Adaptive Real-time Energy Management of a Multi-mode Hybrid Electric Powertrain
2022-01-0822
03/29/2022
- Event
- Content
- Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle (HEV). Equivalent consumption minimization strategy (ECMS) is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this work aims to propose an adaptive real-time ECMS based energy management strategy for a multi-mode hybrid electric powertrain. Optimal solutions of the equivalence factor and the control inputs, i.e., engine speed and torque, are first obtained offline through golden mean search (GMS) algorithm. Then, based on the predicted upcoming driving profile obtained from the geographic information system (GIP) and global position system (GPS), the equivalence factor is updated online periodically to achieve fuel minimization and charge sustenance. Finally, the proposed control strategy is tested on an online HEV simulation platform equipped with high-fidelity engine and transmission models to better understand behaviors of the real hybrid electric powertrain under real-time control. A comparative study is also conducted to verify the effectiveness of the proposed strategy. Performance in terms of computational time and fuel economy is evaluated.
- Citation
- Wang, Y., Biswas, A., Anselma, P., Rathore, A. et al., "Adaptive Real-time Energy Management of a Multi-mode Hybrid Electric Powertrain," SAE Technical Paper 2022-01-0822, 2022, .