Optimizing Energy Harvesting: History
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Anith Khairunnisa Ghazali
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Mohd Khair Hassan
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Mohd Amran Mohd Radzi
,
Azizan As’arry

Recycling braking energy is crucial in increasing the overall energy efficiency of an electric vehicle. Regenerative braking system (RBS) technology makes a significant contribution, but it is quite challenging to design an optimal braking force distribution while ensuring vehicle stability and battery health.

  • regenerative braking
  • super-twisting sliding mode control
  • electric vehicle
  • state of charge (SOC)

1. Introduction

Due to the shortage of resources and environmental problems, electric vehicle development has become a trend in an effort to replace conventional internal combustion engine vehicles [1]. However, the most critical problem of electric vehicles is their limitation in driving range. Therefore, regenerative braking has been introduced to overcome this problem. A regenerative braking system (RBS) is an energy recovery system that converts kinetic energy to electrical or mechanical energy. During deceleration, the vehicle slows down, and kinetic energy is released in the form of heat. Throughout the braking process, the captured kinetic and potential energy are transformed into electrical energy and stored in an energy storage system, such as a battery or a super-capacitor. Regenerative braking is an effective approach that improves vehicle performance, such as range and efficiency, especially in heavy stop-and-go traffic conditions or city driving due to frequent braking [2]. According to [3][4], one-third to one-half of energy is consumed during braking in urban driving. Another finding by [5] determined that there is about 50% or more driving energy lost during braking in urban conditions and 20% in suburban conditions. Consequently, if the wasted energy is successfully recovered, driving mileage may increase by 10% to 30%. Driving range is a vital issue for electric vehicles which depends on several factors such as driving style, weather, and desired comfort. The New European Driving Cycle (NEDC) is used to represent a start-stop drive cycle. Designing an effective braking system would be a good approach to solve this limitation. Even though the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) was introduced as a more accurate testing procedure than the NEDC, but the transition to WLTP is not fully complete in some regions. As a result, many vehicles on the road were tested under the NEDC. Researching EV driving patterns using NEDC allows for standardized testing and comparison while providing insights into the behaviour of existing EVs. It is important to note that as EV technology advances and the transition to WLTP becomes more widespread, researchers are likely to shift towards using WLTP for their studies to capture the most up-to-date and accurate driving patterns and energy consumption data for EVs.
The braking system is a crucial part of a vehicle system. Even though most electric vehicles are equipped with regenerative braking, mechanical braking is still needed to guarantee braking performance [6]. A conventional braking system consists of braking components and braking strategies. Nowadays, the RBS is also included in electric vehicles [7]. The main objective of this research is to achieve better braking performance and higher braking efficiency.
There are two types of RBSs: hydraulic RBS and electric RBS. A hydraulic RBS uses fluid as a working medium. During braking, kinetic energy drives the pump to transfer itself from a low-pressure reservoir to a high-pressure accumulator. Meanwhile, for cruising conditions, the fluid in the high-pressure accumulator drives the motor connected to a drive shaft. Another type of RBS is the electric RBS, which converts kinetic energy to electric energy, which is then stored in a battery. The energy stored in the battery is used to drive the motor connected to the drive shaft [8].

2. Optimizing Energy Harvesting

Zhi-Feng Bai et al.’s research introduced the 𝐻∞ robust controller for the regenerative braking of electric vehicles. The researchers proposed a controller that could make a good combination of regenerative braking and mechanical-friction braking to minimise the effect of disturbance. Based on the comparable result between 𝐻∞ robust control and the proportional-integral derivative (PID) controller, the proposed controller could save more energy and provide a good combination of regenerative braking and mechanical-friction braking [9].
Palanivel et al. proposed a fuzzy logic control, which was used in a three-phase brushless direct current (BLDC) motor to control the four-quadrant operations with no power loss. The execution of the two controllers was analysed based on different control system parameters, such as maximum overshoot, rise time, and settling time, with respect to the simulation results. For the same operating conditions, the control concept employing a fuzzy-tuned PID controller demonstrated better speed regulation and performance than the conventional PID controller [10].
Hao Zhang et al. developed a fuzzy logic control strategy that ensures braking safety and stability by distributing regenerative and friction braking forces reasonably during braking. It enables the motor’s regenerative braking characteristic to be used as much as possible, allowing more kinetic energy to be converted into electric energy and stored in the battery. Based on the findings, the proposed control strategy could recover more braking energy than the ADVISOR’s strategy [11].
Peng Mei et al. developed a novel sliding mode control (SMC) scheme with a fuzzy logic control for energy management in electric vehicles with regenerative braking. A simulation study was performed to validate the proposed controller’s performance and torque distribution strategy. Based on the results, this method effectively allocated hydraulic and motor braking torque, resulting in improved energy recovery and stability [12].
Canciello et al. developed a power transfer optimisation-focused alternative energy management strategy for aeronautical applications. The study used a sliding manifold (SHG)-based high-gain control approach, which resulted in continuous control with robustness properties comparable to classical SMC [13].
The control strategy for energy management onboard the innovative electric aircraft concept was proposed to reduce generator size and onboard weight by utilising battery packs as supplemental energy sources. Sliding mode control was used as the low-level control in the composition of the two-layer controller. Rigorous stability tools based on the theory of SMC and common Lyapunov functions were presented for both controllers, and satisfactory results were obtained [14].
Chu developed an observer-based gain-scheduling path-following control for time-delayed autonomous electric cars. The algorithm schedules the observer and controller gains based on the actual longitudinal velocity. The controller design’s necessary requirements are defined in terms of a series of linear matrix inequalities. Finally, numerical simulations are used to demonstrate the efficacy and superiority of the new method over the existing method. The superiority and efficacy of the proposed controller over other controllers based on simulation results and a thorough evaluation were verified [15].
Allagui proposed a new hybrid fuzzy PID gain-scheduling algorithm parameter with a tuning value A. This tuning parameter enables the elimination of certain shortcomings, such as oscillations in robot motion curvature. The developed platform improved the process of design modifications and contributed to a solution of the motion control problem in terms of evaluating the designed control algorithm in its attainment of the desired output motion characteristics. Based on the outcome, sufficient and robust results in path tracking were produced, confirming the benefit of the combined fuzzy and PID control strategy [16].

This entry is adapted from the peer-reviewed paper 10.3390/en16124561

References

  1. Kuntanapreeda, S. Traction control of electric vehicles using sliding-mode controller with tractive force observer. Int. J. Veh. Technol. 2014, 2014, 829097.
  2. Fajri, P.; Lee, S.; Prabhala, V.A.K.; Ferdowsi, M. Modeling and Integration of Electric Vehicle Regenerative and Friction Braking for Motor/Dynamometer Test Bench Emulation. IEEE Trans. Veh. Technol. 2016, 65, 4264–4273.
  3. Kumar, C.S.N.; Subramanian, S.C. Cooperative control of regenerative braking and friction braking for a hybrid electric vehicle. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2016, 230, 103–116.
  4. Khorsravinia, K.; Hassan, M.K.; Rahman, R.Z.A.; Al-Haddad, S.A.R. Integrated OBD-II and mobile application for electric vehicle (EV) monitoring system. In Proceedings of the 2017 IEEE 2nd International Conference on Automatic Control and Intelligent System, Kota Kinabalu, Malaysia, 21 October 2017; Volume 2017, pp. 202–206.
  5. Chen, J.; Liu, K.; Lan, F.; Liu, M. Braking Stability-Oriented Regenerative Braking Control Strategy of Twin Motor 4WD Electric Vehicle. DEStech Trans. Eng. Technol. Res. 2017, 2017, 58–65.
  6. Lv, C.; Zhang, J.; Li, Y.; Yuan, Y. Regenerative Braking Control Algorithm for an Electrified Vehicle Equipped with a by-Wire brake System; SAE International: Warrendale, PA, USA, 2014; Volume 1.
  7. Zheng, L.; Shi, Z.; Luo, Y.; Kang, J. A Study of Energy Recovery System during Braking for Electric Vehicle. In 2016 6th International Conference on Applied Science, Engineering and Technology; Atlantis Press: Amsterdam, The Netherlands, 2016; pp. 8–13.
  8. Shewate, S.; Kumbhalkar, M.A.; Sonawane, Y.; Salunkhe, T.; Savant, S. Modelling And Simulation of Regenerative Braking System for Light Commercial Vehicle—A Review. IOSR J. Mech. Civ. Eng. 2018, 8, 52–56.
  9. Bai, Z.F.; Li, S.X.; Cao, B.G. H∞ Control Applied to Electric Torque Control for Regenerative Braking of an Electric Vehicle. J. Appl. Sci. 2005, 5, 1103–1107.
  10. Palanivel, P.; Alemayehu, H.; Chandramouli, B.; Hiremath, R. Design and Analysis of BLDC Motor Drive Based on Fuzzy-PID Controller. Int. J. Electr. Eng. Technol. 2020, 11, 281–290.
  11. Zhang, H.; Xu, G.; Li, W.; Zhou, M. Fuzzy logic control in regenerative braking system for electric vehicle. In Proceedings of the 2012 IEEE International Conference on Information and Automation, Shenyang, China, 6–8 June 2012; pp. 588–591.
  12. Mei, P.; Yang, S.; Xu, B.; Sun, K. A Fuzzy Sliding-Mode Control for Regenerative Braking System of Electric Vehicle. In Proceedings of the 2021 7th International Conference on Control, Automation and Robotics (ICCAR), Singapore, 23–26 April 2021; pp. 397–401.
  13. Canciello, G.; Russo, A.; Guida, B.; Cavallo, A. Supervisory Control for Energy Storage System Onboard Aircraft. In Proceedings of the 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Palermo, Italy, 12–15 June 2018; pp. 1–6.
  14. Cavallo, A.; Canciello, G.; Russo, A. Supervised Energy Management in Advanced Aircraft Applications. In Proceedings of the 2018 European Control Conference (ECC), Limassol, Cyprus, 12–15 June 2018; pp. 2769–2774.
  15. Chu, S.; Xie, Z.; Wong, P.K.; Li, P.; Li, W.; Zhao, J. Observer-based gain scheduling path following control for autonomous electric vehicles subject to time delay. Veh. Syst. Dyn. 2022, 60, 1602–1626.
  16. Allagui, N.Y.; Salem, F.A.; Aljuaid, A.M. Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms. Comput. Intell. Neurosci. 2021, 2021, 5542888.
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