A globally tuned load-leveling strategy for energy management of hybrid electric vehicles

This paper presents a novel rule-based control strategy, the globally tuned load-leveling strategy (GTLS), for the energy management of hybrid electric vehicles (HEVs) with series powertrain architecture. The proposed methodology amalgamates the most effective features of existing rule- and optimization-based control strategies and produces a new strategy that operates as simple rules to reduce fuel consumption in an optimal manner. As with the classical Thermostat control strategy, it is based on the load leveling mechanism that operates the engine only at a constant power, whereas the battery works as an energy buffer. On the other hand, the activation of the engine is controlled by a memoryless state-of-charge dependent threshold, which tends towards making the strategy charge sustaining. The decision parameters are small in number and freely tunable, and the control performance can be fully optimized for different HEV model parameters and driving cycles by a systematic tuning process. Dynamic Programming (DP) and an Equivalent Consumption Minimization Strategy (ECMS) are utilized for benchmarking the GTLS respectively for series HEV models representative of two main classes: a simple, but widely applicable for HEV analysis and control design, model, and a high-fidelity model capable of realistic transient analysis and prediction. Simulation results show that the GTLS consumes only 0.6% to 2% more fuel than the DP, demonstrating near-optimal performance with substantially lower computational burden.