As the third generation battery product, the lithium-ion battery has the advantages of high specific capacity, long cycle life, low self-discharge rate, and high-cost performance. Its reliability and safety management technologies are increasingly mature. Especially, the rapid reduction of cost lays the foundation for storage power stations, aerospace, and other fields. It provides an effective way for renewable energy grid connection and smart management. In particular, the long endurance performance of electric vehicles has promoted the rapid development of high energy density lithium-ion batteries.
Significant research progress has been made recently, but there is still a lack of systematic solutions. The modeling and prediction theory cannot meet the development needs of the industry, which has become a bottleneck restricting the sustainable industrial promotion and application of lithium-ion battery. Throughout the current solutions, the battery management research has the following urgent problems to be solved. (1) State of battery estimation lacks uniform standards and is difficult to evaluate effectively and accurately. (2) Lithium-ion battery application scenarios are very complex, and the management methods often lack universality. (3) The battery management system is difficult to achieve the compatibility of multiple functions, which cannot meet the needs of practical applications. In this chapter, contributors are expected to carry out relevant research on the above issues and put forward their unique views to further promote the continuous progress of the new energy field.
Researchers can carry out the research topics including:
• State estimation of the battery
• Remaining useful life prediction
• Aging mechanism analysis
• Battery high-precision equivalent modeling
• Design of battery management system
• The model-driven methods of the Kalman filtering and particle filtering
• Data-driven methods in core state parameter estimation procedure
• Convolutional neural networks
• Short and long-term memory neural network
• Support vector machine
• Intelligent neural network
• The fusion methods
As the third generation battery product, the lithium-ion battery has the advantages of high specific capacity, long cycle life, low self-discharge rate, and high-cost performance. Its reliability and safety management technologies are increasingly mature. Especially, the rapid reduction of cost lays the foundation for storage power stations, aerospace, and other fields. It provides an effective way for renewable energy grid connection and smart management. In particular, the long endurance performance of electric vehicles has promoted the rapid development of high energy density lithium-ion batteries.
Significant research progress has been made recently, but there is still a lack of systematic solutions. The modeling and prediction theory cannot meet the development needs of the industry, which has become a bottleneck restricting the sustainable industrial promotion and application of lithium-ion battery. Throughout the current solutions, the battery management research has the following urgent problems to be solved. (1) State of battery estimation lacks uniform standards and is difficult to evaluate effectively and accurately. (2) Lithium-ion battery application scenarios are very complex, and the management methods often lack universality. (3) The battery management system is difficult to achieve the compatibility of multiple functions, which cannot meet the needs of practical applications. In this chapter, contributors are expected to carry out relevant research on the above issues and put forward their unique views to further promote the continuous progress of the new energy field.
Researchers can carry out the research topics including:
• State estimation of the battery
• Remaining useful life prediction
• Aging mechanism analysis
• Battery high-precision equivalent modeling
• Design of battery management system
• The model-driven methods of the Kalman filtering and particle filtering
• Data-driven methods in core state parameter estimation procedure
• Convolutional neural networks
• Short and long-term memory neural network
• Support vector machine
• Intelligent neural network
• The fusion methods
