Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems
Liang He1, Lipeng Gu2, Linghe Kong1, Yu Gu1, Cong Liu3, Tian He4
1Singapore University of Technology and Design, Singapore2Shanghai University of Engineering Science, China
3University of Texas at Dallas, USA4University of Minnesota, USA
Battery-Powered Cyber-Physical Systems
single cell a few cells tens of cells hundreds of cells
system scale
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13. 2
Large-Scale Battery Systems• Example: 6,800 battery cells for Tesla electric vehicles
• Challengeso cell unbalance issue
• Dynamic battery characteristics during operating cycleso non-linear cell properties: rate-capacity effects
• More energy can be delivered with smaller discharge currentso inevitable cell failure over time and backup cells
3Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Reconfigurable Battery Packs
• Reconfigurable batter packs:adjustable connectivity among battery cells
o Controllable discharge pattern to take
advantages of rate-capacity effects
o Balancing cells
o By-pass failed cells
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Reconfigurable Battery Pack
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Reconfigurable Battery Packs
• Existing efforts: achieving high configuration flexibility with low complexity
• Our investigation: utilizing the configuration flexibility to improve system energy efficiency
5Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
How could Reconfiguration Help
3.7 x 4 = 14.8 volt
Load required voltage7.4 volt
Voltage Regulator
3.7 x 2 = 7.4 volt
with reconfiguration
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Design principle 1: Match the supplied and required voltage
levels
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
• This also reduces discharge current for individual cells: rate-capacity effect
Design principle 2: Minimize the discharge
current of individual cells
How could Reconfiguration Help
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Iload
0.5 I
0.5 I
load
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Adaptive Reconfiguration to Supply Load <V, P>
Identify cell strings matching load voltage V
Select as many as such disjoint cell strings and connect them in parallel
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• To match the supplied and required voltages, we need to
• To minimize the discharge current of battery cells, we need to
𝑣𝑣1 + 𝑣𝑣2 + 𝑣𝑣3= V
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Cell-Graph Representation• A directed Cell-Graph G = <V, E, W>
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• Bridges the gap between graph theory and battery system optimization
Identify All Feasible Voltage-V Cell Strings
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Identify all simple paths of weight sum V in graph
• NP-hard in theory (O(NN)), but practically solvableo number of vertices c in the path
is constraint (O(Nc))• e.g., the number of involved cells
could only be c = 8~10 when supplying a 30 V load with Lithium-ion batteries, whose voltage is typically 3.2V-4.2V
o vertex degree d is constraint due to limited configuration flexibility (O(Ndc))
• e.g., 10s(1-4)p for commercial A123 power module
Depth-First-Search with Pruning
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Minimize the Discharge Current by Connecting as Many Feasible Cell String as Possible in Parallel
Double the discharge current!Formulated as a 0-1 integer programming problem with
one constraint
find the maximum disjoint subset of all these paths
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min max {discharge current of cells}
• Identify all simple paths of weight sum Vi for each load, similar to single-load scenario
• Heterogeneous load requirements
• Batteries supporting different loads may have different discharge currents
• A greedy algorithm• Select the load with the largest
discharge current
• Select the string with the least conflict
Supply a Set of Loads <Vi, Pi>
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Select the String with the Least Conflict
“String1 conflicts with String 2”
String 1
String 2
String 3String 4
select the path with the least confliction
String 1 String 2 String 3 String 4String 1 1 1 1String 2 1 0 0String 3 1 0 1String 4 1 0 1
13Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Practical Issues
• Energy Consumption for Relay Operationo 25 mA driven current and 3 ms delay per relay operation
o for a typical 2900 mAh battery cell, the ratio between the relay energy cost and the battery capacity is
• 25 mA x 3 ms / 2900 mAh < 7.2 x 10 -6
• Power supply during Configuration Switchingo super-capacitor is a straightforward approach
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Experiment
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Battery Pack16 2450mAH AA batteries organized into 8 two-cell modulesLoads1-4 bulb clusters consisting of three 0.5A 3.6W bulbsBaseline (4S2P)Non-reconfigurable conf.: two parallel connected 4-module strings
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
Experiment: System Operation Time
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the lighter the load, the larger the gap between supplied and required voltage, and thus the larger the improvement
1-2 bulb clusters 1-4 bulb clusters 2-4 bulb clusters
4.3 hour 2.9 hour 1.8 hour
Experiment: Temperature
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*Environment Temperature: 28oC
Methods Cell Temperature after ExperimentsNon-Reconf. 4S2P 43oC
Adaptive Reconf. 31oC
High cell temperature degrades performance in terms of both energy efficiency and reliability.
Burned battery pack in Boeing 787, 2012[1]
[1] Boeing 787 Dreamliner battery problems, http://en.wikipedia.org/wiki/Boeing_787_Dreamliner_battery_problems, 2012.
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
EV Driving Trace based Emulation
Emulated LoadsDrive the Mitsubishi Mi Electric Vehicle in downtown Singapore for 45 minutes, and record the discharge/charge voltage & power during this process
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EV Driving Trace based Emulation
Emulated BatteriesDischarge profile of Panasonic NCR18650 battery cellsA battery pack of 64 x 64 cells is emulated
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13. 19
EV Driving Trace based Emulation
the more unbalanced the cells, the larger the improvementExploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.
• A control parameter α captures the unbalance degree of battery cells: a smaller α indicates a more unbalance degree
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Conclusion• Proposed adaptive reconfiguration schemes to
optimize energy efficiency in large-scale battery systems
• Transformed the problem of system reconfigurations to the topology control of the battery cell graph
• Designed practical algorithms to identify desirable reconfigurations for discharging
• Experimental results demonstrate the effectiveness of our proposed adaptive reconfiguration schemes
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Thanks!
Questions?
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AMP20 Energy Modules by A123 Systems
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13. 23
6S – 28S (Nominal voltage: 19.2 – 89.6V)1P – 13P (Nominal capacity: 20 – 260Ahs
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Load #2Load #1 Load #3Dis
char
ge c
urre
nt select a new string for load #1
Load #2Load #1 Load #3Dis
char
ge c
urre
nt
min max {discharge current of cells}
• Identify all simple path of weight sum Vi in graph for each load, similar to single-load scenario
• Heterogeneous load requirements
• Batteries supporting different loads may have different discharge currents
Provide a Set of Loads <Vi, Pi>
Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems, IEEE RTSS'13.