The global economy is shifting towards a circular, lean, inclusive and clean model. This will significantly affect many industries. Historically, the electrification of a country was a hallmark of modernisation, providing lighting and other amenities to every home. Today, we are witnessing changes in the energy landscape, with all areas of life turning to electricity and moving away from fossil fuels as a power source.
In today’s technologically advanced world, design plays a crucial role in modern engineering. Electrical systems lie at the heart of almost every product and process, from electronic devices to industrial machinery. The demand for efficient and reliable electrical designs has never been greater.
Engineers are inventing and optimising products based on a new energy paradigm that is becoming an inevitable goal for the global economy. So, how can we embrace new technologies in our approach to design and use them deliberately to create unique and efficient electrification solutions?
There are many design tools on the market that enable the use of machine and deep learning in the processes of creating, testing, and implementing specific AI-based models. While non-linear models require huge amounts of time for simulations — sometimes days — and the analysis and design of entire systems can consume thousands of simulations to obtain meaningful results, software using machine and deep learning can significantly speed up simulations and analyses. Many companies in industries such as aerospace, automotive, energy and construction are moving in this direction in design and simulation, as safety components play a critical role in these sectors.
When we enter the factory, we immediately notice that internal transport is already dominated by electric machines. This demonstrates the rapid growth in the use of mobile robotics in intralogistics in recent years. The manufacturing and logistics sector is moving rapidly towards the electrification of its ecosystems.
According to analysts, the drive by many industries to achieve net-zero emissions will lead to a nearly 50% increase in electricity consumption by 2030, creating greater demand for all types of energy systems. Average annual global investment in electricity grids is expected to rise to $520 billion during this period — almost double the 2021 figure.
Companies are increasingly replacing current fossil fuel-based technologies with electric solutions. The introduction of electric propulsion in the automotive, rail, marine and aviation industries, as well as in heavy process industries such as steel production, oil and gas extraction, and the chemical industry, is inevitable in the near future. Large stationary battery energy storage systems are developing rapidly, and pure hydrogen obtained from electrolysis is attracting growing interest.
Simulation, modelling and design
In order to be competitive in the market, engineers must use advanced technologies in the design of their products. AI and machine learning (ML) algorithms can optimise designs, predict performance and automate tasks, resulting in more efficient, cost-effective solutions. Virtual simulations can identify potential problems before physical prototyping, saving time and development costs.
With MathWorks solutions, you can model the behaviour of complex electrical components and speed up simulations by creating AI-based ROM (Reduced Order Modelling) models. You can create, train and test virtual sensors and AI-based automation control strategies for motors, batteries, power converters, energy management systems, electric vehicles and network systems. This environment helps ensure the safe and efficient operation of electrical systems by integrating AI-based energy consumption forecasting and predictive maintenance, both of which are based on artificial intelligence.
The electrification of various applications, including electric vehicles, ships, aircraft, energy storage systems connected to the power grid and photovoltaic systems, primarily drives the use of electric batteries. These applications have different battery system design requirements in terms of cell selection, power/energy density, volume, weight, and service life.
Mathematical modelling using Simscape Electrical
Lockheed Martin, one of the largest companies in the aerospace sector, uses software to simulate Orion spacecraft missions with a multi-domain power system model. The Orion spacecraft will carry a crew of four into space, provide the ability to abort missions, support the crew during travel and ensure a safe return from deep space at high speeds.
In this case, engineers needed to simulate the spacecraft’s power system in order to verify the design and test conditions that could lead to failures during missions lasting up to 21 days without docking with another spacecraft.
Four solar panels and four lithium-ion battery packs will power Orion’s life support, propulsion, guidance, and other systems. To ensure these systems have sufficient power for long space missions, engineers are conducting simulations using a power system model of the spacecraft developed with Simulink and Simscape Electrical tools. Lockheed Martin engineers used this software to model and simulate the power system of the Orion spacecraft.
By simulating numerous mission profiles, they verified the correct selection of the solar panels and batteries and predicted how the power system would perform in scenarios involving insufficient sunlight for energy accumulation over many hours.
According to Hector Hernandez, the lead Orion power system analyst at Lockheed Martin, Simscape Electrical enabled the team to create an integrated power system model combining electrical and thermal domains. This gave them a complete picture during mission-level simulations. Additionally, they will be able to integrate mechanical components if they need to model the operation of the motors that drive the photovoltaic panels.
Designing efficient battery packs: from a single cell to a complete solution
However, electrification cannot happen without basic products such as batteries, which store energy and release it when needed.
Battery power is a necessity for modern devices — even devices connected to the mains often have internal batteries to protect critical systems in the event of a power failure. Today, the power cord’s role is largely limited to charging the battery.
But is battery technology already well understood? Of course not. Improper design of the battery system is one of the causes of many fires in machines, vehicles and devices, or even product recalls and explosions. As end users, we have all probably experienced the frustration of using devices with insufficient battery capacity when something runs out of power, leaving us wishing the device could last a full day until it can be connected to the mains. So, how can batteries be designed to maximise the operating time of a given device or machine?
The automotive and energy storage industries are developing at an unprecedented rate. In the automotive industry, for example, sensors, microcontrollers and power semiconductors are helping car manufacturers around the world to achieve increasingly ambitious goals in terms of safety, affordability and efficiency. So, how should a battery system be designed to best meet the power requirements of a given system?
The energy consumption of electronic systems can vary greatly depending on the application. On the one hand, cars consume megawatts, while modern electronic watches operate with minimal power.
Cell design and engineering is a complex process. Understanding the relationship between mechanical, electrical and thermal design is essential for creating an efficient battery pack. Designing a battery pack requires an understanding of the electrification system and the conditions in which it will operate. This applies equally to batteries for a remote control and a space station.
Put simply, we need to ask ourselves some basic questions, the number of which will increase over time:
How much energy do we need?
How much power do we need?
What is the system voltage?
Using the Battery Builder application in MATLAB, we can design a battery pack for a target device, vehicle or machine. The design involves connecting cells in parallel and in series to create modules for the battery pack.
Using battery pack simulation models helps engineers to achieve an appropriate level of representation, optimising the duration of calculations for designing the battery management system (BMS) and analysing thermal effects through simulation.
The software provides capabilities such as:
- modelling of electrothermal behaviour, taking into account charge dynamics, ageing, thermal effects, and heat transfer in battery cell models;
- use of parameterised cells based on manufacturer data sheets;
- visualisation of battery models with different geometries and topologies, from cell to module and from module to pack;
- modelling cooling plates with configurable fluid paths and thermal connections to the battery pack;
- we can also view temperature changes between cells and evaluate cooling efficiency;
- we can set the appropriate model resolution to balance realism with simulation speed.
Designing battery packs from single cells to complete packs / Photo credit: MathWorks
Undoubtedly, the huge advantage of all this is that we remain in the same design environment. MathWorks provides great peace of mind when working on the entire project, as there is no need to switch to software from another manufacturer.
Battery temperature management
The development of the electric vehicle, industrial, marine and home energy storage markets is and will continue to be driven by the use of high-energy battery packs. As the number of cells and operating voltage continue to increase, these packs require battery management systems (BMS) to monitor and maintain the battery cells in good condition, maximising power output while ensuring user safety.
A BMS is a hardware and software unit that controls a battery pack. This essential component measures cell voltage, temperature and current in the battery. It also detects insulation faults and controls contactors and the temperature management system. The BMS protects both the operator of the battery-powered system and the battery pack itself from overcharging, excessive discharge, electrical overcurrent, cell short-circuiting and extreme temperatures.
When selecting a battery pack, attention should be paid to its thermal and electrical parameters. This is the first thing we should consider. Engineers can use MATLAB and Simulink to design a battery temperature management system that regulates the battery temperature to ensure it remains within specifications and performs optimally under various operating conditions.
Mahindra Electric, an electric vehicle manufacturer, uses simulation to optimise the battery thermal management system in electric vehicles. Electric cars are powered by lithium-ion batteries, which require an effective yet energy-efficient BTMS.
The team developing BTMS algorithms for electric vehicles had to determine the optimal cell configuration to maximise cooling and develop an efficient cooling circuit algorithm for these cells.
Drawing on their experience of Model-Based Design, the team created and verified a simulation model of an electric vehicle incorporating a battery, cooling circuit, refrigeration circuit, vehicle and driver, using Simulink and Simscape tools. They then used this model to develop concepts and tune parameters in order to select the most suitable components for the optimal operating point.
Summary
Battery design requires consideration of safety and durability issues. Advanced simulation solutions offer capabilities that comply with industry standards. These simulations enable engineers to optimise the design of the battery housing and reinforcements, for example, by identifying potential weaknesses and vulnerabilities in the battery structure and layout.
The result is a battery pack that can withstand mechanical stress, vibration and impact, for example in cars. This reduces safety risks.
Furthermore, advanced simulations assess the thermal behaviour of the battery. By simulating various thermal scenarios, engineers can design efficient cooling systems that dissipate heat effectively, ensuring the battery operates within a safe temperature range. The simulations also ensure compliance with industry standards, guaranteeing that battery manufacturers meet the highest safety and quality requirements.
Battery life is a critical factor in ensuring the long-term performance of a solution. Advanced simulations evaluate battery cell behaviour under various operating conditions, enabling engineers to predict cell degradation and identify possible electrical system failure scenarios. This enables them to optimise the battery’s chemical composition and design to enhance its durability.
With advanced tools such as the MathWorks environment, the battery industry can develop safer, more durable and efficient energy storage solutions with confidence, helping to create a sustainable future. Every year, the company transforms the way its customers design their products, accelerating research and development cycles and increasing product efficiency. Its software helps them to solve new engineering challenges.
