Energy optimization for intelligent hybrid electric vehicles based on hybrid system approach in a car‐following process

Abstract

Considering the hybrid dynamical characteristics of multimode hybrid electric vehicle during a car-following process, a novel energy management strategy based on hybrid system theory is proposed in this article. Firstly, nonlinear powertrain model and longitudinal dynamics of a single-shaft parallel hybrid electric vehicle is built, and hybrid characteristics combined with continuous dynamics and discrete events during operating mode transition are described. Secondly, the nonlinear powertrain model and characteristics are approximated by the piecewise affine method, the mixed-logic dynamic modeling method is introduced and system variables are defined. Then the hybrid dynamical model for multimode hybrid electric vehicle in a car-following process is established with the hybrid system description language. Thirdly, according to the principle of receding horizon control, a hybrid model predictive controller is designed and applied for optimizing the multiobjective energy management problem in the car-following process. Finally, the proposed control strategy is compared with a rule-based control strategy, and the simulation results show that the proposed control strategy achieves fuel efficiency improvement while ensure the dynamic performance, driving safety and ride comfort during the car-following process, and the designed controller meets the requirement of deep fusion of car-following and energy management.

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

“…EMS aims to improve the drivability and overall user experience of HEVs, ensuring smooth power delivery and responsive performance [29,64,66,67,71]. It can adapt to different driving conditions and traffic patterns by optimizing the operation of the powertrain for maximum efficiency and performance [6,11,58].…”

“…Online control strategies are based on local optimization and can be implemented in real-time since no driving cycle prior knowledge is required and there is a limited computational load [2,6,10,20]. Offline energy management strategies are designed before real-time implementation using historical driving data or simulation models and provide optimized power management solutions [53,68,71,82,83]. Offline optimization requires a priori knowledge about upcoming driving cycles and searches for a global optimal solution [2].…”

Adaptive coexistence of synchronization and anti-synchronization for a class of switched chaotic systems

“…Switched systems are a class of hybrid systems, which arise in various fields of real life world, such as electrical system 1,2 , communication networks 3,4 , flight control 5 , network control systems [6][7][8][9] , traffic control 10 , and processes control 11,12 , etc. In the past two decades, increasing attention has been paid to the analysis and synthesis of switched systems due to their significance in both theory and applications, and many significant results have been obtained for the analysis and design of switched systems.…”