Effective monitoring of the health status of polymer lithium-ion batteries is the key to ensuring the safety of polymer lithium-ion batteries and extending their service life.
The voltage of polymer lithium-ion batteries is an important indicator of their health status. By real-time monitoring of the voltage changes of polymer lithium-ion batteries, it is possible to promptly detect whether the polymer lithium-ion battery is overcharged or over-discharged, and thus take measures to protect the polymer lithium-ion battery.
The increase in the internal resistance of polymer lithium-ion batteries is an important sign of polymer lithium-ion battery aging. By regularly measuring the internal resistance of polymer lithium-ion batteries, the life and performance degradation of polymer lithium-ion batteries can be evaluated. The internal resistance measurement method is a simple and effective method for evaluating the health status of polymer lithium-ion batteries.
The capacity of polymer lithium-ion batteries is an important indicator of the energy storage capacity of polymer lithium-ion batteries. By comparing the current capacity of polymer lithium-ion batteries with the initial capacity, the degree of attenuation of polymer lithium-ion batteries can be determined. Commonly used methods include coulomb counting method, etc., which can accurately measure the capacity changes of polymer lithium-ion batteries.
BMS can monitor various parameters of polymer lithium-ion battery in real time, including voltage, current, temperature, etc., and analyze the health status of polymer lithium-ion battery through algorithms. BMS can provide early warning and fault diagnosis functions to ensure the safe operation of polymer lithium-ion battery system.
The temperature of polymer lithium-ion battery is one of the key factors affecting its performance and life. Too high or too low temperature will cause the performance of polymer lithium-ion battery to deteriorate. Therefore, by monitoring the temperature of polymer lithium-ion battery, it is possible to timely discover and take measures to adjust the working environment of polymer lithium-ion battery.
Based on data-driven methods such as machine learning algorithms, the health status of polymer lithium-ion battery can be predicted by analyzing the external health characteristics of polymer lithium-ion battery (such as charging curve, temperature difference curve, etc.). This method can obtain higher prediction accuracy under the premise of reliable training set.
In order to more accurately monitor the health status of polymer lithium-ion battery, a variety of monitoring methods can be combined for comprehensive evaluation. For example, multiple parameters such as voltage, internal resistance, capacity and temperature of polymer lithium-ion battery can be monitored simultaneously, and the health status of polymer lithium-ion battery can be more comprehensively understood through comprehensive analysis.
