Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA

2013 ◽  
Vol 53 ◽  
pp. 603-611 ◽  
Author(s):  
Wen-Yeau Chang
Keyword(s):  
Neural Network ◽  
Hybrid Method ◽  
Rbf Neural Network ◽  
The State ◽  
State Of Charge

State of Charge Estimation for LFP Battery Using the Hybrid Method

2013 ◽  
Vol 431 ◽  
pp. 221-225 ◽  
Author(s):  
Wen Yeau Chang
Keyword(s):  
Neural Network ◽  
Hybrid Method ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Estimation Method ◽  
State Of Charge ◽  
Portable Devices ◽  
Neural Network Structure ◽  
Enhanced Particle Swarm Optimization ◽  
State Of Charge Estimation

A method to accurately estimate the state of charge (SOC) for LiFePO4(LFP) batteries is urgently required, to address the issues associated with the increased use of LPF batteries for portable devices. This paper proposes a hybrid method that combines a radial basis function (RBF) neural network and enhanced particle swarm optimization (EPSO) algorithm for SOC estimating. With a RBF neural network structure, the EPSO algorithm is used to tune the parameters of the RBF neural network, including the centers and widths of the RBF and the connection weights. The trained RBF neural network is then used to estimate the SOC of a LFP battery. In order to demonstrate the effectiveness of the proposed estimation method, the method is tested using 12.6V, 52Ah LFP batteries under varied discharging condition. The effectiveness of the proposed method is compared with the Coulomb integration method and the back propagation (BP) neural network. The results show that the proposed method outperforms the other methods.

Research of MH-Ni Battery State of Charge Estimation Based on Grey System Neural Network Model

2011 ◽  
Vol 66-68 ◽  
pp. 583-587 ◽  
Author(s):  
Jian Xiong Long
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Pulse Current ◽  
The State ◽  
State Of Charge ◽  
Current Response ◽  
Grey System ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

In order to effectively achieve MH-Ni battery state of charge estimation, grey system neural network model is put forward to predict battery state of charge by using the parameters of battery pulse current response signal as input for grey system neural network. The state of charge is as the network output and the response parameters of the battery pulse current as the input. The results show that its prediction accuracy of the state of charge can be achieved to requirements of the electric vehicles in applications by this method to predict the state of charge.

scholarly journals Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy

2021 ◽  
Vol 9 (11) ◽  
pp. 1228
Author(s):  
Seongwan Kim ◽  
Jongsu Kim
Keyword(s):  
Neural Network ◽  
Hybrid System ◽  
Control Method ◽  
The State ◽  
State Of Charge ◽  
Energy Control ◽  
Optimal Energy ◽  
The Neural Network ◽  
Equivalent Consumption Minimization Strategy ◽  
Hidden Layer

This paper introduces an optimal energy control method whose rule-based control employs the equivalent consumption minimization strategy as the design standard to support a neural network technique. Using the proposed control method, the output command values for each power source based on the load of the ship and the state of charge of the battery satisfy the target of energy optimization. Based on the rules, the load of the ship and the state of charge of the battery were the input in the neural network, and the outputs of two generators were recorded as the output values of the neural network. To optimize the weights of the neural network and reduce the error between the predicted values and results, the Bayesian regularization method was employed, and a single hidden layer with 20 nodes, 2 input layers, and 2 output layers were considered. For the hidden layer, the tansigmoid function was applied, and for the activation functions of the output layers, linear functions were adopted considering the correlation between the input and output data used for training the neural network. The propulsion motor was fitted with a speed controller to ensure a stable speed, and a torque load was applied on the propulsion motor. To verify the accuracy of the neural network learning, a generator–battery hybrid system simulation was conducted using MATLAB Simulink, and the neural network learned values were compared with the generator output command values obtained based on the load of the ship and the battery state of charge. Additionally, it was confirmed that the generator command values were consistent with the neural network learned values, and the stability of the system was maintained by controlling the speed, voltage, and current control of the propulsion motor under various loads of the ship and different battery charge statuses.

A hybrid method for lithium-ion batteries state-of-charge estimation based on gated recurrent unit neural network and an adaptive unscented Kalman filter

2021 ◽  
pp. 1-36
Author(s):  
Shuai Xu ◽  
Fei Zhou ◽  
Yucheng Liu
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Hybrid Method ◽  
Unscented Kalman Filter ◽  
Absolute Error ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Adaptive Unscented Kalman Filter ◽  
Battery State Of Charge ◽  
Gated Recurrent Unit

Abstract Among the battery state of charge estimation methods, the Kalman-based filter algorithms are sensitive to the battery model while the neural network-based algorithms are decided by hyperparameters. In this paper, a hybrid approach composed of a gated recurrent unit neural network and an adaptive unscented Kalman filter method is proposed. A gated recurrent unit neural network is first used to acquire the nonlinear relationship between the battery state of charge and battery measurement signals, and then an adaptive unscented Kalman filter is utilized to filter out the output noise of the neural network to further improve estimation accuracy. The hybrid method avoids the establishment of accurate battery models and the search for optimal hyperparameters. The data of dynamical street test and US06 test are used as training dataset and validation dataset, respectively, while the data collected from the tests under federal urban driving schedules and Beijing driving cycle conditions are taken as testing dataset. As compared with some hybrid methods proposed in other literature, the hybrid method has the best estimation accuracy and generalization for various driving cycles at different ambient temperatures. The root mean square error and the mean absolute error all are less than 1.5%, and the maximum absolute error are less than 2%. In addition, it also exhibits powerful robustness against the abnormal values of the battery signals and can converge to the true value in just 5 seconds.

The State Recognition for Rotary Machines Based on Fractal Theory and Neural Network

2013 ◽  
Vol 404 ◽  
pp. 485-489 ◽  
Author(s):  
Qiang Huang ◽  
Xiao Zhuo Ouyang ◽  
Qiu Ping Huang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Fractal Theory ◽  
Fractal Dimensions ◽  
The State ◽  
Connecting Rod ◽  
Wavelet Theory ◽  
Vibration Signals ◽  
State Recognition ◽  
Rotary Machines

A new state recognition method for rotary machines based on the fractal theory and neural network is proposed, and it is analyzed with the example of bushing abrasion of the connecting rod in diesel engine. Firstly, the wavelet theory is used to reduce noises in the vibration signals and then pick up the generalized fractal dimensions with different iterative steps. They will be the input parameters of the RBF neural network, and the output ones are the four working states. After being trained, the model of neural network can identify the states by the vibration signals. According to the experiment and simulation, the wavelet noise reduction can reproduce the vibration signals clearly and optimize the state recognition. The method based on the fractal theory and neural network is demonstrated to be efficient and feasible, and it can identify the states correctly. It has preferable engineering applicability and the referenced value to other vibration diagnosis of rotary machines.

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