SOC and SOH Joint Estimation of Lithium-Ion Battery Based on Improved Particle Filter Algorithm

In order to improve the estimation accuracy of the state of charge (SOC) of lithium ion batteries and accurately estimate the state of health (SOH), this paper proposes an improved firefly algorithm to optimize particle filter algorithm to estimate the SOC and SOH of lithium batteries. Aiming at the particle degradation problem of the traditional sequential importance sampling in the standard particle filter algorithm, the improved firefly algorithm is used to replace the re-sampling of the traditional particle filter to suppress the particle depletion during the execution of the standard particle filter algorithm; Establishing a second-order RC equivalent circuit model and use the recursive least square method with forgetting factor to identify relevant battery parameters. The ohmic resistance is regarded as a characteristic parameter of the battery state of health (SOH), and the battery SOH is estimated on this basis. IFA-PF algorithms are used for the joint estimation of SOC and SOH. Through simulation verification under DST conditions, the accuracy of using the improved particle filter algorithm to estimate battery SOC is within 2%, with an average error of 0.81%, and its SOH estimation accuracy remains at about 2%, with an average error of 1.34%, which proves the superiority of the joint estimation algorithm.