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.

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 Idleness-Aware Dynamic Power Mode Selection on the i.MX 7ULP IoT Edge Processor

2020 ◽  
Vol 10 (2) ◽  
pp. 19
Author(s):  
Alfio Di Mauro ◽  
Hamed Fatemi ◽  
Jose Pineda de Gyvez ◽  
Luca Benini
Keyword(s):  
Power Consumption ◽  
Power Management ◽  
Management Strategy ◽  
Low Cost ◽  
Mode Selection ◽  
Worst Case ◽  
Dynamic Power ◽  
Dynamic Tuning ◽  
Power Management Strategy ◽  
Power Mode

Power management is a crucial concern in micro-controller platforms for the Internet of Things (IoT) edge. Many applications present a variable and difficult to predict workload profile, usually driven by external inputs. The dynamic tuning of power consumption to the application requirements is indeed a viable approach to save energy. In this paper, we propose the implementation of a power management strategy for a novel low-cost low-power heterogeneous dual-core SoC for IoT edge fabricated in 28 nm FD-SOI technology. Ss with more complex power management policies implemented on high-end application processors, we propose a power management strategy where the power mode is dynamically selected to ensure user-specified target idleness. We demonstrate that the dynamic power mode selection introduced by our power manager allows achieving more than 43% power consumption reduction with respect to static worst-case power mode selection, without any significant penalty in the performance of a running application.

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