Development of logic for pollutant emission reduction of a Hybrid Electric Vehicle

Nowadays, environmental preservation and pollutant reduction are critical points on which the research is focused to reduce the carbon footprint of humans as much as possible. In particular, the mobility industry, responsible for almost 12% of global CO2 emissions, is investing more and more resources in research and development of solutions to face this problem. This thesis is collocated to improve vehicles’ efficiency and reduce their pollutant emissions, being part of a project AutoECO/Pitef that aims at improving the ecology of a light-duty commercial vehicle by hybridizing it and developing low and high-level, ECO-oriented control logic. This study developed and optimized two algorithms to pursue the project’s objective. An improvement of the standard ECMS logic for power splitting management is introduced in the first algorithm. The newly proposed solution gains one degree of freedom more on the decision of the best power unit’s working point through the control of the gear engaged by the drive line. The algorithm will choose the best power split and gear arrangement between all the possible conditions that make the drive-line satisfy the request of the driver by means of a suitable and complete cost function that weights the equivalent fuel consumption, the actuation of the gear shift, the transient of the combustion engine and the fulfillment of the driver’s request. A reduction of the CO2 emissions of 0.73% with respect to the standard ECMS formulation on a WLTP cycle has been achieved during the testing phase. The second algorithm improves the standard Start&Stop logic performances extending the stop time of the engine and avoiding disadvantageous start-stop events. The start of the engine is delayed until the ECMS decides that its contribution is needed through the integration of this decision into the algorithm in the form of an additional weight activated when the engine is off. The engine stop is anticipated thanks to the information about the environment given by the ADAS signals that can detect a stop situation in the near future making the algorithm decide to turn off the engine since it will not be used during braking. An increase in the stop time of 168% has been measured in simulations which causes a reduction of the CO2 emissions of 2.13% with respect to the standard Start&Stop logic on a WLTP cycle.