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A real-time implementable model-predictive cruise controller for electric vehicles and energy-efficient driving

机译:用于电动汽车和节能驾驶的实时可实施模型预测巡航控制器

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This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. The controller and the underlying dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified by measurements. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. In this work, a coordinate transformation is used which leads to a linear differential motion equation without loss of information. The energy consumption map is approximated by the maximum of a set of linear functions which is implicitly determined in the optimisation problem. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the MPC controller in closed-loop operation finally show the effectiveness of the approach.
机译:本文提出了一种用于电动汽车的新型节能模型预测巡航控制公式。控制器和基本的动态模型旨在满足批量生产的电动汽车的特性,这些电动汽车的特征通过测量得以识别。预测性生态巡航控制器涉及到与行驶速度和能耗相关的术语之间的折衷最小化,这些术语通常都由非线性微分方程描述。在这项工作中,使用了坐标变换,该变换导致了线性微分运动方程,而不会丢失信息。能量消耗图由一组线性函数的最大值近似,该线性函数在优化问题中隐式确定。重新制定最终导致了一种模型预测控制方法,该方法具有二次成本函数,线性预测模型和线性约束,它们对应于分段线性系统行为,并允许在保证收敛性的情况下实现快速实时实施。 MPC控制器在闭环运行中的仿真结果最终证明了该方法的有效性。

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