battery modeling presentationeda.ee.ucla.edu/.../703991396chengruchangmengjiehuang/present… ·...
TRANSCRIPT
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Battery modeling PresentationBattery modeling Presentation
MengJie HuangCheng‐Ru ChangCheng Ru Chang
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A new BMS system based on cell redundancy
Antonio Manenti, Andrea Abba, Alessandro Merati, Sergio M. Savaresi
IEEE Transactions on Industrial Electronics
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OutlineOutline
• IntroductionIntroduction• Switch networkSi l i i i• Signal acquisition
• Balancing Algorithm• SOC estimation• PrototypingPrototyping• Conclusion
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IntroductionIntroduction
• Each cell in battery pack has different characteristicsEach cell in battery pack has different characteristics• Disconnected the cell when a single cell reaches its limitlimit
• Balancing in both charge and discharge• BMS should identify and bypass damaged cell permanently
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ArchitectureArchitecture
• Previous workPrevious work– DC‐DC converter, PWM
• Standard Li‐ion cellStandard Li ion cell– 6 connected at the same time, only 1 disconnected
– 4.2V of full charge voltage– 4400mAh of capacity– 10A of maximum continuous current load– 3A of maximum charge current
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Switch networkSwitch network
• Switch resistance directly impacts on the y pperformance of the system
• Switch have to interrupt current flow in both charge and discharge phasecharge and discharge phase
• Connect switch – NMOS switchesNMOS switches (low on‐state resistance)
• Bypass switch – PMOS switches
• Only one cell is bypassed
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Protection systemProtection system
• Prevent floating situationPrevent floating situation• BJT in open‐collector with pull‐up resisterp p
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Border cellBorder cell
• Bottom cell 0Bottom cell 0– Using both NMOS‐based switchesMore efficient due to great conductivity– More efficient due to great conductivity
• Top cell N‐1– both PMOS‐based switches
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Terminal voltage jumpingTerminal voltage jumping
• Due to pack reconfigurationDue to pack reconfiguration• But not a issue since
R fi ti d 100– Reconfiguration needs 100us– Standard load (electric motor) has slower dynamics (10ms)dynamics (10ms)
– Load control system between BMS and load can handle and level voltage jumpshandle and level voltage jumps.
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AcquisitionAcquisition
• Worst caseWorst case– 25V if all connected cell are fully charged (6 cells)– 6mV resolution for 12‐bit ADC
• Hardware solution– 0~5V 2.5~4.2V0 5V 2.5 4.2V– 6mV 2.4mV
• Software solutionSoftware solution– Oversampling to reduce noise
• Finally 6mV 600uVFinally, 6mV 600uV
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MicrocontrollerMicrocontroller
• Microchip (dsPIC30F3014)Microchip (dsPIC30F3014)• Large pinout
2 bi C• 12‐bit ADC
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Balancing algorithmBalancing algorithm
• ACQACQ– Cell voltage, pack voltage, current
• Voltage mode– No current acquisition
• SOC mode– OCV, impedance, neural
t k f l i inetwork, fuzzy logic in previous work
• Ԑvm Ԑsm : deviationԐvm, Ԑsm : deviation• m:cell index
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Balancing algorithmBalancing algorithm
• Charge and dischargeCharge and discharge– Find min and max deviation
• Selected cell is bypassed ,and
l b dpreviously bypassed one is reconnected
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SOC estimation algorithmSOC estimation algorithm
• Coulomb‐countingInitial value of SOC– Initial value of SOC
– Only on the current measurement• Model‐based
– Need a good cell model– Need voltage and current input
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Voltage mode vs SOC modeVoltage mode vs SOC mode
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Refresh time calculationRefresh time calculation
• Ts: SOC estimation time intervalTs: SOC estimation time interval• Tref: pack configuration refresh time interval
l f• Too large Tref– Loss accuracy
• Too small Tref– Increase the stress of the system and cells due to spikes (Voltage jumping)
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Refresh time calculationRefresh time calculation
• Q is the integrated absolute error in SOCQ is the integrated absolute error in SOC• Q is low when balancing effect is high
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Refresh time calculationRefresh time calculation
• ά is a coefficient related to the discharge rateά is a coefficient related to the discharge rate
T1 T2 T3
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Refresh time calculationRefresh time calculation
• The SOC mean valueThe SOC mean value
• The deviation of the SOC of the m‐th cell with respect to average SOC results p g
=
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Refresh time calculationRefresh time calculation
• Q is proportional to TrefQ is proportional to Tref• Increase Tref worsen the balancing effect
h b l i li• Increase N worsen the balancing quality
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Theoretical trend vs Measured resultTheoretical trend vs Measured result
• Quality factor versus number of cells(N) andQuality factor versus number of cells(N) and refresh time (Tref)
• Discharged at 1C• Discharged at 1C
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EfficiencyEfficiency
• Switches that are connected in series to theSwitches that are connected in series to the current flow could overheating of devices and determine a efficiency lossdetermine a efficiency loss
• Best caseF ll h d ll ith l t– Fully charged cell with a low current
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ConclusionConclusion
• Optimal balancing of the battery pack duringOptimal balancing of the battery pack during operation
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A supervisory control strategy for series hybrid electric vehicles withseries hybrid electric vehicles with
two energy storage systemsPierluigi Pisu and Giorgio Rizzoni
V hi l P d P l i 2005Vehicle Power and Propulsion, 2005 IEEE Conference
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Series Hybrid Electric VehicleSeries Hybrid Electric Vehicle
Fig. 1 Schematic representation of a series hybrid configuration.
Fig. 2 Schematic representation of a connection of two electricala connection of two electrical power sources configuration.
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Energy Management Control ProblemEnergy Management Control Problem
• The overall fuel consumption over a given trip:The overall fuel consumption over a given trip:
• The local criteria becomes at all times:
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Equivalent Fuel Consumption Minimization h i l i iStrategy – Physical Viewpoint
• The main idea of the strategy is:The main idea of the strategy is:A present discharge of the RESS corresponds to a future consumption that will be necessary tofuture consumption that will be necessary to recharge the RESS;A present RESS charge corresponds to a future fuelA present RESS charge corresponds to a future fuel savings because this energy will be available in the future to be used at a lower cost.
• The instantaneous fuel consumption:
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Equivalent Fuel Consumption Minimization h i l i iStrategy – Physical Viewpoint
Fig. 3 Energy path for equivalent fuel: (a) consumption during RESS discharge; (b) consumption during RESS recharge.
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Mathematical Formulation: Discharging Mode for a Single Component RESS
• The future cost of dischargingThe future cost of discharging
• can be represented as:
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Mathematical Formulation: Discharging d f i l
• The total energy recharged in the future is:Mode for a Single Component RESS
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Mathematical Formulation: Discharging d f i l
• The cost of the total energy recharged in the
Mode for a Single Component RESS
The cost of the total energy recharged in the future is
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Mathematical Formulation: Discharging d f i l
• After manipulating and approximating we get
Mode for a Single Component RESS
After manipulating and approximating, we get the future cost of :
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Mathematical Formulation: Discharging d f i l
• The instantaneous fuel flow rate caused by
Mode for a Single Component RESS
The instantaneous fuel flow rate caused by RESS:
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Mathematical Formulation: Charging Mode f i l
• The instantaneous fuel flow rate caused by
for a Single Component RESS
The instantaneous fuel flow rate caused by RESS:
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Equivalent Fuel Consumption of a lSingle Component RESS
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Simulation ResultSimulation Result
Fig.7(a) Batteries SOE for HDUD cycle
Fig.7(b) Battery pack current for HDUD cycle
Fig. 6 HDUD driving cyclecycle
Fig.7(c) UltracapacitorsSOE for HDUD cycle
Fig. 8(d) Ultracapacitorscurrent for HDUD cycle
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ConclusionsConclusions• it requires the only knowledge of the efficiency maps for the various systems in the powertrain architecturefor the various systems in the powertrain architecture, and their torque and power limits;
• it requires a limited number of inputs that include the SOEi of the RESSi (i=1,2) and the torque requested at the wheels by the driver (this can be calculated from y ( fthe accelerator and brake pedal position);
it i t i l t i l ti b th• it is easy to implement in real‐time because the optimal power split can be determined by an easy and fast minimization of the function
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Conclusions
• in many cases, the optimal power split can be
Conclusions
a y cases, t e opt a po e sp t ca bepre‐calculated and saved in a multi‐dimensional map as a function of the input variables, avoiding
l d d h fon‐line minimization procedures and therefore, reducing the computational time; it i it b t t ti ti i th• it is quite robust to estimation errors in the recharging and charging efficiencies and in the power split.power split.
• It can be easily extended to any number of RESS in parallel. p