Hybrid electric vehicles that can combine the advantages of traditional and new energy vehicles have become the optimal choice at present in the face of increasingly stringent fuel consumption restrictions and emission regulations. Range-extended hybrid electric vehicles have become an important research topic because of their high energy mixing degree and simple transmission system. A compact traditional fuel vehicle is the research object of this study and the range-extended hybrid system is developed. The design and optimization of the condition prediction energy management strategy are investigated. Vehicle joint simulation analysis and bench test platforms were built to verify the proposed control strategy. The vehicle tracking method was selected to collect real vehicle driving data. The number of vehicles in the field of view and the estimation of the distances between the front and following vehicles are calculated by means of the mature algorithm of the monocular camera and by computer vision. Real vehicle cycle conditions with driving environment and slope information were constructed and compared with all driving data, typical working conditions under NEDC, and typical working conditions under UDDS. The BP neural network and fuzzy logic control were used to identify the road conditions and the driver’s intention. The results showed that the equivalent fuel consumption of the control strategy was lower than that of the fixed-point power following control strategy and vehicle economy improved.
In this paper, a field–circuit combined simulation method, based on the magnetic scalar potential volume integral equation (MSP-VIE) and its fast algorithms, are proposed for the transient simulation and nonlinear distortion analysis of the magnetic balance current sensor. The magnetic part of the sensor is modeled and simulated by MSP-VIE with the field matrices extracted by the method of moments. By directly implementing the magnetic field equation in the circuit, these field matrices can be regarded as equivalent circuit parameters of the magnetic part, to construct the corresponding SPICE model. Finally, the field–circuit combined model of the entire sensor is unified in a circuit, so with the SPICE solver, the transient simulation is accomplished in the time domain. Moreover, aiming at the time-consuming problem, this paper presents a corresponding fast method to accelerate the simulation. The comparison of measurement and simulation demonstrates that the proposed method not only realizes the transient simulation of the whole sensor, but also simulates some hidden performance details; thus, it can be applied to practical engineering, to guide and test the early design of the product.
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