scholarly journals Influence of State of Charge estimation uncertainty on energy management strategies for Hybrid Electric Vehicles

2011 ◽  
Vol 44 (1) ◽  
pp. 9703-9708 ◽  
Author(s):  
V. Larsson ◽  
L. Johannesson ◽  
B. Egardt
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Management Strategies ◽  
State Of Charge ◽  
Estimation Uncertainty ◽  
Hybrid Electric ◽  
State Of Charge Estimation

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.

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.

Innovative Antriebe 2018

2018 ◽  
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
Management Strategies ◽  
Battery Management System ◽  
Use Case ◽  
Battery Management ◽  
Hybrid Electric

Zukünftiges Mobilitätsverhalten Mobilität 2050 – Selfdriving-eCo-Hyperflyyer, Drahtesel, oder was? . . . . . . . . . . . . . . . . . . .1 K. C. Keller, Aveniture GmbH, Freinsheim Ökobilanzierung Einfluss von Zellbauform und Zellchemie auf die Ökobilanz von batterieelektrischen Fahrzeugen . . . . . . . . . .5 T. Semper, M. Clauß, IAV GmbH, Stollberg; A. Forell, IAV GmbH, Bad Cannstatt Anwendungsfallabhängige CO2 -Bilanzen elektrifizierter Fahrzeugantriebe –Use case driven CO2 footprint of electrified powertrains . . . . . . . . . . . . . . .17 O. Ludwig, J. Muth, M. Gernuks, H. Schröder, T. Löscheter Horst, Volkswagen AG, Wolfsburg Prädiktion der Lebensdauer von Traktionsbatteriesystemen für reale Nutzungsszenarien . . . .33 M. Ufert, Professur für Fahrzeugmechatronik, Technische Universität Dresden; A. Batzdorf, L. Morawietz, IAM GmbH, Dresden Predictive Energy Management Strategies for Hybrid Electric Vehicles: eHorizon for Battery Management System. . . . . 49 M. ...

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