An equipment for estimating a motor parameter, the equipment comprising an input interface configured to receive an operating parameter ( u d,q, i d,q, ω e) of an electric motor, a first device configured to perform a first parameter estimation algorithm estimating an estimated first motor parameter ( Ψ PM,1) based on the operating parameter ( u d,q, i d,q, ω e) and on an initially determined second motor parameter ( R s,0), and a second device configured to perform a second parameter estimation algorithm estimating an estimated second motor parameter ( R s,1) based on the operating parameter ( u d,q, i d,q, ω e) and on an initially determined first motor parameter ( Ψ PM,0), wherein the equipment further comprises a third device configured to perform a third parameter estimation algorithm estimating a revised estimated second motor parameter ( R s, 2) based on the estimated first motor parameter ( Ψ PM,1) and on the operating parameter ( u d,q, i d,q, ω e), and a fourth device configured to perform a fourth parameter estimation algorithm estimating a revised estimated first motor parameter ( Ψ PM,2) based on the estimated second motor parameter ( R s,1) and on the operating parameter ( u d,q, i d,q, ω e), wherein the operating parameter ( u d,q, i d,q, ω e) comprises at least one of a detected voltage ( u d,q), a detected current ( i d,q), and a detected electrical speed (ω e), a first motor parameter ( Ψ PM) comprises a permanent magnet flux linkage, and a second motor parameter (R s) comprises a phase resistance, characterized in that the first device and the fourth device comprise an artificial neuronal network model performing the first and fourth parameter estimation algorithm (5', 8'), and the second device and the third device comprise a model reference adaptive control (MRAC) model performing the second and third parameter estimation algorithm (6', 7'). an input interface configured to receive an operating parameter ( u d,q, i d,q, ω e) of an electric motor, a first device configured to perform a first parameter estimation algorithm estimating an estimated first motor parameter ( Ψ PM,1) based on the operating parameter ( u d,q, i d,q, ω e) and on an initially determined second motor parameter ( R s,0), and a second device configured to perform a second parameter estimation algorithm estimating an estimated second motor parameter ( R s,1) based on the operating parameter ( u d,q, i d,q, ω e) and on an initially determined first motor parameter ( Ψ PM,0), wherein the equipment further comprises a third device configured to perform a third parameter estimation algorithm estimating a revised estimated second motor parameter ( R s, 2) based on the estimated first motor parameter ( Ψ PM,1) and on the operating parameter ( u d,q, i d,q, ω e), and a fourth device configured to perform a fourth parameter estimation algorithm estimating a revised estimated first motor parameter ( Ψ PM,2) based on the estimated second motor parameter ( R s,1) and on the operating parameter ( u d,q, i d,q, ω e), wherein the operating parameter ( u d,q, i d,q, ω e) comprises at least one of a detected voltage ( u d,q), a detected current ( i d,q), and a detected electrical speed (ω e), a first motor parameter ( Ψ PM) comprises a permanent magnet flux linkage, and a second motor parameter (R s) comprises a phase resistance, characterized in that the first device and the fourth device comprise an artificial neuronal network model performing the first and fourth parameter estimation algorithm (5', 8'), and the second device and the third device comprise a model reference adaptive control (MRAC) model performing the second and third parameter estimation algorithm (6', 7').