scholarly journals Integrated Design of Powertrain Controllers in Series Hybrid Electric Vehicles for Efficiency Enhancement and Battery Lifetime Extension

2009 ◽  
Vol 3 (4) ◽  
pp. 843-848 ◽  
Author(s):  
Xi Zhang ◽  
Chris Chunting Mi
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Integrated Design ◽  
Efficiency Enhancement ◽  
Battery Lifetime ◽  
In Series ◽  
Hybrid Electric ◽  
Lifetime Extension

scholarly journals Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Nejra Beganovic ◽  
Bedatri Moulik ◽  
Ahmed Mohamed Ali ◽  
Dirk S¨offker
Keyword(s):  
Fuel Consumption ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Operating Conditions ◽  
Battery Lifetime ◽  
Conflicting Objectives ◽  
Battery Degradation ◽  
Lifetime Model ◽  
Hybrid Electric

Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on the them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimalpower flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to batteries lifetime, it becomes unavoidable to develop battery lifetime models, which do not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetimeoriented battery models have to be integrated in 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 is developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance to current operating conditions. Here, the change in charging/ discharging rate (C-rate), overcharging/undercharging of the battery (depth-of-discharge), and the temperature are taken in consideration to define machine model states. The End-of-Lifetime (EoL) is defined as 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 serve as a prerequisite forits integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.

Modeling on a Three-Way Catalyst Used in Series Hybrid Electric Vehicles Considering Its Specific Engine Operation Attribute

2021 ◽  
Vol 60 (4) ◽  
pp. 1583-1601
Author(s):  
Toshinori Okajima ◽  
Suchitra Sivakumar ◽  
Hajime Shingyouchi ◽  
Kyohei Yamaguchi ◽  
Jin Kusaka ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Engine Operation ◽  
In Series ◽  
Hybrid Electric ◽  
Three Way Catalyst

Energy management for series-production plug-in-hybrid electric vehicles based on predictive DP-PMP

2021 ◽  
Vol 69 (1) ◽  
pp. 52-64
Author(s):  
Roland Schmid ◽  
Johannes Buerger ◽  
Naim Bajcinca
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Simulation Studies ◽  
Series Production ◽  
Energy Management Strategy ◽  
Time Operation ◽  
Real Time Operation ◽  
In Series ◽  
Hybrid Electric

AbstractThis paper presents an energy management strategy for parallel plug-in-hybrid electric vehicles which combines Dynamic Programming (DP) and Pontryagin’s Minimums Principle (PMP). In particular, this paper focusses on the practical challenges encountered in series-production vehicles and develops corresponding extensions: First, the paper considers the effects of uncertain prediction data received from a navigation unit. Secondly, we consider engine starting costs in the DP-PMP framework and thirdly, we allow to constrain the engine state (on/off) in certain parts of the driving cycle. These three components are integrated into a unified DP-PMP framework. Simulation studies demonstrate the practical benefit of the algorithm and show close to optimal performance in terms of fuel consumption. At the same time the algorithm is computationally cheap and allows real-time operation on series-production ECUs.

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