A Bidirectional Versatile Buck–Boost Converter Driver for Electric Vehicle Applications

González-Castaño, Catalina; Restrepo, Carlos; Kouro, Samir; Vidal-Idiarte, Enric; Calvente, J.

doi:10.3390/s21175712

Cited by 15 publications

(7 citation statements)

References 47 publications

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“…The electric machine allows the vehicle's kinetic energy to be recovered during braking. A vehicle with such a system is equipped with a battery with a larger capacity [13][14][15].…”

Section: Factors Influencing the Dynamic Characteristics Of Car Controlmentioning

confidence: 99%

Analysis of Energy Flow in Hybrid and Electric-Drive Vehicles

Parczewski,

Wnęk

2024

Energies

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Nowadays, the automotive market offers cars equipped with various types of drive systems, including a classic system in which the internal combustion engine burns fuel to power the vehicle, a hybrid system in which the internal combustion engine is supported by an electric machine (MHEV), and an electrical system in which energy to power the vehicle is drawn from the battery (BEV). Hybrid and electric vehicles have the ability to recover energy during deceleration and braking. Choosing a car with the right drive system is not easy for the average vehicle user. The article presents a comparison of vehicles with various drive systems. For this purpose, a diagnostic scanner was created that allows reading data from the car’s CAN network. The comparison of the obtained data allows for the presentation of more information about vehicles equipped with different drive systems. In particular, parameters such as energy consumption, the possibility of its recovery during braking and the instantaneous power of electric machines were subjected to comparative analysis. Vehicle tests were carried out on a chassis dynamometer in accordance with the WLTP test.

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“…The electric machine allows the vehicle's kinetic energy to be recovered during braking. A vehicle with such a system is equipped with a battery with a larger capacity [13][14][15].…”

Section: Factors Influencing the Dynamic Characteristics Of Car Controlmentioning

confidence: 99%

Analysis of Energy Flow in Hybrid and Electric-Drive Vehicles

Parczewski,

Wnęk

2024

Energies

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“…Speed controller plays an important role in EVs, thus researchers focused to establish the speed controller for commercial and domestic applications. [1][2][3][4] The EV controller should obtain the required speed with low error and low power consumption. However, due to changing road dimensions, load parameters, and external disturbances, the control system is nonlinear, timedependent, and unpredictable.…”

Section: Introductionmentioning

confidence: 99%

“…EVs increase momentum as they have several factors, including price reduction and environmental awareness. Speed controller plays an important role in EVs, thus researchers focused to establish the speed controller for commercial and domestic applications 1–4 . The EV controller should obtain the required speed with low error and low power consumption.…”

Section: Introductionmentioning

confidence: 99%

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Priya,

Balakumaran

2023

Asia-Pacific J Chem Eng

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Nowadays, the use of fuel cells in various applications has become very widespread. Some of their most important applications are in electric vehicles (EVs) and local off‐grid power systems. An EV works as an electric motor that burns a mixture of fuel and gases to generate electricity. One of the challenges with fuel cells is the slow dynamic response to load power changes. In the past few decades, the modeling and control of DC‐DC converters have undergone extensive research and development. However, the error, settling time, and peak overshoot performance are not reduced by the robust nonlinear controller and current mode controller. To overcome these problems, quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is developed. The goal of the QRESCSO‐MFPIC approach is to reduce the integral time absolute error (ITAE) for tuning the fuzzy PI controller. Initializing the fuzzy PI controller parameter is an input in the QRESCSO‐MFPIC approach. For every parameter value, the fitness function is determined and quantile regression analysis is carried out. The efficiency of the QRESCSO‐MFPIC approach is measured by evaluating the settling time and peak overshoot. Tuning of the PI controller is carried out with cat swarm optimization (CSO) and particle swarm optimization (PSO) based on the objective function of ITAE. The result analysis shows that the QRESCSO‐MFPIC approach improves the efficiency of an optimized Fuzzy PI controller compared to the existing methods. Peak overshoot reduction and settling time are improved through the QRESCSO‐MFPIC approach than the proportional integral particle swarm optimization controller (PI‐PSO).

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“…The controller was designed using a linear model of a DC series motor considering the magnetic saturation of the motor. A bi-directional versatile buck-boost converter with a coupled inductor was designed and implemented in [18]. In this case, the current controller employed sliding-mode control, while the voltage controller used PI control.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation and Control of Multi-Source Electric Vehicle Traction Motor under Various Drive Conditions

Prasanthi,

Shareef,

Errouissi

et al. 2023

Energies

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The hybridization of multiple energy sources is crucial for electric vehicles to deliver the same performance as modern fossil fuel-based automobiles. This paper presents a torque and speed control strategy for an electric vehicle DC traction motor by regulating the power flow from two energy sources, namely battery and supercapacitor systems, to resist unpredictable disturbances. A control system with three control loops is applied to regulate the speed of the traction motor. The outer speed control loop is a nonlinear state feedback controller with a disturbance observer, which is capable of handling non-linear systems with unpredictable disturbances. The inner voltage and current control loop are precisely tuned PI controllers. Using an adaptive energy management method that varies depending on the state of charge of the source, the total reference current needed to support the load demand is split between two sources. A laboratory prototype system model is generated, and the performance is analyzed under motoring and regenerative braking conditions. For monitoring and controlling the system, the dSPACE DS1202 real-time simulator is employed. The performance of the proposed control system is evaluated and it is found that the settling time to settle within a tolerance band of 1% is 0.75 s.

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A Bidirectional Versatile Buck–Boost Converter Driver for Electric Vehicle Applications

Cited by 15 publications

References 47 publications

Analysis of Energy Flow in Hybrid and Electric-Drive Vehicles

Analysis of Energy Flow in Hybrid and Electric-Drive Vehicles

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Real-Time Implementation and Control of Multi-Source Electric Vehicle Traction Motor under Various Drive Conditions

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