scholarly journals Online Identification of Power Required for Self-Sustainability of the Battery in Hybrid Electric Vehicles

2014 ◽  
Author(s):  
Andreas A. Malikopoulos
Keyword(s):  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Control Policy ◽  
Management Control ◽  
State Of Charge ◽  
Control Algorithms ◽  
Online Identification ◽  
Hybrid Electric

Hybrid electric vehicles have shown great potential for enhancing fuel economy and reducing emissions. Deriving a power management control policy to distribute the power demanded by the driver optimally to the available subsystems (e.g., the internal combustion engine, motor, generator, and battery) has been a challenging control problem. One of the main aspects of the power management control algorithms is concerned with the self-sustainability of the electrical path, which must be guaranteed for the entire driving cycle. This paper considers the problem of identifying online the power required by the battery to maintain the state of charge within a range of the target value. An algorithm is presented that realizes how much power the engine needs to provide to the battery so that self-sustainability of the electrical path is maintained.

scholarly journals Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jielin Jiang ◽  
Qinting Jiang ◽  
Jinhui Chen ◽  
Xiaotong Zhou ◽  
Shengkai Zhu ◽  
...  
Keyword(s):  
Internal Combustion Engine ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Management Strategies ◽  
Internal Combustion ◽  
Pros And Cons ◽  
Hybrid Electric ◽  
And Control

With the trend of low emissions and sustainable development, the demand for hybrid electric vehicles (HEVs) has increased rapidly. By combining a conventional internal combustion engine with one or more electric motors powered by a battery, HEVs have the advantages over traditional vehicles in better fuel economy and lower tailpipe emissions. Nevertheless, the power management strategies (PMSs) for conventional vehicles which mainly focus on the efficiency of internal combustion engine are no longer applicable due to the complex internal structure of HEVs. Hence, a large number of novel strategies appropriate for HEVs have been surveyed, but most of the researches concentrate on discussing the classifications of PMSs and comparing their cons and pros. This paper presents a comprehensive review of power management strategies adopted in HEVs aiming at specific challenges for the first time. The categories of the existing PMSs are presented based on the different algorithms, followed by a brief study of each type including the analysis of its pros and cons. Afterwards, the implementation and optimization of power management strategies aiming at proposed challenges are introduced in detail with the description of their optimization objectives and optimized results. Finally, future directions and open issues of PMSs in HEVs are discussed.

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.

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