Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook

Zhang, Fengqi; Hu, Xiaosong; Langari, Reza; Cao, Dongpu

doi:10.1016/j.pecs.2019.04.002

Cited by 355 publications

(113 citation statements)

References 103 publications

Supporting

Mentioning

112

Contrasting

Order By: Relevance

“…Vehicles operate in the three longitudinal driving modes of free-flow, car-following and platooning. Accordingly, studies on CACC/ACC and EMS co-optimization can be divided into the following three groups [14]: 1) Studies for free-flow scenarios [15]- [17] usually deal with road constraints such as speed limits, traffic lights, and road intersections. For example, a powertrain and speed integrated control was proposed to achieve 5.0 -16.9 % fuel economy benefits, by utilizing the road topography and the dynamic speed limit [18].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs

Zhou

Makridis

et al. 2020

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multi-objective co-optimization problems often overlook trade-offs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this study proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for PHEVs. The high-level Pareto knowledge assists in finding a best-compromise solution. The results of this work suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57 %) and tracking error (by 68.94 %), while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.

show abstract

Section: Introductionmentioning

confidence: 99%

Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs

Zhou

Makridis

et al. 2020

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

show abstract

“…Hybrid electric vehicles (HEVs) have been widely considered as one of the most promising solutions to achieve superior mileage, lower fuel consumption and less tailpipe emission compared to conventional internal combustion engine based vehicles [16,[23][24][25]. Generally, the HEV is composed of an internal combustion engine (ICE) (gasoline or diesel fueled) with one or more electric motors (EMs).…”

Section: Introductionmentioning

confidence: 99%

Architectures of Planetary Hybrid Powertrain System: Review, Classification and Comparison

et al. 2020

Self Cite

View full text Add to dashboard Cite

Increasing environmental issues and energy crises led to rapid developments of hybrid electric vehicles, especially the planetary hybrid powertrain system (PHPS). This paper presents a comprehensive review of the PHPS, focusing primarily on contributions in the aspect of configuration, classification and comparison. In this work, a new classification method for PHPS architectures is proposed according to the number of electric motors (EMs). In addition, two kinds of PHPS, in the new classification framework, are extensively emphasized in terms of its architectures, advantages and disadvantages. Furthermore, the port diagrams of representative architectures are presented to provide an intuitive method for power flow representation. Finally, a conclusion is made to provide an insight for developing PHPS as well.

show abstract

“…To mitigate the energy crisis and environmental pollution problems, the plug‐in hybrid electric vehicle (PHEV) has been gained great concern all over the world . In practice, energy management is of great importance for fuel economy improvement .…”

Section: Introductionmentioning

confidence: 99%

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

Guo¹,

Zhao²,

Guo³

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

A self-learning energy management is proposed for plug-in hybrid electric bus, by combining Q-Learning (QL) and Pontryagin's minimum principle algorithms. Different from the existing strategies, the expert experience and generalization performance are focused in the proposed strategy. The expert experience is designed as the approximately optimal reference state-of-charge (SOC) trajectories, and the generalization performance is enhanced by a multiply driving cycle training method. In specific, an efficient zone of SOC is firstly designed based on the approximately optimal reference SOC trajectories. Then, the agent of the QL is trained off-line by taking the expert experience as reference SOC trajectories.Finally, an adaptive strategy is proposed based on the well-trained agent. Specially, two different reward functions are defined. That is, the reward function in the off-line training mainly considers the tracking performance between the expert experience and the SOC, while mainly considering the punishment in the adaptive strategy. Simulation results show that the proposed strategy has good generalization performance and can improve the fuel economy by 22.49%, compared to a charge depleting-charge sustaining (CDCS) strategy. K E Y W O R D Senergy management, expert experience, generalization performance, plug-in hybrid electric bus, Q-learning

show abstract

Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook

Cited by 355 publications

References 103 publications

Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs

Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs

Architectures of Planetary Hybrid Powertrain System: Review, Classification and Comparison

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

Contact Info

Product

Resources

About