scholarly journals Simultaneous Optimization of Supervisory Control and Gear Shift Logic for a Parallel Hydraulic Hybrid Refuse Truck Using Stochastic Dynamic Programming

2011 ◽  
Author(s):  
Rajit Johri ◽  
Simon Baseley ◽  
Zoran Filipi
Keyword(s):  
Dynamic Programming ◽  
Power Management ◽  
Stochastic Dynamic Programming ◽  
Management Strategies ◽  
Infinite Horizon ◽  
Simultaneous Optimization ◽  
System Level ◽  
Stochastic Dynamic ◽  
Gear Shift ◽  
Hydraulic Hybrid

The power management controller of a hybrid vehicle orchestrates the operation of onboard energy sources, namely engine and auxiliary power source with the goal of maximizing performance objectives such as the fuel economy. The paper focuses on optimization of the power management strategy of the refuse truck with parallel hydraulic hybrid powertrain. The high power density of hydraulic components and high charging/discharging efficiency of accumulator with no power constraint make hydraulic hybrid an excellent choice for heavy-duty stop and go application. Two power management strategies for a parallel hydraulic hybrid refuse truck are compared; heuristic and stochastic dynamic programming based optimal controller. For designing a SDP based controller, an infinite horizon problem is setup with power demand from driver modeled as random Markov process. The objective is to maximize system level efficiency by optimizing (i) the power split between engine and hydraulic propulsion unit, and (ii) gear shift schedule. This combines the optimization of powertrain parameters with power management design.

scholarly journals Dynamic Programming, An Approach for Analysing Competition Strategies

1979 ◽  
Vol 10 (2) ◽  
pp. 183-194
Author(s):  
T. Pentikäinen
Keyword(s):  
Dynamic Programming ◽  
Future Development ◽  
Stochastic Dynamic Programming ◽  
Management Strategies ◽  
Stochastic Dynamic ◽  
The Future ◽  
New Business ◽  
Insurance Business ◽  
Simulated Market

Stochastic-dynamic programming provides a technique for forecasting limits within which the insurance business will flow by a prefixed probability. The future development depends, among numerous other things, on management strategies, especially resources, which are planned for allocation in the acquisition of new business and for competition. This technique can be used to analyse different market situations. Various competitive measures and eventual counteractions by competitors can be assumed and simulated for the purpose. In this way the consequences of different strategies can be studied in order to find the most appropriate one. Our approach is similar to the well-known business games where teams play business in a simulated market. The idea of applying dynamic programming to business games was suggested by Esa Hovinen (discussion at the Astin Colloquium in Washington in 1977).

scholarly journals A stochastic power management strategy with skid avoidance for improving energy efficiency of in-wheel motor electric vehicles

2018 ◽  
Vol 233 (5) ◽  
pp. 1306-1319 ◽  
Author(s):  
Mehdi Jalalmaab ◽  
Nasser L Azad
Keyword(s):  
Dynamic Programming ◽  
Power Management ◽  
Electric Vehicles ◽  
Power Distribution ◽  
Stochastic Dynamic Programming ◽  
Management Strategy ◽  
Stochastic Dynamic ◽  
Power Demand ◽  
Rule Based ◽  
Power Management Strategy

In this study, a stochastic power management strategy for in-wheel motor electric vehicles is proposed to reduce the energy consumption and increase the driving range, considering the unpredictable nature of the driving power demand. A stochastic dynamic programming approach, policy iteration algorithm, is used to create an infinite horizon problem formulation to calculate optimal power distribution policies for the vehicle. The developed stochastic dynamic programming strategy distributes the demanded power, Pdem between the front and rear in-wheel motors by considering states of the vehicle, including the vehicle speed and the front and the rear wheels’ slip ratios. In addition, a skid avoidance rule is added to the power management strategy to maintain the wheels’ slip ratios within the desired values. Undesirable slip ratios cause poor brake and traction control performances and therefore should be avoided. The resulting strategy consists of a time-invariant, rule-based controller which is fast enough for real time implementations, and additionally, it is not expensive to be launched since the future power demand is approximated without a need to vehicle communication systems or telemetric capability. A high-fidelity model of an in-wheel motor electric vehicle is developed in the Autonomie/Simulink environment for evaluating the proposed strategy. The simulation results show that the proposed stochastic dynamic programming strategy is more efficient in comparison to some benchmark strategies, such as an equal power distribution and generalized rule-based dynamic programming. The simulation results of different driving scenarios for the considered in-wheel motor electric vehicle show the proposed power management strategy leads to 3% energy consumption reduction in average, at no additional cost. If the resulting energy savings is considered for the total annual trips for the vehicle and also the total number of electric vehicles in the country, the proposed power management strategy has a significant impact.

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

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