Hybrid electric vehicles (HEVs) are irreplaceable in attaining sustainable development in contemporary society. Owing to the extra degree of freedom in supplying traction power, HEVs resort to appropriate energy management strategies (EMSs) to present their superiority over conventional internal combustion engine vehicles and pure electric vehicles. Existing EMSs suffer from heavy computation overheads and excessive mode switches. This thesis proposes several novel methods for developing online EMSs for parallel HEVs that achieve both compelling fuel economy and excellent computation efficiency and adaptivity in online applications with uncertain driving conditions. First, the solutions of offline dynamic programming (DP) are exploited to develop online EMSs for close-to-optimal control performances. The optimal speed profile serves as the reference in online control and the optimal value function (VF) is utilized to design control methods. To avoid the “curse of dimensionality”, the tabular VF is approximated by piecewise polynomials to substantially decrease the computation and memory overheads in online usage. Second, to reduce the search space for optimal control actions, two types of special internal combustion engine (ICE) configurations are adopted and analyzed. The first type forces the ICE to strictly operate at the optimal operation line (OOL), whereas the second one allows a narrow band around the OOL. The second one outperforms the first one because it contributes to more robust ICE operations with slightly higher computation complexity. Third, a hierarchical architecture is proposed for online EMSs so that the transient powertrain mode and torque split scheme are optimized by different methods in sequence. To avoid the exponential complexity of finding the optimal trajectory of the powertrain mode, the optimal VF is leveraged for an optimal decision within one sampling period with the aid of simplified assumptions. Model approximations on the ICE and the electric motor are conducted so as to convert ...
Computationally Efficient and Adaptive Energy Management Strategies for Parallel Hybrid Electric Vehicles
2023-01-01
Theses
Electronic Resource
English
Hybrid Electric Vehicle , Energy Management Strategy , Computation Efficiency , Value Function , Adaptive Learning , Processor-in-the- Loop Simulation , Elhybridfordon , Energihanteringsstrategi , Beräkningseffektivitet , Värdefunktion , Adaptiv Inlärning , Processor-in-the-loop , Control Engineering , Reglerteknik , Vehicle Engineering , Farkostteknik , Energy Engineering , Energiteknik
DDC: | 629 |
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