Battery Management System Development in Simulink

Summary

The video script details the design and simulation of a Battery Management System (BMS) in Simulink, focusing on its application in an electric vehicle powertrain. The BMS model is used to simulate various usage cycles and environmental conditions to evaluate the system's response to unsafe conditions, such as temperature, voltage, or current outside recommended limits. The script explains the model's components, including the battery pack with different cell configurations, the BMS ECU with monitoring and control algorithms, and the charger and inverter contactor circuitry. It also discusses the importance of accurate State of Charge (SOC) estimation and presents three methods for this purpose: Coulomb counting, Unscented Kalman Filter (UKF), and Extended Kalman Filter (EKF). The video emphasizes the need for balancing individual battery cells to prevent underutilization and the significance of active thermal management to maintain cell condition uniformity. The script concludes by highlighting the use of Simulink, Stateflow, and Control System toolboxes for designing the BMS through modeling and simulation.

Takeaways

• 🚗 The BMS model in Simulink is used for desktop simulations to evaluate the system's response to unsafe conditions such as temperature, voltage, or current outside recommended limits.
• 🔋 The model can simulate various usage cycles and environmental conditions, aiding vehicle designers in testing scenarios without damaging real batteries.
• 📊 The BMS ECU within the model has monitoring and control algorithms connected to a block representing the battery pack and its circuitry and peripherals.
• 🔌 The model includes two versions of the battery pack: a small one with six cells and a larger one with 16 modules, each containing six cells in series.
• 🔥 The thermal layout of the battery pack is asymmetrical, leading to significant temperature differences among cells, which is crucial for understanding real-life performance.
• 🔄 Each cell in the model has an equivalent circuit that includes temperature, SOC, and aging dependencies, mimicking real-life lithium chemistry behaviors.
• 🔄 The BMS algorithm includes passive balancing circuitry to maintain battery cell modules' balance and better utilize total storage capacity.
• 🔌 The model also includes charger and inverter contactor circuitry to prevent high current from damaging the battery pack during charging.
• 🔍 The BMS subsystem uses individual cell voltages and temperatures to calculate maximum allowable charge and discharge current levels for safety.
• 🔄 State of charge (SOC) estimation is crucial and is achieved through three methods: Coulomb counting, unscented Kalman filters, and extended Kalman filters, each with its merits and limitations.
• 🔧 The model allows for the assessment of SOC estimation accuracy and the effectiveness of balancing procedures, highlighting the need for active thermal management.

Q & A

What is the primary purpose of the BMS model in Simulink?

The primary purpose of the BMS model in Simulink is to simulate desktop simulations for reproducing diverse usage cycles and environmental conditions to evaluate the system's response to potentially unsafe conditions such as temperature, voltage, or current outside the recommended limits.

How does the BMS model help in testing different scenarios without damaging real batteries?

The BMS model allows vehicle designers to test various situations like different temperatures, state of charges, and aggressive drive cycles in simulation, which helps in understanding the system's behavior without risking damage to the real battery.

What are the two versions of the battery pack included in the model?

The model contains two versions of the battery pack: a small one with just six cells connected in series, and a larger one with 16 modules, each module containing the six-cell series string, all connected in one parallel string.

Why is it important to model the physical domain of each cell in the battery pack?

Modeling the physical domain of each cell, indicated by different colors (e.g., blue for electrical, orange for thermal), helps in understanding how cells exchange heat with one another and how thermal layouts can affect temperature differences among cells, which is crucial for battery performance and safety.

What role does the equivalent circuit play inside each unit cell?

The equivalent circuit inside each unit cell, which should have a topology and parameters that mimic real-life lithium chemistry responses, is essential for providing an equivalent response to what would be observed experimentally, including dependencies on temperature, SOC, and possibly aging.

How does the BMS algorithm manage cell balancing to optimize battery utilization?

The BMS algorithm manages cell balancing by selectively closing switches in the passive balancing circuitry, which allows cells to undergo a partial discharge to lower their voltage, maintaining the battery cell modules' balance and enabling better utilization of the total storage capacity.

What is the significance of the charger and inverter contactor circuitry in the BMS model?

The charger and inverter contactor circuitry are important for safely connecting the battery pack to a charger. They prevent excessively high current from rushing into the pack, which could potentially cause damage, by implementing a special pre-connection sequence.

How does the BMS algorithm protect the battery pack from physical damage due to temperature extremes?

The BMS algorithm uses a lookup table with a rising or falling s-shaped profile to specify a current threshold based on temperature, modulating the allowable current delivery to avoid physical damage to the cell materials at high temperatures during charge and discharge, and at low temperatures during charge.

What are the three methods for state of charge (SOC) estimation presented in the BMS model?

The three methods for SOC estimation presented in the BMS model are Coulomb counting, unscented Kalman filter (UKF), and extended Kalman filter (EKF). Each method has its merits and limitations and is used to estimate the SOC based on different principles and data.

How does the state machine in the BMS algorithm define the main operating states?

The state machine in the BMS algorithm uses a state flow diagram to define the main operating states, which include standby, driving, charging, and fault states. It uses components representing states that are active or inactive depending on conditions, with code executing during entry, during, or exit from the state.

What is the importance of accurate SOC estimation in a battery management system?

Accurate SOC estimation is crucial as it determines how much longer a vehicle can be driven before needing to recharge. It is more challenging than traditional fuel gauge design because SOC is not directly measurable; instead, other measurements are related to SOC, which requires sophisticated algorithms to estimate correctly.

How does the BMS model handle temperature differences among cells to prevent uneven degradation?

The BMS model addresses temperature differences by showing the significant discrepancy among the hottest and coldest cells due to layout symmetry in thermal behavior. It highlights the need for active thermal management to keep thermal differences within a few degrees Celsius to prevent faster degradation of hotter cells.

What are the capabilities and limitations of Coulomb counting for SOC estimation?

Coulomb counting is simple and has a very low computational cost, making it beneficial for SOC estimation. However, its limitations include the accumulation of current sensor error and the inability to recover from a wrong initial condition due to the lack of feedback from voltage measurements.

Why is it necessary to activate passive balancing in the BMS model?

Passive balancing is necessary to keep individual battery cells at roughly the same state of charge. Without it, the cell with the highest SOC would limit the amount of charge that can be put into the pack, leading to underutilization of the system and reduced performance.

Outlines

00:00

🔋 Introduction to BMS Model in Simulink

The first paragraph introduces the Battery Management System (BMS) model in Simulink, which is used for desktop simulations to evaluate the system's response to various usage cycles and environmental conditions. It highlights the ability to test scenarios like temperature, voltage, or current outside recommended limits without damaging a real battery. The script discusses a specific example involving an electric vehicle powertrain with a 75% charged battery at 15 degrees Celsius, and the subsequent driving, charging, and resting stages. It emphasizes the model's utility in testing different conditions, such as varying temperatures and states of charge, and its components including the BMS ECU, monitoring and control algorithms, and the representation of the battery pack and its circuitry. Two versions of the battery pack are mentioned: a smaller one with six cells and a larger one with 16 modules, each containing six cells in series. The paragraph also touches on the thermal layout and the importance of modeling each cell to represent real-life lithium-ion cells, including their chemistry and equivalent circuit parameters.

05:01

🚦 BMS Algorithm and Battery Pack Protection

The second paragraph delves into the BMS algorithm, which is responsible for monitoring, protecting, and reporting measurements from the battery pack. It explains how the system uses cell voltages and temperatures to calculate maximum allowable charge and discharge current levels, preventing physical damage to the cells at high or low temperatures. The paragraph outlines the state machine within the BMS that defines the main operating states, including standby, driving, charging, and fault states. It also discusses the charging sequence, emphasizing the importance of pre-connecting the battery to the charger via a resistor to prevent high current inrush. Furthermore, the paragraph explores different methods for State of Charge (SOC) estimation, including Coulomb counting and the Unscented and Extended Kalman filters, and their respective advantages and limitations.

10:04

🔧 Balancing and Thermal Management

The third paragraph focuses on the importance of balancing individual battery cells to maintain similar states of charge, which prevents underutilization of the battery pack. It describes the state logic that calculates voltage differences between cells and activates passive balancing to reduce the SOC of higher cells. The paragraph also discusses the simulation results, showing how cell voltages converge due to the balancing procedure and how charging currents are managed to prevent voltage thresholds from being exceeded. The temperature traces highlight the thermal discrepancy among cells due to the module layout, emphasizing the need for active thermal management to maintain cell condition uniformity. Lastly, the paragraph presents SOC estimation results using different methods, demonstrating the recovery from initial errors in the estimation algorithms.

15:05

📈 Simulation Results and Conclusion

The fourth and final paragraph summarizes the simulation results, showcasing the convergence of cell voltages and the effectiveness of the balancing procedure. It also discusses the BMS state and balanced command signals, providing insights into the system's operation. The paragraph concludes by highlighting the use of Simulink, Stateflow, and Control System toolboxes for designing the BMS through modeling and simulation, and then hands back the discussion to Chirag.

Keywords

💡BMS (Battery Management System)

Battery Management System (BMS) is a critical component in the video that monitors, controls, and protects the battery pack from overcharging, over-discharging, and overheating. It is essential for the safe and efficient operation of batteries, especially in electric vehicles. In the video, the BMS is modeled in Simulink to simulate various usage scenarios and environmental conditions to evaluate the system's response to potentially unsafe conditions.

💡Simulink

Simulink is a software platform used for modeling, simulating, and analyzing multidomain dynamical systems. In the context of the video, Simulink is utilized to create a model of the BMS and the battery pack. This allows for the simulation of different driving scenarios and environmental conditions to test the system's performance without risking damage to a real battery.

💡State of Charge (SOC)

State of Charge (SOC) is a measure of the amount of charge left in the battery. It is a critical parameter for determining how much longer a vehicle can be driven before it needs to be recharged. In the video, the BMS uses different methods to estimate the SOC, such as Coulomb counting, unscented Kalman filters, and extended Kalman filters. Accurate SOC estimation is vital for the efficient use of the battery's capacity.

💡Electric Vehicle (EV)

An electric vehicle (EV) is a vehicle that uses electric motors and batteries for propulsion. The video discusses how the BMS is part of an EV powertrain and how it plays a crucial role in managing the battery pack to ensure the vehicle's performance and safety. The script mentions a scenario where the battery is 75% charged and the outside temperature is 15 degrees Celsius, illustrating the conditions under which the BMS operates within an EV.

💡Temperature, Voltage, and Current Limits

These are the operational limits that the BMS must ensure the battery pack does not exceed to prevent damage or unsafe conditions. The video explains that the BMS model allows for the simulation of scenarios where these limits are potentially exceeded, such as high temperatures or undervoltage situations. The BMS algorithm calculates the maximum allowable charge and discharge current levels based on individual cell voltages and temperatures.

💡Aging Dependencies

Aging dependencies refer to the changes in a battery's performance over time due to usage and environmental factors. In the video, it is mentioned that the equivalent circuit components within each unit cell of the battery pack should include aging dependencies to accurately simulate the response of a real-life lithium-ion battery. This is important for the BMS to predict and manage the battery's performance throughout its lifecycle.

💡Passive Balancing Circuitry

Passive balancing circuitry is a component of the BMS that helps maintain the battery cell modules' balance by allowing cells with higher voltage to discharge slightly. This is important for maximizing the utilization of the battery's total storage capacity. The video describes how the BMS algorithm commands the passive balancing circuitry to selectively close switches for partial discharge of cells.

💡Charger and Inverter Contactor Circuitry

This refers to the electrical components that control the connection between the battery pack and the charger or inverter. The video emphasizes the importance of a special sequence for pre-connecting the battery pack to the charger via a resistor to prevent high current from potentially damaging the battery. This sequence is part of the BMS's protective measures.

💡Equivalent Circuit

An equivalent circuit is a simplified electrical model that represents the behavior of a more complex system, such as a battery cell. In the video, it is stated that each unit cell in the battery pack model has an equivalent circuit with topology and parameters that mimic the response of a real-life lithium-ion cell. This model includes temperature, SOC, and aging dependencies, which are crucial for simulating the cell's performance accurately.

💡State Machine

A state machine is a computational model used to design logic in a system that can be in one of a finite number of states at any given time. In the context of the video, the state machine within the BMS defines the main operating states, such as standby, driving, charging, and fault. It uses state flow in Simulink to represent the logic that determines the actions of the BMS based on the current state and conditions.

💡Coulomb Counting

Coulomb counting is a method for estimating the SOC of a battery by integrating the current that enters and leaves the cell over time. It is mentioned in the video as one of the three SOC estimation methods implemented in the BMS model. While it is simple and has a low computational cost, it has drawbacks such as the accumulation of sensor errors and the inability to recover from a wrong initial condition without voltage feedback.

Highlights

The BMS model in Simulink can simulate desktop scenarios to evaluate the system's response to potentially unsafe conditions like temperature, voltage, or current outside recommended limits.

The model is used to test various situations without risking damage to the real battery, aiding vehicle designers in assessing diverse usage cycles and environmental conditions.

The battery system can be part of an electric vehicle powertrain, with the model simulating different states such as driving, charging, and resting.

The model includes two versions of the battery pack: a small one with six cells and a larger one with 16 modules, each containing six cells in series.

Each unit cell within the battery pack is representative of a real-life lithium-ion cell, with an equivalent circuit that includes temperature, SOC, and aging dependencies.

The BMS algorithm includes monitoring and control algorithms to manage the battery pack and associated circuitry and peripherals.

Passive balancing circuitry is used to maintain the battery cell modules' balance, allowing better utilization of the total storage capacity.

The charger and inverter contactor circuitry are modeled to prevent high current from potentially damaging the battery pack upon connection.

The BMS algorithm uses individual cell voltages and temperatures to calculate maximum allowable charge and discharge current levels.

Current thresholds are determined based on temperature to avoid physical damage to the cell materials at high temperatures during charge and discharge.

The state machine within the BMS defines the main operating states, including standby, driving, charging, and fault.

Accurate estimation of the battery's state of charge (SOC) is crucial for determining how long the vehicle can be driven before needing to recharge.

Three different methods for SOC estimation are presented: Coulomb counting, unscented Kalman filter (UKF), and extended Kalman filter (EKF).

Coulomb counting is simple and low-cost but lacks the ability to recover from a wrong initial condition due to the absence of voltage information.

Kalman filter algorithms, UKF and EKF, recover from initial errors and rely on a model of the unit cell to predict terminal voltage for SOC estimation.

Active thermal management is necessary to keep thermal differences within a few degrees Celsius, preventing uneven cell degradation.

The simulation results show how individual cell voltages converge towards each other as a result of the balancing procedure.

The BMS state and balanced command signals are monitored to ensure the battery pack remains within recommended electrical and thermal limits.

The use of Simulink, Stateflow, and Control System toolboxes allows for comprehensive design and simulation of the battery management system.

Transcripts

in the next few minutes I'll explain the

main components of the BMS modeled in

Simulink we can use this model for

desktop simulations where we can for

example reproduce diverse usage cycles

and environmental conditions to evaluate

the system's response to a potentially

unsafe condition for example a

temperature voltage or current outside

the recommended limits let's say this

battery system is part of an electric

vehicle powertrain say the battery is

75% charged and the outside temperature

is 15 degrees Celsius in these

conditions we start driving for a while

and stop and charge the battery

finally the battery is at rest and the

balancing cycle kicks in how do we know

that during these three typical usage

stages the battery pack remains within

recommended electrical and thermal

limits what if the temperature is 40

degrees Celsius instead of 15 how about

if the initial state of charge is 30%

well an aggressive drive cycle will

cause an under voltage situation the

model allows the vehicle designer to

test all these situations in simulation

without risking causing damage to the

real battery this is the BMS model in

simulink the battery and its management

system are inside this model reference

on the top left we define a different

driving scenarios that determine the

test sequence provided by the subsystem

on the bottom left the green lights at

the top right indicates whether there

has been a fault

for example any cell has reached an over

temperature condition the system itself

has a model reference representing the

BMS ECU with its various monitoring and

control algorithms connected to a block

with a representation of the battery

pack and associated circuitry and

peripherals this model contains two

versions of the battery pack a small one

with just six cells connected in series

and a larger one with 16 modules

each module containing the six cell

series string in all cases we model just

one parallel string let's start with a

description of the battery pack and

spare Ferrell's the variant subsystem on

the Left contains the two versions of

the battery pack mentioned before the

one with the six cells and the large one

with 96 cells let's take a look at the

small one first this battery packs model

in Sims Cape where the component color

tells us its physical domain for example

blue indicates electrical an orange

indicates a thermal we can see that the

six cells are connected in series and

can exchange heat with one another the

thermal layout is asymmetrical with cell

number six at the bottom insulated on

one side so no heat can dissipate in

that direction and cell number one at

the top exposed to the outside

atmosphere and therefore getting rid of

the heat by convection this asymmetry

will be responsible for a significant

temperature difference among the six

cells so what makes each unit cell

representative of a real-life lithium

and chemistry say nickel manganese

cobalt or NMC well inside each cell

there's an equivalent circuit whose

topology and parameters should give me a

response equivalent to the one I would

observe experimentally the equivalent

circuit components should also include

temperature SOC and possibly aging

dependencies if you're interested in

cell characterization a detailed account

on how to perform this parameter

estimation on battery cells is available

on our website searching for battery

modeling next to the battery pack

there's a subsystem with a passive

balancing circuitry commanded by the

balancing logic from the BMS algorithm

these switches selectively close

their corresponding cell needs a partial

discharge to lower its associ

maintaining the battery cell modules

imbalance allows me to better utilize

its total storage capacity as we will

see in a few minutes another important

element of the plant model is the set of

charger and inverter contactor circuitry

prior to connecting a battery back to a

charger it is important to pre connect

them via a resistor to prevent an

excessively high current from rushing

into the pack and potentially damage it

this pre connection needs a special

sequence that we will show and we

describe the BMS algorithm finally the

last piece of the battery plan that we

model here are the charger and the load

both simply represented here by current

sources commanded to follow the charging

and driving profiles from the source

block at the model top level let's now

focus on the BMS algorithm this part of

the battery management system monitors

protects limits and reports measurements

from the battery pack the subsystem left

uses individual cell voltages and

temperatures to calculate maximum

allowable charge and discharge current

levels when a cell is at low SOC its

voltage is low and it is important to

prevent the cell from delivering a large

amount of current since this would cause

an excessively large voltage drop that

could potentially be below the cutoff

voltage

specified by the cell manufacturer

comparing the minimum cell voltage in

the module against this lower threshold

and dividing it by the maximum internal

resistance value calculated for this

cell we can compute a voltage based

current threshold

we also know that it is important to

limit current deliver your intake when

temperature is too high or too low

using a lookup table with a rising or

falling s-shaped profile we can specify

a current threshold based on temperature

and modulate the allowable current

delivery this is very important to avoid

physical damage to the cell materials at

high temperatures both during charge and

discharge and at low temperatures during

charge since doing so below freezing

temperature is now allowed these two

thresholds are then compared against one

another and the lowest becomes the

current limit

the subsystem labeled state machine

defines the main operating state of the

BMS it is represented here using state

flow as Simulink add-on toolbox meant

for designing state logic in state law

we used components representing states

that are active or inactive depending on

the conditions and the text were writing

inside a state is code that executes

saan entry curing or an exit from the

state the state machine has four

parallel States parallel here meaning

that they can be active at the same time

the first one defines the variable BMS

state which indicates standby driving

charging and fault

charging comprises constant currents and

constant voltage stages the second state

switch is the fault state on incase of a

current voltage or temperature value

reaches an unsafe level the third and

fourth states define the contactor on

and off switching sequence for the

charger and inverter this is needed to

avoid an excessively large current in

rush at the beginning of the charging

stage knowing how much longer we will be

able to drive our car before we need to

stop for a recharge depends on accurate

estimation of the battery SOC this is of

great importance and it is much more

challenging than in the case of

conventional vehicle fuel gauge design

where the measurement is direct in

battery systems we do not measure state

of charge which is not directly

measurable we actually measure something

else and hopefully relate that to the

SOC the third subsystem contains three

different methods for state of charge

estimation in practice the BMS developer

would only use one of them but here we

present all three

illustrate their individual merits and

limitations

the first method known as Coulomb

counting consists of integrating the

current that enters and leaves the cell

to keep track of the state of charge

over time a benefit of this method is

its simplicity and very low

computational cost its drawbacks include

the accumulation of current sensor error

and its inability to recover from a

wrong initial condition because of the

lack of feedback from voltage

measurements the second and third SOC

emission methods implemented here are

the unscented and extended Kalman

filters both are variations of nonlinear

common filters and they rely on a model

of the unit cell to predict a terminal

voltage resulting from a current

stimulus estimating the internal cell

states the SOC among them by comparing

this prediction against measurement of

the terminal voltage the choice between

EKF and UKF is typically made based on

the severity of the systems

non-linearity in this case the only

non-linearity presence is given by the

OSI vsoc relationship and it is a mild

one so it is expected that the EKF

should give adequate results the common

filter algorithm has two parts the state

update and the measurement update the

state update predicts the current state

based on the previous state value and

the input and the measurement update

corrects this prediction using newly

acquired data the cell model we use is

implemented here as a MATLAB script and

corresponds to the equivalent circuit we

use to simulate the battery pack the

next task to consider is balancing it is

important to keep individual battery

cells roughly at the same state of

charge

otherwise the cell with high

so sea level will limit the amount of

charge that we can put into the pack

rendering the system underutilized this

state logic calculates the voltage

difference between the highest and

lowest cell voltages and based on

whether this difference exceeds a design

value activate pest balancing balance

commands is a boolean vector that

indicates which bleach resistor to

activate so that the cell SOC is slowly

reduced doing this with all cells but

the one whose SOC is the lowest

eventually makes all associates converge

within a prescribed tolerance let's now

take a second look at the simulation

results in this driving charging

balancing sequence example we first

observed individual cell voltages

changing as a result of current flowing

in and out at the beginning of the

simulation they are slightly different

because we initialize the model with a

slight SOC imbalance towards the end of

the simulation the values converge

towards one another as a result of the

balancing procedure and how about

currents look at the charging period

during the constant current stage the

current is generated because the maximum

module cell voltage is high enough

compared to a prescribed four point four

volt limit that an excessively high

current could try the voltage beyond the

threshold significantly limiting the

life of the battery since we calculate

the limits based on the maximum

resistance value within the battery cell

locum table we're in conservative a less

conservative current limiting

calculation could use the actual battery

cell resistance at the estimated SOC and

temperature since this information is

available at all operating conditions

the temperature traces show a

significant discrepancy among the

hottest and coldest sell the reason is

mainly be a symmetry in the module

layout in terms of thermal behavior cell

number six gets significantly hotter

than seller one because it is thermally

insulated on one side even when the

maximum temperature reached during the

simulation is not of a medium concern in

terms of safety the temperature

difference exhibited here will

eventually cause a much faster

degradation of cell six

compared to cell one leading to an

undesirable unevenness in cell condition

hence the need for active thermal

management to keep thermal differences

within a few degrees Celsius the graph

at the top right shows three SOC

estimation traces of the same battery

cell each performed with a different

method yellow corresponds to cool of

counting blue corresponds to UKF and

orange to EKF the initial SOC in the

simulation is seventy-five percent but

the SOC estimators were initialized to

eighty percent to assess their

capability to recover it is apparent

that Coulomb counting never does because

it has no way to realize it is wrong due

to the absence of voltage information

both Kalman filter algorithms on the

other hands recover from the initial

error within the first hour of simulated

time with the EKF slightly outperforming

the UK earth and finally the other two

scopes indicates that BMS state and each

of the six balanced command signals

respectively in summary we've utilized

Simulink state low sim scape and control

system toolboxes to design a battery

management system using modeling and

simulation now I'll turn it over back to

Chirag