| 1. |
- Ali M., Ahmed, et al.
(författare)
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A State-of-Health-Oriented Power Management Strategy for Multi-Source Electric Vehicles Considering Situation-Based Optimized Solutions in Real-Time
- 2019
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Ingår i: Proceedings of the Annual Conference of the PHM Society 11 (1). - Scottsdale, Arizona, USA : PHM Society. - 9781936263295 - 9781936263059 ; , s. 1-10
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Konferensbidrag (refereegranskat)abstract
- 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 electrical efficiency of the fuel cell. On the other hand, lifetime degradation of the battery is associated with the recurrent charging / discharging cycles. The proposed power management strategy addresses the trade-off between these contradictory 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 optimization 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 life. Simulation tests using different driving cycles are conducted for evaluation purpose.
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| 2. |
- Beganovic, Nejra, et al.
(författare)
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Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation
- 2019
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Ingår i: Proceedings of the Annual Conference of the PHM Society 2019. - : PHM Society. - 9781936263295 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimal power flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to battery life, it becomes unavoidable to develop battery lifetime models, which not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetime-oriented battery models have to be integrated into power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source was developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance with current operating conditions. Here, the change in charge / discharge rate (C-rate), overcharging / undercharging of the battery (depth-of-discharge), and the temperature are taken into consideration to define machine model states. The End-of-Lifetime (EoL) is defined as the deviation between nominal and current ampere-hour (Ah) throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serves as a prerequisite for its integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.
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| 3. |
- Moulik, Bedatri, et al.
(författare)
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Adaptive prioritization of a situation-based power management for hybrid electric vehicles
- 2020
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Ingår i: 2020 IEEE Vehicle Power and Propulsion Conference (VPPC). - : IEEE. - 9781728189598
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Konferensbidrag (refereegranskat)abstract
- Hybrid electric vehicles can provide better performance assuming the power is intelligently and adaptively managed among the multiple sources in real-time. Most of the available power management strategies are either non-optimal or are not real-time applicable. Only a few focus on multiple and conflicting challenges of optimization objectives. In this contribution a fuel cell-battery-supercapacitor EV is considered with optimized rule-based power management. An important aspect of the optimized rule-based controller concept is its ability to offer flexibility of changing the priority or weights between the objectives so as to obtain an improved battery life or a better fuel economy or a better drivability. The weights are a representative of different rule-sets which prioritize either or a combination of the objectives. A concept of adaptive prioritization is proposed which can limit/allow the usage of each of the HEV sources depending on the driver/situation requirements and assumed future priorities. The simulation results with two different drive cycles, indicate the switching between rule sets to give better results with reference to one or two objectives at the cost of the other and vice versa.
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