Electrical energy consumption is an important component of energy consumption for internal combustion engine vehicle, which directly affects the fuel economy. A bus-based electrical energy management system is built, and an electrical energy management strategy based on driving cycle recognition and electrical load perception is presented to achieve the refined management of vehicle energy. Six typical driving cycles are selected to establish an improved learning vector quantization neural network model for driving cycle recognition. The corresponding model training algorithm is designed by utilizing a similar driving cycle classification and the gradient optimization so that the better recognition accuracy and the less computation intensity can be obtained. An online recognition mechanism based on sliding time window is devised, and the optimal time window length is determined. To minimize fuel consumption, a dynamic optimal regulation strategy for the output power of the alternator and battery, which considers driving cycle recognition and electrical load perception, is proposed. Experimental results show that the strategy can effectually improve the vehicle fuel economy according to the driving cycle and the electrical load change and decrease the fuel consumption per 100 miles of vehicle.
FlexRay has been extensively applied to safety-critical systems, such as powertrain, chassis, and X-by-wire system in vehicles. Its transmission reliability considerably influences vehicle safety. In this study, frame coding and media access control in the FlexRay protocol are analyzed, and calculation equations are deduced for the length of time slot and coded frame of FlexRay. Redundant transmission of FlexRay static frame is introduced to solve the transmission failure caused by the bit error in the physical layer, and the transmission reliability of a static segment is defined for the mechanism. Considering the demands on transmission reliability and network schedulability, the configuration of FlexRay parameters, including communication cycle, time slot allocation, and segment length, is formulated as a generalized optimization problem with constraints on predefined transmission reliability and response time of messages. A Reliability-Based Parameter Optimization algorithm and its sub-algorithms, Response Time of Dynamic Segment and Slot Allocation for the Predefined Reliability, are presented to approximately solve the optimization model. Experiment results validate the proposed method, which can efficiently improve the transmission reliability and guarantee the real-time performance of the FlexRay network.
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