The　economical　and　safe　energy　management　strategy　(EMS)　is　essential　for　a　battery　electric　vehicle　(BEV).　With　an　improved　deep　deterministic　policy　gradient　(DDPG)-based　EMS　proposed　for　a　dual-motor　BEV,　energy　consumption　and　electric　component　aging　are　minimized.　Firstly,　this　study　incorporates　health　awareness　of　electric　components　into　EMS.　Then,　this　study　employs　a　gated　recurrent　unit　(GRU)　as　well　as　a　self-attention　(SA)　mechanism　for　improving　the　original　DDPG＇s　performance.　The　GRU　enhances　the　performance　of　DDPG　agents,　post-convergence,　by　furnishing　them　with　additional　state　information.　The　SA　mechanism　empowers　agents　to　distinguish　the　importance　of　state　information,　thus　improving　its　adaptability　in　new　environments.　The　proposed　EMS　decreases　battery　energy　consumption　to　7.95　kW-hour　and　enhances　battery　state　of　health　(SOH)　to　96.83　%　of　the　dynamic　programming　(DP)-based　EMS.　The　two　motors　of　the　proposed　EMS　possess　the　highest　end-value　SOH,　i.e.,　99.99929　%　and　99.99978　%,　respectively.　The　proposed　EMS　demonstrates　superior　adaptability　to　other　EMSs,　regardless　of　the　initial　state　of　charge　(SOC).　It　shows　the　best　adaptability　at　an　initial　SOC　of　0.6,　and　achieves　95.87　%　and　94.67　%　of　the　energy　consumption　of　DP-based　EMS　in　the　mixed　and　actual　test　cycles,　respectively.