RLS with optimum multiple adaptive forgetting factors for SoC and SoH estimation of Li-Ion battery

Abstract

Recursive least square (RLS) with a single forgetting factor has been commonly used for parameter and state estimation of dynamical systems. In many applications such as robotics, electric vehicles, renewable energy systems, and smart-grid, accurate battery state of charge (SOC) and state of health (SOH) estimation is essential for the safe and efficient operation. To this end, the challenge lies in identifying and parameterization the temporal behavior of Lithium-Ion batteries, because their response is nonlinear and time-varying. This paper proposes a new RLS algorithm with optimum multiple adaptive forgetting factors (MAFFs) for SOC and SOH estimation of Li-ion batteries. Particle swarm intelligence is employed for identifying the system parameters. The performance of the optimum MAFF-RLS algorithm is compared to RLS with multiple fixed forgetting factors (MFFFs). Performance evaluation is carried out using the Urban Dynamometer Driving Schedule (UDDS). The simulation results indicate the better performance of MAFF-RLS algorithm compared to MFFF-RLS algorithm in terms of mean square error of SOC and internal resistance.