This thesis explores how to maximize traction for battery electric heavy-duty trucks under challenging road conditions. On road surfaces with low grip, requesting a high torque from the electric motor can lead to excessive slip and reduction of tractive force as a consequence. The aim of this thesis is to implement a control strategy that regulates the torque to achieve maximum tractive force between the wheels and the road surface. This is done by using a gradient ascent method and iteratively looking at estimates of the driveshaft torsion and rotational speed of the electric motor and the wheels to find the peak of the friction coefficient-slip ratio curve, that describes the relation between slip and the friction coefficient. A reference generator is developed, outputting a reference in slip ratio for the controller to follow, directing it towards the peak of the friction coefficient-slip ratio curve.

Two types of controllers are tested, PI controller and an LQR controller. As the dynamics of the driveline differ for each gear, the controllers where tuned differently at each gear. PI controllers are the ones used for the majority of the simulations. The performance of the reference generator and the controllers are evaluated using simulations in Matlab/Simulink where the driveline model has been implemented. The results show that the control framework is able to find and stay at the maximum tractive force in multiple simulation configurations. The drawback of the gradient ascent method is its sensitivity to noise and future work should include better and more reliable ways to filter and estimate states and signals.