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(a) Accuracy of LSTM prediction of HI; (b) error of predicted capacity for different quantities of HI; (c) LSTM+GPR prediction accuracy scatter plot.

(a) Accuracy of LSTM prediction of HI; (b) error of predicted capacity for different quantities of HI; (c) LSTM+GPR prediction accuracy scatter plot.

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Figure 5. (a) Accuracy of LSTM prediction of HI; (b) error of predicted...
Figure 6. Markov decision process.
Figure 7. Double DQN neural network structure.
Figure 8. (a) Contribution rate and cumulative contribution rate...
+3 Figure 9. Overall scheduling framework. Figure 9. Overall scheduling...
Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
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  • Jun 2023
Power battery scheduling optimization can improve the service life of the battery, but the existing heuristic algorithm has poor adaptability, and the capacity fluctuates significantly in the cycle aging process, which makes it easy to fall into the local optimal. To overcome these problems, we take the battery cycle life maximization as the goal,...
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Context 1
... root mean square error (RMSE) and the mean absolute error (MAE) [33] are used as measures of the evaluation accuracy of the proposed method. Figure 5a shows the ability of the LSTM to predict health indicators. The LSTM can mine past input information and better learn the time−domain characteristics of the data. ...
Context 2
... results show that MAE and RMSE are less than 10%, which is accurate for predicting health indicators. The statistical results for the battery input of three HIs and input of two HIs, and a single HI, are shown in Figure 5b. The goal of capacity estimation with a single HI is to evaluate the performance of that HI. ...
Context 3
... with the HI degradation prediction model and capacity estimation model, the test set is used for verification. Figure 5c shows the statistical results of partial dispersion points utilizing this strategy, indicating that the capacity prediction strategy is effective. Through confirmation, we can learn that the output prediction capacity error is within the acceptable range and can guarantee a good accuracy to predict the capacity by the trained model. ...
Context 4
... root mean square error (RMSE) and the mean absolute error (MAE) [33] are used as measures of the evaluation accuracy of the proposed method. Figure 5a shows the ability of the LSTM to predict health indicators. The LSTM can mine past input information and better learn the time−domain characteristics of the data. ...
Context 5
... results show that MAE and RMSE are less than 10%, which is accurate for predicting health indicators. The statistical results for the battery input of three HIs and input of two HIs, and a single HI, are shown in Figure 5b. The goal of capacity estimation with a single HI is to evaluate the performance of that HI. ...
Context 6
... with the HI degradation prediction model and capacity estimation model, the test set is used for verification. Figure 5c shows the statistical results of partial dispersion points utilizing this strategy, indicating that the capacity prediction strategy is effective. Through confirmation, we can learn that the output prediction capacity error is within the acceptable range and can guarantee a good accuracy to predict the capacity by the trained model. ...