Due to increasingly strict emissions legislation, car manufacturers are forced to reduce the production of vehicle exhaust emissions. Today, the most debated topic is greenhouse gas emissions, which, when using fossil fuels, can only be reduced by cutting down on fuel consumption. For hybrid vehicles, this goal can be achieved by making the control logic more efficient.
This thesis developed a control strategy for hybrid vehicles based on the principle of a finite-state machine, using a local optimization method. The goal was to improve the efficiency of the hybrid vehicle's control logic by predicting the driving profile of a regular commute based on previously completed trips along the same route.
To ensure the predictive control strategy is activated only at the appropriate time, an algorithm to identify repeating driving patterns was created. By subsequently adapting the existing control logic based on the predicted driving profile, it's possible to reduce fuel consumption and, consequently, lower CO2 emissions.
To verify the control strategy, validation drives were conducted on a predetermined circuit. These drives showed that for a mild-hybrid vehicle, the control strategy leads to a reduction in fuel consumption and CO2 emissions of approximately 2.3 %. For full-hybrid vehicles, a significantly higher savings is expected due to a greater degree of regenerative braking, which further increases the potential of the proposed solution.