An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

Liang, Meng Di; Wu, Tie Zhou

doi:10.4028/www.scientific.net/amr.953-954.800

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…[18][19][20][21] Therein, NN, developed as SOC estimators, have been studied extensively in the literature, including back-propagation neural networks (BP-NN), 22,23 radial basis function neural networks (RBFNN). 24,25 In the NN model for SOC estimation, a large mass of known input data and expected output data obtained from the battery charging and discharging experiments is required to train the network, thereby self-learning the network parameters and extracting the fitting relationship. Instead, a mathematical model of the system and the complex reaction mechanism inside the battery need not to be considered.…”

Section: State-of-the-artmentioning

confidence: 99%

Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology

et al. 2020

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Faced with the ever-increasing urban environmental pollution, the electric vehicles (EVs) have received increasing attention in the automotive industry. Lithium-ion batteries, serving as electrochemical power storage, have been extensively used in EVs because of the lightweight, no local pollution and high power density. The increasing awareness on the safe operation and reliability of the battery requires an efficient battery management system (BMS), among the parameters monitored by which, state-of-charge (SOC) is critical in preventing overcharge, deep discharge, and irreversible damage. This article investigates the neural network (NN)-based modeling, learning, and estimation of SOC by comparing two different methodologies, that is, direct structure with SOC as network output and indirect structure with voltage as output. Firstly, the nonlinear autoregressive exogenous neural network (NARX-NN) is introduced, in which SOC is directly deemed as an NN output for learning and estimation. Secondly, a radial basis function (RBF)-based NN with unscented Kalman filter (RBFNN-UKF) is proposed, in which the terminal voltage is used as output. Instead, SOC is deemed as an internal state which would be estimated indirectly based on the feedback error of voltage. Experimental results demonstrate that both estimators can achieve accurate SOC estimation for regular cases, in spite of the inaccurate initial conditions. However, the direct NN structure is revealed as not capable of dealing with the cases with sensor bias, which, however, can be well accommodated in the indirect structure by extending the sensor bias as an augmented state. Benefiting from the uncertainty augmentation and feedback compensation, the indirect RBFNN-UKF shows superiority over the direct estimation in the practical experiments, depicting a promising prospect in the future onboard EV-BMS application. K E Y W O R D S lithium-ion battery, NARX neural network, sensor bias, state-of-charge (SOC), unscented kalman filter (UKF)

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Section: State-of-the-artmentioning

confidence: 99%

Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology

et al. 2020

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“…The data-driven method can effectively solve the problems of nonlinearity and instability in battery data collection [23]. This method is based on a large amount of experimental offline data, and the characteristics of current, voltage and temperature are trained to establish a mapping model of the SOC, including neural network (NN) [24,25], support vector machine (SVM) [26] and deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Wei

Min

et al. 2020

IEEE Access

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Highly accurate state of charge (SOC) estimation of lithium-ion batteries is one of the key technologies of battery management systems in electric vehicles. The performance of SOC estimation directly influences the driving range and safety of these vehicles. Due to external disturbances, temperature variation and electromagnetic interference, accurate SOC estimation becomes difficult. To accurately estimate the SOC of lithium-ion batteries, this paper presents a novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM).The proposed hybrid NARX model embeds LSTM memory, which provides jump-ahead connections in the time-unfolded model. These jump-ahead connections provide a shorter path for the propagation of gradient information, therefore reducing long-term dependence on the recurrent neural network. Experimental results show that the estimation performance root mean square error (RMSE) of the proposed model is less than 1%, and this model has better multitime prediction performance. Finally, the hybrid NARX and LSTM model is compared with the standard back propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) and LSTM existing models under urban dynamometer driving schedule (UDDS) and dynamic stress test (DST) conditions. The proposed hybrid NARX-LSTM model yield relative to other methods and can estimate the battery SOC with high accuracy. The RMSE of proposed model is improved by approximately 60% compared with the standard LSTM INDEX TERMS electric vehicles; state of charge; lithium-ion batteries; NARX; LSTM

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A review of the state of health for lithium-ion batteries: Research status and suggestions

Tian

Qin

et al. 2020

Journal of Cleaner Production

447

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An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

Cited by 4 publications

References 9 publications

Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology

Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

A review of the state of health for lithium-ion batteries: Research status and suggestions

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