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

Abstract

The invention relates to a method for estimating the state of charge of a lithium-ion battery based on an improved fractional model, which belongs to the field of battery technology and includes steps: S1: selecting a power battery to be tested, collecting and sorting out technical data of the power battery, and establishing the power battery The improved fractional-order battery model, and determine the model parameters required for state-of-charge estimation of the power battery; S2: Conduct charge-discharge experiments with a current rate of C / 20 and electrochemical impedance spectroscopy (EIS) on the tested battery at 25°C Experiment, and then establish the experimental database of charging and discharging open circuit voltage and battery model parameters, simulate various real vehicle working conditions, and establish a working condition test experimental database; S3: Perform parameter identification on EIS data to obtain battery model parameters, and obtain OCV through data fitting Mapping relationship with SOC; S4: Combine the improved fractional-order battery model of the power battery with the FEKF algorithm to estimate the SOC state of 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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