scholarly journals Adaptive energy control strategy of PHEV based on the Pontryagin’s minimum principle algorithm

2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110355
Author(s):  
Dapai Shi ◽  
Kangjie Liu ◽  
Yun Wang ◽  
Ruijun Liu ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Control Problem ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Control Strategies ◽  
Minimum Principle ◽  
Optimization Methods ◽  
Pontryagin’S Minimum Principle ◽  
Energy Control ◽  
Pontryagin's Minimum Principle ◽  
Equivalent Fuel

The optimization of energy control strategy is one of the key technologies of plug-in hybrid vehicles (PHEVs) to improve the capabilities of energy saving and emission reduction. In order to improve fuel economy of PHEV, adaptive equivalent minimum fuel consumption strategy (A-ECMS) is proposed. Firstly, optimization methods of different energy control strategies are analyzed, and the Pontryagin’s Minimum Principle (PMP) and the equivalent fuel consumption theory are selected to optimize energy control strategy of the PHEV. Secondly, the configuration of PHEV and research objectives of the power control system are determined. Thirdly, the energy control problem is analyzed by the PMP theory, and the improvement measures for the energy control problem are put forward by the equivalent minimum fuel consumption strategy (ECMS). Fourthly, after analyzing the relationship between the equivalent factor and reference SOC, adaptive equivalent minimum fuel consumption strategy (A-ECMS) model is established by MATLAB/Simulink. Finally, combined with Cruise software, the PHEV simulation model is simulated, and the simulation results are analyzed. The results show that compared with the CD/CS energy control strategy, the A-ECMS energy control strategy reduced the 100 km fuel consumption of the vehicle by 7% under three times WLTC driving condition.

scholarly journals Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haitao Yan ◽  
Yongzhi Xu
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Control ◽  
Time Performance ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.

Online optimization of Pontryagin’s minimum principle for a series hydraulic hybrid wheel loader

2021 ◽  
pp. 095440702110398
Author(s):  
Qi Zhang ◽  
Feng Wang ◽  
Bing Xu ◽  
Zongxuan Sun
Keyword(s):  
Dynamic Programming ◽  
Fuel Consumption ◽  
Minimum Principle ◽  
Pontryagin’S Minimum Principle ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
Rule Based ◽  
Wheel Loader ◽  
Hydraulic Hybrid ◽  
Pontryagin's Minimum Principle

The hydraulic hybrid powertrain has great potential for reducing fuel consumption and emission of off-road vehicles. The energy management strategy is the key to hybrid powertrain and currently there are many well-developed strategies. Of which the Pontryagin’s minimum principle is of research interest since it is a global optimization method while less computational burden than dynamic programming. However, it requires full cycle information to calculate co-state value in the principle, making it not implementable. Therefore in this study an implementable Pontryagin’s minimum principle is proposed for a series hybrid wheel loader, where the optimal co-state value in the principle is trained through repetitive wheel loader duty cycle. The Pontryagin’s minimum principle formulations of hybrid wheel loader are developed. The online co-state training algorithm is presented. A dynamic simulation model of hybrid wheel loader is developed. The fuel consumption of hybrid wheel loader with proposed strategy is compared with dynamic programming strategy and rule-based strategy in wheel loader long and short loading cycles. Results show the fuel consumption with proposed strategy is close to dynamic programming result and is lower than rule-based strategy. Finally, the influence of pressure level of hybrid powertrain on vehicle fuel consumption is studied.

A Computationally Efficient Optimal Power Management for Power Split Hybrid Vehicle Based on Pontryagin’s Minimum Principle

2017 ◽  
Author(s):  
Masood Ghasemi ◽  
Xingyong Song
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Optimization Problem ◽  
Minimum Principle ◽  
Hybrid Vehicle ◽  
Algebraic Equations ◽  
Pontryagin’S Minimum Principle ◽  
Computationally Efficient ◽  
Power Split ◽  
Pontryagin's Minimum Principle

The need for less fuel consumption urges effective powertrain management optimization in hybrid vehicles. In this study, we consider the real time power optimization problem of a power split hybrid vehicle. Assuming that the power on demand at the driveline can be predicted and known for each driving cycle, the powertrain management and optimization are conducted at the hybrid powertrain system’s level in a computationally efficient fashion. Specifically, we provide an analytical formulation of the powertrain optimization for the hybrid vehicle by using the Pontryagin’s minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations. In order to justify the applicability of the methodology for real-time implementations, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach.

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