scholarly journals SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

10.29007/m89x ◽  
2020 ◽  
Author(s):  
Jong Hyun Lee ◽  
Hyun Sil Kim ◽  
In Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
Discharge Voltage ◽  
State Of Charge ◽  
State Of Health ◽  
Soc Estimation ◽  
Multilayer Neural Networks

This paper presents a battery monitoring system using a multilayer neural network (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. In this system, the MNN utilizes experimental discharge voltage data from lithium battery operation to estimate SOH and uses present and previous voltages for SOC estimation. From experimental results, we know that the proposed battery monitoring system performs SOC estimation and SOH diagnosis well.

scholarly journals Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

Batteries ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 47 ◽  
Author(s):  
Angelo Bonfitto ◽  
Stefano Feraco ◽  
Andrea Tonoli ◽  
Nicola Amati ◽  
Francesco Monti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lithium Batteries ◽  
Hybrid Electric Vehicle ◽  
Electronic Control Unit ◽  
Computational Cost ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Soc Estimation ◽  
Artificial Neural

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

scholarly journals State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1546
Author(s):  
M. S. Hossain Lipu ◽  
M. A. Hannan ◽  
Aini Hussain ◽  
Afida Ayob ◽  
Mohamad H. M. Saad ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Optimal Number ◽  
State Of Charge ◽  
Optimization Approach ◽  
Safe Driving ◽  
Soc Estimation ◽  
Improved Firefly Algorithm

The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.

Effects of Nonlinear Synapses on the Performance of Multilayer Neural Networks

1996 ◽  
Vol 8 (5) ◽  
pp. 939-949 ◽  
Author(s):  
G. Dündar ◽  
F-C. Hsu ◽  
K. Rose
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Neural Networks ◽  
Circuit Simulations ◽  
Multiplier Circuit

The problems arising from the use of nonlinear multipliers in multilayer neural network synapse structures are discussed. The errors arising from the neglect of nonlinearities are shown and the effect of training in eliminating these errors is discussed. A method for predicting the final errors resulting from nonlinearities is described. Our approximate results are compared with the results from circuit simulations of an actual multiplier circuit.

A Taguchi-Neural-Based In-Process Tool Breakage Monitoring System in End Milling Operations

2010 ◽  
Vol 450 ◽  
pp. 251-254 ◽  
Author(s):  
Po Tsang B. Huang ◽  
James C. Chen ◽  
Chiuhsiang Joe Lin ◽  
Peng Hua Lyu ◽  
Bo Chen Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
End Milling ◽  
Computer Integrated Manufacturing ◽  
Cnc Machine ◽  
Machining Processes ◽  
Tool Breakage ◽  
Milling Operations

The use of CNC machines is one of the successful factors in the computer integrated manufacturing (CIM). Even though the CNC machine can automatically perform the machining processes, some of the situations that may significantly influence the quality of product such as a cutting tool breakage. Therefore, to prevent the machine from damaging and ensure the quality of product, it is important to develop a system that can monitor the tool conditions. The purpose of this study is to develop a Taguchi-neural-based in-process tool breakage monitoring system in end milling operations that can monitor the tool conditions and immediately response a proper action. For an in-process tool breakage monitoring system, a neural network was applied to making decisions of monitoring. One of the disadvantages of neural network is the training processes. It is difficult to determine an optimal combination of training parameters of neural networks. Traditionally, the try-and-error method is time-consuming and without systematic base. Therefore, the optimization of training parameters for neural networks using Taguchi design was applied to training the neural network model and to enhance the accuracy of the tool breakage monitoring system.

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