Lithium battery equivalent circuit model parameter online robust adaptive identification method

Abstract

The invention discloses a lithium battery equivalent circuit model parameter online robust adaptive identification method. The method comprises the following steps of: establishing an SOC-OCV relation curve according to a battery open-circuit voltage and a state of charge; initializing system parameters, and setting initial values of theta(0) and P(0), an initial SOC state, lambda and a sampling period T; calculating Soc(k) of the k-th step by adopting an ampere-hour integral method, wherein k=1, 2,..., n; according to the collected battery current and terminal voltage observation data, calculating to obtain a matrix by combining an SOC-OCV function relationship; starting an RLsign algorithm, and calculating a1, a2, a3, a4 and a5 according to an iterative formula; further solving parameters R0, R1, R2, C1 and C2 in a battery model; and circulating the steps to carry out online identification on the model parameters so as to realize dynamic adjustment of the battery model parameters. According to the lithium battery equivalent circuit model parameter online robust adaptive identification method, the influence of non-Gaussian noise on parameter identification can be effectively reduced, and the effectiveness of parameter estimation precision is ensured; and the algorithm is stable, the calculation complexity is low, and the steady-state convergence precision is higher than that of other similar traditional algorithms under the non-Gaussian noise condition.

Description

technical field [0001] The invention belongs to the technical field of parameter identification methods in signal processing, and in particular relates to an online robust self-adaptive identification method for lithium battery equivalent circuit model parameters. Background technique [0002] In the past ten years, as traditional energy vehicles powered by fossil energy have been adversely affected by energy constraints and environmental pollution, petroleum-powered vehicles are being replaced by electric vehicles powered by new energy. Compared with traditional fossil energy vehicles, electric vehicles have shown advantages over traditional vehicles in terms of energy consumption, which makes the development of electric vehicles a mainstream trend in the current automotive industry. [0003] As an important part of the battery management system of electric vehicles, state of charge estimation (State of Charge, SOC) of lithium batteries has become a research hotspot in elec...

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Problems solved by technology

When the above method is applied to model parameter estimation, it is assumed that the system measurement noise obeys Gaussian distribution, but the actual lithium battery working environment is easily disturbed by physical or artificial non-Gaussian distribution noise. In this case, the above method is applied to lithium There will be a large deviation in the identification accuracy of the battery equivalent circuit model parameters

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