Energy flow management of P2 hybrid vehicle based on ADAS sensors

The optimization of consumption is one of the main challenges of today’s automotive world. The increasing imposed restrictions require seeking means to reduce consumption. The proposed work aims to reduce the consumption of an existing vehicle through the exploitation of currently available technologies. The vehicle is characterized by a P2 parallel type full-hybrid electric architecture. In this context three possible approaches are used. The first involves the study and comparison between the automatic transmission present in the standard vehicle and a Dual Clutch Transmission (DCT). This comparison wanted to evaluate the efficiency performance among different control logics implemented in the DCT and the automatic transmission. For the evaluation, the energy required for the imposition of a load state is measured. Also, regarding the DCT logics an estimator capable of obtaining the future next gear request imposed by the ECMS-GC control logic was implemented. The second study is the creation of control logics that is able to reduce consumption by supervising the human driver to discourage its imposed inefficiencies speed profiles. This supervisor is created by exploiting a fuzzy logic that is capable of categorize the speed profiles using the input information coming from the ADAS sensor integration. The discouragement of the inefficient speed pattern is implemented by a dynamic calibration of the throttle PID controller. The third aims to create a logic capable of imposing a longitudinal speed profile that can improve the efficiency of the vehicle. This logic is implemented taking the braking scenario as a reference. In this context, the algorithm developed imposes a profile capable of maximizing the regenerated energy during braking. The implementation of this logic must be evaluated in terms of real-time implementation: imposing a solution capable of obtaining the overall maximum of energy recovered during braking would be too time-consuming. To overcome this drawback, the implemented algorithm is able to obtain the speed profile in less time, which however is only sub-optimal. The results of these logics will examine the amount of energy recovered.