Dual-stage adaptive control of hybrid energy storage system for electric vehicle application

Arslan, Muhammad; Ahmad, Iftikhar; Azeem, Muhammad Kashif; Liaquat, Muwahida

doi:10.1016/j.est.2021.103165

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…Similarly, Moreno et al 134 applied NN on hybrid electric vehicles in which SC was used as a secondary source, and the range was increased up to 8.9% in the city test. Deep reinforcement learning (DRL) is another type of learning‐based approach, which is used widely for EMS designing 135‐137 . These methods behave like a “black box,” as shown in Figure 12, in which internal phenomena are unseen, but it can deal with the complex system quickly depending upon the system design.…”

Section: Different Suitable Control Algorithms For Lib‐sc Hessmentioning

confidence: 99%

“…Deep reinforcement learning (DRL) is another type of learning-based approach, which is used widely for EMS designing. [135][136][137] These methods behave like a "black box," as shown in Figure 12, in which internal phenomena are unseen, but it can deal with the complex system quickly depending upon the system design. The system design depends on the prior knowledge of this approach and the database used for tuning and obtaining global optimization solutions; this approach should be combined with some offline optimization techniques.…”

Section: Learning-based Approachmentioning

confidence: 99%

See 1 more Smart Citation

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Ahsan

Mekhilef

Soon

et al. 2022

Intl J of Energy Research

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Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium-ion battery (LIB) and a supercapacitor (SC)-based HESS (LIB-SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal-sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system-level optimizations, include component sizing (CS) and power-energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB-SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB-SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB-SC HESS are provided to inspire researchers for further studies. Highlights• Lithium-ion battery (LIB) and supercapacitor (SC)-based hybrid energy storage system (LIB-SC HESS) suitable for EV applications is analyzed comprehensively.

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Section: Different Suitable Control Algorithms For Lib‐sc Hessmentioning

confidence: 99%

Section: Learning-based Approachmentioning

confidence: 99%

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Ahsan

Mekhilef

Soon

et al. 2022

Intl J of Energy Research

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“…It also displays warning signals if a fault in any of the subsystems is detected by the diagnostic system so that necessary action can be taken immediately. 158,159 All the internal signal transmission and communication is achieved through the CAN as it serves as the prime 117 communication medium between various systems in the vehicle. 160 Here, the communication with the battery management system (BMS) is necessary because if the SOC falls below a certain level, the MCU will receive a signal to control the motor work in the generator to charge the battery, hence making sure that the SOC does not drop below a certain value.…”

Section: Motor Control Systemmentioning

confidence: 99%

“…The respective output signals are sent to the optimum power control unit, which splits the brake force between the vehicle's braking systems based on driver demand. Then, the adaptive control employs various modelling approaches for maximizing the energy recuperation efficiency such as adaptive MPC, 116 modified direct torque control, 117 adaptive neuro‐based FLC, 118 back‐stepping based adaptive controller, 119 and adaptive fuzzy control, 101 as mentioned below in Table 5. According to a study aimed at control of regenerative braking of a downhill cruising EV, the adaptive MPC is designed, which can realize the adaptive control of the braking systems with the change of the road gradient, stability, and robustness 120 .…”

Section: Regenerative Braking Control Strategies In Electric Vehiclesmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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“…Control algorithms have been derived from mathematical control theory and artificial intelligence (AI) in several research investigations [15]- [17]. The majority of HESS-related research, however, is still in the simulation phase and has not been extensively implemented [18].…”

Section: Introductionmentioning

confidence: 99%

Control energy management system for photovoltaic with bidirectional converter using deep neural network

Widjonarko,

Utomo,

Omar

et al. 2024

IJECE

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Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.

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Dual-stage adaptive control of hybrid energy storage system for electric vehicle application

Cited by 11 publications

References 34 publications

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Control energy management system for photovoltaic with bidirectional converter using deep neural network

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