OPTIMAL POWER MANAGEMENT OF HYDROGEN FUEL CELL VEHICLES Giuliano Premier Sustainable Environment Research Centre (SERC) Renewable Hydrogen Research & Demonstration Centre University of Glamorgan Baglan Energy Park Port Talbot SA12 7AX, UK Contributions: Kary Thanapalan, Fan Zhang, Alan Guwy, SERC, University of Glamorgan, UK Heiko Juraschka, Lars Gusig, University of Applied Sciences and Arts Hannover, Germany 1
Outline of the Presentation I. Introduction 2. Classification of vehicle systems 3. Improvement of driving range for HEV 4. Determination of power usage in vehicle using priory knowledge and prediction tools 5. Optimal power management 6. Summary / Discussion 2
1. Introduction Increasing global energy demands lead to energy shortages. Hydrogen as an energy carrier Advantages Produced from renewable resources Zero / low emissions Scalable technology Better fuel economy & total efficiency potential Barriers Cost effectiveness & performance Public awareness & safety concerns 3
2. Classification of Vehicle systems Several variations and definition are recently under investigation for alternative car concepts Type of motor to transform energy into torque EV Electric vehicle (DC-motor) CV Conventional (ICE-motor) Serial hybrid Parallel hybrid Conv. Supply of energy to motor BEV Battery EV FCV Fuel-Cell EV HEV Hybrid electrical vehicle Various ICEtechnologies in production Dedicated additional power source for hybridization HHEV Hydrogen-Hybrid-EV REEV Range-Extended-EV Hydrogen-FC in combination with battery and others energy sources (UC, PV etc.) EV combining battery with additional electrical power source in general (ICE, FC or other) 4
2. Classification of Vehicle systems HHEV University of Glamorgan Hydrogen Vehicles Hydrogen Bus The powertrain of UoGHB consists of a 12kW PEM fuel cell stack (Hydrogenics) 288v, 132 Amp/hr lead-acid battery pack 375v, 63F Maxwell ultracapacitor 70kW DC motor Towards the Green Economy.. The cost of hydrogen, it s storage and lack of infrastructures are among the main problems K.K.T.Thanapalan, J.G.Williams, G.C.Premier, A.J.Guwy (2011), Design and Implementation of Renewable Hydrogen Fuel Cell Vehicles, Renewable Energy & Power Quality Journal, No.9. 5
2. Classification of Vehicle systems Range of EV and HEV is strongly dependent on temperature Energy demand for cooling and heating passenger compartment and HV-battery-pack at temperatures other than 20 C reaches up to 7kW which can reduce the vehicle range by 59% Vehicle-class B (comparable to Mini E, Volkswagen Polo 6R), Drive cycle: NEDC usable battery capacity 33.67 kwh (Li-Ion) Weight 1467kg drag coefficient 0.322 frontal area 2.05 m² As the average temperature for driving conditions is depended on the climate region range and CO2- emissions depend on outside temperature for different car concepts Temperature data for UK/Cardiff, average temperature 10 C, vehicle-class B (comparable to Mini E, Volkswagen Polo 6R), REEV-full/empty referring to SOC at beginning of cycle Energy-mix, CO2-emissions: 450g/kWh - 2.32 kg/l gasoline 6
2. Classification of Vehicle systems CO2-emissions are strongly dependent on drive-cycle, car concept (BEV/REEV) and range Calculated CO2-Emissions of different vehicle concepts (outside temperature for average of 10 C, UK/Cardiff) ECE NEDC EUC REEV-car with pre-conditioned battery show the best results for low and high driving ranges, only within a small band the BEV shows lower emissions. 7
3. Determination of energy usage in vehicles Determination of energy usage in vehicles using priory knowledge and prediction tools Priory knowledge of trip energy requirements via prediction tools: drive cycle libraries, google map, GPS data etc Source: Shams-Zahraei and Kouzani (2010) Priory knowledge of information may include: - Distance and elevation, - Temperature and humidity conditions, - Wind force and direction, - Vehicle location and load conditions, - Driver characterization and profile etc. Source: mobility5sure. IAV-GmbH, Nietschke (2012) 8
3. Determination of energy usage in vehicles Broader details of this subject can be found in recent research outputs, see for example; R. Li and G. Rose (2011), Incorporating uncertainty into short-term travel time predictions, Transportation Research Part C: Emerging Technologies, vol.19(6), pp.1006-1018 FO. Johansson and A. Rabiei (2010), Energy management in a hybrid vehicle using predicted road slope information, Dept. of Energy and Environment, Chalmers University of Technology, Sweden. M. Shams-Zahraei and AZ. Kouzani (2010), Power-cycle-library-based control strategy for plug-in hybrid electric vehicles, IEEE-Vehicle Power and Propulsion Conference (VPPC), pp.1-6 W. Nietschke (2012), Mobility4Sure, Development of a mobility-app for range prediction in electronic vehicles. in: automotion, IAV-GmbH, Germany. 9
3. Determination of energy usage in vehicles Following on this... This work focus on the design and implementation of optimal power management strategies for the predicted range, for fuel efficiency and economy to route for the particular journey. 10
4. Optimal power management Optimal power management of hydrogen fuel cell vehicles Hydrogen hybrid electric vehicles (HHEV): UoGHB model is used for this example case Power components: FC stack, UC, Battery, Motor HHEV supervisory controller Optimization of power usage: Compared responses of power consumption The results indicate that the combined optimal SC control strategy with DSO mechanism has improved the HHEV system efficiency and the total power saving of 10kW is achieved for the given UoGHB drive cycle 11
4. Optimal power management Power components: FC stack, UC, Battery, Motor FC Stack model The stack voltage v v n st v fc fc E v act v st v ohm is given by v con UC model V uc R uc I uc 1 C id t 0 I uc dt V id 0 12
4. Optimal power management Power components: FC stack, UC, Battery, Motor Equivalent circuit models 1 UC model Vuc RucIuc C id t 0 I uc dt V id 0 t 1 Battery model S max. Q Ib dt. Qmax 0 Dynamic behaviour of a motor is given by: J V m d dt R m i b m L di dt V L e t i V e e 13
4. Optimal power management HHEV supervisory controller (SC) The SC operates at the HHEV system level and interacts with low level subsystems Power balance equation p fc p uc p b p d 14
4. Optimal power management Power source configuration of a HHEV system 15
4. Optimal power management HHEV supervisory controller 1. Rule based controller for HHEV: Based on the drivability requirements and power balance of the overall HHEV system The rule based controller is based on fuzzy rules to coordinate the power flow operation of the HHEV system 2. Direct search optimization method (DSO): The DSO algorithm works faster than many other optimization methods It can exploit all local information in an effective way Optimal power management approach for HHEV : SC control +DSO method 16
speed(km/h) Optimal power management Optimization of power usage: Drive cycle: The drive cycle used here is generated through experimental driving of UoGHB 80 70 60 50 40 30 20 10 0 0 200 400 600 800 1000 time(sec) 17
SOC H2(kg) SOC Optimal power management 1 SOC of the battery 0.8 0.6 0.4 0.2 0 200 400 600 800 1000 1200 time(sec) 0.12 0.1 H2 consumption 0.08 0.06 0.04 0.02 0 0 200 400 600 800 1000 1200 time(sec) 0.67 0.66 SOC of the UC 0.65 0.64 0.63 0.62 0.61 0 200 400 600 800 1000 1200 time(sec) 18
speed(km/h) Optimal power management Optimization of power usage: NEDC Drive cycle: 150 100 50 0 0 200 400 600 800 1000 1200 time(sec) 19
SOC H2(kg) SOC Optimal power management For the NEDC Drive cycle: 0.8 SOC of the battery 0.7 0.6 0.5 0.4 0.1 0 200 400 600 800 1000 1200 time(sec) H2 consumption 0.08 0.06 0.04 0.02 0 0 200 400 600 800 1000 1200 time(sec) 0.83 0.82 SOC of the UC 0.81 0.8 0.79 0 200 400 600 800 1000 1200 time(sec) 20
4. Optimal power management Optimal power management of hydrogen fuel cell vehicles The results indicate that the combined optimal SC control strategy with DSO mechanism has improved the HHEV; system efficiency and the total energy saving of 0.046kWh is achieved for the given UoGHB drive cycle The proposed power saving mechanism require a priori knowledge of the drive cycle. 21
5. Summary In this paper an investigation and development of an optimal power management strategy of hydrogen fuel cell vehicles is carried out with reference to the UoGHB. Analyses are based on the overall progress in the development of different classification of vehicle systems and energy usage and its future.. 22
OPTIMAL POWER MANAGEMENT OF HYDROGEN FUEL CELL VEHICLES Thank You Sustainable Environment Research Centre Renewable Hydrogen Research & Demonstration Centre University of Glamorgan Baglan Energy Park Port Talbot SA12 7AX, UK 23