Kalman　filters　(KFs)　are　widely　used　for　state-of-charge　(SOC)　estimation　of　Li-ion　batteries　due　to　their　excellent　dynamic　tracking　capability.　Especially　the　cubature　KF　(CKF),　with　the　computational　efficiency　and　nonlinear　processing　ability,　is　an　outstanding　candidate　for　SOC　estimation.　However,　the　actual　working　conditions　are　complex　and　changeable,　and　the　measurement　data　is　usually　accompanied　by　non-Gaussian　noise　(outliers).　Therefore,　the　performance　of　the　original　CKF　with　minimum　mean　square　error　(MMSE)　criterion　may　be　degraded　seriously　in　these　cases.　In　order　to　enhance　the　robustness　of　CKF,　the　MMSE　in　the　CKF　framework　is　substituted　by　the　generalized　maximum　correntropy　criterion　(GMCC),　and　thus　a　robust　CKF　with　GMCC　(GMCC-CKF)　is　developed　by　fixed　point　iteration　approach　in　this　work.　Furthermore,　a　SOC　estimation　model　via　the　GMCC-CKF　is　proposed　to　improve　estimation　accuracy　under　non-Gaussian　noise　environments.　The　simulation　results　show　that,　compared　with　the　traditional　KFs,　the　proposed　GMCC-CKF　can　accurately　estimate　the　SOC　of　lithium　batteries　under　different　temperatures　and　operating　conditions　considering　non-Gaussian　noise　interference.　The　results　of　mean　absolute　error　(MAE)　and　root　mean　square　error　(RMSE)　are　less　than　1%,　which　verifies　the　excellent　performance　of　GMCC-CKF.　©　2022　Elsevier　Ltd