Lithium battery SOC online estimation method based on backward smooth filtering framework

Abstract

The invention discloses a lithium battery SOC online estimation method based on a backward smooth filtering framework, and the method comprises the following steps: 1, testing a lithium battery so asto obtain a function of an open-circuit voltage with respect to a state of charge, and obtaining an initial SOC of the lithium battery through the open-circuit voltage; 2, establishing a lithium battery equivalent circuit model, and determining a discrete state equation and an observation equation of the lithium battery; 3, carrying out model parameter identification, and identifying the parameters of the equivalent circuit model of the lithium battery; 4, establishing a backward smooth square root cubature Kalman filter; and 5, acquiring real-time voltage and current data of the lithium battery, and estimating the SOC of the battery. According to the algorithm, on the basis of a traditional square root cubature Kalman algorithm, a backward smooth filtering framework is combined, backwardsmooth recursion operation is carried out by utilizing latest measurement information, the influence of factors such as observation noise and observation errors is reduced, and the convergence speed is increased while the estimation precision is improved.

Description

technical field [0001] The invention relates to the field of battery management systems for electric vehicles, in particular to an online estimation method for lithium battery SOC based on a backward smoothing filter framework. Background technique [0002] In recent years, pure electric vehicles and hybrid vehicles with batteries as the main power source have been widely put into use. This emerging means of transportation has attracted people's attention for its low-carbon emission, energy-saving and light-weight features. Lithium-ion batteries, as energy storage devices for electric vehicles and hybrid vehicles, need to adopt effective battery management methods to slow down battery capacity decay, improve battery life, and ensure the reliability and safety of electric vehicles. Among them, battery state of charge (SOC) estimation has always been a hot topic in battery management research. Accurate estimation of battery SOC can be used to prevent battery overcharge and ov...

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Problems solved by technology

However, CKF also has problems that the spherical volume point may exceed the integration area, and the positive definiteness of the covariance matrix may be destroyed during the calculation process.

Literature (Leng Yan. Lithium battery SOC estimation and battery management system research based on CKF [D]. Jiangsu University, 2016.) proposed the square root volumetric Kalman filter (SRCKF), which solved the covariance matrix in the calculation process of CKF The problem that the positive definiteness of the system is destroyed, but the problems of filter divergence and general convergence speed have not been solved.

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