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Increasingly strict CO2 and emissions norms are pushing the automotive industry towards increasing adoption of Hybrid Electric Vehicle (HEV) technology. HEVs are complex hardware systems which are often controlled by software that is complex to maintain, time-consuming to calibrate, and not always guaranteed to deliver optimal fuel economy. Hence, coordinated, systematic control of different subsystems of HEV is an attractive proposition. In this paper, Model Predictive Control (MPC) and Equivalent Consumption Minimization Strategy (ECMS) based supervisory controllers have been developed to coordinate the power split between the two prime movers of an HEV – internal combustion engine and electric motor. A dynamical physics based HEV model has been developed for simulation of the system behavior. A cost function has been formulated to improve fuel economy and battery life.

The constant pressure on reducing fuel consumption and emissions in cost sensitive automotive markets has brought focus on retrofit HEV solutions. Through retrofit mechanism, existing conventional (solely engine powered) vehicles can be quickly converted into HEVs. However, the retrofit HEV, although cost effective, poses challenges in developing strategies to control the motor for a given fuel economy, emissions, drivability, battery life requirements and driver inputs. These challenges are primarily due to non-availability of calibration data from the OEMs. This paper focuses on the benefits and challenges with design, tuning and performance of MPC based supervisory controller against a conventional one for a retrofit HEV, using practical data in simulation environment. The inherent characteristics of MPC will lead to the choice of best possible inputs, while respecting the constraints.

High-end vehicles with latest technology and autonomous driving experience have to bear the cost of increasing number of sensors on-board. It would be beneficial to reduce some of the sensors in the vehicle and make use of other available resources, retaining the same functionality. This paper discusses a novel technique of estimating the weight of seat occupant from an already existing DC motor without using additional pressure sensors. Passenger weight information is important for seat-belt reminder system as well as supplemental restraint system that will decide the air-bag deployment. The mathematical model for a series-type DC motor is analyzed and simulated using MATLAB. Further, results of the experiment performed on a lower capacity motor are shared and compared with the simulation results. Formulating a linear relation gives a possibility to develop a device for occupant weight measurement inside the high-end vehicles.