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Electric vehicles (EVs) or Battery Electric Vehicles (BEVs) are vehicles that rely completely on batteries for power. Battery cells used in vehicles are largely made from chemical compounds such as Lithium which are susceptible to temperature and pressure changes. In order to ensure battery longevity, the cells must be operated within a safe operating boundary. In our August 2022 blog, we covered the basics of a battery management system which is to ensure the state of a battery is adequately reported and regulated, and detailed why it is a crucial part of battery system design.

In this tutorial, we will learn how to design a simple battery management system in Collimator.

The battery management system design is made up of five components which we will model in Collimator as follows:

- Vehicle load model - Simulates the charging and discharging current cycles of the car
- BMS controller model - Simulates the monitoring of all battery parameters, including the main state machine and the State of Charge (SOC)
- Battery Plant model - Battery plant specified to an industry standard lithium ion battery in the form of a lookup table
- BMS voltage and current sensor
- BMS temperature sensor

An overview of the model is shown below:

The vehicle load demand model, shown below, emulates the charging and discharging current cycles of the vehicle. We model two different vehicle load demands: 1) a pulse with an offset and 2) a constant:

We employ an if/else logic statement to switch between the two:

The primary function of a BMS is to meet the safety requirements for operating a battery under various load and environmental conditions. This ensures lower risk during the operation. Our Battery Management System is composed of 4 parts:

- Current saturation
- Cell current monitoring
- Cell voltage monitoring
- Temperature monitoring

When done well, this will allow us to increase the efficiency of the battery and prolong battery life, thus reducing the total cost of ownership of our electric vehicle.

Current saturation is used to moderate the demand from the driver and ensure that the vehicle does not draw more current than is safe or good for the battery. In our case, we will limit the vehicle load current to predetermined min and max constant values 1 and -1 respectively. Below, we compare the max current (batt_current_max_A) to the min current (batt_current_min) and vehicle demand (veh_load_current_ref_A_0).

We then use a saturation block to limit the output current within the constants defined.

The cell current monitoring begins with the ADC current count:

We run a digital audio conversion of the ADC count, add the offset constant, and multiply this by the resistor constant in order to get the battery current. We'll employ some fault detection using logic operators to ensure our current is within the maximum and minimum amp values.

Next, the main state machine sub-model takes the current and employs if/else statements in Python to identify the state of the battery.

Lastly, our fuel gauge monitor sub-model will estimate the battery percentage and the amount of current stored in the battery.

Similar to the cell current monitoring, the cell voltage monitoring is responsible for converting ADC voltage count in bits to voltage in volts. We will also ensure fault detection is in place to check for overvoltage and undervoltage.

If either the current or voltage is at fault, the battery as a whole will be inoperable and at fault.

For the purpose of this simulation we are assume a constant battery temperature.

The battery plant model is implemented using physics based electrical circuit equations along with data from the manufacturer to closely model a generic Lithium-ion (Li-Ion) battery. The model calculates the internal voltage drop from internal resistance and battery current information. Then a 1D lookup table is used to estimate open circuit voltage from the SOC which is estimated using coulomb counting and continuous integration. Since the battery temperature is modeled as a constant. Both of these are combined to calculate closed circuit voltage which is the battery output voltage.

\begin{equation} V_{CCV} = V_{OCV} + V_R \end{equation} \begin{equation}V_{OCV} = f(SOC)\end{equation}\begin{equation}V_R = I_{battery} * R_{internal} \end{equation}\begin{equation} R_{internal} = f(SOC, T) \end{equation} \begin{equation} SOC = \frac{1}{Capacity_{cell, max}} \int_{0}^t I_{battery}dt \end{equation}

**Where: **

- \(V_{OCV}\)is Cell's Open Circuit Voltage
- \(V_{CCV}\) is Cell's Closed Circuit Voltage
- \(SOC\) is Cell's State of Charge
- \(T\) is Cell's Temperature
- \(R_{internal}\) is Cell's Internal Resistance

- Batteries can be modeled using physics-based equations and data. In this model, OCV-SOC data is employed along with the basic electrical equations to achieve the required battery characteristics

- Charging and discharging current cycles, coulomb counting algorithms, et al. can be quickly developed in the rapid prototyping and early design phases to understand the system and discard designs that do not show promise. This can save development time and reduce risk substantially

- The final complexity of BMS controller models depends on the exact system and the requirements. Collimator's model based design (MBD) environment can be used to meet most automotive standard specifications