UKF algorithm-based lithium ion power battery state estimation method

Abstract

The invention discloses an UKF algorithm-based lithium ion power battery state estimation method. According to the method, on-line estimation and updating are performed on parameters of a time-variable charge state space model and a health state space model by a joint estimation strategy, so that model accuracy under a dynamic test working condition can be effectively ensured, and the lithium ionpower battery state estimation accuracy is improved. Moreover, during unscented transformation, quasi-linear processing is performed on a measurement equation, so that calculation cost during the cycle iterative process is reduced very well; by on-line estimation and correction on statistic characteristic parameter of process noise, and the self-adaptive fault-tolerant capability of the UKF algorithm is greatly improved; and the convergence of filtering is traced in real time, filtering divergence is prevented by correcting covariance when a filtering result is in a divergent trend, and the numerical value stability during the filtering process is ensured.

Description

technical field [0001] The present application relates to the technical field of lithium-ion power batteries, in particular to a method for estimating the state of lithium-ion power batteries based on the UKF algorithm; it also relates to a battery state estimation device, equipment and computer-readable storage medium based on the UKF algorithm. Background technique [0002] As an important energy source for new energy electric vehicles, lithium-ion power batteries can accurately estimate their state of charge (SOC) and state of health (SOH), and can effectively estimate the mileage of a car, which is fully reasonable The use of lithium-ion power batteries to prolong the service life of lithium-ion power batteries and improve the operating efficiency of the vehicle has important practical significance for accelerating the process of electrification of vehicles. [0003] At present, there are many methods for estimating the state of charge of lithium-ion power batteries, but...

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Problems solved by technology

[0003] At present, there are many methods for estimating the state of charge of lithium-ion power batteries, but all of them have technical defects in varying degrees. For example, the ampere-hour counting (AH) method has accumulated errors, The problem of calibration; the open circuit voltage (OCV) method requires a long time to stand still, and is not suitable for battery state estimation during work; the neural network method has a strong dependence on the data set, and the algorithm's anti-disturbance ability Insufficient; based on unscented Kalman filter (unscented Kalman filter, UKF) for state estimation, UKF uses a series of determined samples to approximate the posterior probability density distribution of the system state, which can effectively solve the problem of filter divergence caused by the aggravation of system nonlinearity , but its estimation accuracy is affected by the accuracy of the battery equivalent model

[0004] In addition, the methods for estimating the state of health of lithium-ion power batteries mainly include the internal resistance method (for milliohm-level internal resistance, it is difficult to measure); the electrochemical impedance method (often used in laboratories to analyze the state of health of batteries) Mathematical model method (the nonlinearity of the battery system and the uncertainty of the environment will affect the accuracy of mathematical modeling, thereby affecting the estimation accuracy)

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