This　study　proposes　an　efficient　estimator　and　uses　it　to　estimate　the　health　of　a　lithium-ion　battery　and　a　supercapacitor　in　the　hybrid　energy　storage　system　(HESS).　A　new　type　of　online　health　estimator　that　uses　a　fuzzy　brain　emotional　learning　neural　network　(FBELNN)　is　proposed.　This　neural　network　is　different　to　a　conventional　brain　emotional　learning　neural　network　where　the　fuzzy　inference　system　and　a　new　reward　signal　are　used.　The　effect　capacity　fading　on　the　output　of　energy　storage　components　is　also　determined.　The　proposed　method　uses　a　discrete　wavelet　transform　(DWT)　and　principal　component　analysis　(PCA)　to　extract　features　from　the　response　signal　for　the　impulse　load.　The　DWT-PCA　can　reduce　the　workload　for　feature　extraction.　The　parameter　adaptation　laws　and　convergence　analysis　for　the　FBELNN　are　derived　and　the　internal　parameters　for　the　FBELNN　are　optimized　using　a　genetic　algorithm　(GA).　A　neural　network　estimates　the　capacity　of　a　supercapacitor　and　lithium-ion　battery　in　real-time　to　better　ensure　the　safety　of　HESS.　The　sample　set　is　collected　from　the　voltage　response　signal　in　the　HESS　simulation　platform　and　practical　experimental　platform.　Simulation　and　experimental　results　show　that　the　proposed　method　has　a　faster　learning　speed　and　is　more　accurate　than　other　methods.