Observer techniques for estimating the State-of-Charge and State-of-Health of VRLABs for Hybrid Electric Vehicles

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.

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State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers

2005 European Conference on Power Electronics and Applications ◽

10.1109/epe.2005.219601 ◽

2005 ◽

Cited By ~ 13

Author(s):

B.S. Bhangu ◽

P. Bentley ◽

D.A. Stone ◽

C.M. Bingham

Keyword(s):

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

State Of Charge ◽

State Of Health ◽

Lead Acid Batteries ◽

Non Linear ◽

Hybrid Electric ◽

Lead Acid

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Effects of Using an Electrically Heated Catalyst on the State of Charge of the Battery Pack for Series Hybrid Electric Vehicles at Cold Start

10.4271/2020-01-0444 ◽

2020 ◽

Author(s):

Suchitra Sivakumar ◽

Hajime Shingyouchi ◽

Xieyang Yan ◽

Toshinori Okajima ◽

Kyohei Yamaguchi ◽

...

Keyword(s):

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Cold Start ◽

The State ◽

State Of Charge ◽

Battery Pack ◽

Hybrid Electric

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Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid-Electric Vehicles

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2004.842461 ◽

2005 ◽

Vol 54 (3) ◽

pp. 783-794 ◽

Cited By ~ 303

Author(s):

B.S. Bhangu ◽

P. Bentley ◽

D.A. Stone ◽

C.M. Bingham

Keyword(s):

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

State Of Charge ◽

State Of Health ◽

Nonlinear Observers ◽

Lead Acid Batteries ◽

Hybrid Electric ◽

Lead Acid

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Impedance measurements on lead–acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles

Journal of Power Sources ◽

10.1016/j.jpowsour.2004.10.028 ◽

2005 ◽

Vol 144 (2) ◽

pp. 418-425 ◽

Cited By ~ 159

Author(s):

Holger Blanke ◽

Oliver Bohlen ◽

Stephan Buller ◽

Rik W. De Doncker ◽

Birger Fricke ◽

...

Keyword(s):

Charge State ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

State Of Charge ◽

State Of Health ◽

Impedance Measurements ◽

Lead Acid Batteries ◽

Hybrid Electric ◽

Lead Acid

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

Mathematics ◽

10.3390/math10020260 ◽

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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