Variable recursive least square algorithm for online battery equivalent circuit model parameters identification for electric vehicles

El Marghichi, Mouncef; Loulijat, Azeddine; El Hantati, Issam

doi:10.1007/s00202-023-02064-2

Cited by 1 publication

(1 citation statement)

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“…However, such models are characterized with augmented complexity and are not suitable for optimization problems 34 . In this work, a second-order Thevenin (PNGV) model has been considered for the battery-pack, which is a common approach to provide a scalable and computationally-feasible optimization of electric powertrains without mitigating the fidelity and integrity of individual cell dynamics 35 , 36 . The implemented PNGV model proved an accuracy up to 98% compared to individual cell modeling in vehicular applications, where cell monitoring is not the main scope 37 .…”

Section: Vehicle Model and Driving Cycle Synthesismentioning

confidence: 99%

Optimal charge scheduling and on-board control of an urban electrified BRT fleet considering synthetic representative driving cycles

Ali,

Ayad,

Asfoor

2024

Sci Rep

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This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet . Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution.

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Section: Vehicle Model and Driving Cycle Synthesismentioning

confidence: 99%