Lithium ion battery charge state estimation method based on improved fractional order model

Abstract

The invention relates to a lithium ion battery charge state estimation method based on an improved fractional order model, belonging to the technical field of batteries. The method comprises the following steps: S1, selecting a to-be-tested power battery, collecting and tidying technical materials of the power battery, building the improved fractional order model of the power battery, and determining model parameters needed for charge state estimation of the power battery; S2, at the temperature of 25 DEG C, performing a charging and discharging experiment with current rate of C / 20 and an electrochemical impedance spectroscopy EIS experiment for the tested battery, consequently, building an experiment database of charging and discharging open circuit voltage and battery model parameters, simulating various real vehicle working conditions, and building a working condition test experiment database; S3, performing parameter recognition for the EIS data to obtain battery model parameters,and obtaining a mapping relation between OCV and SOC through data fitting; S4, combining the improved fractional order model of the power battery with a FEKF algorithm to perform SOC state estimationfor the battery.

Description

technical field [0001] The invention relates to the field of battery technology, in particular to a method for estimating the state of charge of a lithium-ion battery based on an improved fractional model. Background technique [0002] Lithium-ion batteries have become the preferred power source for electric vehicles due to their large capacity, long cycle life, and low memory effect. SOC is the most important state that needs to be supervised and monitored by the Battery Management System (BMS) of electric vehicles. High-precision and fast-converging SOC state estimation can provide more accurate mileage estimation and expand the available SOC range to avoid battery loss, accelerated life decay, combustion and even explosion caused by overcharge and overdischarge. [0003] Common SOC estimation methods can be divided into model-free and model-based algorithms. The simple and easy-to-use ampere-hour integration method is commonly used in the model-free method, but due to it...

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Problems solved by technology

On the other hand, it is based on data-driven artificial intelligence algorithms such as support vector machines, neural networks, etc. to simulate the complex nonlinear relationship between SOC and its influencing factors, but these black-box models have a high dependence on the quality and quantity of training data , relatively poor adaptability to unknown data

In the model-based method, the electrochemical model is suitable for macroscopic and microscopic predictions with high accuracy, but the computational requirements are high and the parameters are difficult to obtain

The equivalent circuit model based on the external dynamic characteristics can simulate the working characteristics of different types of batteries, thereby avoiding the detailed calculation of the internal electrochemical process, which has obvious flexibility and simplicity and high accuracy, and combined with Kalman Filter (Kalman Filter) , KF), Extended Kalman Filter (Extended Kalman Filter, EKF), Particle Filter (Particle Filter, PF) and other algorithms design observers to estimate SOC, but the commonly used RC integer order model cannot accurately simulate the nonlinearity of the battery. thus limiting the estimation accuracy

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