RETRACTED: Energy control strategy of plug-in hybrid electric vehicle based on pattern recognition

For tracking control issues of distributed electric-driven agricultural vehicles in the complex road conditions driving state, a 6-degree-of-freedom model of agriculture vehicle was established. On this basis, a mathematical model for control system design was derived using the significant advantages of real-time online estimation of vehicle yaw angular velocity of nonlinear third-order extended state observer. Based on the nonlinear third-order extended state observer, a fast and smooth terminal sliding mode control algorithm was designed using the mathematical model. Afterward, the stability of system was analyzed based on Lyapunov theory. The control algorithm used the fast and smooth terminal sliding mode control instead of the normal terminal sliding mode control required control coefficient matrix inverse calculation, which improved the real-time of the control algorithm. Meanwhile, the smooth sliding mode approach law could converge to the sliding surface in finite time and effectively eliminate chattering, and it had strong adaptability to perturbation and perturbation of system parameters. Simulation and experimental results showed that compared with the traditional nonlinear feedback control algorithm based on nonlinear third-order extended state observer, the proposed control algorithm had short adjustment time, small overshoot and good tracking control accuracy.

Section: Introductionmentioning

confidence: 99%

The tracking control study of distributed electric-driven agricultural vehicles based on real-time online estimation

Zhou

2019

“…Many previous studies have been conducted on logic threshold control, fuzzy control, dynamic programming, and model predictive control, which have been developed and used in HEVs. [1][2][3][4][5] These methods have higher control precision and make a positive contribution to fuel economy.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

Shi

Qiu

et al. 2018

For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.

“…5 This strategy can be divided into two stages: the propulsion is mainly or exclusively provided by the electrical motor when SOC is high, namely, CD mode; SOC is confined to a predefined small range once the threshold is reached, and the vehicle is powered by the motor and engine like the conventional HEV, namely, CS mode. 6 Only suboptimal result is expected to be achieved with this strategy, because the threshold is unlikely to meet all the driving conditions. Equivalent fuel consumption minimization strategy (ECMS) improves CD/CS strategy by continuously computing the equivalent fuel consumption cost for different power splits between the internal combustion engine and the electric motor as a basis to select the most efficient operating point.…”

Section: Introductionmentioning

confidence: 99%

Traction control–integrated energy management strategy for all-wheel-drive plug-in hybrid electric vehicle

Song

Zeng

et al. 2017

Plug-in hybrid electric vehicle is one of the potential candidates to tackle the stringent fuel economy standards. Many previous studies focus on optimal fuel economy by coordinating primary power source and assistant power source in plug-in hybrid electric vehicles, which usually neglects vehicle dynamic performance. All-wheel-drive plug-in hybrid electric vehicle has advantages both in fuel economy and dynamic performance. However, when all-wheel-drive plug-in hybrid electric vehicle is traveling on low cohesive roads, how to balance the conflict between traction performance and fuel economy undoubtedly will be the crucial problem. To address the issue, an integrated control strategy is proposed for all-wheel-drive plug-in hybrid electric vehicle in this article, namely, traction control system-integrated energy management strategy. A charge depletion/charge sustaining strategy is employed to realize the torque distribution mainly according to the engine optimal working region to achieve better fuel economy. Traction control system is integrated to prevent slipping of the wheels during starting and acceleration by applying engine throttle control, motor torque control, and active braking control, which improves the vehicle dynamic performance. The traction enhancement is beneficial to the improvement of fuel efficiency. By the co-simulation of MATLAB/Simulink and AMESim, the results demonstrate the effectiveness of the proposed integrated control strategy, which increases the vehicle velocity greatly and optimizes fuel economy as well.