The energy management strategy of the multi-source parallel power system for the self-reconfigurable ground vehicle

The self-reconfigurable ground vehicle (SRGV) has the ability of self-assembly and self-disassembly, which is a disruptive innovation to the traditional fixed configuration ground vehicle. The basic component of the SRGV is defined as a cell unit (CU), which is a complete system capable of working independently and has the basic function of the ground vehicle. The reconfiguration of the SRGV is not only the connection of the mechanical systems but also the integration between the power sources of different CUs. To this end, this paper proposes a novel multi-source parallel power system (MSPPS) for the SRGV, whose key characteristics are multi-branch and co-bus. The MSPPS can extend any number of power sources, which greatly improves the power level of SRGV. In this paper, the MSPPS with battery power source is discussed. The disassembly and assembly of the SRGV could lead to some inconsistencies such as SoC between the battery packs of each CU. To prolong the lifetime of the battery packs and working time of the SRGV, a hierarchical proportional control (HPC) strategy and a filtered model predictive control (FMPC) strategy are proposed. Both energy management strategies can reasonably allocate the output energy between different battery packs to meet the power demand and reduce battery inconsistencies. To verify and compare the effectiveness of the proposed two strategies, numerous simulations are carried out. The simulation results show that the FMPC strategy has faster convergence speed and lower power fluctuations in the energy management process. A SRGV prototype consisting of three CUs is developed, and the experimental platform for the power system of the SRGV is successfully established. The feasibility of the proposed MSPPS architecture is validated. The proposed HPC strategy is deployed in the rapid ECU. The experiment results are similar to the simulations and effectively demonstrate the real-time performance.