Effect of Ambient Temperature on EV Charging Curves After Seven Years of EV Operation

Blažek, Vojtěch; Pergl, Ivo; Kedron, Pavel; Piecha, Marian; Bajaj, Mohit

doi:10.1109/epe58302.2023.10149281

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…But as the number of cars increased in cities, so did air pollution [2,3]. Statistics from the European Union show that transportation makes up nearly 27% of total greenhouse gas emissions [4,5], with automobiles being responsible for around 70%. EVs have been widely adopted and recognized worldwide as a practical solution to the emissions problem due to their many benefits, including the reduction of greenhouse gas emissions and mitigation of global warming [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Electric Vehicle Battery Management System Using an Artificial Neural Network-Based Adaptive Droop Control Theory

Afzal

Aurangzeb

Iqbal

et al. 2023

International Journal of Energy Research

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The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system’s ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS’s efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management.

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