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One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

The Illinois Institute of Technology (IIT) Interprofessional Projects (IPRO®) Program engages multidisciplinary teams of students in semester-long projects, with a total of thirty to thirty-five different projects offered every semester. This program greatly contributes to IIT's signature undergraduate education experience, with each interprofessional course delivering a team-oriented, project-based requirement within the undergraduate curriculum. Among its many benefits, each interprofessional course offers students the opportunity to integrate the education and research environment of the university to tackle real-world problems. In the process, students get the chance to develop and emerge from the experience with maturity, confidence, and valuable professional skills that are highly sought after in the workplace, simultaneously preparing them for the realities of today's global, highly-competitive environment [1].

Intensifying demands for higher fuel economy from one hand and environmental concerns from the other are driving advanced automotive power systems to be more electric. As a result, automotive electrical systems with higher capacity and more complexity are needed to cope with this expanding electrification trend. As different electrical applications and loads are being introduced in automobiles, multi-converter intensive power electronic systems are emerging as the next generation of the advanced automotive electrical systems. In fact, power electronic converters and electric motor drives are inevitable parts of more electric automotive power systems. When power electronic converters and electric motor drives are tightly regulated to improve system performance and efficiency, they present negative impedance characteristics of constant power loads to the entire automotive electrical system. This destabilizing effect may cause system instability.

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin’s minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude.