Under　the　dual　challenges　of　traditional　energy　shortages　and　environmental　degradation,　lithium-ion　batteries　have　become　a　core　driving　force　for　new　energy　technologies　because　of　their　high　energy　density　and　long　lifespan.　However,　estimating　the　remaining　useful　life　(RUL)　of　lithium-ion　batteries　faces　multiple　challenges,　and　accurately　assessing　battery　health　is　vital　for　enhancing　battery　management　systems　(BMS).　Recently,　significant　progress　has　been　made　in　RUL　prediction　through　deep　learning　and　machine　learning　algorithms,　but　existing　models　still　face　issues　with　hyperparameter　dependence,　data　noise,　and　complex　metric　handling.　To　solve　these　challenges,　this　paper　set　a　hybrid　prediction　model　that　combines　traditional　machine　learning　with　deep　learning.　First,　using　principal　component　analysis　(PCA)　for　feature　reduction,　and　a　belief　rule　base　(BRB)　module　is　introduced　to　improve　data　interpretability.　Next,　LSTM　and　an　improved　Transformer　network　are　employed　for　time　series　modeling,　incorporating　temporal　embedding　mechanisms　to　better　capture　degradation　trends.　Finally,　particle　swarm　optimization　(PSO)　is　employed　to　optimize　hyperparameters,　improving　prediction　accuracy.　The　experimental　results　demonstrate　that　the　proposed　model　performs　better　than　other　models　on　the　NASA　lithium-ion　battery　dataset.　Through　ablation　experiments,　the　key　roles　of　each　component　in　the　prediction　process　were　verified.　©　2025　IEEE.