In this paper, dynamic programming (DP) economical cruise control algorithms under discrete distance and discrete time strategies are designed to improve battery electric vehicle (BEV) energy consumption and riding comfort. In our experiment, energy consumption of constant-speed driving is reduced by up to 21.6% using the optimum velocity profiles from discrete distance DP. ‘Variable step length and boundary conditions’ are utilized to reduce discrete distance DP processing time by 90.8% with no effect on accuracy. Under discrete time DP, position is introduced as an additional variable, so discrete time DP allows more inputs including real-time preceding vehicle position and traffic information. The experiment results indicate that under traffic situation, discrete time DP further improves energy consumption and riding comfort by avoiding unnecessary stop at intersections compared with discrete distance DP. Further analysis indicates that under properly controlled velocity, the transfer between potential energy and kinetic energy is more efficient than that between potential energy and electric energy. These DP economical cruise control algorithms are effective for BEVs, hybrid electric vehicles and plug-in hybrid electric vehicles. ‘Variable step length and boundary conditions’ method in DP can also be applied in other domains where DP processing time is crucial.

中文翻译：

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实时交通情况下经济巡航控制中的高效动态规划

本文设计了离散距离和离散时间策略下的动态规划（DP）经济巡航控制算法，以提高纯电动汽车（BEV）的能耗和乘坐舒适性。在我们的实验中，使用离散距离 DP 的最佳速度分布，恒速驱动的能耗降低了 21.6%。“可变步长和边界条件”用于将离散距离 DP 处理时间减少 90.8%，而不会影响精度。在离散时间 DP 下，位置作为附加变量被引入，因此离散时间 DP 允许更多输入，包括实时前车位置和交通信息。实验结果表明，在交通情况下，与离散距离 DP 相比，离散时间 DP 通过避免在路口不必要的停车，进一步提高了能耗和乘坐舒适性。进一步分析表明，在适当控制速度下，势能与动能之间的传递比势能与电能之间的传递更为有效。这些 DP 经济型巡航控制算法对 BEV、混合动力汽车和插电式混合动力汽车有效。DP 中的“可变步长和边界条件”方法也可以应用于 DP 处理时间至关重要的其他领域。势能和动能之间的传递比势能和电能之间的传递效率更高。这些 DP 经济型巡航控制算法对 BEV、混合动力汽车和插电式混合动力汽车有效。DP 中的“可变步长和边界条件”方法也可以应用于 DP 处理时间至关重要的其他领域。势能和动能之间的传递比势能和电能之间的传递效率更高。这些 DP 经济型巡航控制算法对 BEV、混合动力汽车和插电式混合动力汽车有效。DP 中的“可变步长和边界条件”方法也可以应用于 DP 处理时间至关重要的其他领域。