The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO₄) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.

State of charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state of charge is thus particularly important for electric vehicles (EVs), hybrid EVs, or portable devices. The aim of this innovative study is to estimate the SOC of a high-capacity lithium iron phosphate (LiFePO₄) battery cell from an experimental dataset obtained in the University of Oviedo Battery Laboratory using the multivariate adaptive regression splines (MARS) technique. An accurate predictive model able to forecast the SOC in the short term is obtained and it is a first step using the MARS technique to estimate the SOC of batteries. The agreement of the MARS model with the experimental dataset confirmed the goodness of fit for a limited range of SOC (25-90% SOC) and for a simple dynamic data profile [constant-current (CC) constant-voltage charge-CC discharge].

In the last few years, several Li-ion battery technologies have been studied and developed for their use in electric vehicles (EVs). Among them, lithium iron phosphate (LFP) batteries are considered a promising battery technology for EVs, due to their key advantages, such as cycle life, efficiency, and reliability, to name a few. This paper evaluates several commercial LFP batteries, studied under various testing scenarios, including cycle life, energy efficiency, power capability, or internal resistance test. The obtained results are compared with the long-term U.S. Advanced Consortium (USABC) goals, in order to validate the feasibility of this technology for its use in EVs. We found that although the tested LFP batteries successfully met some important USABC goals, there are still several technical challenges to be addressed, such as the increase of the energy density or the reduction of the final cost.