Volume 19, Issue 1 (January 2019)
Modares Mechanical Engineering 2019, 19(1): 43-52 |
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Nouri Khajavi M, Bayat G. Comparison of Li-Ion Battery State of Charge Prediction by Artificial Neural Network and Adaptive Neuro Fuzzy Inference System. Modares Mechanical Engineering 2019; 19 (1) :43-52
URL: http://mme.modares.ac.ir/article-15-18157-en.html
URL: http://mme.modares.ac.ir/article-15-18157-en.html
1- Automotive Department, Mechanical Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran , mnouri@sru.ac.ir
2- Automotive Department, Mechanical Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran
2- Automotive Department, Mechanical Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran
Abstract: (9271 Views)
An accurate estimation of the state of charge is necessary not only for optimal management of the energy in the electric vehicles (EV) and smart grids, but also to protect the battery from going to the deep discharge or overcharge conditions that degrades battery life and may create potentially dangerous situations like explosion. Despite the importance of this parameter, the state of charge cannot be measured directly from the battery terminals. In this research, an electric equivalent circuit model is simulated in the Simulink environment with two RC networks. This model has the advantage of providing a quick test for the extraction of parameters and dynamic characteristics of the battery model, but is not suitable for on-line applications in an EV. This is why algorithms need to be developed to estimate the SOC of the battery pack and the individual cells based on the measured data of each one. In this paper, for the validation of the neural network, a discharge rate of 0.6A and in the adaptive neuro fuzzy inference system (ANFIS) network, the discharge rate of 0.8, 0.1, and 0.45 was used. The comparison of ANFIS method with the neural method in this study showed that the ANFIS method is more accurate in estimating the state of charge and correlates the experimental points and the output of the network , so that ANFIS error in some states of charge is less than 2%.
Article Type: Original Research |
Subject:
Mechatronics
Received: 2018/03/25 | Accepted: 2018/09/17 | Published: 2019/01/1
Received: 2018/03/25 | Accepted: 2018/09/17 | Published: 2019/01/1
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