Implementable Strategy Research of Brake Energy Recovery Based on Dynamic Programming Algorithm for a Parallel Hydraulic Hybrid Bus

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.

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Optimized Control Strategy Based on the Driving Cycle Type for a Hydraulic Hybrid Bus

Volume 1: Advanced Computational Mechanics; Advanced Simulation-Based Engineering Sciences; Virtual and Augmented Reality; Applied Solid Mechanics and Material Processing; Dynamical Systems and Control ◽

10.1115/esda2012-82673 ◽

2012 ◽

Author(s):

Ali Safaei ◽

Vahid Esfahanian ◽

Mohammad Reza Ha’iri-Yazdi ◽

Mohsen Esfahanian ◽

Masood Masih Tehrani ◽

...

Keyword(s):

Fuel Consumption ◽

Intelligent Control ◽

Control Strategy ◽

Dynamic Programming Algorithm ◽

Driving Cycle ◽

Programming Algorithm ◽

Hybrid Powertrain ◽

Hydraulic Hybrid ◽

Hybrid Bus ◽

Degree Of Similarity

Using hybrid powertrains is an attractive idea to reduce the fuel consumption in vehicles. Control strategy is the most challenging subject in designing of a hybrid powertrain. In this paper, an optimized control strategy based on the driving cycle type designed for a hydraulic hybrid bus has been presented. Because of considering the type of the driving cycle, the proposed control strategy can be named as an intelligent one. In this controller, at first, four standard driving cycles have been defined as the reference clusters. Then the optimized control strategy for each cluster has been derived using a dynamic programming algorithm. In addition, several multi-layered perceptron networks are modeled in order to use the output of each optimized control strategy. After that a clustering method with a feature selection algorithm has been implemented to assign degree of similarity to each cluster for the unknown driving cycle. Finally, a linear combination of four optimized control strategy outputs has been used for generating final output of the intelligent control strategy. In this combination, each output is weighted by the corresponding degree of similarity. Here, the hydraulic hybrid bus model is a feed forward one and has been simulated using a compound driving cycle. The compound driving cycle consists of six distinct 100s long portions of the Nuremburg driving cycle. The simulation results show that by using the intelligent control strategy, the fuel consumption of the hybrid bus has been reduced by almost 12% in comparison with the results of a rule-based control strategy.

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Approximate Stochastic Differential Dynamic Programming for Hybrid Vehicle Energy Management

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4042253 ◽

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.

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