Expedited Distributed Convex Optimization Strategy for Energy Management of Series-Parallel Hybrid Electric Vehicles

Mode switching decision and power allocation are both regarded as critical technologies that influence the operational performance of series-parallel hybrid electric vehicles (SPHEV). The inclusion of integer decision variables associated with mode switching complicates the problem into mixed integer optimal control problem (MIOCP) and weakens the applicability of the strategy. To address this challenge, this article proposes an expedited distributed convex optimization strategy for energy management of SPHEV. By leveraging the distributed convex optimization framework, the formulated MIOCP is effectively simplified, achieving collaborative resolution of mode switching decision and power allocation. To enhance the strategy's capability in coping with stochastic scenarios, an alternating direction method of multipliers with time-varying penalty parameter is designed to facilitate efficient solution. Furthermore, the integer optimal control problem associated with mode switching is transformed into tractable convex quadratic programming problem with relaxation approach, and an integral rounding strategy is constructed to balance the requirements for improved fuel economy and reduced switching frequency. Simulation and hardware-in-the-loop (HIL) tests are conducted to validate the performance of the proposed strategy. Compared with adaptive equivalent consumption minimization strategy, the proposed strategy achieves economic improvement of 7.003% and 5.781% under two testing scenarios. The results also exhibit that the proposed strategy attains comparable performance to dynamic programming, while exhibiting significantly fewer switching occurrences and efficient computational capabilities. The HIL tests further substantiate the superiority of the proposed strategy in computational efficiency.