2012, Article in monograph or in proceedings (EVS26)A driving cycle defines the speed profile as requirement for a propulsion systems or vehicles. As a single driving cycle can hardly be considered representative for all situations a vehicle will experience, attempts are
made to classify driving cycles, using techniques as fuzzy logic, statistics and principal component analysis. The need for a definition of the required speed also explains the many driving cycles available in literature. Objective of this study is to classify driving cycles not in terms of speed, but in terms of power, with a focus is on electric vehicles.
2012, Article / Letter to editor (IEEE Transactions on Vehicular Technology, vol. 61, iss. 5, (2012), pp. 1986-1998)The objective of an energy management strategy for fuel cell hybrid propulsion systems is to minimize the fuel needed to provide the required power demand. This minimization is defined as an optimization problem. Methods such as dynamic programming numerically solve this optimization problem. Strategies such as the equivalent consumption minimization strategy derive an analytical solution based on low-order models that approximate fuel cell stack and battery behavior. This paper presents an analytical solution based on models of the fuel cell system and battery close to physics. Apart from an analytical solution, this solution provides a fundamental understanding of the energy management problem. Because the solution is analytic and does not need a priori knowledge, the computation time is limited, and real-time implementation is possible. The solution presented is validated against existing optimizing energy management strategies in both simulations and experiments. For simulations, a midsize distribution truck is chosen. Experiments are carried out on a 10-kW scale test facility that comprises a fuel cell system, a battery, a motor with load, and an electronic load. In both simulations and measurements, the solution presented in this paper performs best compared to the equivalent consumption minimization strategy and a range-extender strategy, although the differences are within 3%. In the simulations, the solution presented approaches a minimum in fuel consumption, derived offline using dynamic programming, within 1%.[
2012, Article / Letter to editor (Oil & Gas Science and Technology, vol. 65, iss. 1, (2012), pp. 103-113)Fuel cell hybrid vehicles are believed to provide a solution to cut down emissions in the long term. They provide local zero-emission propulsion and when the hydrogen as fuel is derived from renewable energy sources, fuel cell hybrids enable well-to-wheel zero-emission transportation,
2012, Article / Letter to editor (Oil & Gas Science and Technology, vol. 67, iss. 4, (2012), pp. 563-573)An existing fuel cell hybrid distribution truck, built for demonstration purposes, is used as a case study to investigate the effect of stack (kW) and battery (kW, kWh) sizes on the hydrogen consumption of the vehicle. Three driving cycles, the NEDC for Low Power vehicles, CSC and JE05 cycle, define the driving requirements for the vehicle. The Equivalent Consumption Minimization Strategy (ECMS) is used for determining the control setpoint for the fuel cell and battery system. It closely approximates the global minimum in fuel consumption, set by Dynamic Programming (DP). Using DP the sizing problem can be solved but ECMS can also be implemented real-time. For the considered vehicle and hardware, all three driving cycles result in optimal sizes for the fuel cell stack of approximately three times the average drive power demand. This demonstrates that sizing the fuel cell stack the average or maximum power demand is not necessarily optimal with respect to a minimum fuel consumption. The battery is sized to deliver the difference between specified stack power and the peak power in the total power demand. The sizing of the battery is dominated by its power handling capabilities. Therefore, a higher maximum C-rate leads to a lower battery weight which in turn leads to a lower hydrogen consumption. The energy storage capacity of the battery only becomes an issue for C-rates over 30. Compared to a Range Extender (RE) configuration, where the stack size is comparable to the average power demand and the stack is operated on a constant power level, optimal stack and battery sizes with ECMS as EnergyManagement Strategy significantly reduce the fuel consumption. Compared to a RE strategy, ECMS makes much better use of the combined power available from the fuel cell stack and the battery, resulting in a lower fuel consumption but also enabling a lower battery weight which consequently leads to improved payload capabilities.
2011, Article in monograph or in proceedings (EEVC)The objective of an energy management strategy for fuel cell hybrid propulsion systems is to minimize the fuel needed to provide the required power demand. This minimization is defined as an optimization problem. Methods such as dynamic programming numerically solve this optimization problem. Strategies such as the equivalent consumption minimization strategy derive an analytical solution based on low-order models that approximate fuel cell stack and battery behavior. This paper presents an analytical solution based on models of the fuel cell system and battery close to physics. Apart from an analytical solution, this solution provides a fundamental understanding of the energy management problem. Because the solution is analytic and does not need a priori knowledge, the computation time is limited, and real-time implementation is possible. The solution presented is validated against existing optimizing energy management strategies in both simulations and experiments. For simulations, a midsize distribution truck is chosen. Experiments are carried out on a 10-kW scale test facility that comprises a fuel cell system, a battery, a motor with load, and an electronic load. In both simulations and measurements, the solution presented in this paper performs best compared to the equivalent consumption minimization strategy and a range-extender strategy, although the differences are within 3%. In the simulations, the solution presented approaches a minimum in fuel consumption, derived offline using dynamic programming, within 1%.
2009, Article / Letter to editor (World Electric Vehicle Journal, vol. 2009, iss. 3, (2009))Optimization routines for battery, supercap and fuel cell stack in a fuel cell based propulsion system face two problems: the tendency to cycle beating and the necessity to maintain identical amounts of stored energy in battery and supercap at the start and end of the driving cycle used in the simulation. A method is proposed to reduce these problems. The proposed method characterizes driving cycles and generates alternative cycles with an arbitrary length from an existing cycle, based on the characteristics of the original. The method is demonstrated with an existing driving cycle for buses and validated with measurements from a trolley bus in the region of Arnhem, the Netherlands.