Method for estimating remaining capacity of battery based on threshold extension Kalman algorithm

Abstract

A method for estimating the remaining capacity of a battery based on a threshold extension Kalman algorithm includes the following steps: 1) using an EKF algorithm to obtain estimated values of respective state variables at a current time, the state variables including remaining battery capacity, first RC link terminal voltage, and second RC link terminal voltage; 2) setting the thresholds of state variables in a state equation in combination with short-term historical current data, and determining whether the estimated values of the state variables exceed respective threshold ranges, and if so, restricting the corresponding state variable to the threshold range. Based on the EKF algorithm, the method add thresholds to state variables in the model by using historical data, restricts the range of state changes, and prevents an decrease in the accuracy of SOC estimation caused by the divergence of state variables, and has better robustness than the existing model-based SOC estimation method.

Description

technical field [0001] The invention relates to the field of battery remaining capacity (SOC) estimation, in particular to a method for estimating the battery remaining capacity based on a threshold value extended Kalman algorithm. Background technique [0002] Battery storage systems (BSS) are widely used in power smoothing systems for new energy power generation and energy management systems for electric vehicles. The performance and life of the battery are easily affected by temperature, charge and discharge times, and charge and discharge rate. The remaining battery capacity (SOC) is the basis for the battery management system (BMS) to optimize the battery working state, prolong the battery life, and ensure the safe operation of the system. [0003] Theoretically, the ampere-hour method and the open-circuit voltage method can realize accurate estimation of battery SOC. However, due to factors such as measurement noise, error accumulation, and inaccurate initial SOC, the...

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Problems solved by technology

[0007] The main purpose of the present invention is to overcome the inaccurate estimation of SOC by the model-based SOC estimation method in the prior art under the conditions of local model distortion, short-term sensor failure, large initial error, fast error accumulation, non-Gaussian noise interference, etc. problem, a battery remaining capacity estimation method based on the threshold extended Kalman algorithm is proposed

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