scholarly journals A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3438
Author(s):  
Xiaobo Sun ◽  
Weirong Liu ◽  
Mengfei Wen ◽  
Yue Wu ◽  
Heng Li ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Energy Allocation ◽  
Driving Cycle ◽  
Cruise Control ◽  
Car Following ◽  
Multi Objective ◽  
Control Framework ◽  
Equivalent Factor ◽  
Hybrid Electric

This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller.

scholarly journals Organic Rankine Cycle Waste Heat Recovery for Passenger Hybrid Electric Vehicles

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4532 ◽  
Author(s):  
Wan Rashidi Bin Wan Ramli ◽  
Apostolos Pesyridis ◽  
Dhrumil Gohil ◽  
Fuhaid Alshammari
Keyword(s):  
Electric Vehicles ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Hybrid Electric Vehicles ◽  
Waste Heat ◽  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Hybrid Vehicle ◽  
Driving Cycle ◽  
Hybrid Electric

Electrification of road transport is a major step to solve the air quality problem and general environmental impact caused by the still widespread use of fossil fuels. At the same time, energy efficiency in the transport sector must be improved as a steppingstone towards a more sustainable future. Multiple waste heat recovery technologies are being investigated for low-temperature waste heat recovery. One of the technologies that is being considered for vehicle application is the Organic Rankine Cycle (ORC). In this paper, the potential of ORC is discussed in detail for hybrid vehicle application. The modelling and testing of multiple systems such as the hybrid vehicle, engine, and ORC waste heat recovery are performed using the computational approach in GT-SUITE software environment correlated against available engine data. It was found that the maximum cycle efficiency achieved from the ORC system was 5.4% with 2.02 kW of delivered power recovered from the waste heat available. This led to 1.0% and 1.2% of fuel economy improvement in the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicle Test Procedure (WLTP) driving cycle test, respectively. From the driving cycle analysis, Hybrid Electric Vehicles (HEV) and ORC are operative in a different part of the driving cycle. This is because the entire propulsion power is provided by the HEV system, resulting in less engine operation in some part of the cycle for the ORC system to function. Apart from that, a brief economic analysis of ORC Waste Heat Recovery (WHR) is also performed in this paper and a comparative analysis is carried out for different waste heat recovery technologies for hybrid vehicle application.

scholarly journals Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2074 ◽  
Author(s):  
Hangyang Li ◽  
Yunshan Zhou ◽  
Huanjian Xiong ◽  
Bing Fu ◽  
Zhiliang Huang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Speed Ratio ◽  
Driving Pattern ◽  
Variable Transmission ◽  
Equivalent Factor ◽  
Hybrid Electric

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

scholarly journals Predictive Cruise Control in Hybrid Electric Vehicles

2009 ◽  
Vol 3 (3) ◽  
pp. 494-504 ◽  
Author(s):  
Thijs van Keulen ◽  
Gerrit Naus ◽  
Bram de Jager ◽  
René van de Molengraft ◽  
Maarten Steinbuch ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Cruise Control ◽  
Hybrid Electric

scholarly journals Multi-Objective Optimal Charging Control of Plug-In Hybrid Electric Vehicles in Power Distribution Systems

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2563 ◽  
Author(s):  
Wei Li ◽  
Zhiyun Lin ◽  
Kai Cai ◽  
Hanyun Zhou ◽  
Gangfeng Yan
Keyword(s):  
Electric Vehicles ◽  
Power Distribution ◽  
Distribution Systems ◽  
Hybrid Electric Vehicles ◽  
Power Distribution Systems ◽  
Power Distribution System ◽  
Multi Objective ◽  
Power Load ◽  
Hybrid Electric ◽  
Coordinated Charging

With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), the coordinated charging of PHEVs has become an important issue in power distribution systems. This paper employs a multi-objective optimization model for coordinated charging of PHEVs in the system, in which the problem of valley filling and total cost minimization are both investigated under the system’s technical constraints. To this end, a hierarchical optimal algorithm combining the water-filling-based algorithm with the consensus-based method is proposed to solve the constrained optimization problem. Moreover, a moving horizon approach is adopted to deal with the case where PHEVs arrive and leave randomly. We show that the proposed algorithm not only enhances the stability of the power load but also achieves the global minimization of vehicle owners charging costs, and its implementation is convenient in the multi-level power distribution system integrating the physical power grid with a heterogeneous information network. Numerical simulations are presented to show the desirable performance of the proposed algorithm.

Adaptive Equivalent Factors of Multi-Objective Energy Management for Fuel Cell Hybrid Electric Vehicles

2020 ◽  
Author(s):  
Yan Ma ◽  
Jian Chen ◽  
Junmin Wang
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Cell System ◽  
Fuel Cell System ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Equivalent Factor

Abstract In this paper, a multi-objective energy management strategy with an adaptive equivalent factor is proposed to improve the fuel economy, system durability, and charge-sustenance performance of fuel cell hybrid electric vehicles. Firstly, the total hydrogen consumption and degradation cost of power sources can be calculated by flexible empirical models. Then, the multi-objective optimization problem can be transformed into an objective function, which can be solved by quadratic programming to improve the real-time performance. Furthermore, an adaptive Unscented Kalman filter is designed to estimate the aging state of the fuel cell system. The equivalent factor in the objective function can be adaptively updated by the estimated aging state, which can balance the conflict between the fuel economy and the system durability while keeping the state-of-charge in an ideal range. Finally, simulation results show that when the fuel cell system is obviously damaged during the operation, the proposed energy management strategy still can minimize the total cost and maintain the charge-sustenance performance under different driving cycles compared with other methods.

Robust Predictive Energy Management of Connected Power-split Hybrid Electric Vehicles Using Dynamic Traffic Data

2021 ◽  
Author(s):  
Seyedeh Mahsa Sotoudeh ◽  
Baisravan HomChaudhuri
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Traffic Data ◽  
Energy Management Strategy ◽  
Short Term ◽  
Low Level ◽  
Hybrid Electric

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEV) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudospectral optimal controller with discounted cost is utilized at the high-level to find an approximate optimal solution for the entire driving cycle. At the low-level, a Long Short-Term Memory neural network is developed for higher quality driving cycle (velocity) predictions over the low-level's short horizons. Tube-based model predictive controller is then used at the low-level to ensure constraint satisfaction in the presence of driving cycle prediction errors. Simulation results over real-world driving cycles show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

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