Battery state of charge estimation method and estimation device

Abstract

The invention provides a battery charge state estimation method and a battery charge state estimation device. According to the method and device, after determining the EKF discrete state space model for the battery, the absolute value of the input current and the SOC value at the previous moment are fuzzified, and the Kalman gain correction coefficient is finally obtained through fuzzy reasoning, thereby correcting the Kalman gain , and finally get the SOC value at each moment. Compared with the traditional Kalman filter method, it can reduce the influence of current and SOC on the accuracy of the battery model, and estimate the SOC more accurately.

Description

technical field [0001] The invention relates to the technical field of batteries, in particular to a method for estimating the state of charge of a battery and an estimating device for running the algorithm. Background technique [0002] The state of charge (SOC) of the battery represents the remaining available power of the battery, and its accurate estimation is one of the core functions of the battery management system. The premise is that its estimation accuracy directly affects the efficiency of the entire battery management system. However, since the battery itself is a closed system, it cannot be directly measured and obtained by directly placing sensors inside the battery. It can only be estimated based on the relationship between other measurable parameters (voltage, current, temperature). Since this relationship has strong nonlinear characteristics, and is often affected by many factors such as operating conditions, temperature, and aging degree, it will change in...

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Problems solved by technology

The shortcomings of the existing methods are: 1) the internal resistance method has the problem that the internal resistance does not change much in a range where the state of charge is close to 1; 2) the ampere-hour integration method is strongly dependent on the accurate initial value of SOC The accuracy requirements of the current acquisition equipment are very high. If the initial value of the SOC is inaccurate or the ammeter is offset, a large cumulative error will be generated; 3) The open circuit voltage method needs to stand the battery for a long time, which is not suitable for real-time estimation; 4) Extended The Kalman filter (EKF) method is a model-based algorithm. Because it overcomes the strict requirement of the initial SOC value of the ampere-hour integration method, it has become a hot spot in research and application, but its accuracy is affected by the battery model and sensor measurement errors. Influenced by other factors, in the actual application process, the battery model parameters change with the SOC, current, temperature and battery aging degree, and the statistical information of the sensor error may be unknown or time-varying, which will lead to the estimation of the traditional EKF method Low accuracy and poor robustness

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