Approximate Stochastic Differential Dynamic Programming for Hybrid Vehicle Energy Management

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Kyle Williams ◽  
Monika Ivantysynova
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Energy Management ◽  
Environmental Protection Agency ◽  
Dynamic Programming Algorithm ◽  
Hybrid Vehicle ◽  
Programming Algorithm ◽  
Differential Dynamic Programming ◽  
Hydraulic Hybrid ◽  
Behavior Learning

This paper develops a new computational approach for energy management in a hydraulic hybrid vehicle. The developed algorithm, called approximate stochastic differential dynamic programming (ASDDP) is a variant of the classic differential dynamic programming algorithm. The simulation results are discussed for two Environmental Protection Agency drive cycles and one real world cycle based on collected data. Flexibility of the ASDDP algorithm is demonstrated as real-time driver behavior learning, and forecasted road grade information are incorporated into the control setup. Real-time potential of ASDDP is evaluated in a hardware-in-the-loop (HIL) experimental setup.

scholarly journals Parameter Matching Analysis of Hydraulic Hybrid Excavators Based on Dynamic Programming Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Shen ◽  
Jihai Jiang ◽  
Xiaoyu Su ◽  
Hamid Reza Karimi
Keyword(s):  
Dynamic Programming ◽  
General Principle ◽  
Hot Spot ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Hydraulic Hybrid ◽  
Parameter Matching ◽  
Calculation Results ◽  
Hybrid Excavator ◽  
Common Pressure Rail

In order to meet the energy saving requirement of the excavator, hybrid excavators are becoming the hot spot for researchers. The initial problem is to match the parameter of each component, because the system is tending to be more complicated due to the introduction of the accumulator. In this paper, firstly, a new architecture is presented which is hydraulic hybrid excavator based on common pressure rail combined switched function (HHES). Secondly, the general principle of dynamic programming algorithm (DPA) is explained. Then, the method by using DPA for parameter matching of HHES is described in detail. Furthermore, the DPA is translated into the M language for simulation. Finally, the calculation results are analyzed, and the optimal matching group is obtained.

Optimal Flight Path Planning of Cruising Phase with No-Fly Zone Constraints Based on Dynamic Programming Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 5911-5917
Author(s):  
Su Xiao Wang ◽  
Yong Sheng Yang ◽  
Zhong Liang Jing
Keyword(s):  
Dynamic Programming ◽  
Path Planning ◽  
Real Time ◽  
Optimal Path ◽  
Dynamic Programming Algorithm ◽  
Optimization Method ◽  
Flight Path ◽  
Programming Algorithm ◽  
Effective Implementation ◽  
Arbitrary Selection

The purpose of flight path planning is to find the optimal path from the real-time and conflict-free airspace to meet the targets, according to one or several performance index. Effective avoiding the no-fly zones, such as the areas of martial movement and the areas of rain and thunderstorm, has great significance to the current flight management system (FMS) that is real-time and effective implementation of the flight plan. The dynamic optimization method of level route based on DP (Dynamic Programming) algorithm without no-fly zone constraints is discussed. Quick and effective to find out an optimal path from the waypoints of arbitrary selection and input can be realized. On this basis, the situation of adding no-fly zone constraints is focused on. In order to ensure that the aircraft is able to effectively avoid no-fly zone constraints in actual flight, Gauss Kruger projection method to convert geographic coordinates to plane coordinates is adopted. Simulation results show that the method used can not only effectively avoid no-fly zone constraints, and the path passed is still optimal.

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