Adaptive energy management in automated hybrid electric vehicles with flexible torque request

Zhang, Fengqi; Hu, Xiaosong; Langari, Reza; Wang, Lihua; Cui, Yahui; Pang, Hui

doi:10.1016/j.energy.2020.118873

Cited by 54 publications

(25 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third variant F EEM (3) takes into account situation (1) when the DCD voltage range is exceeded U DCD . In this case, the EEM algorithm based on (2) will select P SEL values for all SAs to ensure condition (9).…”

Section: The Elastic Energy Management Algorithmmentioning

confidence: 99%

“…There are many concepts employed in controlling the demand for electricity by the use of a smart appliances (SA) described in the scientific literature. This article will present the concept of using and integrating smart home devices and consumer devices (e.g., electric vehicles [9], small energy storage) for energy balancing in a local low-voltage distribution system. An algorithm for elastic energy management will be proposed which takes into account the priorities of device operation set by the user.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices

et al. 2022

View full text Add to dashboard Cite

In modern Electric Power Systems, emphasis is placed on the increasing share of electricity from renewable energy sources (PV, wind, hydro, etc.), at the expense of energy generated with the use of fossil fuels. This will lead to changes in energy supply. When there is excessive generation from RESs, there will be too much energy in the system, otherwise, there will be a shortage of energy. Therefore, smart devices should be introduced into the system, the operation of which can be initiated by the conditions of the power grid. This will allow the load profiles of the power grid to be changed and the electricity supply to be used more rationally. The article proposes an elastic energy management algorithm (EEM) in a hierarchical control system with distributed control devices for controlling domestic smart appliances (SA). In the simulation part, scenarios of the algorithm’s operation were carried out for 1000 households with the use of the distribution of activities of individual SAs. In experimental studies, simplified results for three SA types and 100 devices for each type were presented. The obtained results confirm that, thanks to the use of SAs and the appropriate algorithm for their control, it is possible to change the load profile of the power grid. The efficacious operation of SAs will be possible thanks to the change of habits of electricity users, which is briefly described in the article.

show abstract

Section: The Elastic Energy Management Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Electrical and hybrid energy systems have been going through massive growth in recent years. This has been observed in different sectors, such as in vehicles [1,2]. Hybridization has shown great potential to improve the efficiency of various energy-related systems.…”

Section: Introductionmentioning

confidence: 99%

A review of geothermal energy-driven hydrogen production systems

Mahmoud

Ramadan

Naher

et al. 2021

Thermal Science and Engineering Progress

View full text Add to dashboard Cite

“…ECMS can be used as an instantaneous optimization algorithm, therefore it does not necessarily require knowledge of the future power demand. In [15] an adaptive energy management strategy based on ECMS framework is developed to optimise both flexible torque request and the gearshift commands. Some works presented offline optimal control techniques [16] [17], which cannot be implemented in real time, but they can provide references for tuning or design rule-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Battery Aging-Aware Online Optimal Control: An Energy Management System for Hybrid Electric Vehicles Supported by a Bio-Inspired Velocity Prediction

Valenti¹,

Pagot

Pascali

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In this manuscript we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed by the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances INDEX TERMS hybrid electric vehicles (HEVs), online optimal control, velocity prediction algorithm, energy management system (EMS), battery electrochemical model

show abstract

Adaptive energy management in automated hybrid electric vehicles with flexible torque request

Cited by 54 publications

References 18 publications

An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices

An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices

A review of geothermal energy-driven hydrogen production systems

Battery Aging-Aware Online Optimal Control: An Energy Management System for Hybrid Electric Vehicles Supported by a Bio-Inspired Velocity Prediction

Contact Info

Product

Resources

About