Advances in machine learning inspire novel solutions for the validation of complex vehicle models and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel-drive passenger vehicle configuration, is validated through an efficient virtual test controller (VTC) developed in this article. The VTC is designed via a novel approach based on the least-squares support vector machine and random forest with the inner-interim data filtered by the ReliefF algorithm to validate the vehicle model as necessary. This article discusses the process and highlights the accuracy improvements of the PHEV model that is achieved by implementing the VTC. The validity of the VTC is addressed by examining the PHEV model to mimic the characteristics of the internal combustion engine, motor, and generator behaviors observed through the benchmark test. Sufficient simulations and hardware-in-the-loop tests are employed to demonstrate the capability of the novel VTC-based model validation method in practical applications. The major novelty of this article lies in the development of a VTC, by which the vehicle model can be efficiently developed, providing a solid framework and enormous convenience for control strategy design.

中文翻译：

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基于高效数值方法的增强模型预测控制的插电式混合动力汽车最优控制策略


机器学习的进步激发了验证复杂车辆模型的新颖解决方案，并激发了一种简单的方法来提高插电式混合动力电动汽车（PHEV）等复杂配置车辆的能源管理性能。基于四轮驱动乘用车配置构建的 PHEV 模型通过本文开发的高效虚拟测试控制器 (VTC) 进行了验证。 VTC 采用基于最小二乘支持向量机和随机森林的新颖方法设计，并通过 ReliefF 算法过滤内部临时数据，以根据需要验证车辆模型。本文讨论了该过程并重点介绍了通过实施 VTC 实现的 PHEV 模型的准确性改进。 VTC 的有效性通过检查 PHEV 模型来模拟通过基准测试观察到的内燃机、电动机和发电机行为的特性来解决。通过充分的仿真和硬件在环测试，证明了基于 VTC 的新型模型验证方法在实际应用中的能力。本文的主要创新点在于VTC的开发，通过VTC可以高效地开发车辆模型，为控制策略设计提供坚实的框架和极大的便利。