scholarly journals Swarm intelligence based State-of-Charge optimization for charging Plug-in Hybrid Electric Vehicles

2015 ◽  
Author(s):  
I. Rahman ◽  
P. M. Vasant ◽  
B. S. M. Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Swarm Intelligence ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
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.

A Dynamic Programming Control Strategy for HEV

2012 ◽  
Vol 263-266 ◽  
pp. 541-544 ◽  
Author(s):  
Babici Leandru Corneliu Cezar ◽  
Onea Alexandru
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
The State ◽  
State Of Charge ◽  
Battery Pack ◽  
Matlab Simulink ◽  
Hybrid Electric

Dynamic programming is a very powerful algorithmic paradigm which solves a problem by identifying subproblems and tackling them one by one. First the smallest are solved, and then using their answers, it can be figured out larger ones, until the whole lot of them is solved. This paper presents a control strategy for hybrid electric vehicles, based on the dynamic programming, applied in MATLAB, Simulink environment, using ADVISOR. It was tried this method due to the calculation speed of the suitable torque and speed required from the engine, considering the driver power request (torque and speed), and the state of charge (SOC) of the batteries. Using the fuel converter (FC) fuel map, and the remaining SOC of the battery pack, it was designed an algorithm that will chose at each time the required torque and speed from the first and second source of power.

scholarly journals Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms

Energies ◽  
2010 ◽  
Vol 3 (10) ◽  
pp. 1654-1672 ◽  
Author(s):  
Jingyu Yan ◽  
Guoqing Xu ◽  
Huihuan Qian ◽  
Yangsheng Xu
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Hybrid Electric ◽  
State Of Charge Estimation

Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

2010 ◽  
Author(s):  
Simona Onori ◽  
Lorenzo Serrao ◽  
Giorgio Rizzoni
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Optimal Solution ◽  
State Of Charge ◽  
Management Problem ◽  
Equivalence Factor ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy ◽  
Adaptation Law

This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.

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