Power Management for a Fuel Cell/Battery/Supercapacitor Hybrid Locomotive

2020 ◽  
Author(s):  
Qishen Zhao ◽  
Tianheng Feng ◽  
Dongmei Chen ◽  
Wei Li
Keyword(s):  
Fuel Cell ◽  
Power Management ◽  
Management Strategy ◽  
Stochastic Dynamic ◽  
Hybrid Powertrain ◽  
Power Sources ◽  
Management Framework ◽  
Power Management Strategy ◽  
Driving Cycles ◽  
Efficiency Performance

Abstract Electrification of locomotive with hybridized fuel-cell, battery and supercapacitor has drawn much attention from both the academia and industry. Unlike traditional powertrain, hybrid powertrain consists of multiple power sources with a complex drivetrain structure, various efficiency performance, and different dynamics. Therefore, it is necessary to develop a power management strategy to make sure each power source operates under a quasi-optimal condition and maximize the overall powertrain efficiency. This paper presents the development of a power management framework for a novel hybrid locomotive consisting of PEM fuel cell, battery, and supercapacitor. Both the equivalent consumption management strategy (ECMS) and the stochastic dynamic programming (SDP) are applied to solve for the optimal power split strategy. The resulted power management strategy is presented in the form of policy maps, which makes it convenient for real-time in-vehicle implementations. Simulation results indicate that the SDP demonstrates advantages over the ECMS in terms of equivalent hydrogen consumption over typical locomotive driving cycles.

scholarly journals State-of-Health-Oriented Power Management Strategy for Multi-Source Electric Vehicles Considering Situation-Based Optimized Solutions in Real-Time

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Ahmed Mohamed Ali ◽  
Bedatri Moulik ◽  
Nejra Beganovic ◽  
Dirk Söffker
Keyword(s):  
Fuel Cell ◽  
Power Consumption ◽  
Power Management ◽  
Management Strategy ◽  
Health Management ◽  
Power Management Strategy ◽  
Performance Requirements ◽  
Driving Cycles ◽  
Weighted Fusion ◽  
Power Handling

This paper presents a novel situation-based power and battery health management strategy for fuel cell vehicles. In such hybrid powertrains, the synergy role of batteries is essential to minimize overall power consumption and maintain higher electric efficiency of the fuel cell. On the other side, lifetime degradation of the battery is associated to the recurrent charging/discharging cycles. The proposed power management strategy addresses the trade-off between these contradictive objectives. Vehicle states in each situation are defined in terms of driver-related identification parameters (power demand and speed) corporately with powertrain related ones (on-board battery’s state of charge). Optimal power handling solution for each situation is searched offline considering different optimizations criteria: range extension, lifetime maximization, or power consumption minimization. A weighted fusion of these optimized solutions can be implemented online based on desired driving strategy, leading to situation-based optimized solution. This contribution aims to provide flexible power handling options meeting performance requirements (energy efficiency and driveability) without scarifying battery’s lifetime. Simulation tests using different driving cycles are conducted for evaluation purpose.

Real-Time Applicable Power Management of Multi-Source Fuel Cell Vehicles Using Situation-Based Model Predictive Control

2020 ◽  
Author(s):  
Ahmed M. Ali ◽  
Dirk Söffker
Keyword(s):  
Fuel Cell ◽  
Model Predictive Control ◽  
Power Management ◽  
Predictive Control ◽  
Control Strategies ◽  
Management Strategies ◽  
State Transitions ◽  
Multiple Characteristic ◽  
Power Sources ◽  
Driving Cycles

Abstract Power management in all-electric powertrains has a significant potential to optimally handle the limited energy and power density of electric power sources. Situation-based power management strategies (SB-PMSs), defining optimized solutions related to specific vehicle situations, offer the ability to reduce computational requirements and enhance the solution optimality of simple rule-based algorithms. Moreover, the local optimality of SB-PMSs can be addressed by considering online optimization of the situated solutions for limited horizons. This paper presents a novel PMSs using model predictive control (MPC) to define optimal control strategies based on situated solutions for fuel cell hybrid vehicles. Vehicle states are defined in terms of multiple characteristic variables and power management decisions are optimized offline for each vehicle states. Prediction of vehicle states is conducted using statistical predictive model based on state transitions in a number of driving cycles. Preoptimized solutions related to predicted states are iterated online to achieve better optimality over the look-ahead horizon. Results analysis from online testing revealed the ability of SB-MPC to improve the optimality of situation-based solutions and hence reduce total energy cost in different driving cycles.

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