Long short-term memory networks with keras pdf download

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The experimental results show that the RNN model has good results on battery aging, hysteresis, dynamic current curve, nonlinear dynamic characteristics and parameter uncertainty.

Due to the so-called gradient vanishing problem that occurs during traditional back-propagation training, RNNs them- selves cannot capture long-term dependencies [12]. Long short-term memory can solve the problem of gradient van- ishing and long-term dependence, while LSTM adjusts the estimation method and data-driven estimation method [2].

Yang et al. Anton et al. However, LSTM used support vector machines to estimate SOC from cannot avoid the defect of long-term forgetting, which means battery current, voltage, and temperature measurements.

Recently, Therefore, using only LSTM to process long-term predictions with the improvement of the computing power provided cannot achieve better accuracy.

Optimal parameters using one dataset as the training data. TABLE 3. Optimal parameters using two datasets as the training data. In analyzing image and time-series data, better results can to LSTM networks based on evolutionary attention for time be obtained by incorporating the attention layer into the series prediction [21]. At the same time, prediction errors are LSTM model compared to other ordinary deep learning mod- sent as feedback to guide the search process.

Note that the model only helps to select As a result, attention mechanisms soon expanded to vari- the output of earlier layers that are critical to each subsequent ous fields, including time series prediction [22]—[24]. In this stage of the model.

It allows the network to selectively focus study, attention mechanisms are proposed to address two on specific information and determine which part of the infor- shortcomings of LSTM.

The attention mechanism replaces mation may be more valuable for the current task. The attention mechanism is located on the output layer to estimate the SOC of two Li-ion batteries under three dif- of LSTM to model long-term dependencies. We define a historical sequence of targets as the DE algorithm. Note that make conclusions and suggestions for future research. I represents current, V represents voltage, and T represents temperature.

If there is no chance in the network to 1. Therefore, the output of the 2. Since 3. It includes inputs, outputs, and forgets the gate so that the network can learn longer 4. Output gate o modulates the amount of the output sequences, manage longer dependencies, and converge on memory content, and specific solutions.

The attention mechanism was originally devel- oped as a way to improve the accuracy of machine translation. This not only bc , and bo are the bias parameters to be learned. Attention weights predict the final SOC, as shown in Figure 1. TABLE 5. Comparison of different models using the FUDS dataset. Step 3. Select the parameters of the DE algorithm, such as Step 1. Define and normalize the target and input features. Determine the fitness function.

The mean absolute are [5, 10, 20], [30, , ], 50, 0. Output the optimal value for each parameter. The test plat- automobile. Three separate test plans were battery capacity regeneration. The FUDS profile simulates executed on the battery test bench at low temperature, room a city driving profile with fast speed fluctuations, and the temperature, and high temperature in the chamber room.

The US06 simulates highway driving with high acceleration and battery open circuit voltage OCV test under low-current rapid speed fluctuations. The second dataset, the Panasonic NCRPF cell, was We control its ambient temperature and record all test data at obtained from Mendeley data [37].

The test platform is given one second intervals. Due to the steady growth of the global in [38]. For each test, first fully charge the battery, and EV industry over the past decade, EV battery packs must then calculate the driving cycle power curve based on the operate under a variety of dynamic loads and temperature battery during discharge until the battery reaches the cut- conditions [31]. Therefore, maintaining the accuracy of the off voltage of 2. Several driving cycle tests, such as monitoring system has become a major challenge.

DE algorithm. Some specifications of these two v u n batteries are shown in Table 1. The proposed model SOC at time t, and n is the total number of the test data. During model training, the parameters using the LSTM model with attention is 1. In all cases, the SOC estimation. In the second experiment, the model was trained using two The number of training datasets may affect the estimation datasets and the remaining dataset was used as test data.

For results. In the first experiment, the model was trained using example, the DST and US06 datasets are used as training one dataset and the remaining datasets were used as test data. The comparison result of the 1. Therefore, we conclude SOC estimation is shown in Figure 3. When training the model with two datasets, models.

The optimal parameters for LSTM The performance of the proposed model is compared without and with attention models are [5, 96, 64, 50, ] and with some published methods in Zhang et al. The mean tion performs best using the FUDS dataset as the test set. Note that LB and UB are lower and upper n bounds, respectively.

The SOC estimation error is less than 1. Figure 12 b. However, the estimation error at the starting The MAE and MAX of the models are shown in point for both the training condition and the end-point for the Figures LSTM with attention are 0.

Moreover, Figures show the estimated SOC for operating conditions. The DE algorithm obtains the tures. Therefore, we can conclude that LSTM with attention optimal parameters of the model.

Data collected from differ- mechanism performs better than LSTM without attention. Then, using one dataset and constructing the prediction interval and SOC estimation error. The variance and bias of the proposed model. The model is run results show that the LSTM model with an attention mech- times with random dropout, which will produce pre- anism can provide better estimation accuracy and stability.

Then, we can calculate Besides, when using two datasets for model training, the SOC the empirical mean and variance of the output to obtain the estimation can be improved. The estimation accuracy of the prediction interval for each time step. Qin, D. Song, H. Chen, W. Cheng, G. Jiang, and G. Joint Conf. Therefore, it can be [18] Y. Liang, S. Ke, J. Zhang, X. Yi, and Y. Li, Y. Shen, and Y. Asian Conf. Kaji, J. Zech, J. Kim, S. Cho, N. Dangayach, A. Costa, and E.

Liu and J. In the future, we will work on other advanced deep learning [22] X. Ran, Z. Shan, Y. Fang, and C. Yu and Y. Chemali, P. Kollmeyer, M. Preindl, and A. Liang, A. Zhiyuli, S. Zhang, R. Xu, and B. Power Sources, vol. Bian, H. He, and S. Storn and K.