<p>The energy consumption issues in existing commercial vehicles are significant, therefore economic driving of commercial vehicles has been a key research focus. With the proliferation of intelligent and connected vehicles and the advancement of cloud control technologies, economic driving techniques have been further developed. Coasting, as a method of economic driving, has not been adequately planned by existing economic driving technologies, which leads to certain losses in vehicle economy. The speed cannot be directly controlled and it only depends on the coasting distance, which makes the optimization difficult. Therefore, this paper proposes an eco-coasting system architecture for commercial vehicles based on a cloud control system with multi-objectives of fuel consumption and travel time. By deploying the eco-coasting planning algorithm in the cloud, the advantages of the cloud’s high computational power and large storage capacity are fully leveraged. The paper proposes an eco-coasting planning algorithm that uses DP’s property of optimal substructure and road segmentation to reduce the difficulty of solving the optimal speed-gear sequence, and determines the cost-minimized speed-gear sequence as the final execution sequence through a selecting method. Subsequently, simulation experiments demonstrate that the proposed eco-coasting algorithm can reasonably plan the in-gear driving speeds and coasting time of vehicles. Compared to the PCC algorithm, it achieves a fuel saving rate of 2.96%–3.43% on the premise of ensuring driving efficiency, achieving economical driving.</p>