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Journal of power sources (2.Issues and challenges of battery health (2.2…
Journal of power sources
2.Issues and challenges of battery health
2.1.Issues of battery aging
It is possible to to monitor the
gradual degradation
When battery age,the tiny lithium make
fiber structure as dendrite
and cause fire.
2.2 Degradation factors
1,Environment temperature
2,Discharging current rate
3,Charging rate (fast charging)
4,Depth of Discharge (DoD)
5,Time intervals between full charge cycles
2.3. Challenges of monitoring battery health
1,Noise in collecting data
2,Cannot measure the measurements from unexpected battery behavior
3,Inaccurate results from online monitoring
4,Small errors will accumulate over time
2.4. Consideration of features for monitoring battery health
Architecture,load profile,control
To review battery prognostics and health management(PHM)techniques
From
battery manufacturers,
car designers,
and electric vehicle drivers
1.Two battery prognostics addresses two critical issues
1,Uncertainty of battery behavior and internal characteristics
It's impossible to observe the internal battery electrochemical process.
Misunderstanding of battery characteristics causes substantial issues.
2,Safety assurance
Such as li battery,if it temperature experiences high speed of increasing,then one cell will spread to neighboring cells,could have explore or fire
3.Prognostics of battery health
3.1. Overview of battery PHM technologies
Prognostics and health management (PHM)
is a science using tools and techniques to evaluate the reliability a system or component.
It gives a predicted condition of a product or a system
3.2. State of charge estimation
See picture in the article
3.3. State of health assessment
Internal resistance and capacity,could use as a basis for providing and indication of the SoH of the battery
Low charge rate help to control balancing cells on battery
4.Techniques for battery prognostics
4.1. Physical-models approach
When battery fully charged ,voltage can be acquired from open-circuit measurement and one extra measurement with load at the terminal.
Model is only valid under steady load condition
4.1.1. Thevenin model
4.1.2. Runtime-based electrical model
4.1.3. Combined electric model
4.2. Data-driven approach
4.2.1. Neural network
4.2.2. Support vector machine
4.2.3. Fuzzy logic
4.3. Fusion approach
4.3.1. Kalman filter
4.3.2. Other techniques
5.Battery safety and realiablity
The Li-ion battery can be broken during use