To prevent battery thermal runaway for electric vehicles (EVs), it is necessary to figure out and apply the connections between temperature consistency in battery pack (TCBP) and driving condition to achieve accurate evaluation and diagnosis for temperature inconsistency. This article designed and conducted the naturalistic driving experiments on EVs, and the long-term and high-frequency vehicular running data was used to explore the connection characteristics between TCBP and driving condition for the first time. The microtrip method was adopted to divide EVs’ running segments, and 24 driving condition parameters (DCPs) are extracted for each segment. The principal component analysis (PCA) and k-means algorithm were used to cluster segments into congested, moderate, and smooth driving condition (SDC). For the three driving conditions, the correlation between DCPs and variation coefficient of probe temperature (VCPT) was obtained by calculating their maximum information coefficient (MIC). The importance and influence pattern of DCPs to VCPT was analyzed using random forest (RF) model and ALEs plot, and their quantitative effect on VCPT was calculated by data statistics. Moreover, key DCPs preferably used for TCBP estimation or prediction modeling were identified. The research results provide important insights for the development of adaptive threshold-based evaluation and diagnosis method for temperature inconsistency in EVs’ battery pack.


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    Title :

    Connections Between Temperature Consistency in Battery Pack and Driving Condition of Electric Vehicles: A Naturalistic Driving Study


    Contributors:
    Li, Shaopeng (author) / Zhang, Hui (author) / Ding, Naikan (author)


    Publication date :

    2025-02-01


    Size :

    7897045 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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