Estimating the Charge and Temperature States for Li-Ion Batteries by Coupling Single Particle Kinetics and Electrothermal Effects

Battery state of charge (SoC) and state of temperature (SoT) are critical information to make efficient management strategies. This article proposes a multiphysics model-based SoC and SoT estimation method for li-ion batteries (LiBs). First, to describe battery electrochemical and thermal characteristics, an extended single particle model (eSPM) and a thermal model are constructed and coupled with bridge variables of temperature and li-ion concentration. Second, aging related parameters of the eSPM are identified by using the genetic algorithm to track battery deterioration progress. Third, battery electrochemical states regarding li-ion concentrations at anode/cathode electrodes are estimated by using the adaptive unscented kalman filter to deal with the issues of nonlinearity and noise, wherein the eSPM parameters are online adjusted according to offline calibrations to adapt to temperature changing. Fourth, the estimated li-ion concentrations are used to obtain the SoC and the entropic power, which is the key to determine the heat-generation power. Finally, the proposed method is verified on 18 650 LiB cells under 0–50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C ambient temperatures and high-dynamic load excitations. Experimental results show that the proposed method can accurately and reliably reproduce battery voltage, SoC, and SoT with maximum root mean square errors of 0.055 V, 0.016, and 0.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C, respectively.