Optimal Energy Management of Series Hybrid Electric Vehicles With Engine Start-Stop System

This article develops energy management (EM) control for series hybrid electric vehicles (HEVs) that include an engine start-stop system (SSS). The objective of the control is to optimally split the energy between the sources of the powertrain and achieve fuel consumption minimization. In contrast to existing works, a fuel penalty is used to characterize more realistically SSS engine restarts, to enable more realistic design and testing of control algorithms. This article first derives two important analytic results: 1) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks and 2) proof of optimality of charge sustaining (CS) operation in series HEVs. It then proposes a novel heuristic control strategy, the hysteresis power threshold strategy (), by amalgamating simple and effective control rules extracted from the suite of derived analytic EM optimal solutions. The decision parameters of the control strategy are small in number and freely tunable. The overall control performance can be fully optimized for different HEV parameters and driving cycles by a systematic tuning process while also targeting CS operation. The performance of is evaluated and benchmarked against existing methodologies, including dynamic programming (DP) and a recently proposed state-of-the-art heuristic strategy. The results show the effectiveness and robustness of the and also indicate its potential to be used as the benchmark strategy for high-fidelity HEV models, where DP is no longer applicable due to computational complexity.

Automated summary

This article presents an energy management (EM) control strategy for series hybrid electric vehicles (HEVs) with an engine start-stop system (SSS), aiming to optimize energy distribution and minimize fuel consumption. The control strategy incorporates a fuel penalty mechanism and proposes the hysteresis power threshold strategy () to realistically model engine restarts. The article demonstrates the effectiveness and robustness of the control strategy, suggesting its potential as a benchmark strategy for high-fidelity HEV models.