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We develop an adaptive neuro-fuzzy inference system (ANFIS) to achieve the goal. First in this paper, nonconventional input variables of the ANFIS are selected ...
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But if the only three variables are used as inputs, the ANFIS model can not predict battery SOC accurately. Some new input variables must be added to the model ...
We develop an adaptive neuro-fuzzy inference system (ANFIS) to achieve the goal. First in this paper, nonconventional input variables of the ANFIS are selected ...
This paper proposes a lead–acid battery SOC estimation method using an artificial NN (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The ANN ...
Discover an advanced battery performance model using Adaptive Neuro-Fuzzy Inference Systems. Achieve accurate SOC estimation with high precision and ...
Moreover, the BMS must be able to estimate and prognosticate some important parameters of the battery bank such as the State of Charge (SOC), the State of ...
An accurate battery model is crucial to exactly estimate the SOC. In order to improve the model accuracy, this paper presents an improved parameter ...
Oct 25, 2024 · This study presents a novel approach utilizing an artificial neural network to estimate the state of charge of a battery based on key variables
Adaptive neuro fuzzy inference system, a data driven approach, is given the input from extended Kalman filter for minimizing the error in the estimate value of ...
The objective of this paper is to estimate the SOC of a lithium-ion battery (LIB) using an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural ...