How can quantum computing revolutionize the electric vehicle industry?
Jhis year, the Nobel Prize in Physics was awarded to Alain Aspect, John Clauser and Anton Zeilinger for their groundbreaking experiments in quantum physics.
Many real-life problems like modeling the brain, simulating the spread of cancer in the human system, discovering drugs to fight drug-resistant bacteria, understanding the impact of our decisions on the carbon footprint and global warming, forecasting weather and natural disasters, reducing the design cycle time of complex systems such as electric vehicles, airplanes and simulating their performance before physical manufacturing, can be better solved using quantum-inspired algorithms running on quantum computers. Classical computers (working on bit-zeros and ones) impose massive constraints on complex modeling phenomena, and the execution time of precise algorithms is very long. In this article, we discuss how the quantum-inspired algorithm can solve a complex problem of battery thermal management systems in electric vehicles, which has caused several accidents in the recent period.
In today’s geopolitical climate, it’s hard to deny that electric vehicles (EVs) are pioneering the transportation industry’s transition from fossil fuels to renewable energy sources. Countries around the world have pledged to become carbon neutral, with electric vehicles playing a major role in key strategies for reducing greenhouse gas emissions and decreasing dependence on fossil fuels. The United Nations Framework Convention on Climate Change has set guidelines for the contribution required by each of its member countries to reduce greenhouse gas emissions. For India, electric vehicles must gain a 30% market share by 2030. The push to transition to electric vehicles is significant as transport accounts for 10% of all emissions. However, the reliability of electric vehicles is poor, as evidenced by numerous battery-related accidents. To overcome this problem, adapting to advanced computing technologies like quantum computing can ensure increased reliability and security while maintaining cost effectiveness.
The adoption of electric vehicles is essential for India to reduce its carbon footprint and reach its goal of becoming a net zero nation by 2070. The Indian government is investing 9,000 crore rupees (1 billion dollars) until 2024 as part of the faster adoption and manufacturing of (Hybrid &) Electric Vehicles (FAME) II to mitigate the consequences of global warming, with the audacious goal of creating a net zero economy. But even with all the prestige and glory that EVs possess, they still have a fundamental problem. Electric vehicles are highly susceptible to catching fire and even sometimes exploding, as incidents across India this summer have demonstrated. These dangerous incidents are not limited to India, as there is evidence that such cases occur all over the world. Due to faulty battery systems, five major automakers have recalled their vehicles equipped with LG batteries. Battery thermal runaway difficulties are the primary cause of these issues. This conflict is influenced by several variables, including external damage to the battery, improper charging, weather conditions, and improper cooling.
Fires in electric vehicles in India have occurred for several reasons. If the EV detects an overheating problem, it should ideally stop powering the batteries. Additionally, many of these vehicles had no ventilation mechanism to prevent the entire car from igniting. These issues highlight some of the biggest issues electric vehicles are facing today. The main question is how to properly design the battery packs to prevent the batteries from overheating and isolate the problem to inflict the least possible damage.
Read also: Electric vehicles: From transition to transformation in 3 steps
In electric vehicles, the battery thermal management system (BTMS) typically regulates the heat generated by the batteries. Thermal regulation is important for the optimal and safe operation of EVs because EV batteries have a narrow operating temperature range and are dangerous if used above. A BTMS can contain various cooling methods to keep the batteries within the desired temperature window. These include forced air or liquid cooling using phase change materials, heat pipe cooling, or even a combination of these. When designing a BTMS unit, computer simulations and physical testing of battery thermal loads play an important role. In addition, when designing, manufacturers must consider many criteria, such as size, material cost, manufacturability, reliability, and safety. The secret to creating the right design for BTMS is to consider these external criteria.
However, the behavior of BTMS cannot be fully modeled by current techniques and therefore cannot be optimized for these extrinsic factors. The inability to model accurately is due to the heavy computing resources required to perform these calculations. Therefore, it is necessary to explore more advanced computing techniques to speed up the computing process and resolve these issues within a realistic time frame. Some of these methods include graphene-based transistors, DNA computing, neuromorphic computing, and quantum computing. Quantum computing has the best chance of providing a solution that improves the performance of these EV productions out of all the aforementioned possibilities.
Quantum computing is a new computing technology that calculates using qubits instead of traditional 0 or 1 bits. This revolutionary computational method is a probabilistic computational technique, where a qubit is described as a probability of being 0 or 1. Therefore, when combining this new processing system with the principles of quantum mechanics, such as superposition, entanglement and quantum tunneling, quantum computing can perform calculations exponentially faster than the classical computer system. Although currently, the real benefits of quantum computers are only sought for specialized applications, such as finance or drug discovery. However, there is a breed of quantum algorithms – quantum-inspired (QI) algorithms – that can execute instructions on classical machines while improving performance. In the short term, QI algorithms have outperformed classical routines without relying on quantum machines, which can have complications such as decoherence and other quantum noise.
In recent times, there has been a wave of Quantum-inspired optimization algorithms. Integrating them into design optimization will be the key to solving the BTMS problem. The biggest benefit of IQ optimization is the increased size of the design space to help create innovative designs that were previously unexplored. Since IQ optimization algorithms compute faster than traditional optimization methods, IQ optimization methods can easily take into account external factors, such as reliability and security. Thus, a better design can be achieved while maintaining enriched performance. Therefore, the use of QI optimization technique for battery thermal management systems can generate ideal designs to reduce hazardous incidents.
Therefore, optimizing the battery thermal management system with a quantum-inspired approach can modernize its design, thereby reducing the number of associated incidents.
Many problems in the EV space need to be solved. However, the growth of the electric vehicle sector has provided a large number of new technologies in battery materials and faster charging technology. The growth of electric vehicles in India is unprecedented, as highlighted in a forecast report by NITI Aayog (National Institution for Transforming India), a government think tank focused on India’s social, cultural, technological and economic development. In the report, the number of two-wheeled electric vehicles is expected to grow from 2 million vehicles in 2022 to more than 23 million by 2030, and overall electric vehicle sales by about six times in eight years. But this predicted growth is only possible if consumers consider electric vehicles as an alternative to gasoline-powered vehicles. However, the desire for electrification to curb the effects of climate change is weakened by the fatal misadventures of poor thermal management. To combat this problem, pioneering methods of IQ algorithms can reduce the problem by providing an advantage. QI algorithms can improve the battery thermal management system to create immense value for the global e-mobility movement.
Professor Chandan Chowdhury, ISB and Rut Lineswala, BosonQ Psi
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[This article has been reproduced with permission from ISBInsight, the research publication of the Indian School of Business, India]