Browsing by Author "Cai, Chengxi"

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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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    Dataset "A Novel Hybrid Electrochemical Equivalent Circuit Model for Online Battery Management Systems"
    (Cranfield University, 2024-08-02) Cai, Chengxi; Auger, Daniel
    A Novel Hybrid Electrochemical Equivalent Circuit Model for Online Battery Management Systems
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    Enhanced online identification of battery models exploiting data richness
    (IEEE, 2023-05-11) Cai, Chengxi; Auger, Daniel J.; Perinpanayagam, Suresh
    The online model parameter identification is essential to ensure the accuracy and dependability of other battery management system (BMS) tasks in the case of battery degradation and operational environment change. Traditional recursive least squares (RLS) algorithms have always been dependent on persistently exciting data, which limits their ability to operate online when this cannot be guaranteed. This paper proposed a modified RLS method that selects the data richest point for parameter identification. In this model, Fisher information matrix and Cramer-Rao bound are utilised to evaluate the data richness. The final algorithms solve the operational limitations of RLS algorithms, enabling a reliable online model parameter identification under real-world dynamic conditions. The identified model parameters from the single cycle dynamic stress test (DST) of an NCM battery are verified by terminal voltage and state of charge (SoC) estimation with the Root Mean Square Error (RMSE) 0.0332 and 0.0131, respectively.