The supervisory control strategy of a hybrid vehicle coordinates the operation of vehicle sub-systems to achieve performance targets such as maximizing fuel economy and reducing exhaust emissions. This high-level control problem is commonly referred as the power management problem. In the past, many supervisory control strategies were developed on the basis of a few pre-defined driving cycles, using intuition and heuristics. The resulting control strategy was often inherently cycle-beating and lacked a guaranteed level of optimality. In this study, the power management problem is tackled from a stochastic viewpoint. An infinite-horizon stochastic dynamic optimization problem is formulated. The power demand from the driver is modeled as a random Markov process. The optimal control strategy is then obtained by using stochastic dynamic programming (SDP). The obtained control law is in the form of a stationary full-state feedback and can be directly implemented. Simulation results over standard driving cycles and random driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach. It was found that the obtained SDP control algorithm outperforms a sub-optimal rule-based control strategy trained from deterministic DP results.
A stochastic control strategy for hybrid electric vehicles
American Control Conference, 2004 ; 4710-4715
2004
6 Seiten, 11 Quellen
Aufsatz (Konferenz)
Englisch
Adaptive Hybrid Thermostat Control Strategy for Series Hybrid Electric Vehicles
British Library Conference Proceedings | 2021
|Adaptive Hybrid Thermostat Control Strategy for Series Hybrid Electric Vehicles
SAE Technical Papers | 2021
|Optimization-Based Control Strategy for Large Hybrid Electric Vehicles
SAE Technical Papers | 2018
|Power control strategy for fuel cell hybrid electric vehicles
Kraftfahrwesen | 2003
|Optimization-Based Control Strategy for Large Hybrid Electric Vehicles
British Library Conference Proceedings | 2018
|