Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The
development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is
a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve
these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control
techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent
control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a
neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic
behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity
vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the
efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator
which provides 3 times more accurate estimations than analytical approaches.The research leading to these results has been supported by
the ECSEL Joint Undertaking under Grant agreement no.
662192 (3Ccar).This Joint Undertaking receives support from
the European Union Horizon 2020 research and innovation
program and the ECSEL member states