SOC online estimation method and system of lithium ion battery

Abstract

The invention provides an SOC online estimation method and system of a lithium ion battery. The SOC online estimation method comprises the following steps: determining a battery SOC-OCV relationship;establishing a dual-temperature gas-liquid kinetic battery analysis model: establishing the dual-temperature gas-liquid kinetic model, simulating the dual-temperature gas-liquid kinetic battery analysis model by using the dual-temperature gas-liquid kinetic model, and calculating to obtain an estimated value of a battery end voltage value through the dual-temperature gas-liquid kinetic battery analysis model; obtaining a current real-time battery SOC priori estimated value through an ampere-hour integral method; and obtaining an SOC optimal estimated value by using a Kalman filtering algorithm: subtracting the estimated value of the voltage value of the battery end from the acquired end voltage to obtain an error value, and correcting the SOC priori estimated value through the error valueand Kalman gain to obtain the SOC optimal estimated value, thereby improving the SOC estimation precision.

Description

technical field [0001] The invention belongs to the field of electric vehicle power batteries, and in particular relates to an online SOC estimation method and system of a lithium-ion battery. Background technique [0002] In recent years, electric vehicles have developed rapidly, but drivers' anxiety about the range of electric vehicles is a huge obstacle restricting the further popularization of electric vehicles. The early method of increasing battery energy density by improving materials is now getting closer to the bottleneck. With the increase of battery energy density, its safety performance will become more and more worrying. [0003] Therefore, in order to give full play to the performance of the battery system, prevent its overcharge and overdischarge from causing safety hazards, and prolong the cruising range of electric vehicles, it is necessary to carefully manage the battery system. The premise of fine management of the battery system is to accurately estimate...

AI Technical Summary

Problems solved by technology

However, due to the characteristics of the battery, the open-circuit voltage method needs to be left for more than 2 hours to obtain an accurate open-circuit voltage, so it cannot be applied in real vehicle driving; and the ampere-hour integral method, because it is too dependent on the initial value, and will continue to change during the estimation process. Accumulated errors, so in long-term estimation, its reliability will drop sharply; and neural network, Kalman filter and other algorithms, due to the inaccuracy of the equivalent circuit model itself, it is particularly difficult and complicated to improve the estimation accuracy

In the battery SOC estimation based on the electrochemical model, due to the complexity of the electrochemical model itself, the estimation complexity will be much higher than other estimation methods under the premise of ensuring the estimation accuracy.

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