Better EV Batteries with MATLAB and AI

Electric Vehicles (EVs) are vehicles that are propelled by electric motors, and use batteries or fuel cells as their energy source. EVs have no tailpipe emissions, which makes them cleaner and more environmentally friendly than traditional gas-powered vehicles. There are four main types of EVs: Battery electric vehicles (BEVs), Plug-in hybrid electric vehicles (PHEVs), Hybrid electric vehicles (HEVs) and Fuel cell electric vehicles. There are essentially only two important components of an EV – the motor and the battery. In this article, we will concentrate on how MATLAB and AI can help improve the efficiency of EV battery. There are two ways in which the battery can be improved – by increasing its life and by increasing its range. To do that, engineers need to consider the parameters that affect its performance. The most commonly used EV battery today is the Lithium-ion battery. The primary parameters that affect its life are voltage, current, and the temperature; the secondary parameters include depth of discharge, charging speed and storage temperature.

Voltage, current, and temperature can all affect the longevity of an EV battery in different ways:

• Voltage: Higher voltages can cause lithium plating, which is a process where lithium ions deposit on the surface of the graphite anode. This can reduce the battery's capacity and shorten its lifespan.
• Current: Higher currents can generate heat, which can damage the battery and reduce its lifespan.
• Temperature: Higher temperatures can accelerate the battery's degradation process. This is because the chemical reactions that take place inside the battery are temperature-dependent.

For an EV to be commercially successful, it is important to make the battery as long lasting as possible. Before we discuss how MATLAB and AI can optimize lifespan of an EV battery, let us first get to know about MATLAB in brief.

Introduction to MATLAB
MATLAB is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. MATLAB is used by engineers, scientists and even students worldwide for a variety of tasks, including data analysis and visualization, algorithm development and prototyping, modelling and simulation, scientific and engineering graphics and application development. MATLAB is particularly well-suited for tasks that involve matrices and vectors, as its core language is based on matrix operations. It also includes a large library of built-in functions and toolboxes for a wide range of applications. With artificial intelligence (AI) taking centre stage recently, the latest version of MATLAB now incorporates AI. It can be used to design new algorithms, train models more efficiently and to improve and debug the models quicker. MATLAB makes it easier for users to get started with AI, even if they don't have a lot of experience with machine learning or deep learning. It also improves the integration of AI with other MATLAB tools, such as Simulink and the MATLAB Coder. This makes it easier to use AI in a wider range of applications.

AI Incorporated MATLAB for EV Battery Design
AI incorporated MATLAB can be used to model EV battery parameters and predict how they will affect the longevity. For example, MATLAB can be used to simulate the effects of different driving patterns and environmental conditions on the battery. This information can be used to develop strategies for extending the battery's lifespan. AI plays a number of important roles in modelling EV battery longevity. Together, MATLAB and AI can be used to:

• develop more accurate battery models. Traditional battery models are often based on simplified equations that do not fully capture the complex behaviour of batteries. AI can be used to develop more accurate battery models that take into account a wider range of factors, such as the battery's chemistry, age, and operating conditions.
• predict battery longevity more accurately. Traditional battery longevity prediction methods are often based on statistical analysis of historical data. AI can be used to develop more accurate battery longevity prediction methods that take into account the battery's current condition and future operating conditions.
• develop new strategies for extending battery lifespan. AI can be used to develop new strategies for charging and discharging batteries in a way that maximizes their lifespan. For example, AI can be used to develop algorithms for adaptive charging, which adjusts the charging rate based on the battery's current condition.

Here are some specific examples of how AI incorporated MATLAB can be used to model the effects of voltage, current, and temperature on EV battery longevity:

• Voltage: MATLAB can be used to simulate the effects of lithium plating at different voltages. This information can be used to set the maximum voltage that the battery can be charged to.
• Current: MATLAB can be used to simulate the effects of heat generation at different currents. This information can be used to set the maximum current that the battery can be charged and discharged at
• Temperature: MATLAB can be used to simulate the effects of temperature on the battery's degradation process. This information can be used to develop strategies for keeping the battery cool, such as using a battery management system (BMS).

In addition to the above, MATLAB and AI can also be used to develop new battery chemistries with longer lifespans, design new battery management systems that can protect the battery from damage and extend its lifespan and develop new algorithms for charging and discharging batteries in a way that maximizes their lifespan. MATLAB provides a number of tools to accomplish this. These tools include Simscape Electrical, Simulink, Battery Builder App, and the Statistics and Machine Learning toolbox. These tools enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP).

In India and abroad, EVs are gaining in prominence for the advantages they offer. Optimizing the battery for improving its longevity and range is crucial to the success of commercial EVs. By combining the capabilities of AI and MATLAB, it is possible to develop more accurate battery models, optimize battery management, extend battery life, and improve the overall performance and reliability of batteries used in electric vehicles. This, in turn, contributes to the advancement and adoption of electric mobility.