Smart Safety Management of Energy Storage Batteries
portes grátis
Smart Safety Management of Energy Storage Batteries
Xie, Yanxin; Huang, Qi; Wang, Yujie; Zhang, Gexiang; Fernandez, Carlos; Liu, Guangchen; Wang, Shunli
Elsevier - Health Sciences Division
03/2026
541
Mole
Inglês
9780443275784
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1. Introduction
2. Power storage battery testing
3. Smart energy modeling analysis
4. Intelligent core algorithm for predicting the state of energy storage batteries
5. SOC estimation of energy storage battery based on LSTM neural network
6. Data-driven state-of-health (SOH) estimation for energy storage battery clusters
7. Battery peak power estimation based on long- and short-term memory networks
8. Design and optimization of energy state assessment algorithms for energy storage batteries
9. Improved firefly optimized method for lithium-ion batteries of the co-estimation of state of charge and state of health
10. Joint estimation of state of charge and state of power for lithium-ion battery based on H? filtering
11. Battery RUL prediction based on multicore correlation vector machine
12. Intelligent balancing management of new power storage batteries
2. Power storage battery testing
3. Smart energy modeling analysis
4. Intelligent core algorithm for predicting the state of energy storage batteries
5. SOC estimation of energy storage battery based on LSTM neural network
6. Data-driven state-of-health (SOH) estimation for energy storage battery clusters
7. Battery peak power estimation based on long- and short-term memory networks
8. Design and optimization of energy state assessment algorithms for energy storage batteries
9. Improved firefly optimized method for lithium-ion batteries of the co-estimation of state of charge and state of health
10. Joint estimation of state of charge and state of power for lithium-ion battery based on H? filtering
11. Battery RUL prediction based on multicore correlation vector machine
12. Intelligent balancing management of new power storage batteries
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Electric energy storage system; Battery characteristics; Model construction; State estimation; Kalman filtering; Data driven
1. Introduction
2. Power storage battery testing
3. Smart energy modeling analysis
4. Intelligent core algorithm for predicting the state of energy storage batteries
5. SOC estimation of energy storage battery based on LSTM neural network
6. Data-driven state-of-health (SOH) estimation for energy storage battery clusters
7. Battery peak power estimation based on long- and short-term memory networks
8. Design and optimization of energy state assessment algorithms for energy storage batteries
9. Improved firefly optimized method for lithium-ion batteries of the co-estimation of state of charge and state of health
10. Joint estimation of state of charge and state of power for lithium-ion battery based on H? filtering
11. Battery RUL prediction based on multicore correlation vector machine
12. Intelligent balancing management of new power storage batteries
2. Power storage battery testing
3. Smart energy modeling analysis
4. Intelligent core algorithm for predicting the state of energy storage batteries
5. SOC estimation of energy storage battery based on LSTM neural network
6. Data-driven state-of-health (SOH) estimation for energy storage battery clusters
7. Battery peak power estimation based on long- and short-term memory networks
8. Design and optimization of energy state assessment algorithms for energy storage batteries
9. Improved firefly optimized method for lithium-ion batteries of the co-estimation of state of charge and state of health
10. Joint estimation of state of charge and state of power for lithium-ion battery based on H? filtering
11. Battery RUL prediction based on multicore correlation vector machine
12. Intelligent balancing management of new power storage batteries
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
