Battery modeling Presentation Battery modeling Presentation MengJie - - PowerPoint PPT Presentation

Battery modeling Presentation Battery modeling Presentation

MengJie Huang Cheng‐Ru Chang Cheng Ru Chang

A new BMS system based on cell redundancy

Antonio Manenti, Andrea Abba, Alessandro Merati, Sergio M. Savaresi IEEE Transactions on Industrial Electronics

Outline Outline

• Introduction

Introduction

• Switch network

Si l i i i

• Signal acquisition
• Balancing Algorithm
• SOC estimation
• Prototyping

Prototyping

• Conclusion

Introduction Introduction

• Each cell in battery pack has different characteristics

Each cell in battery pack has different characteristics

• Disconnected the cell when a single cell reaches its

limit limit

• Balancing in both charge and discharge
• BMS should identify and bypass damaged cell

permanently

Architecture Architecture

• Previous work

Previous work

– DC‐DC converter, PWM

• Standard Li‐ion cell

Standard 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 network Switch network

• Switch resistance directly impacts on the

y p performance of the system

• Switch have to interrupt current flow in both

charge and discharge phase charge and discharge phase

• Connect switch

– NMOS switches NMOS switches (low on‐state resistance)

• Bypass switch

– PMOS switches

• Only one cell is bypassed

Protection system Protection system

• Prevent floating situation

Prevent floating situation

• BJT in open‐collector

with pull‐up resister p p

Border cell Border cell

• Bottom cell 0

Bottom cell 0

– Using both NMOS‐based switches More efficient due to great conductivity – More efficient due to great conductivity

• Top cell N‐1

– both PMOS‐based switches

Terminal voltage jumping Terminal voltage jumping

• Due to pack reconfiguration

Due 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 jumps handle and level voltage jumps.

Acquisition Acquisition

• Worst case

Worst case

– 25V if all connected cell are fully charged (6 cells) – 6mV resolution for 12‐bit ADC

• Hardware solution

– 0~5V  2.5~4.2V 0 5V  2.5 4.2V – 6mV  2.4mV

• Software solution

Software solution

– Oversampling to reduce noise

• Finally 6mV  600uV

Finally, 6mV  600uV

Microcontroller Microcontroller

• Microchip (dsPIC30F3014)

Microchip (dsPIC30F3014)

• Large pinout

2 bi C

• 12‐bit ADC

Balancing algorithm Balancing algorithm

• ACQ

ACQ

– Cell voltage, pack voltage, current

• Voltage mode

– No current acquisition

• SOC mode

– OCV, impedance, neural t k f l i i network, fuzzy logic in previous work

• Ԑvm Ԑsm : deviation

Ԑvm, Ԑsm : deviation

• m:cell index

Balancing algorithm Balancing algorithm

• Charge and discharge

Charge and discharge

– Find min and max deviation

• Selected cell is

bypassed ,and l b d previously bypassed

• ne is reconnected

SOC estimation algorithm SOC estimation algorithm

• Coulomb‐counting

Initial 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 mode Voltage mode vs SOC mode

Refresh time calculation Refresh time calculation

• Ts: SOC estimation time interval

Ts: 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 calculation Refresh time calculation

• Q is the integrated absolute error in SOC

Q is the integrated absolute error in SOC

• Q is low when balancing effect is high

Refresh time calculation Refresh time calculation

• ά is a coefficient related to the discharge rate

ά is a coefficient related to the discharge rate

T1 T2 T3

Refresh time calculation Refresh time calculation

• The SOC mean value

The SOC mean value

• The deviation of the SOC of the m‐th cell with

respect to average SOC results p g

=

Refresh time calculation Refresh time calculation

• Q is proportional to Tref

Q 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 result Theoretical trend vs Measured result

• Quality factor versus number of cells(N) and

Quality factor versus number of cells(N) and refresh time (Tref)

• Discharged at 1C
• Discharged at 1C

Efficiency Efficiency

• Switches that are connected in series to the

Switches that are connected in series to the current flow could overheating of devices and determine a efficiency loss determine a efficiency loss

• Best case

F ll h d ll ith l t – Fully charged cell with a low current

Conclusion Conclusion

• Optimal balancing of the battery pack during

Optimal balancing of the battery pack during

• peration

A supervisory control strategy for series hybrid electric vehicles with series hybrid electric vehicles with two energy storage systems

Pierluigi Pisu and Giorgio Rizzoni V hi l P d P l i 2005 Vehicle Power and Propulsion, 2005 IEEE Conference

Series Hybrid Electric Vehicle Series Hybrid Electric Vehicle

• Fig. 1 Schematic representation of

a series hybrid configuration.

• Fig. 2 Schematic representation of

a connection of two electrical a connection of two electrical power sources configuration.

Energy Management Control Problem Energy 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 i Strategy – 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 to future consumption that will be necessary to recharge the RESS; A present RESS charge corresponds to a future fuel A 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 i Strategy – 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 discharging

The 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 l Single Component RESS

Simulation Result Simulation Result

Fig.7(a) Batteries SOE for HDUD cycle Fig.7(b) Battery pack current for HDUD cycle

• Fig. 6 HDUD driving

cycle cycle Fig.7(c) Ultracapacitors SOE for HDUD cycle

• Fig. 8(d) Ultracapacitors

current for HDUD cycle

Conclusions Conclusions

• it requires the only knowledge of the efficiency maps

for the various systems in the powertrain architecture for 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 ( f the 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
• ptimal 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 be pre‐calculated and saved in a multi‐dimensional map as a function of the input variables, avoiding l d d h f

• n‐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