring safety and reliability across the entire battery life cycle. Equivalent circuit models (ECM) are convenient but do not represent physical characteristics well; in contrast, electrochemical models with strong physical meaning are hard to parameterizing in an online setting. To address these challenges, this paper introduces a novel hybrid electrochemical Equivalent Circuit Model (eECM), which integrates electrochemical processes into an ECM, representing slow-dynamic internal processes with a simplified representation of solid- and liquid-phase diffusion; fast-dynamics are represented by ECM terms. The model is supported by an Adaptive Extended Kalman Filter (AEKF) to manage battery state changes and mitigate noise. To enhance parameter identification, a Fisher information matrix-enhanced Variable Forgetting Factor Recursive Least Squares (Fisher-VFFRLS) approach is employed, guided by the Cramér–Rao bound for identifying the most sensitive data points directly from the discharge cycle. Electrochemical parameters are determined via post-charging rest via a Genetic Algorithm (GA). The proposed methodology is validated on three dynamic cycles—DST, US06, and FUDS-demonstrates the effectiveness of the proposed eECM and parameter identification strategy, with maximum Root Mean Square Error (RMSE) for terminal voltage and State of Charge (SoC) estimation below 0.0076 and 0.0122, respectively.
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    ItemOpen Access
    A new topology for electric all-terrain vehicle hybrid battery systems using low-frequency discrete cell switching
    (IEEE, 2022-07-12) Gong, You; Auger, Daniel J.; Fotouhi, Abbas; Hale, Christoper J.
    This paper presents an investigation into the feasibility of a novel discrete-switched topology for an electric all-terrain vehicle (e-ATV) hybrid battery system that avoids expensive and bulky DC-DC converters using a simpler discrete-switched structure together with an intelligent low-frequency switching algorithm. Hardware is simplified at the expense of more complex control. The algorithm switches cells in and out of series strings, based on their state of charge relative to other cells in the pack and the power being drawn from the pack. The principles are demonstrated using a simulated model combining lithium-titanate-oxide (LTO) and lithium-ion-phosphate (LFP) cells together in an e-ATV battery pack. Despite its simplicity, the intelligent switching algorithm, successfully allocates power to different elements of the battery and ensures that state of charge remains broadly balanced throughout discharge, with the pack ending up in good balance: the LFP cells are in balance to within 0.01% of each other, and the LTO cells within 0.1% of each other. While the paper focuses on the essential feasibility of the concept, it also identifies future research for including thermal effects, uncertainties in state estimation, cell ageing and non-uniformly, and consideration of other powertrain components, e.g. motor and power electronics.