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A novel systematic algorithm paradigm for the electric vehicle data anomaly detection based on association data mining
Author(s) -
Wang Yan,
Wu Mengnan
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5073
Subject(s) - electric vehicle , regenerative brake , brake , automotive engineering , association rule learning , computer science , energy (signal processing) , anomaly detection , tracking (education) , engineering , data mining , power (physics) , psychology , physics , pedagogy , statistics , mathematics , quantum mechanics
Summary The electric automobile electric regenerative braking control strategy request achieves two goals, namely, enhances the entire vehicle the energy returns‐ratio as well as optimizes the pilot to the feel. When the former request brake the electric regenerative braking first, it can satisfy the brake request under the premise first to then use the electric regenerative braking recycling brake energy, and before, the latter request rational distribution the trailing wheel braking force uses in tracking pilot's deceleration intention. Under this basis, this paper proposes the novel systematic algorithm paradigm for the electric car data anomaly detection based on association data mining. With the continuous improvement of the intelligent vehicle and the continuous improvement of the function of the system, the system can be used as a hybrid vehicle detection and vehicle maintenance standard equipment and has broad application prospects. The effectiveness of the proposed system is verified through the experiment.