Multi-time-scale state-of-charge estimation method for lithium battery of hybrid electric vehicle

Abstract

The invention aims to solve the problem of state-of-charge estimation error caused by inconsistency of a battery model state and a battery system state due to the influence of working conditions such as temperature and current of a battery management system of a hybrid electric vehicle. The invention discloses a multi-time scale state-of-charge estimation method based on an artificial bee colony algorithm and an adaptive particle filter algorithm, which comprises the following steps: firstly, selecting a certain power lithium battery, carrying out constant-temperature constant-current pulse discharge experiments on the power battery at different temperatures, recording experimental data, and establishing an open-circuit voltage temperature model of the lithium battery; secondly, establishing an equivalent circuit model to determine a system model state equation and identification parameters; and finally, performing system online identification and SOC accurate estimation when the power battery works based on an artificial bee colony algorithm and an adaptive particle filter algorithm of multiple time scales. The model adopted by the invention is based on an equivalent circuit model, and SOC accurate estimation can be performed on the lithium battery under different working conditions by combining an open-circuit voltage temperature model.

Description

technical field [0001] The invention relates to the technical field of battery management, in particular to a multi-time-scale state-of-charge estimation method for a lithium battery of a hybrid electric vehicle. Background technique [0002] State of Charge (SOC) estimation of power lithium batteries is one of the core technologies of battery management systems for hybrid electric vehicles. Accurate battery state-of-charge estimation can quantify the remaining mileage of electric vehicles and optimize the energy management of the whole vehicle. It can improve battery efficiency, prevent battery overcharge or overdischarge, improve battery cycle life, and ensure battery safety. [0003] The SOC of lithium batteries cannot be measured by sensors, and needs to be obtained by means of other measurable physical quantities such as current, voltage and temperature, through the established theoretical model and model parameters, combined with estimation methods. Considering the te...

AI Technical Summary

Problems solved by technology

Inconsistent rates of change of these parameters increase the micro- and macro-complexity of battery state-of-charge estimation

[0004] In the traditional SOC estimation method, the identification of battery model parameters is different due to changes in operating conditions such as temperature or current, but several calculations are required in the process of using the parameter identification method to achieve parameter regression.

However, when the operation is performed synchronously with the filtering algorithm, the inconsistency between the system state of the lithium battery and the model state parameters may lead to inevitable errors in the estimation of SOC

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