Project 2
Aim: Study of Li-ion Battery System Design using MATLAB/Simulink.
Objective:
1. To find the max heat generation of the battery.
2. Estimate the SOC of the battery at 2 *104second of the battery operation.
Theory:
A] Heat Generation:
Large battery packs are used in electric vehicles. Heat is generated when the battery pack is being
used. Therefore, it is necessary to predict battery heat generation.
Operating temperature of lithium-ion battery is an important factor influencing the performance of
electric vehicles. During charging and discharging process, battery temperature varies due to internal
heat generation, calling for analysis of battery heat generation rate. The generated heat consists of
Joule heat and reaction heat, and both are affected by various factors, including temperature, battery
aging effect, state of charge (SOC), and operation current. In this article, a series of experiments
based on a power-type lithium manganese oxide/graphite battery was implemented under different
conditions. The parameters for Joule heat and reaction heat are determined, and the Joule heat,
reaction heat as well as total heat generation rate is detailed and analyzed considering the influence of
temperature, aging, SOC, and current. In order to validate the accuracy of heat generation rate, a
lumped battery heat transfer model is applied to calculate the temperature variation, and the
estimated temperature variation shows good correspondence with experimental results under different
currents and aging conditions. Due to its simplicity, the temperature variation estimation method is
suitable for real time applications.
What factors affect the generated heat in a battery?
The generated heat consists of Joule heat and reaction heat, and both are affected by various
factors, including temperature, battery aging effect, state of charge (SOC), and operation
current.
Controlling heat build up in EVs:
Battery thermal management is a huge challenge in electric vehicles. In addition to increasing the risk
of fire, high temperatures can have a direct influence on range. By efficiently controlling EV
temperature levels, battery range can be maximized.
Just like internal-combustion engine vehicles, when an electric power train works harder, it gets
hotter. Therefore, EVs require effective, efficient and durable cooling systems. Heat management can
have a major impact on not only the components within a vehicle, but also its overall performance.
Importance of Thermal Management for EVs?
TMS is generally used for batteries operating under a high discharge rate, and especially for a large-
scale battery pack that requires a long life cycle (>10 years) and is pricy to replace.
The factors that are traditionally considered in the TMS trade-off analysis include energy efficiency,
capital costs, ease of operation, maintenance requirements, and reliability. Cold plate and
thermoelectric devices are not recommended for use in commercial systems, owing to their high
thermal resistance and low coefficient of performance, respectively.
Battery Thermal Runaway in Electric Vehicles:
Thermal runaway begins when the heat generated within a battery exceeds the amount of heat that is
dissipated to its surroundings.
What causes the battery to overheat which leads to thermal runaway?
1. Ambient Temperature
2. Age of the Battery
3. Float Charging Voltage
4. Overcharging
Approaches to Minimize Thermal Runaway
The reduction of the Thermal Runway hazard can be fulfilled on three levels-
1. First: improve the intrinsic safety of anti-TR properties by material modification;
2. Second: set passive defense against the practical abuse conditions and develop an early warning
algorithm before TR;
3. Third: postpone or inhibit the secondary hazard, such as Thermal Runway propagation, to win
sufficient time for the passenger to escape from the EV after the accident.
Formula:
Heat Generation = Joule Heat + Entropic Heat
Joule Heat = i^2 X r X t
Where, t = time (sec/hr)
i = battery current (A)
r = resistance (mohm)
Joule heating, also known as ohmic heating, is used to directly heat water with electric current.
Joule heating is sometimes also referred to as ohmic heating or electrical resistance heating. It refers
to the method of heating electrolyte-filled water by exposing it directly to electrical current.
The entropic coefficient of a lithium-ion battery cell is used to calculate the reversible heat of a
battery during operation, which is a nonnegligible part of the battery thermal modeling.
SOC % (State Of Charge) of a Battery pack:
The SOC is the measurement of the amount of charge remaining on a battery. It represents the ratio
of the current capacity of the battery to its nominal capacity after a period of use or long-term
shelving, which also indicates the maximum amount of power that can be stored in the battery. The
SOC is calculated as a percentage: when it’s at 0, it means the battery is completely discharged.
When the SOC is at 100, it means the battery is completely full.
Absolute Charge State
There is also what’s called an absolute charge state, which is a reference value calculated from the
fixed capacity value when the cell is manufactured. The absolute SOC of a new fully charged battery is
100% while an aged battery, even if fully charged, cannot reach 100% in different charge and dis-
charge situations.
Influencing Factors
Most SOC estimations require precise data measurements, whether it is the chemistry of the cell (type
of electrolyte), its operating conditions, or the variables of the cell (voltage, current), etc., thus being
suitable only in the laboratory and not in real applications. The SOC of a battery is a combination of
voltage, charge/discharge current and internal resistance parameters to estimate its size. These
parameters are also affected by a variety of uncertainties such as battery aging, temperature changes
and usage status.
Accurate SOC estimations reflect important information, such as battery performance, remaining
battery life, etc., which ultimately leads to efficient management and utilization of battery power and
energy. In addition, SOC estimation can be used to regulate battery life degradation, explosions or
fires due to battery over-discharge and overcharge, as well as aging and permanent damage to the
battery cell structure. Therefore, the SOC indicator on a BMS is vital for user convenience, as well as
the efficiency, safety and life of the battery.
Calculations:
1. To find the maximum heat generation of the battery.
Given ;
Battery resistance = 0.5 mohm
Entropic factor = 2
delta time = 0.1
Currrent = -0.896.....(from the drive cycle )
Formula:
Maximum Heat Generated = Joule Heat + Entropic Heat
Now, Joule heat = i^2 X r X t
= (-0.896)^2 X 0.5 x 120 sec
Joule heat = 48 mJ
Therefore,
Maximum Heat Generated = 48 mJ + 2 mJ
Maximum Heat Generated = 50 mJ
2. The SOC of the battery at 2 *104second of the battery operation.
The SOC of the battery pack can be calculated using the MATLAB/Simulink.
Case 1:-
Simulink Model:
In this simulation we are using Drive cycle source FTP75(2474 Second) to estimaye the SOC of a
battery pack for 2*104 sec of time.
Components used:
1. Drive cycle source
2. Controlled current source
3. Battery
4. Bus Selector
5. powergui block
6. Series RLC branch
7. Scope block
Results in Scope:
Case 2:-
If we change the drive cycle source to NYCC (598 Seconds) now the variation occured in SOC
compared with the previous drive cycle source of a battery pack.
Simulation Model:
Results in Scope:
Here from the above both results it can be seen that for various drive cycle source ,in SOC%
variation also takes place, i.e. , For 1st drive cycle source SOC% = 91.8% and for 2nd drive cycle
source SOC% = 96.4%
Battery parameters:
Bus selector:
Resistance:
Drive cycle source:
Given drive cycle can be Import in MATLAB Workspace in a following manner,
1] First we to have prepare excel sheet with time and current values. Which is sown below,
2] Import this excel sheet in a MATLAB workspace.
3] Result in Command window will now look like as shown below.
https://drive.google.com/file/d/1uaMgE80DqslUkFBuPH-_K3xCsnJXfDT9/view?usp=sharing
Kindly open this simulink file with MATLAB Simulink.
Conclusion:
1. Internal heat generation during the operation of a battery is a critical concern. If batteries get too hot,
they can rupture or explode. And Lithium and Lithium-ion cells/batteries can catch on fire when they
rupture, creating even more of a safety hazard.
2. Here, Maximum heat generated in a battery pack is 50 mJ.
3. Accurate SOC estimations reflect important information, such as battery performance, remaining
battery life, etc., which ultimately leads to efficient management and utilization of battery power and
energy.
4. Estimated SOC% of a battery pack is 91.8% and 96.4% for 2 different drive cycle.