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Battery modeling PresentationBattery modeling Presentation
MengJie HuangCheng‐Ru ChangCheng Ru Chang
A new BMS system based on cell redundancy
Antonio Manenti, Andrea Abba, Alessandro Merati, Sergio M. Savaresi
IEEE Transactions on Industrial Electronics
OutlineOutline
• IntroductionIntroduction• Switch networkSi l i i i• Signal acquisition
• Balancing Algorithm• SOC estimation• PrototypingPrototyping• Conclusion
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
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
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
Protection systemProtection system
• Prevent floating situationPrevent floating situation• BJT in open‐collector with pull‐up resisterp p
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
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.
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
MicrocontrollerMicrocontroller
• Microchip (dsPIC30F3014)Microchip (dsPIC30F3014)• Large pinout
2 bi C• 12‐bit ADC
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
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
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
Voltage mode vs SOC modeVoltage mode vs SOC mode
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)
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
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
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
=
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
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
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
ConclusionConclusion
• Optimal balancing of the battery pack duringOptimal balancing of the battery pack during operation
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
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.
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:
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:
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.
Mathematical Formulation: Discharging Mode for a Single Component RESS
• The future cost of dischargingThe future cost of discharging
• can be represented as:
Mathematical Formulation: Discharging d f i l
• The total energy recharged in the future is:Mode for a Single Component RESS
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
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 :
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:
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:
Equivalent Fuel Consumption of a lSingle Component RESS
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
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
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