Abstract：The safe and efficient operation of lithium batteries depends on accurate state of charge（SOC）

estimation. However，the traditional battery model and SOC estimation have poor robustness and reliability under

noise interference. Aiming at the problem of SOC cooperative estimation under noise interference，firstly，the

maximum available capacity and open circuit voltage（OCV）characteristics of the battery were analyzed，and the

curve characteristics of lithium battery SOC—OCV were studied. Then，the problem of online model parameter

identification and SOC estimation under noise interference was studied，and a two-swarm cooperative particle

swarm optimization（TCPSO）method based on adaptive dynamic sliding window was proposed. Experimental

results show that the maximum SOC estimation error of the proposed method is less than 1%，which shows that the

proposed method can realize online parameter identification，and it is superior to the existing collaborative

estimation methods in terms of anti-noise performance and SOC estimation accuracy.

      Key words：state of charge （SOC） estimation；noise interference；parameter identification；two-swarm

cooperative particle swarm optimization（TCPSO）