A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview

Feng, Tianheng; Yang, Lin; Gu, Qing; Hu, Yanqing; Yan, Ting; Yan, Bin

doi:10.1109/tvt.2014.2336378

Cited by 160 publications

(57 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3!9 (15) By assuming 3!9 , "!9 , , , $$ , , 9 , are all constants and the fact " is a constant (neglecting the transition process from on to off and off to on), this equation can be rewritten including the dependence on time as:…”

Section: A Vehicle Dynamicsmentioning

confidence: 99%

A Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles

Wang

Shekhar

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Increasing the fuel economy of hybrid electric vehicles (HEVs) and extended range electric vehicles (EREVs) through optimization-based energy management strategies (EMS) has been an active research area in transportation. However, it is difficult to apply optimization-based EMS to current in-use EREVs because insufficient knowledge is known about future trips, and because such methods are computationally expensive for large-scale deployment. As a result, most past research has been validated on standard driving cycles or on recorded highresolution data from past real driving cycles. This paper improves an in-use rule-based EMS that is used in a delivery vehicle fleet equipped with two-way vehicle-to-cloud connectivity. A physics model-guided online Bayesian framework is described and validated on large number of in-use driving samples of EREVs used for last-mile package delivery. The framework includes: a database, a preprocessing module, a vehicle model and an online Bayesian algorithm module. It uses historical 0.2 Hz resolution trip data as input and outputs an updated parameter to the engine control logic on the vehicle to reduce fuel consumption on the next trip. The key contribution of this work is a framework that provides an immediate solution for fuel use reduction of in-use EREVs. The framework was also demonstrated on real-world EREVs delivery vehicles operating on actual routes. The results show an average of 12.8% fuel use reduction among tested vehicles for 155 real delivery trips. The presented framework is extendable to other EREV applications including passenger vehicles, transit buses, and other vocational vehicles whose trips are similar dayto-day.Index Terms-Extended range electric vehicle, energy management strategies, online Bayesian algorithm, rule-based blended method.

show abstract

Section: A Vehicle Dynamicsmentioning

confidence: 99%

A Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles

Wang

Shekhar

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Supervisory-control-strategy-based energy demand prediction is helpful for minimizing energy consumption in real-time operations. For instance, for applications related to plug-in hybrid electric vehicles, energy demand prediction is performed via three successive steps [23]: a neural network model is used to predict a vehicle's energy demand, a mathematical model is used to translate the predicted energy demand into a state of charge (SOC), and an adaptive equivalent consumption minimization strategy is used to track the SOC for power-state determination.…”

Section: Efficient Meteringmentioning

confidence: 99%

Supply and Demand Oriented Energy Management in the Internet of Things

Liu²,

et al. 2016

AIT

View full text Add to dashboard Cite

The Internet of Things (IoT) is emerging as an attractive paradigm involving physical perceptions, cyber interactions, social correlations and even cognitive thinking through a cyber-physical-social-thinking hyperspace. In this context, energy management with the purposes of energy saving and high efficiency is a challenging issue. In this work, a taxonomy model is established in reference to the IoT layers (i.e., sensor-actuator layer, network layer, and application layer), and IoT energy management is addressed from the perspectives of supply and demand to achieve green perception, communication, and computing. A smart home scenario is presented as a case study involving the main enabling technologies with supply-side, demand-side, and supply-demand balance considerations, and open issues in the field of IoT energy management are also discussed.

show abstract

“…Different control tradeoff of energy management target is mentioned in related literatures [4,5] including fuel economy improvement [6], and tailpipe emission reduction [7]. Rule based and optimization based methods are mostly considered, as discussed by the authors of [8]. Rule based methods are relatively easier to exploit and are widely employed in practice [9,10].…”

Section: Introductionmentioning

confidence: 99%

Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning

Chen

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

This paper proposes an energy management strategy for a power-split plug-in hybrid electric vehicle (PHEV) based on reinforcement learning (RL). Firstly, a control-oriented power-split PHEV model is built, and then the RL method is employed based on the Markov Decision Process (MDP) to find the optimal solution according to the built model. During the strategy search, several different standard driving schedules are chosen, and the transfer probability of the power demand is derived based on the Markov chain. Accordingly, the optimal control strategy is found by the Q-learning (QL) algorithm, which can decide suitable energy allocation between the gasoline engine and the battery pack. Simulation results indicate that the RL-based control strategy could not only lessen fuel consumption under different driving cycles, but also limit the maximum discharge power of battery, compared with the charging depletion/charging sustaining (CD/CS) method and the equivalent consumption minimization strategy (ECMS).

show abstract

A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview

Cited by 160 publications

References 23 publications

A Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles

A Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles

Supply and Demand Oriented Energy Management in the Internet of Things

Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning

Contact Info

Product

Resources

About