BMS lithium-ion battery development -Lithium - Ion Battery Equipment

BMS lithium-ion battery development -Lithium - Ion Battery Equipment

The primary problem of BMS is security, which needs to have:

1) Overcharge protection, that is, the charging needs to be terminated in time when the voltage exceeds the standard.

2) Over-discharge protection, that is, the discharge is terminated when the voltage of any cell in the group is lower than the over-discharge threshold.

3) Over-current and short-circuit protection, the main function is that the output can be automatically turned off and enter the self-locking state when excessive discharge current or even short-circuit is accidentally caused.

At present, these basic protection functions can be realized by professional lithium battery protection chips, such as TI, Linear Technology and other special lithium battery protection ICs. Balance is the core issue of battery management, and research at home and abroad is very active. The specific realization method of equalization can be divided into dissipative and non-dissipative types according to the energy processing method. Dissipative equalization is achieved by consuming excess energy by shunt resistors connected in parallel at both ends of the battery. This method has the advantages of simple implementation and low cost. Inexpensive advantage, but has thermal management issues. Non-dissipative balance means that energy is transferred between cells in the battery pack to achieve balance. There are various circuit topologies in this balance method, but there are often problems such as complicated circuits, low balance efficiency, and slow balance speed. It limits its use in large-capacity fields such as electric vehicles and energy storage.(Lithium - Ion Battery Equipment)

Due to the complexity of the structure and model of lithium batteries, the SOC characteristics of lithium batteries are affected by many uncertain factors, such as charge-discharge rate, temperature and charge-discharge times. Therefore, how to make an accurate estimate of SOC based on measurable parameters is currently Urgent problem to be solved. At present, the commonly used SOC estimation methods in the industry include the discharge method, the ampere-hour integration method, the open circuit voltage method, and the Kalman filter method. The discharge test method is to discharge the battery at a constant current and count the discharged power until the terminal voltage reaches the discharge cut-off voltage. This method is relatively reliable and suitable for different types of batteries; but the main disadvantage is that the test process is long and cannot be estimated online in real time, so this method is generally used to determine battery model parameters. The ampere-hour integration method calculates the SOC by calculating the inflow or outflow capacity of the battery during charging and discharging for a period of time. After calculating the SOC value, it is compensated according to the influencing factors such as the ambient temperature and the charging and discharging rate. This method has the problem of integral cumulative error and initial value prediction. The open circuit voltage method uses the corresponding relationship between the open circuit voltage of the battery and a certain SOC curve to estimate the SOC. However, in order to measure the open circuit voltage, the self-recovery effect of the battery needs to be eliminated, which takes a long time. Therefore, the open circuit voltage method cannot estimate the SOC in real time, but it can provide the initial SOC value for other algorithms. Kalman filtering is a method to solve the filtering problem in discrete equations through recursive iteration, which can estimate the state value of the current moment by recursion according to the state of the previous moment. Therefore, we can use the last state parameter of the battery to estimate the working state at the current moment, that is, the battery current, working temperature and other parameters are used as the input of the system, the SOC is used as the state parameter, and the battery voltage is used as the output. estimate. At present, the Kalman filter method is still in the theoretical simulation stage, and there are few reports of practical application. The current BMS system mainly uses analog/digital temperature sensors to monitor the temperature, and the thermistor can be used for cost-constrained occasions.

At present, the United States, Japan and Germany are at the forefront of the world in terms of BMS research and productization. A123System Company of the United States first developed an iron-lithium energy storage system, and in 2011 built the world's largest 32MWh lithium battery energy storage power station. At present, domestic research in the field of energy storage power station BMS has also begun. For example, the special BMS for energy storage power station produced by Ligao Company adopts a three-level system architecture to realize battery monitoring, which can be used for large, medium and small energy storage.



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