Decision-Flux Optimization-based energy management for battery-supercapacitor hybrid storage in electric vehicles: battery lifetime and efficiency enhancement

Battery-supercapacitor hybrid energy storage systems can reduce the transient loading of lithium-ion batteries in electric vehicles, but their benefit strongly depends on the energy-management parameters used to distribute power between the two storage devices. This study proposes Decision-Flux Optimization (DFO), a simulation-driven feasible-descent framework for calibrating the parameters of a battery/supercapacitor energy management strategy under full driving-cycle constraints. DFO uses induced operational trace of a candidate decision vector which comprises of battery state of charge, supercapacitor state of charge, power limits, voltage limits and end-of-cycle recovery constraints. Constraint satisfaction is considered as an admissibility condition and not as a term in the objective function. The objective is a combination of normalized RMS battery power, normalized energy-loss indicators, and only accepted updates are feasible trace-level transitions. The optimized parameters are computed offline and then deployed in the real-time control layer. Evaluation of the method is performed on ARTEMIS driving cycle through simulation and laboratory-scale controller validation. Compared with GA, PSO, RB-EMS, MPC, SRCPO, DSCEO, and ADRL-EMS benchmarks, DFO produced the lowest RMS battery power in the reported case study and maintained feasibility across repeated runs. The aging analysis indicates a model-estimated lifetime of 3720 equivalent cycles before the 80% capacity threshold, which should be interpreted as a comparative model-based durability estimate rather than a direct long-term lifetime measurement. Results indicate that trace level feasibility gating can be used to enhance repeatability, battery stress mitigation, and energy management for efficiency.