Energy Science and Technology III Lecture Winter Term 2015/16. Battery System Engineering II

Transcription

1 Energy Science and Technology III Lecture Winter Term 215/16 Battery System Engineering II Michael A. Danzer Zentrum für Sonnenenergie- und Wasserstoff-Forschung (ZSW) Baden-Württemberg Contents Battery System Engineering Battery System Engineering I Applications and requirements Fundamentals of battery operation Characteristics, selection, and testing of cell types Charging methods Battery System Engineering II Battery modeling Battery modules and systems Battery management system Cell balancing State estimation -2-

2 Modeling and simulation of Li-ion batteries dynamics life/ageing safety power prediction model Battery know-how (electro-)chemistry technology test steady state (OCV), dynamic, t/f domain, state estimation parameter identification cooling simulation pack dimensioning -3- Modeling and simulation of the electrical behavior Transient terminal voltage model Equivalent circuit U cell I cell I mr I sr V Z i R sr terminal voltage cell current main reaction current side reaction current source voltage (open circuit voltage) complex internal resistance (impedance) resistance of side reaction Model structure is applicable to many technologies. Model parameter have to be identified for specific cells. -4-

3 Transient terminal voltage model Source voltage I V as function of state of charge 4,2 discharge NMC - graphite 3,7 discharge LFP - graphite 4 charge 3,5 charge 3,8 3,3 OCV [V] 3,6 OCV [V] 3,1 3,4 2,9 3,2 2, SOC [%] 2, SOC [%] Remark: at operation in wide temperature range entropy has to be regarded (dv/dt). Transient terminal voltage model Source voltage II V dependency on temperature Experimentally determined entropic heat as a function of SOC for manganese spinel (positive electrode) and carbon (negative electrode). Source: V. Srinivasan and C.Y. Wang, Journal of the electrochemical Society 15(1) A98-A16 (23)

4 Transient terminal voltage model Source voltage III Hysteresis Distinct open circuit voltage profiles for charge V,ch and discharge V,dc Modeling Nonlinear transfer function V transfer = f( Q transfer ) between charge and discharge curve. Open circuit voltage (V) V,dc V,ch V transfer,dc V transfer,ch State of charge (%) Source: Günther et al., 3rd IEEE PES ISGT Europe Conference, Berlin, Germany, October 212 Transient terminal voltage model Internal resistance I Complex internal resistance Z i Voltage response to current pulse in time domain Modeling approaches RC V [V] U m 1RC 2RC 3RC RC 3RC R Ω R Ω R 1 R 1 C 1 R 2 R 2 C 2 R 3 e [mv] t [s] 15 1RC 1 2RC 3RC 5 C 1 C 2 C 3 R C = R C resistance capacity time constant t [s]

5 Transient terminal voltage model Internal resistance II Dependencies of model parameters - Temperature - State of charge - Current 5 x Current sign - Hysteresis Example R 1 R 1 R Ω R 2 R 3 Ohmic Resistance R 1 (Ohm) SOC B = 2 % C 1 C 2 C 3 SOC B = 55 % Current (A) Pulse profile and measurement matrix designed according to variables above. Transient terminal voltage model Internal resistance III Frequency dependent complex resistance Electrochemical Impedance Spectroscopy 8 modeling approach measurement.1hz-1khz model 6 Physical effect Modeling 4 electric and ionic conductivity Ohmic resistance -Z'' [m ] 2 charge transfer reaction diffusion processes inductive behavior of cable ZARC element constant phase element inductivity Z' [m ]

6 Modeling and simulation of the thermal behavior Cell temperature model I Equivalent circuit Lumped parameters for - heat conductivity - heat capacity - heat source P heat = P irrev + P rev = P Joule + P entropic Measurement method of power loss calculation of energy balance at balanced charging Charge (Ah) Energy (Wh) Power loss (W) Time (h) Time (h) Time (h) Modeling and simulation of the thermal behavior Equivalent circuit Directional heat transfer Measurement of core temperature

7 Modeling and simulation of the thermal behavior Cell temperature model II Inhomogeneity of temperature - Thermographical analysis - Spatial resolution of models Source: Stumpp et al., ANSYS Conference & 31th CADFEM Users Meeting, Mannheim, Germany, 213. Modeling and simulation of the battery life State of Health activation production formation usage / degradation / ageing recycling Definition BOL EOL battery life SOH = 1 SOH = SOH is 1% at BOL, will decrease over time and use, and is % at EOL ( SOH 1) Calculation based on capacity EOL is reached when 2% of the initial capacity is irreversibly lost. Capacity at BOL: C =C(t=) SOH = 1 Capacity at EOL: e.g. 8% C SOH = Calculation based on resistance EOL is reached when the internal resistance increased by 5%. Resistance at BOL: R =R(t=) SOH = 1 Resistance at EOL: e.g. 15% R SOH = C( t ) C( t) SOH 1 C( t ) R( t) R( t ) SOH 1 R( t ) 1 2% 1 5%

8 Modeling and simulation of the battery life Ageing contributions and models Calendar life - Degradation processes without usage - Independent from current and energy throughput - Dependent on temperature, state of charge, time - Follows Arrhenius equation A cal f ( t, T, SOC) Cycle life - Degradation processes under current flow - Dependent on current, temperature, depth of discharge, state of charge, energy throughput - Very specific for load profile in the application f ( I, T, SOC, SOC, Q, E, cyc,...) A cyc Assumption Superposition of calendar and cycle ageing SOH 1 A 1 ( A cal Acyc) Modeling and simulation of the battery life Ageing tests Ageing as a function of various variables SOH 1 A 1 f ( t, I, T, SOC, SOC, Q, E, cyc,...) Statistical influences production tolerances cell to cell variation material variation lot to lot variation Fundamental modeling Electrochemistry Material science Life testing Selection of relevant variables, ranges and level test matrix Statistical design of experiment reduced test plan Run life tests with replicates for each condition (Nonlinear) regression analysis

9 HEV-Batterie Modeling and simulation of Li-ion batteries Ageing inhomogeneities 96s1p, liquid cooling S. Paul et al. Journal of Power Sources 239 (213) Calendar life test Modeling and simulation of the battery life Ageing results and models relative capacity [ ] NMC//C 1 Ah pouch float 5% SOC, 25-5 C Arrhenius and square root life model T = 25 C t EOL = d T = 4 C t EOL = 365 d E A rc( t, T, θ) 1 A exp RT K A = d -.5 E A = J mol -1 R = J K -1 mol -1 t.84 measurement.82 model, fit = 99.2% confidence interval T = 5 C.8 t EOL = 122 d time [d]

10 Modeling and simulation of Li-ion batteries Simulation model Structure diagram Input values State values Output values Modeling and simulation of Li-ion batteries Model validation cell voltage Validation test Artemis drive cycle 1 Dynamic drive profile Measurement T amb = 3 C SOC = 8 % Results error e max( e ) = 14 mv median( e )= 2 mv current [A] voltage [V] error [mv] Simulation Measurement time [s] 1 Source: M. André, Science of The Total Environment, vol , pp , Dec

11 Modeling and simulation of Li-ion batteries Model validation cell temperature Validation test Artemis drive cycle 1 Dynamic drive profile Measurement T amb = 3 C SOC = 8 % Results error e max( e ) =.6 K median( e )=.13 K current [A] temperature [ C] error [K] simulation measurement time [s] 1 Source: M. André, Science of The Total Environment, vol , pp , Dec. 24 Battery modules and systems from cell to module and system

12 Cells, modules, and systems Glossary, definitions, and fundamentals I cell module battery pack/system The smallest unit of a battery, consisting of a positive and a negative electrode, a separator and the electrolyte. It stores electrical energy and forms the fundamental cornerstone of a battery if it is placed into a case and equipped with electrical connectors. The capacity of a cell is determined by its size. The cell voltage, however, depends on the electrochemical system of the element. Subunit of a battery pack. Consists of 6-14 cells coupled into a single unit and equipped with attachments for external electrical connections. Often equipped with voltage, current, or temperature sensors and basic safety circuit. The voltage of a module is mostly kept below 6 V. Overall system: comprises modules, switchbox, fuses, battery management, thermal management, housing, and interfaces. cell battery system module Cells, modules, and systems Configurations Parallel and serial connection of cells and modules Serial connection: 2s Parallel connection: 2p - doubling voltage, same current - same capacity -E = U M C M =(2 U C ) C C = 2 U C C C - same voltage, doubling current - doubling capacity -E = U M C M =U C (2 C C )= 2 U C C C Serial-parallel connection: 2s2p Serial-parallel connection: 2p2s

13 S4 hybrid battery pack Cells, modules, and systems Example I cooling module Lithium-ion cell battery management system (BMS) cooling fluid connector high voltage connector cell voltage monitoring Cells, modules, and systems Example II A123 Systems power module and pack for hybrid electric vehicles (HEV) Module specifications Core cell: AHR32113 cylindrical cell Configurations: 1S(1-4)P Capacity range: 4.4Ah to 17.6Ah Power range: 5kW to 2kW Nominal voltage: 33V Thermal management: can be liquid or air cooled Pack specifications Configurations: 8S2P to 12S2P Capacity: 8.8Ah Power: 75kW - 11kW Nominal Voltage: 264V to 393V Thermal management: liquid cooled Sealed System: IP67 rated for temporary immersion Standard vehicle communication and control interface

14 Cells, modules, and systems Example III A123 Systems energy module and pack for electric vehicles and PHEV Module specifications Core cell: AMP2 prismatic cell Configurations: 6S 28S (nominal voltage: V) 1P 13P (nominal capacity: 2 26Ahs) Up to 5.38 kwh per module Discharge power: 3C continuous, 1C peak (1 sec) Thermal management: can be liquid or air cooled Pack specifications Nominal voltage: 393V Nominal energy: 23kWh Tesla Roadster 6831 LCO cells (1865) Cells, modules, and systems Example IV The Energy Storage System contains 6831 lithium ion cells arranged into 11 "sheets" connected in series; each sheet contains 9 "bricks" connected in series; each "brick" contains 69 cells connected in parallel. 11S 9S 69P

15 Nissan Leaf 48 Modules 4 cells each = 192 cells (AESC) Voltage 36V Energy 24 kwh (partially used to increase lifetime) CHAdeMO fast charge: SOC 8% in 2-3 minutes Cells, modules, and systems Example V Smart ED Battery Power Power Torque Energy Consumption Range Top speed Cells, modules, and systems Example VI Deutsche ACCUmotive / LiTec 3 kw (4 hp, continuous) 55 kw (74 hp, 2 min peak) 13 Nm 17.6 kwh 12 kwh/1 km 14 km (NEDC cycle) 12 km/h 2-8 % SOC: 3 h -6 km/h: 5 s

16 Cells, modules, and systems Design of packs Things to consider designing a battery system - Selection of right cell type according to application - Connections and connectors: cost, added resistance, mechanical stability - Cell fixture: glue or metal frames for mechanical stability against shaking and shock - Safety elements: e.g. thermal switches (PTC: positive temperature coefficient) - Insulation: positive terminals, protection against contact - Housing: minimum volume with optimal (thermal) environment for cells - Housing: mechanical stability and protection against environment Cells, modules, and systems Dimensioning of packs Using degrees of freedom to optimize battery life and cost - Selection of cells with higher capacity or adding more cells to pack - Increasing initial cost - Reduction of average state of charge and/or depth of discharge - Reduction of currents and self heating - Prolonging cycle and calendar life - Reducing overall cost (see total cost of ownership)

17 Simulation-based design and sizing of a battery Cells, modules, and systems Total cost of ownership (TCO) TCO is a financial estimate that helps to determine direct and indirect costs of a product. In the automotive market TCO defines the cost of owning a vehicle from the time of purchase by the owner, through its operation and maintenance to the time of selling. TCO studies are used to compare different drive train and vehicle types. Key elements incorporated in the cost of ownership for an (electric) vehicle are: time purchase operation selling cost cost of acquisition, (government) incentives (battery lease), depreciation costs, fuel (charging) costs, insurance, financing, repairs, fees and taxes, maintenance costs, opportunity costs, downtime costs residual value, recycling costs, (second use) Literature: Sources: Feb. 212, ZSW

18 Battery management Introduction Functions of a battery management system (BMS) BMS V I T data acquistion state estimation electrical management safety management monitoring thermal management Battery management Data acquisition variable sensor types desired accuracy voltage voltage sensor ADC, sigma/delta 5-2 mv current temperature shunt magneto resistive sensor thermocouple resistance thermometers (PT1, PT1, NTC) % of maximum current 1-3 C Voltage measurement Every single cell vs. overall voltage vs. pilot cell V m V 1 V 2 V 3 V 4 V N

19 Battery management Safety management Switch off criteria - Over charge - Deep discharge - Short circuit - Over current - Over temperature logic I shunt Protection against short circuit can also be done internally in the cell or externally by thermal switches (PTC: positive temperature coefficient). V T sensor T Battery management Monitoring system BMS V I T data acquistion state estimation electrical management safety management monitoring thermal management The monitoring system - collects measured data (voltage, current, temperature) and derived information like battery statistics (histograms), state of charge (SOC), state of health (SOH), available energy, available charge/discharge power, prediction - provides battery relevant information to higher level systems (e.g. energy management system EMS) The monitoring system does not take action itself

20 Battery management Thermal management Objectives - safety: prevent thermal runaway prevent lithium plating - performance: reduce internal resistance (especially at low temperatures, cold crank) - life: increase calendar and cycle life (chemical reaction rate of side reactions / degradation mechanisms follow an Arrhenius equation) prevent lithium plating Battery management Thermal management Cooling strategies cooling plate air cooling ground cooling cooling plate with liquid coolant flow tab cooling complexity efficiency volume cell types cy,pr,po cy,pr cy,pr,po cy,pr,po cy,pr,po cy: cylindrical, pr: prismatic, po: pouch Source: Dirk Neumeister, BEHR, CTI,

21 Battery management system Introduction The basic task of a Battery Management System (BMS) is to ensure that - the battery delivers the required power to the application, - the battery is operated within its specifications, - the battery s lifetime is maximized, - the risk of damage to the battery, and - any hazard to its surrounding is prevented. This is achieved by - monitoring the battery s states, - controlling the battery's charging and discharging process, - communicating with higher level systems, and - controlling the temperature. V I T BMS Functions of a BMS data acquistion state estimation electrical management safety management monitoring thermal management Battery and energy management Battery management system System overview in EV

22 Battery management system Topologies Star topology (master/slave) ring topology Source: Batteries are no peas In a battery module there are no two batteries like two peas in a pod. Cells are neither created nor aging equally. Cell balancing Introduction Battery failure and life No matter what battery management techniques are used, the failure rate or life expectancy of a multicell battery will always be worse than the quoted failure rate or life expectancy of the single cells. Definition of balancing Battery balancing is a technique that equalizes the state of charge of cells in a battery and therewith maximizes the useable battery capacity as well as increases the battery's lifetime

23 Equal cells at equal states of charge Cell balancing Motivation Unequal cells at various states of charge variations due to cell to cell variation, temperature differences, unequal aging SOC max SOC min unsusable capacity Single cell monitoring (Dis-)/charging stops when first cell voltage reaches (lower)/upper voltage cutoff. Cell balancing Passive balancing Dissipative resistors The dissipative method shunts selected cells with high value resistors to remove charge from cells with high SOC until they match the charge of the lowest cells. This circuit is the simplest and cheapest cell balancing implementation. The resistor value is chosen so that the current is small (<1mA/Ahr capacity). A 1mA/Ahr resistor could balance high cells at a rate of 1% per hour. If operated continuously, such a technique could drain the entire battery pack in a few days. Source: S. Moore and P. Schneider, SAE

24 Charge-shunting Cell balancing Active balancing charge shunting The charge-shunting cell balancing method selectively shunts the charging current around each cell as they become fully charged. The shunt resistor is sized to shunt exactly the charging current when the fully charged cell voltage is reached. If the charging current decreases, resistor will discharge the shunted cell. This method is most efficiently employed - when charge rates are known (preselected size of resistor), - on systems that are charged often with small charge currents to avoid extremely large power dissipations. Disadvantages are the requirement for large power dissipating resistors, high current switches, and thermal management. Source: S. Moore and P. Schneider, SAE 21 Charge shuttling Cell balancing Active balancing charge shuttling This balancing mechanisms consist of a device that removes charge from a selected cell, stores that charge, and then delivers it to another cell. Flying capacitor Cells at high SOC will charge the capacitor and the cells at low SOC will take charge from the capacitor. In this way, the charge of the most charged cells are distributed to the least charged cells. A fixed switching sequence is needed to open and close the proper switches. Disadvantages - A large number of switches (n+5) rated at peak charging current for the capacitor are required. - A significant amount of energy is dissipated as resistive heating in the switches and the capacitor. A large portion of balancing is achieved by dissipating the charge from the higher charged cells. Other embodiments of charge shuttling schemes are charge shuttling between two cells and charge shuttling with several cells. Source: S. Moore and P. Schneider, SAE

25 Energy converters Cell balancing Active balancing energy converters Cell balancing utilizing energy conversion devices employ inductors or transformers to move energy from a cell or group of cells to another cell or group of cells. Switched transformer Current is taken from the entire pack and is switched into the transformer. The transformer output is rectified through a diode and delivered into the selected cell, which is determined by the setting of switches. Electronic control is required to select the target cell and set switches. This method can rapidly balance low cells at the cost of removing energy from the entire pack. Disadvantages include high complexity, high parts count in terms of control, magnetics, and switches, and low efficiency due to switching losses and magnetics losses. Other embodiments of charge shuttling schemes are shared transformer and multiple transformer. Source: S. Moore and P. Schneider, SAE Overview Cell balancing Overview of balancing methods method level time duration cost dissipative resistor charge shuttling charge shunting Leveling Levels downwards to lowest SOC. Pack performance determined by weakest cell. Levels to average SOC. Pack performance determined by average cell. Levels upwards to highest SOC. Maximum energy storage. at rest at rest while charging very long (low bypass currents) very long (slow charging of weaker cells) fast (high bypass currents) lowest in between highest (switch, bypass) before balancing after dissipative balancing charge shuttling charge shunting SOC max SOC max SOC max SOC max SOC min SOC min SOC min SOC min -5-

26 Cell balancing Redox shuttle chemical cell balancing Gassing In Lead-acid batteries, overcharging causes gassing which coincidentally balances the cells. Redox shuttle The redox shuttle is an attempt to provide chemical overcharge protection in Lithium cells using an equivalent method thus avoiding the need for electronic cell balancing. A chemical additive which undergoes reversible chemical action absorbing excess charge above a preset voltage is added to the electrolyte. The chemical reaction is reversed as voltage falls below the preset level. Equivalent circuit of a cell State estimation Methods of SOC determination Methods of SOC estimation - determination of OCV, OCV = f(soc) - integration of main reaction current (Ah-balance, Coulomb-counting, book-keeping) 1 1 SOC( t) SOC( t) Imr ( ) d SOC( t) I( ) Isr ( ) d C C t t - determination of internal resistances Challenges - hysteresis of OCV-SOC-curve - temperature dependency of parameters and profiles - aging - modeling of side reactions - measurement inaccuracies To increase the accuracy of SOC determination single methods are combined: e.g. integration of main reaction current with OCV determination at rest periods

27 State estimation SOC determination by current integration Integration of main reaction current (Ah-balance, Coulomb-counting) 1 1 SOC( t) SOC( t) Imr ( ) d SOC( t) I( ) Isr ( ) d C C t t Block diagram I T U estimation of losses detection of full/empty reset C capacity x -1 Functions needed - accurate (offset- and drift-free) current measurement - detection of full and empty state - model of losses (side reactions, self discharge) - capacity adaptation - adaptation to aging I sr - Q 1/C SOC State estimation SOC determination by OCV measurement SOC can be determined by voltage measurement during rest periods. SOC as a function of OCV, SOC = f(ocv), can be stored in look-up tables. Detection of rest periods by - current (I = ) - time (t > t wait ) - voltage (du/dt < x mv/s) 4,2 4 discharge charge NMC - graphite Challenges - hysteresis of OCV-SOC-curve - flat OCV-SOC curves - voltage measurement accuracy - temperature dependency of OCV-SOC-curve - aging OCV [V] 3,8 3,6 3,4 3, SOC [%]

28 State estimation SOC determination by OCV measurement SOC can be determined by voltage measurement during rest periods. SOC as a function of OCV, SOC = f(ocv), can be stored in look-up tables. Detection of rest periods by - current (I = ) - time (t > t wait ) - voltage (du/dt < x mv/s) 3,7 3,5 discharge charge LFP - graphite Challenges - hysteresis of OCV-SOC-curve - flat OCV-SOC curves - voltage measurement accuracy - temperature dependency of OCV-SOC-curve - aging OCV [V] 3,3 3,1 2,9 2,7 2, SOC [%] State estimation SOC determination by OCV measurement Chemistry dependant voltage window 3,7 3,5 discharge charge LFP - graphite 4,2 4 discharge charge NMC - graphite 3,3 3,8 OCV [V] 3,1 2,9 V = OCV SOC9 -OCV SOC1 1mV OCV [V] 3,6 3,4 2,7 3,2 V = OCV SOC9 -OCV SOC1 6mV 2, SOC [%] SOC [%] Voltage measurement accuracy needed for an SOC-error of less than 5% LFP: 2 mv NMC: 1 mv

29 State estimation SOC determination by a combination of methods SOC estimation can be improved by combining the methods of current integration and OCV measurement. State estimation Advanced methods for dual SOC and SOH estimation Model based state estimation u I T amb SOC x SOH U y T cell battery parameter - y mod battery model based observer SOC xˆ SOH

30 State estimation Advanced methods for dual SOC and SOH estimation Published methods for state of charge estimation - (extended / Sigma-point) Kalman filter - sliding mode observer - state space observer (Luenberger) - adaptive filter - recursive least squares filter - neural network - fuzzy logic - particle filter (sequential Monte Carlo method) Models for battery state estimation

31 Models for observer-based battery state estimation Models for observer-based battery state estimation Klee Barillas et al., Applied Energy 155 (215)

32 Observer-based battery state estimation Klee Barillas et al., Applied Energy 155 (215) Observer-based battery state estimation Klee Barillas et al., Applied Energy 155 (215)

33 Observer-based battery state estimation Klee Barillas et al., Applied Energy 155 (215) Observer-based battery state estimation Klee Barillas et al., Applied Energy 155 (215)

34 Observer-based battery state estimation Klee Barillas et al., Applied Energy 155 (215) Observer-based battery state estimation Validation experiment Driving cycle of an off-road vehicle Battery module and BMS (a) 1 battery current [C-rate] (b) 46 battery voltage [V] time [s] Module (Akasol) 46 Ah NMC pouch cell (Kokam) 12s1p BMS (STW) 32-Bit Microcontroller 2.5 mv resolution ADC Klee Barillas et al., Applied Energy 155 (215)

35 Observer-based battery state estimation Results Comparison of SOC and voltage measurement and estimation (a) 7 LO SMO EKF Ref. (b) (c) 1 SOC [%] SOC error [%] voltage error [V] cell voltage [V] (d) Klee Barillas et al., Applied Energy 155 (215) time [s] time [s] Observer-based battery state estimation Results Comparison convergence behavior (a) 62 SOC [%] (b) 5 SOC error [%] -5 LO SMO EKF Ref. Klee Barillas et al., Applied Energy 155 (215) time [s]

36 Observer-based battery state estimation Results Robustness analysis LO SMO EKF Voltage measurement noise Current measurement noise (a) 2 (b) 2 SOC error [%] time [s] Current sensor drift (c) SOC error [%] time [s] SOC error [%] time [s] Parameter uncertainty (d) 2 SOC error [%] time [s] Klee Barillas et al., Applied Energy 155 (215) Observer-based battery state estimation Performance summary of estimation algorithms LO SMO EKF SOC accuracy Computational time Dynamik RAM convergence Stack-size 5 Complexity Klee Barillas et al., Applied Energy 155 (215)

37 Further Reading Journal of power sources IEEE Xplore digital library Electrochimica Acta EDS the society for solid-state and electrochemical science and technology Journal of Applied Electrochemistry A. Jossen, W. Weydanz: Moderne Akkumulatoren richtig einsetzen; Leipheim and Munich (Germany), 26. (Book in German language). J. Garche (ed. in chief): Encyclopedia of electrochemical power sources; Elsevier, New York (USA), 29. Pop et al.: Battery Management Systems; Springer, 28 Linden D., Reddy T.B., Handbook of Batteries Third Edition, McGraw-Hill, 22 Links IEA Technology Roadmap Electric and plug-in hybrid electric vehicles Varta: Battery know-how / battery glossary / e-learning Maxim application notes and tutorials Battery types, chemistries, performance, management, chargers

38 Organisation EST III & EPS 2 Lecture (mandatory): Seminar (mandatory): Lab: Thursdays, 2:15 pm 4 pm (c.t.)! ZSW, Helmholtzstraße 8 Seminar room, 77 Fridays, 8:15 am 1: am (c.t.)! ZSW, Helmholtzstraße 8 ZSW, Seminar room, 77 see lab schedule, 13: pm WBZU, Helmoltzstraße 6 Semester break: December 24, 213 January 6, 214 Exam (closed exam): February 26, 216, 1 am 12 am, ZSW Participation in lectures, seminars & lab is mandatory Thank you for your attention! michael.danzer@zsw-bw.de Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg Lise-Meitner-Str. 24, 8981 Ulm -75- Stuttgart Widderstall Photovoltaics -76- & Solab Solar test-field Energy Policy & Energy Ulm Carriers University EST-III 215/16 BSE M. Danzer Ulm Electrochemical Energy Technologies Ulm elab

39 Examination At the end of the semester an exam will take place; duration is 12 min. the examination will be a closed examination Each of the six ZSW lecturers will prepare questions to his lecture. Questions are either calculations or comprehensive questions to the lectures. No additional literature is needed to answer them. A formulary (ZSW Fuel Cell Formulary by Alexander Kabza) will be available at the examination room A pocket calculator (non programmable) can be used. Not allowed are: Books, own scripts, smartphones, tablets or laptops. Example exams from former lectures are NOT available. All potential questions are subject of the seminars -77-