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首页> 外文会议>International Conference on Mechanical Engineering and Mechanics vol.2; 20051026-28; Nanjing(CN) >Power Management Strategy for Vehicle with Electric Control Hydrostatic Drive Using Fuzzy Prediction and Genetic Optimization
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Power Management Strategy for Vehicle with Electric Control Hydrostatic Drive Using Fuzzy Prediction and Genetic Optimization

机译：基于模糊预测和遗传优化的电液静力车辆动力管理策略。

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摘要

This paper presents a method to develop a power management strategy for vehicle with electric control and hydrostatic drive (ECHDV) using predictive information. The ECHDV system is modelled in MATLAB/SIMULINK environment to facilitate the evaluation of power management strategy. Fuzzy and Genetic algorithm is used to obtain the predictive information and find the optimal solution respectively assuming the Chinese bus driving cycle as evaluation example. The predictive information with fuzzy table forecast algorithm is integrated into the optimization of power management strategy. Predictive information represents the tendency of load varying and be approximate to it which is not only accelerates the solution but also to feed the optimal algorithm, while a genetic algorithm is adopted to act as optimizer. ECHDV system behaviours indicate that the control strategy using predictive information takes advantages of improvement in fuel economy, and the simulation results show that there is about 40% fuel economy improvement for ECHDV.
机译：本文提出了一种使用预测信息为电动控制和静液压驱动（ECHDV）的车辆开发电源管理策略的方法。 ECHDV系统在MATLAB / SIMULINK环境中建模，以方便评估电源管理策略。以中国公交车的驾驶周期为评价样本，分别采用模糊遗传算法获得预测信息并找到最优解。具有模糊表预测算法的预测信息被集成到电源管理策略的优化中。预测信息代表了负载变化的趋势，并且与之近似，不仅加快了求解速度，而且为优化算法提供了依据，而遗传算法则用作优化器。 ECHDV系统行为表明，使用预测信息的控制策略利用了改善燃油经济性的优势，并且仿真结果表明ECHDV的燃油经济性提高了约40％。

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