You will get information about battery management system from above ppt

1. Design of an Intelligent Battery Management System
(BMS)
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Guide Name :- Prof. A. D. Dharmadhikari
Presented By:-Rupesh R. Dhule
Roll No:- IR111(RBT19ME225)
JSPM’S
RAJARSHI SHAHU COLLEGE OF ENGINEERING
TATHAWADE,PUNE-33.
(An Autonomous Institute Affiliated to Savitribai Phule Pune University, Pune)

2. content
• Why BMS
• Applications
• Overall Topology
• Model Description
• Solar PV Array model
• DC-DC Buck Boost Converter model
• Battery RC model
• Controller Algorithm Block
• Results
• Future Work-plan
• References
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3. Need of Battery Management System
• Heart of all types of energy storage technology.
• Ensures optimum usage of the energy inside the battery powering
the portable/stationary system.
• Risk of damage inflicted upon the battery is minimized.
• Enhances system run-time reliability.
• Increase overall system efficiency.
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4. Applications
• Grid connected & off-grid
• Utility grid
• Off grid power storage and power transfer as required.
• Storage in electric automobiles
• Applications in astronomy:
• Power supply and transfer in space stations and satellites.
• Power to run remotely controlled automobiles and rover on other planets
surfaces.
• Intermittent & renewable energy applications as backup [solar, wind,
etc.]
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5. BMS for solar PV system
• BMS for solar PV systems are designed to enhance the battery storage life
time and to ensure power system reliability.
• BMS is being extensively used in Grid connected and off-grid solar PV
applications (Stand-alone solar Pump, Electric vehicle, rural electrification
etc.)
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6. Major subsystems
• Solar PV array
• Electrical Interface (PCU)
• DC-DC buck-boost converter topology
• Battery
• Controller Algorithm Block
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7. Model designed in MATLAB
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8. Solar PV array
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9. MATLAB model of Solar PV array
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10. DC-DC buck-boost converter model
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11. Battery
• Energy storing device.
• Redox reaction between electrodes and electrolyte.
• Two types mainly: Primary and secondary
Types of conventional batteries:
• Nickel Cadmium (Ni-Cd) battery.
• Nickel-Metal Hydride (Ni-MH) battery.
• Lead Acid battery.
• Lithium Ion battery.
• Lithium Polymer battery.
• NaS battery
• New Generation large scale stationary batteries:
• Redox Flow Batteries
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12. Detailed Comparative
study of different
parameters of popular
batteries in use.
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13. Lithium Ion Battery
 Lightest metal.
 Provides very high energy density in terms of weight (twice that of the
standard Ni- Cd batteries).
 It has a cycle life of 1200 – 2000 which is reasonably good for automotive
applications.
 Self-discharge is less than half compared to nickel-cadmium (Ni-Cd),
making lithium-ion well suited for modern fuel gauge applications.
 Does not need prolonged priming when it’s new.
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14. Electrical equivalent model of Battery
• Equivalent circuit model of a battery
Thevenin battery model
E0 – Open-circuit Battery Voltage
R – Solution Resistance
C0 – Electrode Capacitance
R0 – Electrode Resistance
MATLAB model designed
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15. 2 stage conventional CC-CV charging characteristics
CHARGING CHARACTERISTICS OF BATTERY
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16. Charging characteristics of battery
• 3 stage CC-CV charging characteristics
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17. 3 stage Charge control Algorithm
Initialize battery
OCV , SOC
If
VOCV > VTrickle
If
VOCV > VBulk
NO YES
(C.C. Mode)
NO
YES
ICh = IBulk
(C.C. Mode)
ICh = ITrickle
If
ICh > IFloat
NO
YES
ICh = 0
(C.V. Mode)
VCh = VOCV
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18. Logic Model
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19. Future Work-Plan
• The performance of the proposed charge controller shall be improved
by proper choice of L-C filter.
• Maximum power point tracking (MPPT) will be introduced in the PCU
model to improve the overall system efficiency.
• The effect of temperature rise inside the battery stack shall be taken
care of in the proposed model later on.
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20. References
• D. Sutanto, H.L. Chan, “ A New Battery Model for use with Battery
Energy Storage Systems and Electric Vehicles Power Systems”, Power
Engineering Society Winter Meeting, January 2000.
• John Chiasson, Baskar Variamohan, “Estimating the State of
Charge of a Battery”, Transactions on Control Systems Technology,
Vol. 13, NO. 3, May 2005.
• Jun Xu and Binggang Cao, “Battery Management System for Electric
Drive Vehicles – Modeling, State Estimation and Balancing”.
• Barrie Lawson, “State of Charge (SOC) Determination”, Woodbank
Communications .
• Dirk Uwe Sauer, Heinz Wenzl, “Comparison of different approaches
for lifetime prediction of electrochemical systems-Using lead-acid
batteries as example”, Journal of Power Sources, Vol. 176, NO. 2.
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21. Thank you
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