A transferable perception-guided EMS for series hybrid electric unmanned tracked vehicles

This work investigates the optimal energy allocation considering the different road properties for a series hybrid electric unmanned tracked vehicle. Tracked vehicles operate mostly in off-road conditions, where the energy consumption changes heavily due to the road smoothness. However, few works considered the effect of explicit road properties on energy allocation for tracked vehicles. Besides, conventional energy management strategies are generally difficult to adapt to the fast-changing off-road conditions. To address these challenges, a perception-guided energy management strategy based on deep reinforcement learning that takes road roughness as explicit features into account is proposed. A method of road roughness extraction and quantification is proposed based on the random sample consensus algorithm and singular value decomposition. To enhance the deployment efficiency in different off-road driving conditions, a deep transfer learning framework of the proposed perception-guided energy management strategy is devised. Experimental results demonstrate that the perception-guided energy management strategy improved the fuel economy by 8.15 %. Moreover, the transferable energy management strategy achieves a convergence rate of 34.15 % better than the relearned energy management strategy. Our code is available at https://github.com/BIT-XJY/PgEMS.