LogIn / RegistrationArticles for sale
- Head office address: Moscow, 123317, Moscow City, 8th Floor, Presnenskaya Embankment, 6, bldg. 2
- article@123mi.ru
- Сортировка:
- по горящим
- по цене
- по дате
#6579. A hybrid convolutional neural network-long short term memory for discharge capacity estimation of lithium-ion batteries
| December 2026 | publication date |
| Proposal available till | 05-06-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Electrical and Electronic Engineering; Energy Engineering and Power Technology; Renewable Energy, Sustainability and the Environment; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №6579.1   | Contract №6579.2 | Contract №6579.3 | Contract №6579.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Predicting discharge capacities of lithium-ion batteries (LIBs) is essential for safe battery operation in electric vehicles (EVs). In this paper, a convolutional neural network-long short term memory (CNN-LSTM) approach is proposed to estimate the discharge capacity of LIBs. The parameters such as the voltage, current, temperature, and charge/discharge capacity are recorded from a battery management system (BMS) at various stages of the charge-discharge cycles. The experiments are conducted to obtain the data at different cycles, where each cycle is divided into four steps. Each testing cycle comprises charging, rest, discharging, and rest. In the predictive model, the initial layers are convolutional layers that help in feature extraction. Then, the long and short term memory layer is used to retain or forget related information. Finally, the prediction is completed by selecting the corresponding activation function. The evaluation model is established via the multiple train test split method. The lower values of weighted mean squared error suggest that discharge capacity estimation using CNN-LSTM is a reliable method. The CNN-LSTM approach can further be compiled in BMSs of EVs to get real-time status for state of charge and state of health values.
Keywords:
battery management system; Convolutional neural network-long short term memory network; Electric vehicles; Lithium-ion batteries
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Predicting discharge capacities of lithium-ion batteries (LIBs) is essential for safe battery operation in electric vehicles (EVs). In this paper, a convolutional neural network-long short term memory (CNN-LSTM) approach is proposed to estimate the discharge capacity of LIBs. The parameters such as the voltage, current, temperature, and charge/discharge capacity are recorded from a battery management system (BMS) at various stages of the charge-discharge cycles. The experiments are conducted to obtain the data at different cycles, where each cycle is divided into four steps. Each testing cycle comprises charging, rest, discharging, and rest. In the predictive model, the initial layers are convolutional layers that help in feature extraction. Then, the long and short term memory layer is used to retain or forget related information. Finally, the prediction is completed by selecting the corresponding activation function. The evaluation model is established via the multiple train test split method. The lower values of weighted mean squared error suggest that discharge capacity estimation using CNN-LSTM is a reliable method. The CNN-LSTM approach can further be compiled in BMSs of EVs to get real-time status for state of charge and state of health values.
Keywords:
battery management system; Convolutional neural network-long short term memory network; Electric vehicles; Lithium-ion batteries
Contacts :
