Energy Management in Fuel-Cell\/Battery Vehicles: Key Issues Identified in the IEEE Vehicular Technology Society Motor Vehicle Challenge 2017

Dépature, Clément; Jemeï, Samir; Boulon, Loïc; Bouscayrol, Alain; Marx, Neigel; Morando, Simon; Castaings, Ali

doi:10.1109/mvt.2018.2837154

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…This current profile corresponds to a real In order to cope with the requirements of real transportation applications, the identification process is simulated under a dynamic current profile. This current profile corresponds to a real driving cycle provided in [78] and shown in Figure 6. The simulated battery model is discharged under the dynamic profile current corresponding to this driving cycle and the internal resistance is estimated using the three battery ECM.…”

Section: Simulation Of Battery Internal Resistance Identification Using Rls Algorithmmentioning

confidence: 99%

“…Even with a dynamic current profile, the estimation is accurate and the battery ECM is simple and easy to implement for real time applications. driving cycle provided in [78] and shown in Figure 6. The simulated battery model is discharged under the dynamic profile current corresponding to this driving cycle and the internal resistance is estimated using the three battery ECM.…”

Section: Simulation Of Battery Internal Resistance Identification Using Rls Algorithmmentioning

confidence: 99%

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A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

Noura

Boulon

Jemeï

2020

WEVJ

135

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To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

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Section: Simulation Of Battery Internal Resistance Identification Using Rls Algorithmmentioning

confidence: 99%

Section: Simulation Of Battery Internal Resistance Identification Using Rls Algorithmmentioning

confidence: 99%

A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

Noura

Boulon

Jemeï

2020

WEVJ

135

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“…PHEVs can operate in different modes, such as full electric and power split modes. These modes are controlled by the energymanagement system under diverse driving conditions [1].…”

Section: Overviewmentioning

confidence: 99%

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

et al. 2019

EEE Ind. Electron. Mag.

191

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E nergy management is a critical technology in plug-in hybrid-electric vehicles (PHEVs) for maximizing efficiency, fuel economy, and range, as well as reducing pollutant emissions. At the same time, deep reinforcement learning (DRL) has become an effective and important methodology to formulate model-free and realtime energy-management strategies for HEVs and PHEVs. In this article, we describe the energy-management issues of HEVs/PHEVs and summarize a variety of potential DRL applications for onboard energy management. We also discuss the prospects for various DRL approaches in the energy-management field.

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“…It is essential to equip hybrid electric vehicles with advanced energy management systems to achieve these goals. Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) can operate in a variety of modes such as fully electric or power distribution, which are controlled by an energy management system based on driving conditions [7][8][9][10]. Accordingly, an energy management system tries to improve the power output of multiple sources and supply the necessary power while minimizing the most important and significant costs [11,12].…”

Section: Introductionmentioning

confidence: 99%

Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

Chen

Alazzawi

Guerrero

et al. 2022

Mathematical Problems in Engineering

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The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

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Energy Management in Fuel-Cell\/Battery Vehicles: Key Issues Identified in the IEEE Vehicular Technology Society Motor Vehicle Challenge 2017

Cited by 14 publications

References 18 publications

A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

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