Browse Publications Technical Papers 2012-01-1609
2012-09-10

Development of Energy Management Strategies and Analysis with Standard Drive Cycles for Fuel Cell Electric Vehicles 2012-01-1609

In order to reduce fuel consumption in Fuel Cell Electric Vehicles, effective distribution of power demand between Fuel Cell and Battery is required. Energy management strategies can improve fuel economy by meeting power demand efficiently. This paper explains development of various energy management strategies for Fuel Cell Electric Vehicle with Lithium Ion Battery.
Drive cycles used for optimization and analysis of the strategies are New European Drive cycles (NEDC), Japanese Drive cycles (JAP1015), City Drive cycles, Highway Drive cycles (FHDS) and Federal Urban Drive cycles (FUDS). All Fuel consumption and ageing calculations are done using backward model implemented in MATLAB/SIMULINK.
This work consists of following topics
  • Benchmark calculations for fuel consumption
  • Development of Energy management strategies
  • Optimization of strategies for minimizing fuel consumption
  • Analysis of strategies for fuel consumption with different Drive cycles
  • Analysis of strategies for stack and battery durability with different Drive cycles
Initially, benchmark calculations are performed for Fuel Cell Electric Vehicle to know theoretical/ultimate limits for fuel consumption. Dynamic Programming (DP) is used for benchmark calculations with simple vehicle models. As Dynamic Programming requires lot of computation, it can't be used for complex vehicle models. So a simple and effective method is introduced for benchmark calculations with detailed vehicle models. This new method is developed based on energy and power requirements of Drive cycle. This paper presents results of benchmark fuel consumption for all standard Drive cycles.
Various control strategies are developed from simple to complex. Different strategies like load follower strategy, equivalent consumption minimization (ECMS) strategies, fuzzy based strategy and neural network based strategies are developed. These strategies are optimized for minimizing fuel consumption using backward model.
Detailed analysis of the performance of various control strategies on different Drive cycles is presented. This analysis consists of fuel consumption per 100 km, influence on stack durability and influence on battery durability. Drive cycles are repeated several times for simulation to get effective fuel consumption values and to nullify the effect of state of charge (SOC) of battery on these calculations.

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