Simulating the translatory motion of a vehicle during a gear shift gives a good basis to evaluate performance and comfort of a gear shift. This evaluation can be used for gear shifting strategy in an automatic transmission. A model of a diesel engine and it's electronic control system is developed to capture the engines behaviour in a vehicle simulation environment. The modelled quantities are brake torque, fuel consumption and exhaust gas temperature and are based on engine speed and pedal position. In order to describe these outputs the inlet air flow and boost pressure are also modelled and used as inner variables. The model is intended to be implemented on board a vehicle in a control unit which has limited computational performance. To keep the model as computationally efficient as possible the model basically consists of look-up tables and polynomials. First order systems are used to describe the dynamics of air flow and exhaust temperature. The outputs enables gear shift optimization over three variables, torque for vehicle acceleration, fuel consumption for efficiency and exhaust temperature to maintain high efficiency in the exhaust after treatment system. The engine model captures the low frequent dynamics of the modelled quantities in the closed loop of the engine and it's electronic control system. The model only consists of three states, one for the pressure build up in the intake manifold and two states for modelling the exhaust temperature. The model is compared to measured data from a engine test cell and the mean absolute relative error are lower than 6.8%, 7.8% and 5.8% for brake torque, fuel consumption and exhaust gas temperature respectively. These results are considered good given the simplicity of the model.

One of the largest energy losses in an excavator is the compensation loss. In a hydraulic load sensing system where one pump supplies multiple actuators, these compensation losses are inevitable. To minimize the compensation losses the use of a multi chamber cylinder can be used, which can control the load pressure by activate its chambers in different combinations and in turn minimize the compensation losses.  For this proposed architecture, the control of the multi chamber cylinder systems is not trivial. The possible states of the system, due to the number of combinations, makes conventional control, like a rule based strategy, unfeasible. Therefore, is the reinforcement learning a promising approach to find an optimal control.  A hydraulic system was modeled and validated against a physical one, as a base for the reinforcement learning to learn in simulation environment. A satisfactory model was achieved, accurately modeled the static behavior of the system but lacks some dynamics.  A Deep Q-Network agent was used which successfully managed to select optimal combinations for given loads when implemented in the physical test rig, even though the simulation model was not perfect.