State of charge estimation of lead acid batteries using neural networks

Anand, I; Mathur, B. L.

doi:10.1109/iccpct.2013.6528901

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…It bases on a simple abstraction and simulation of the human brain and accepts the corresponding training mainly through the input and output sample so that it can meet the mapping function relations. It improves the accuracy of the model through the model weight and deviation adjustment [51,52]. This process can be divided into two stages.…”

Section: Neural Network Methodsmentioning

confidence: 99%

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Zhang

Fan

2020

WEVJ

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Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional methods based on the battery experiments, the modern methods based on control theory, and other methods based on the innovative ideas, especially focusing on the algorithms based on control theory. The results imply that the algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish a rich database, improve hardware technology, come up with a much better battery model, and give full play to the advantages of each algorithm.

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Section: Neural Network Methodsmentioning

confidence: 99%

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Zhang

Fan

2020

WEVJ

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“…The better prediction of SOC is observed when we consider the solid /electrolyte phase and the current. Others types of modeling are one-dimension model (ID) [26][27], pseudo -two dimensional (P2D) [28] model and single particle model [29,30,31] which is more popular for predict the state of the charge prediction. Hence, the electrochemical model is very complex and complicated to improve that limits its application especially for electrical engineering and solving differential Equation is time consuming.…”

Section: Review On the State-of-the Art Of Socmentioning

confidence: 99%

State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods

Karrar¹,

Zhang²

2018

IJSE

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The state of charge (SOC) estimation plays important role in the battery energy storage system (BESS).Nowadays many semiconductor companies are paying more and more attention and investment to support many researchers to implement the state of charge for the batteries storage. the key to optimize the batteries storage is determine SOC value based on accuracy methods. a number of brief methods for SOC determination have been studied and compared with traditional methods the adaptive methods shown precise result because didn't consider the dynamic effect of the batteries. In this paper, we use combination methods to estimate the SOC for lead-acid battery storage under two charge techniques namely Maximum Power Point Tracking -Plus Width Module (MPPT-PWM) when considering the effect of voltage drops on the estimation of SOC. The model uses the coulomb counting as an algorithm to determine the SOC and set it as a target in the backpropagation function in artificial neural network in MATLAB program (R2016a 64-bit (win64)). The simulation results show that the model is very precise to estimate the SOC in realistic operation.

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“…One of the most common problems when using lead–acid batteries involves ascertaining that the performance of the batteries has fallen below recommended levels. There are many studies in the literature that focus on the state of charge of a battery (SOC) [1,2,3,4,5], the majority of which are based on inference systems using dynamic learning [6,7]. The control of the energy output of accumulators [8] or modelling their behavior [9,10] also constitutes the focus of numerous pieces of research.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

Romero-Ternero

Oviedo-Olmedo

Carrasco

et al. 2019

Sensors

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A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning.

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State of charge estimation of lead acid batteries using neural networks

Cited by 11 publications

References 4 publications

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

State of Charge Estimation for Off-Grid System Under Two Charge Controller Using Combination Methods

A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

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