scholarly journals Unscented Kalman Filter-Based Battery SOC Estimation and Peak Power Prediction Method for Power Distribution of Hybrid Electric Vehicles

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 35957-35965 ◽  
Author(s):  
Weida Wang ◽  
Xiantao Wang ◽  
Changle Xiang ◽  
Chao Wei ◽  
Yulong Zhao
Keyword(s):  
Kalman Filter ◽  
Electric Vehicles ◽  
Power Distribution ◽  
Peak Power ◽  
Hybrid Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Prediction Method ◽  
Power Prediction ◽  
Soc Estimation ◽  
Hybrid Electric

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

Modeling and Control Strategy of Series Hybrid Electric Vehicles

2014 ◽  
Vol 543-547 ◽  
pp. 1246-1249
Author(s):  
Liang Zhang ◽  
Bin Jiao ◽  
Lei Li
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicles ◽  
System Structure ◽  
Modeling And Control ◽  
Powertrain Control ◽  
Hybrid Electric ◽  
And Control ◽  
Made In

The modeling method and control strategy for series hybrid electric vehicles were presented in this paper. Firstly, the system structure and operation principles are discussed systematically; and then a control strategy is proposed based on the modeling of powertrain. Control strategy focus on the multi-modes switch logic and power distribution. In the last part of this paper, the simulation made in MATLAB/Simulink was introduced, which results indicate that the model and control strategy are correct.

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.

Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems

2013 ◽  
Vol 4 (3) ◽  
pp. 1351-1360 ◽  
Author(s):  
Soroush Shafiee ◽  
Mahmud Fotuhi-Firuzabad ◽  
Mohammad Rastegar
Keyword(s):  
Electric Vehicles ◽  
Power Distribution ◽  
Distribution Systems ◽  
Hybrid Electric Vehicles ◽  
Power Distribution Systems ◽  
Hybrid Electric
Sign in / Sign up

Export Citation Format

Share Document