Impacts of electric vehicle fast charging under dynamic temperature and humidity: Experimental and theoretically validated model analyses

Toward automobile electrification and automation, a smart scenario of DC-charging plug-in electric vehicles (PEVs) at any parking lot equipped with chargers is proposed. In this paper, this scenario is composed of four main stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process of the PEVs is implemented using the constant current-constant voltage (CC-CV) protocol. This was followed by a novel PEV classification model under the impacts of various ambient circumstances. Then an estimation of the charging characteristic parameters at the corresponding conditions is obtained. Finally, the model identification of the battery dynamic behaviour is sufficiently proposed. The feedforward backpropagation neural network (FFBP-NN) as a supervised classification algorithm supported by the statistical analysis of an instant charging current sample is used, which achieves an accuracy of 83.2%. In addition, the FFBP-NN perfectly estimated the charging current, terminal voltage, and charging interval time with a maximum error of 1%. Eventually, a sufficient identification model of the battery dynamic behaviour based on the Hammerstein-Wiener (HW) model is introduced with the best fit of 89.62% and an error of 1.1876%. The experimental and simulated results are within 1%error with the preceding research literature.

Automated summary

This paper proposes a smart scenario for DC-charging plug-in electric vehicles at any parking lot equipped with chargers. It investigates the impact of temperature and/or relative humidity on the charging process of the vehicles and introduces a novel classification model. The paper also successfully identifies the battery's dynamic behavior using a feedforward backpropagation neural network and Hammerstein-Wiener model.