In recent years, there has been an incredible improvement in battery technology because of the occurrence of EVs and
HEVs. However, the State-of-Charge (SoC) estimation remains a challenge in battery engineering. SoC is the ratio of available
capacity and maximum possible charge that can be stored in a battery. SoC estimation is of prime importance with relation to
battery safety and maintenance. This paper shows SoC estimation by optimisation SVM technique. Support Vector Machine
(SVM) is a kind of learning machine based on statistical learning premises. An accurate SoC estimation can improve the
performance of the battery and raise the security of the EVs. The SoC cannot only protect the battery, avoid overcharge or
discharge, but also improve the battery life. Therefore, the aim of this study is correct sampling of voltage, current and
temperature signals. In this project a SVM optimized by Particle Swarm Optimization (PSO) to boost SoC estimation accuracy.
