Energy management of a dual-mode power-split powertrain based on the Pontryagin's minimum principle

2017 ◽  
Vol 11 (9) ◽  
pp. 561-571 ◽  
Author(s):  
Pouyan Ahmadizadeh ◽  
Behrooz Mashadi ◽  
Dhaval Lodaya
Keyword(s):  
Energy Management ◽  
Minimum Principle ◽  
Pontryagin’S Minimum Principle ◽  
Dual Mode ◽  
Power Split ◽  
Pontryagin's Minimum Principle

scholarly journals Computationally Efficient Energy Management in Hybrid Electric Vehicles Based on Approximate Pontryagin’s Minimum Principle

2020 ◽  
Vol 11 (4) ◽  
pp. 65
Author(s):  
Fengqi Zhang ◽  
Lihua Wang ◽  
Serdar Coskun ◽  
Yahui Cui ◽  
Hui Pang
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Piecewise Linear ◽  
Minimum Principle ◽  
Engine Fuel ◽  
Pontryagin’S Minimum Principle ◽  
Torque Distribution ◽  
Computation Efficiency ◽  
Pontryagin's Minimum Principle ◽  
Hybrid Electric

This article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin’s Minimum Principle (A-PMP). The A-PMP optimizes gearshift commands and torque distribution for overall energy efficiency. As a practical numerical solution in PMP, the proposed methodology utilizes a piecewise linear approximation of the engine fuel rate and state of charge (SOC) derivative by considering drivability and fuel economy simultaneously. Moreover, battery aging is explicitly studied by introducing a control-oriented model, which aims to investigate the effect of battery aging on the optimization performance in the development of the HEVs. An approximate energy management strategy with piecewise linear models is then formulated by the A-PMP, which targets a better performance for the Hamiltonian optimization. The gearshift map is extracted from the optimal results in the standard PMP to hinder frequent gearshift by considering both drivability and fuel economy. Utilizing an approximated Hamilton function, the torque distribution, gearshift command, and the battery aging degradation are jointly optimized under a unified framework. Simulations are performed for dynamic programming (DP), PMP, and A-PMP to validate the effectiveness of the proposed approach. The results indicate that the proposed methodology achieves a close fuel economy compared with the DP-based optimal solution. Moreover, it improves the computation efficiency by 50% and energy saving by 3.5%, compared with the PMP, while ensuring good drivability and fuel efficiency.

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