The existing battery SOH estimation method mainly has the following:
1 Direct discharge method: It is a method for evaluating the effect of the load on the battery SOH. This method is complex, requiring the SOH of the offline to test the battery, and cannot be realized online.
2 Measuring the internal impedance method: The SOH of the battery can be characterized by the relationship of ohmic resistance, and the ohmic interformance of the battery is obtained by measuring analysis, thus calculating the SOH of the battery;
3 Electrochemical Impedance Analysis (ELECTROCHEMICAL IMPEDANCE SPECTROPY, EIS): The main idea of this method is to apply a plurality of sinusoidal signals to be measured, and analyze the data information obtained by fuzzy theory, predict the aging of the battery, this method requires a lot Experimental data, low practicality;
4 Chemical Analysis: This method is estimated by measuring the change in electrolyte density, but the method must damage the battery structure so that the battery can no longer use;
5 Modern estimation method: mainly Kalman filtering algorithm, neural network algorithm, fuzzy logic algorithm, etc., these methods can qualitatively analyze the state of the battery, and the actual application effect is better.
Xiahua batteries include: tubular battery, colloidal battery, sealed battery, maintenance-free battery, OPZV tubular colloidal battery, OPZS battery, deep circulation battery
In this paper, the current battery state is low, the difference is poor, and the second-order THEVENIN in-lithium ion battery is used, and the battery status is real-time using adaptive unscented Kalman Filter, Aukf) algorithm. estimate.
Adaptive elsencing algorithm algorithm combined with elsencing Kalman filter algorithm and extended Kalman algorithm, establishing a cyclic iterative relationship, known battery parameters estimates battery state, and then uses the battery status as a known amount of estimated model parameters, in this type Recursive calculation, estimated battery SOC and ohm internal resistance in real time.
Wuxi Huizhong, Nanjing Xia Hua, Wuxi
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