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The selection and configuration of sensors can strongly influence the closed-loop dynamics of a system. Therefore a methodology for finding the best sensor placement is a valuable tool. This paper deals with this problem by formulating an optimization problem and applies the new method on an SI engine. The best sensor configuration is one that minimizes the overall system costs, yet still meets the system constraints. Before solving the optimization problem, the system is modeled, different sensor configurations are defined, the appropriate controller and the feedback term are developed, and the locations and size of the various errors present in the model are determined. Then, the objective function and the system constraints are defined and the optimization problem is solved considering the worst-case combination of modeling errors, which is computed using genetic algorithms. The objective function is defined as the sum of the sensor costs and of a penalty term.

Pneumatic hybridization of internal combustion engines may prove to be a viable and cost-efficient alternative to electric hybridization. This paper evaluates the effects of pneumatic hybridization of various engine concepts using the criteria of fuel efficiency, driveability, emissions, and cost efficiency. The most promising engine concept is found to be the pneumatic hybridization combined with downsizing and supercharging spark-ignited engines. With this concept, a fuel consumption reduction of over 30% compared to a standard engine with the same rated power can be achieved. The poor driveability usually associated with heavily downsized and supercharged engines is completely overcome by injecting additional air during transients. The most important design issues for this new concept are discussed and several possible solutions are presented. Following these considerations, the first fully functional hybrid pneumatic engine was realized.