Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization

Lithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One critical factors correct operation an is estimation battery charge state. In this sense, work presents a comparison state (SoC), tested in four different conduction profiles temperatures, which was performed using Multiple Linear Regression without (MLR) with spline interpolation (SPL-MLR) Generalized Model (GLM). The models were calibrated by three bio-inspired optimization techniques: Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO). computational results showed that MLR-PSO suitable SoC prediction, overcoming all other important proposals from literature.