Charging 2W/3W EV performance with Simulations powered by Quantum
Updated: May 9
The growth of EV sales In India is expected to be staggering, with projections indicating a surge from 2 million vehicles in 2022 to over 23 million by 2030. In just eight years, overall EV sales are anticipated to increase by a remarkable six-fold, highlighting the significant shift towards sustainable transportation.
The electric 2Wheeler (E2W) market is expected to grow faster, with over 80% of the overall 2W market by 2030. The demand has been spurred due to its low-cost affordability at the consumer level - an electric two-wheeler is significantly cheaper per km. Another factor aiding its growth is the rise in e-commerce and delivery-based businesses. In addition, the EV2W is apt for last-mile connectivity and app-based micro-mobility services in a densely populated country like India.
As competition hots up in this space, EV 2-wheelers and 3-wheelers (E2/3W), manufacturers must accelerate innovations to be competitive. Simulations powered by Quantum could be the key to spurring innovations and meeting this demand. Currently, complex simulations using many variables are bottlenecked due to the limited computational power of classical high-performance computing (HPCs). As a result, engineering teams have to trade off between high accuracy and time to market.
Quantum computing can overcome this limitation by processing large amounts of data to make simulations faster and providing simulation solutions with the same computational power.
The exponential power of Quantum computing can offer a significant advantage to the electric vehicle (EV) industry, particularly in the development of EV 2/3 wheelers. By processing large amounts of data faster, Quantum computing can result in more efficient and optimized designs, developing high-performance, safe and sustainable batteries.
Even as battery manufacturers have rapidly advanced battery designs, EVs are highly susceptible to catching fire and occasionally exploding during Indian summers.
The reasons behind catastrophic events in current lithium-ion cells can be categorized into three main types: electrical, mechanical, and thermal.
Thermal stability in Lithium (Li) Ion batteries reduces due to the following reasons:
Figure: Thermal runaway propagation
Reducing Thermal runaway is a critical application for simulations that require high-accuracy results. As E2W and E3W have many end-use cases in a country like India, they are used for different load-carrying capacities, passenger transport for short distances, and last-mile delivery for e-commerce companies. This variety of usage requires different battery and Battery Thermal Management Systems (BTMS) configurations tailored for each use case. Inefficient designs can lead to hazardous battery pack explosions due to extreme temperature, voltage, and charge/ discharge rate.
Quantum-powered Design Optimization can be used for creating safe external battery casings associated with battery pack designs. It can prevent multiple external conditions that lead to thermal, mechanical, or thermal abuse.
Most E2W and E3W OEMs opt for swappable and portable battery packs for their electric scooter and rickshaws. This has many advantages like fast recharge of the packs and packing more capacity into the scooter. However, connecting two or more batteries of like voltages and capacities is yet another design challenge for engineers designing BTMS.
Challenges associated with designing safe BTMS:
Simulating complex behavior of Batteries with multiple loading conditions
Estimating overvoltage, under-voltage, and overheating cut-off
Predicting SOH (State of Health) and SOC (State of Charge) of Li-Ion Batteries at scale for improving battery efficiency. Simulating accurate chemical behavior under different conditions
The battery makes up 25% of an EV’s total cost, and half of that battery cost is materials. Simulations to optimize battery chemistry with new materials and components can reduce battery costs tremendously. In addition, it can help researchers identify the sweet spot between faster charging times and a battery’s expected life. Modeling these simulations involves considering multiple complex variables. As E2W and E3Ws are used for various loads and conditions, especially in Southeast Asia, several external factors come into play.
Quantum-Powered Simulation can explore better design while considering multiple factors such as loading conditions (Voltage and currents), mechanical stresses, and heat dissipation. Accordingly, it improves the rapid cooling of Lithium-Cells and predicts materials for better thermal barriers used between batteries.
Areas, where Quantum Powered Simulation can improve to reduce thermal runaway problems, are as follows:
High-fidelity material and thermal analysis
Optimized Structural designs
Multi-objective optimization for better battery pack designs and thermal performance of battery cooling
Battery OEMs and Manufacturers must consider many criteria, such as size, material cost, manufacturability, reliability, and safety. In addition, they also need various external factors for designing the battery components. This complexity makes simulations by current techniques challenging to optimize due to the heavy computing resources required to perform these calculations. Therefore, exploring more advanced computing techniques like Quantum computing are required to solve these problems in a realistic timeframe.
BQP recently released the first version of BQPhy with its proprietary quantum-inspired algorithms integrated as part of a CAE solver, leveraging the power of quantum computing that can run on both HPC and quantum computers. QI optimization algorithms compute faster than traditional optimization methods and offer a higher exploration rate with the same number of computational resources. In addition, this approach works best with a wide range of variables and constraints and can readily consider external factors such as reliability and safety.