Joint estimation method for branch current, state of charge and power state of parallel battery pack

Abstract

The invention discloses a joint estimation method for branch current, charge state and power state of a parallel battery pack, which comprises the following steps of: firstly, acquiring the branch current of the parallel battery pack, establishing an equivalent circuit model of a battery, and simultaneously, taking the estimated branch current and terminal voltage as input; a recursive least square method with a forgetting factor is used for identifying model parameters, a self-adaptive forgetting factor capable of changing along with a residual error is added into extended Kalman filtering to estimate the state of charge, and power state estimation under the constraint of the state of charge and terminal voltage is carried out. According to the method, the influence of the inconsistency of the lithium ion battery monomers on the branch current is considered, and the adaptive forgetting factor capable of changing along with the residual also improves the adaptability of the extended Kalman filtering to different environments, so that the estimation precision of the branch current, the charge state and the power state is improved.

Description

technical field [0001] The invention relates to battery technology, in particular to the technical field of electric vehicle power battery systems, in particular to a method for joint estimation of branch current, state of charge and power state of parallel battery packs. Background technique [0002] In order to meet the requirements of energy and power, the battery system of electric vehicles usually consists of hundreds of battery cells connected in series or in parallel. However, due to tolerances in the manufacturing process and different use conditions, inconsistencies between battery cells are unavoidable, and inconsistencies in impedance and capacity between parallel batteries can lead to uneven current distribution in each branch. At present, for large parallel battery packs, the battery management system usually only monitors the total current, but cannot obtain the current of each branch. If the average branch current obtained by dividing the total current by the...

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Problems solved by technology

[0003] At the same time, most model-based SOC estimation adopts the Extended Kalman Filter (EKF) method. Due to the error between the equivalent circuit model and the actual physical model of the battery, as the number of filters increases, the error accumulation will lead to a decrease in accuracy. Even divergence, it is necessary to add a suitable forgetting factor to correct the state estimation error covariance, and the constant forgetting factor cannot be adjusted as the system changes

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