Optimal energy allocation strategy for electric vehicles based on the real-time model predictive control technology

Wei, Hongqian; Fan, Likang; Ai, Qiang; Zhao, Wenqiang; Huang, Tianyi; Zhang, Youtong

doi:10.1016/j.seta.2021.101797

Cited by 10 publications

(9 citation statements)

References 36 publications

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“…The simulation results show that the average error of the stability control strategy is reduced by 0.071 m compared with MPC [14,15]. The braking energy recovery strategy based on fuzzy control improves the effective energy recovery rate by 4.95% compared with the logic gate control strategy, and the optimal hydraulic and motor braking ratio can be calculated according to the braking condition of the vehicle, which has a wider range of applicable conditions compared with [30,31]. It can be seen that the control strategy can improve the control accuracy and energy recovery efficiency of hybrid electric vehicles under the premise of ensuring vehicle safety.…”

Section: Discussionmentioning

confidence: 99%

“…How to maintain the stability of ABS (antilock brake system) while considering the braking distribution is the key point. Different from the traditional braking mode, the coordinated control of motor and mechanical braking can ensure the safety of vehicles to the greatest extent [28][29][30][31][32][33][34][35][36]. Some studies apply intelligent control and optimization algorithms to vehicle control strategies [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

et al. 2022

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In order to further improve the stability of path tracking control and fuel economy of new energy vehicles, an optimal control strategy of path tracking and braking energy recovery is proposed. First, a model predictive controller is designed based on the three-degrees of freedom dynamics model of the vehicle according to the idea of hierarchical control, and a fuzzy yaw torque controller is established with the desired yaw velocity and side slip angle of the mass center as constraints. Second, at high-speed driving conditions, the executive layer of the component distributes the braking torque according to the braking energy recovery control strategy. Finally, the optimal control strategy of path tracking and braking energy recovery is verified by Carsim/Advisor/Simulink software under different driving speeds. The results show that the optimized control strategy can improve the tracking accuracy and driving stability of a vehicle with large curvature turning and further improve the fuel economy of new energy vehicles under the premise of meeting the control requirements.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

et al. 2022

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“…Moreover, it combines all control objectives into a single formulation, and the control settings are simple to tune 89 . Nonlinear MPC's are often combined with certain algorithms like the PSO‐based nonlinear MPC as mentioned in Reference 90. The PSO‐non‐linear MPC is adopted to realize the required force and moment with brake torque allocation and pressure regulation along with vehicle stability control features like calculating required longitudinal force, lateral force, etc 91 .…”

Section: Regenerative Braking Control Strategies In Electric Vehiclesmentioning

confidence: 99%

“…Similarly, to enhance regenerative braking performance in EVs with SRM, 111 the MOOS 112,113 is proposed to improve regenerative braking performance under sliding braking conditions. Under sliding braking situations, this MOOS‐based management technique may substantially improve brake smoothness, expand working distance, and lengthen the battery lifespan of EVs 90,114 . This control approach can attain maximum energy efficiency and performance in different braking conditions.…”

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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“…In this context, several methods in the recent literature have been studied and evaluated, such as optimization methods, filter-based methods, controller methods, and rule-based methods. Optimization-based strategies that include Model Predictive Control [ 7 ], Grey Wolf Optimizer [ 8 ], Particle Swarm Optimization [ 9 ], etc, have been investigated in order to deal with complex management objectives (efficiency, cost, lifetime, etc). On the other hand, these strategies are complex and impose an important computation burden.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Direct Torque Control and Fuzzy Logic Energy Management for Multi Power Source Electric Vehicles

Kakouche

Rekioua

Mezani

et al. 2022

Sensors

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This paper proposes a novel Fuzzy-MPDTC control applied to a fuel cell battery electric vehicle whose traction is ensured using a permanent magnet synchronous motor (PMSM). On the traction side, model predictive direct torque control (MPDTC) is used to control PMSM torque, and guarantee minimum torque and current ripples while ensuring satisfactory speed tracking. On the sources side, an energy management strategy (EMS) based on fuzzy logic is proposed, it aims to distribute power over energy sources rationally and satisfy the load power demand. To assess these techniques, a driving cycle under different operating modes, namely cruising, acceleration, idling and regenerative braking is proposed. Real-time simulation is developed using the RT LAB platform and the obtained results match those obtained in numerical simulation using MATLAB/Simulink. The results show a good performance of the whole system, where the proposed MPDTC minimized the torque and flux ripples with 54.54% and 77%, respectively, compared to the conventional DTC and reduced the THD of the PMSM current with 53.37%. Furthermore, the proposed EMS based on fuzzy logic shows good performance and keeps the battery SOC within safe limits under the proposed speed profile and international NYCC driving cycle. These aforementioned results confirm the robustness and effectiveness of the proposed control techniques.

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Optimal energy allocation strategy for electric vehicles based on the real-time model predictive control technology

Cited by 10 publications

References 36 publications

Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

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

Model Predictive Direct Torque Control and Fuzzy Logic Energy Management for Multi Power Source Electric Vehicles

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