Model Predictive Control Based Energy Management Strategy of Series Hybrid Electric Vehicles Considering Driving Pattern Recognition

Hao, Jinna; Ruan, Shumin; Wang, Wei

doi:10.3390/electronics12061418

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…MPC-based energy management strategy for hybrid electric vehicles, considering factors such as driver demand, traffic conditions, and energy availability was developed. The study demonstrated improved fuel economy and emissions reduction compared to traditional control strategies [23]- [26].…”

Section: Introductionmentioning

confidence: 90%

Modelling and performance analysis of free body dynamics of electric vehicles

Sakthivelsamy,

Subramaniyan

2024

IJPEDS

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The modelling and performance analysis of free body dynamics in electric vehicles (EVs) plays a crucial role in understanding and optimizing the vehicle's behavior and performance. This research focuses on accurately modelling the forces and motions acting on the vehicle body, excluding the powertrain components. By considering factors such as vehicle weight, suspension characteristics, tire properties, and aerodynamic forces, a comprehensive mathematical model is developed. This model enables the simulation and analysis of the vehicle's behavior under various operating conditions. The performance analysis involves evaluating key metrics such as vehicle response, stability limits, and ride comfort. The proposed system is compared with the existing electric vehicle in the market. The findings from this research contribute to the design and development of EVs with improved handling, stability, and energy efficiency. Additionally, they inform the development of advanced driver-assistance systems (ADAS) and autonomous driving technologies. Overall, the modelling and performance analysis of free body dynamics in EVs supports the advancement of sustainable and efficient transportation systems.

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Section: Introductionmentioning

confidence: 90%

Modelling and performance analysis of free body dynamics of electric vehicles

Sakthivelsamy,

Subramaniyan

2024

IJPEDS

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“…It has the advantages of strong robustness, good control effect and high stability and has been widely used in linear and nonlinear control systems. Hao et al [30] optimized the predictive model, combining driving intention recognition and driving condition prediction with MPC, with the goal of improving fuel consumption while maintaining the SOC of the battery. To further improve the calculation efficiency, the model was discretized and linearized, and the MPC problem was transformed into a quadratic programming problem, which can be effectively solved using the interior point method.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon

Lin,

Wei,

Feng

et al. 2024

Energies

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Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.

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“…Rule-based strategies are the most widely used and relatively mature due to their simple design, convenient implementation, and excellent real-time performance [10,11]. Ruan et al developed a rule-based shifting schedule and power split strategy for a dual-motor powertrain.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Real-Time Optimal Energy Management Strategy Considering Energy Efficiency and Flexible Torque Response for a Dual-Motor Four-Drive Powertrain

Zheng

Tian

2023

Electronics

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To exhaust the potential of energy efficiency and dynamic performance of the dual-motor four-drive powertrain, this study developed a multi-objective real-time optimal energy management strategy considering energy efficiency and flexible torque response. First, a theoretical analysis of energy loss and operating characteristics was performed to elucidate the energy-saving advantages and control challenges of the dual-motor four-drive powertrain. Second, an economic strategy based on the adaptive nonlinear particle swarm optimization (ANLPSO) and optimization freezing tolerance mechanism was devised to realize real-time optimal power distribution. Then, the pre-shifting recognition schedule and gradient torque recovery strategy were developed to achieve flexible torque response during gear shifting. Finally, smooth switching logic was created to assure a seamless transition between the two strategies. Numerous simulation results indicate that compared with the single-motor drive strategy, the proposed strategy can increase energy efficiency by 8.1%, 4.02%, and 9.49% under NEDC, WLTC, and CLTC, respectively. During shifting, the longitudinal acceleration and jerk of the proposed strategy are significantly superior to those of the original strategy, thereby enhancing the vehicle’s dynamic performance and ride comfort. The results of the drum experiment validate the efficacy of the proposed method for energy consumption optimization and torque coordination control in the actual vehicle environment.

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Model Predictive Control Based Energy Management Strategy of Series Hybrid Electric Vehicles Considering Driving Pattern Recognition

Cited by 8 publications

References 24 publications

Modelling and performance analysis of free body dynamics of electric vehicles

Modelling and performance analysis of free body dynamics of electric vehicles

A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon

Multi-Objective Real-Time Optimal Energy Management Strategy Considering Energy Efficiency and Flexible Torque Response for a Dual-Motor Four-Drive Powertrain

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