Energy-efficient system and charge balancing topology for electric vehicle application

Habib, Arslan; Hasan, Mohammad Kamrul; Islam, Shayla; Sharma, Rohit; Hassan, Rosilah; Nafi, Nazmus Shaker; Yadav, Kusum; Alotaibi, Shoayee Dlaim

doi:10.1016/j.seta.2022.102516

Cited by 13 publications

(3 citation statements)

References 176 publications

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“…This can be justified by higher current capability, powertrain and charging efficiency of super-capacitor and fuel cells [247,248]. Further, the use of control strategies like PWM, pulse amplitude modulation (PAM) and bang-bang type current control with a super-capacitor and fuel cell improves the DR of EV by minimising the power converter losses [249,250].…”

Section: Performance Enhancement Techniques For Ev Output Characteris...mentioning

confidence: 99%

Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques

Gurusamy,

Ashok,

Mason

2023

IEEE Access

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Electric mobility is getting prominence in modern transportation as government policies aim to reduce greenhouse gas (GHG) emissions. In the context of real-time testing, numerical modelling and simulation of electric vehicle (EV) powertrains play a vital role in developing an efficient electric powertrain and charging infrastructure as it consumes less time and cost. Also, it enhances the overall performance by optimizing the size and configuration of the EV powertrain under different driving conditions. Thus, the review paper explores the different modelling approaches used for estimating the energy consumption (EC) and driving range (DR) initially. Further, the vehicle analytical model is discussed in detail with sub-models of powertrain components and vehicle dynamics, which have the mathematical correlation related to power losses and energy flow. Additionally, the necessity, development process, characterization and accuracy of localized driving cycles (DCs) and commonly used driver controller models for EVs are critically elaborated. Further, the impact of various influential input parameters such as vehicle parameters and driving conditions on EV performance characteristics is analyzed along with different improvisation methods utilized in the existing literature. From this extensive review, it can be concluded that simulation results by using an analytical vehicle model have good accuracy with chassis dynamometer-based testing and it can be used for optimizing the size and configuration of EV powertrain components under different scenarios. Finally, the present status and future research required in the field of EV powertrain development through modelling and simulation are summarized to extend the application of EVs in transportation sectors.

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Section: Performance Enhancement Techniques For Ev Output Characteris...mentioning

confidence: 99%

Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques

Gurusamy,

Ashok,

Mason

2023

IEEE Access

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“…Selective control over ISOcount and CHGcount follow the same logic in Eq. ( 8), but referring to the appropriate fault conditions (Abdul-jabbar et al, 2022;Habib et al, 2022;Shukla and Patel, 2022). 4 Results and discussions…”

Section: Cmrr 20 Logmentioning

confidence: 99%

Battery management system enhancement for lithium-ions battery cells using switched shunt resistor approach based on finite state machine control algorithm

2023

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Due to their favorable characteristics, lithium-ion batteries have a dominant share of the battery market. There are a number of issues related to the use and management of Lithium-ion batteries in this paper, specifically with regard to the safe operation of the batteries as well as methods for balancing their cells. With the help of a passive cell balancing algorithm and a cell measurement circuit, a battery management system with a passive cell balancing algorithm has been developed. The purpose of this paper is to improve the efficiency of the balancing algorithm by implementing and analyzing a cell modelling method from the literature, with the aim of improving its performance. The results of this study showed that the use of the cell modelling system was able to improve the balancing algorithm’s balancing and charging times by 12.6%. Further, to validate the results obtained from the measurement system and the cell modelling system, an analysis was conducted of uncertainty propagation in order to validate the results. As part of future research, broader testing conditions may be used in order to better understand the positive impact of the cell modelling system on the balancing algorithm in the future.

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“…In this context, Electric Vehicles (EVs) emerge as a beacon of hope, rapidly gaining traction and popularity (He et al, 2022;Liu et al, 2022a;Zhang et al, 2022). Yet, the adoption curve of EVs is somewhat tempered by persistent user concerns, notably the vehicle's range (Ahasan et al, 2022;Ruan and Lv, 2022;Zhang et al, 2021b) and the inherent charging latency (Aljehane and Mansour, 2022;Boddapati et al, 2022;Ji et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Optimized Speed Control for Electric Vehicles on Dynamic Wireless Charging Lanes: An Eco-Driving Approach

Zhong,

En,

Pei

et al. 2024

J. Int. Con. Veh.

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As the adoption of Electric Vehicles (EVs) intensifies, two primary challenges emerge: limited range due to battery constraints and extended charging times. The traditional charging stations, particularly those near highways, exacerbate these issues with necessary detours, inconsistent service levels, and unpredictable waiting durations. The emerging technology of dynamic wireless charging lanes (DWCLs) may alleviate range anxiety and eliminate long charging stops; however, the driving speed on DWCL significantly affects charging efficiency and effective charging time. Meanwhile, the existing research has addressed load balancing optimization on Dynamic Wireless Charging (DWC) systems to a limited extent. To address this critical issue, this study introduces an innovative eco-driving speed control strategy, providing a novel solution to the multi-objective optimization problem of speed control on DWCL. We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs. Three objective functions are formulated to tackle the challenges at hand: reducing travel time, increasing charging efficiency, and achieving load balancing on DWCL, which corresponds to four control strategies. The results of numerical tests indicate that a comprehensive control strategy, which considers all objectives, achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing. Furthermore, by defining the energy demand and speed range through an upper operation limit, a relatively superior speed control strategy can be selected. This work contributes to the discourse on DWCL integration into modern transportation systems, enhancing the EV driving experience on major roads.

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Energy-efficient system and charge balancing topology for electric vehicle application

Cited by 13 publications

References 176 publications

Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques

Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques

Battery management system enhancement for lithium-ions battery cells using switched shunt resistor approach based on finite state machine control algorithm

Optimized Speed Control for Electric Vehicles on Dynamic Wireless Charging Lanes: An Eco-Driving Approach

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