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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.

This paper addresses feedforward tracking control for electronic throttles. A robust and accurate tracking control scheme based on the training of a Neural Network model and feedback term (PID) is developed. The Neural Network based term can be trained off-line. This feedfoward term serves as a mathematical model capable of describing Electronic Throttle dynamics over a wide range. We have shown that by adding the Neural Network based feedforward control to a common feedback control method, such as the gain-scheduled PID used in many ETC production controllers, that the tracking control performance criteria such as transient errors, steady state errors, response time and overshoot, are greatly improved. Experiments conducted on a production Electronic Throttle Body with a Motorola H-brigde driver IC have shown good results utilizing this approach.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.

As centralization of automotive E/E (Electrical and/or Electronic) architectures becomes reality for future vehicles, it is crucial that existing assets be reused in the most efficient and effective manner. We report on our experience developing a new centralized E/E architecture for a propulsion domain, and migrating the corresponding propulsion elements of an existing decentralized, CAN-based architecture to a prototype of the centralized propulsion domain. Our migration adopts automotive Ethernet and supporting standards as a next-generation communications backbone technology; a next-generation computation platform from automotive supplier NXP; and a new automotive virtualization solution from OpenSynergy. We discuss aspects of legacy software reuse and adaptation; modification of vehicle HiL simulation models used in testing; existing vendor tool support; and implications arising from functional safety and the ISO 26262 standard.