With　the　widespread　adoption　of　electric　vehicles　(EVs)　and　energy　storage　in　renewable　energy　systems,　the　use　of　lithium-ion　batteries　has　increased　significantly,　making　the　battery　safety　performance　a　primary　concern.　The　accurate　state　of　charge　(SOC)　estimation　can　help　mitigate　the　safety　risks　for　the　utilisation　of　EVs　and　renewable　energy　systems.　Due　to　the　dynamic　and　non-linear　properties　of　batteries,　an　adaptive　online　SOC　estimation　is　proposed　in　this　paper　by　combining　the　online　parameters　estimation　using　equivalent　circuit　model　(ECM)　and　the　improved　particle　filter　(PF)　algorithm.　It　firstly　deduces　ECM　parameters　equations　using　bilinear　transformation　with　the　elimination　of　the　variation　caused　by　the　ambient　temperature.　Then,　the　seeker　optimization　algorithm　(SOA)-based　fixed-length　weighted　least　square　(LS)　algorithm　is　introduced　to　online　estimate　the　battery　parameters　accurately.　With　the　established　ECM,　the　battery　SOC　can　be　estimated　by　the　improved　genetic　algorithm　(IGA)　resampling-based　PF　algorithm,　which　effectively　alleviates　the　particle　degeneracy　problem　during　the　estimation,　consequently,　offering　a　better　performance　in　SOC　estimation.　Both　simulations　and　experiments　have　been　conducted　to　validate　the　effectiveness　of　the　proposed　method.　Compared　with　other　existing　algorithms,　it　shows　that　the　proposed　algorithm　can　accurately　model　the　battery　with　the　root　mean　squared　error　(RMSE)　＜0.1　%　and　achieve　the　real-time　SOC　estimation　with　less　computation　burden　and　high　accuracy.