Where new innovations and improvements for Li-ion battery technology are needed and how digital twins can help.
In the last decade, the electric vehicle (EV) industry has grown tremendously from just few offerings to today, when every automaker is working to electrify its vehicle portfolio. A lion’s share of this growth can be attributed to the advancements in lithium-ion (Li-ion) battery technology. Since 2010, Li-ion battery costs have come down by 87% [1] and energy density has tripled [2]. Automakers now offer electric vehicles with a 200+ mile range, compared to 100+ miles just 10 years ago, while maintaining the same price point. As the auto industry enters the new decade, which one can argue is the decade of vehicle electrification, it is focusing on some key challenges where new innovations and improvements for Li-ion battery technology are needed. This article highlights the challenges and how suppliers and OEMs can leverage the digital twin of Li-ion battery design and manufacturing to stay ahead of the curve.
Range anxiety is a strong function of total battery pack energy and the charging time. The significant increase in Li-ion cell energy density and reduction in cost have made 250-mile drive range with 60-100 kWh battery packs a new normal for today’s electric cars. The ability to fast charge, therefore, is increasingly becoming essential to address range anxiety concerns. DC fast chargers with 150 kW to 350 kW power that can recharge 80% of the battery in 15 to 30 minutes have been introduced in recent years.
Current fast charging technologies are posing new challenges for Li-ion battery design, life and safety. To allow fast charging, battery suppliers must optimize cell chemistries, cell design and thermal management strategies. Putting 150 kW to 350 kW power for a short time in a battery pack will cause a rapid temperature increase, which can lead not only to safety issues or unexpectedly rapid aging, but also can trigger the controller to reduce charging power. This in turn can significantly increase charging time. Equally importantly, the impact of fast charging on battery cycle life must be assessed while accounting for user-specific charging behavior such as fast charging whenever one gets an opportunity, or when the battery is about to run out of energy. The implications of vehicle-to-grid (V2G) on battery aging are also critical for battery suppliers and OEMs to understand.
Battery suppliers and OEMs will need to deal with multi-scale challenges – from electrode microstructure to large pack thermal management, charging algorithms and variety of vehicle charging profiles – as the industry accelerates towards offering fast charging. To address these complex challenges, a simulation-driven development for Li-ion battery for fast charging, such as the one Siemens offers, will be playing a key role. Simcenter Battery Design Studio (BDS), a physics-based Li-ion cell design software, can help battery vendors and OEMs to optimize electrode designs to balance fast charging with overall cell energy density and to drive cost reductions. Simcenter BDS predictions of cell electrochemical behavior and internal distribution of Li-ion inside a cell are benefiting cell suppliers to understand the limiting factors and improve their cell designs to allow fast charging.
Simcenter STAR-CCM+, a 3D computational fluid dynamics (CFD) software, enables calculation of coupled 3D thermal and electromechanical behavior with detailed spatial effects of rapid charging at cell, module and pack level. With accurate 3D distribution of Li-ions and temperature, engineers can develop cooling strategies for modules and packs in real rapid-charging conditions. Battery pack thermal runaway simulations with STAR-CCM+ further enable engineers to safeguard their pack designs against potential safety events.
Siemens’ 1D system simulation software, Simcenter Amesim, empowers companies to accurately simulate Li-ion aging as a function of usage and charging profile, and also allows auto OEMs to design and optimize vehicle thermal management networks that account for battery cooling needs during fast charging. Synergy and integrated workflow among these software solutions further builds a digital thread for battery engineering that allows rapid innovations and flexibility to adapt to changing requirements as the industry races towards fast charging.
Fast and accurate battery aging simulation.
Module and pack simulation help optimize thermal management strategies for fast charging.
In the last few years, vehicle electrification has evolved as a key disruptor to the auto industry and supply chain. According to a Siemens analysis, 483 companies in the world are developing electric cars or light-duty trucks. Furthermore, according to recent analysis by McKinsey & Company [3], suppliers are posing increasing competition to OEMs’ in-house strategies or their relations with Tier 1. This disruption combined with unprecedented startup activities is challenging traditional engineering and manufacturing methodologies.
Battery pack manufacturers are confronted with exploding product complexity and are dealing with an emerging workforce with nontraditional development roles. Battery packs are extremely complex, with hundreds or thousands of cells, electronics, high-voltage wires, fans, coolant pumps, and other components. Recently, pack suppliers and automakers are looking to further integrate additional power electronics components in the same housing of the battery pack to more effectively utilize volume and also to innovate novel thermal management schemes. To address these pack engineering issues, engineers need to account for CAD complexities for pack enclosure design and vehicle integration strategies. Also, as more companies are aiming at developing new battery and electric powertrain technologies, they are looking to leverage the power of complex battery pack simulations without over-reliance on CFD experts to solve battery pack engineering problems.
To address these use cases Siemens delivers Simcenter FLOEFD, which allows CFD simulations for battery packs in multi-CAD environments. Since Simcenter FLOEFD runs within the native CAD environment, there is no need to change back and forth between CAD and CFD simulation environments. Ease of handling complex geometries and automated meshing capabilities in FLOEFD enable CAD engineers to accelerate battery pack enclosure design while accounting for vehicle integration constraints. Engineers can not only save time and cost to optimize battery packs but also easily adapt to any changes in battery design to aid ease of manufacturing at module or pack level.
CAD-embedded CFD simulation enables battery pack design engineers to explore design variants and vehicle integration options without relying on CFD experts.
Boston Consulting Group (BCG) forecasts that global capacity for battery cell production will exceed market demand by approximately 40% in 2021, exerting tremendous price pressure. To preserve margins at lower prices, producers will need to reduce manufacturing costs. According to BCG, producers can reduce costs by up to 20% by transitioning to Industry 4.0 technologies. [4]
For battery pack suppliers as well as automakers, factory-of-the-future concepts are crucial to moving EV production into the fast lane and reducing manufacturing costs. As companies re-think manufacturing processes, they must digitally plan and validate novel production approaches before implementing them on the shop floor and apply rigorous design-for-manufacturing principles. Process, plant and human simulation technologies will enable companies to achieve the targeted manufacturing cost reductions. Siemens Tecnomatix is a comprehensive portfolio of digital manufacturing solutions that are immensely helping battery manufacturers and automakers. The portfolio can benefit battery pack manufacturing in several ways.
In the typical process, battery modules are mounted and fixed to the battery pack housing by robotics processes, then human operators connect the modules in a manual process. With human simulation tools, such as Siemens Process Simulate Human, battery module designers can select the target demography and posture for the operation, then design the connectors with appropriate grip space, clearance, and compliance with ergonomics standards.
Process simulation with tools such as Siemens Process Simulate allow manufacturing engineers to design the assembly operation sequences, validate reachability and process cycle times, and generate work instructions from the operator’s point of view. Process simulation also supports flexible workcell design, robotics programming and workcell control and automation for complex processes that are unsuitable or unsafe for manual execution. With these capabilities, companies can realize efficient ramp-up to production and lower implementation risks.
Equally importantly, simulation software such as Siemens Plant Simulation can empower battery pack manufacturers to impact the bottom line by optimizing battery production material flows, logistics, system configurations, equipment and energy usage and product mix. The insights afforded by whole-line simulations help improve efficiency by increasing throughput, capacity utilization and overall equipment efficiency and by reducing bottlenecks and inventory.
3D digital manufacturing solutions allow battery manufacturers to plan and validate processes before floor implementation.
By 2030, nearly 25% of all miles driven in the United States could be in shared autonomous electric vehicles, according to a recent study by the Boston Consulting Group [5]. Bringing autonomous driving and electrification technologies together in a vehicle will pose unique challenges and will also offer unique optimization opportunities for both battery suppliers and automakers. Fully autonomous vehicles (level 5) will have significantly more electronics (sensors, sensor fusion box, ECUs for drive-by-wire systems) than today’s vehicles. With a fully electric powertrain, the electronics that enable autonomous functionality will be powered by the high-voltage battery. This increased burden will reduce electric drive range and thus pose a critical challenge for developers of batteries for autonomous electric vehicles. For instance, in a level 5 autonomous car, the functionality-enabling electronics power demand can be 1 kW or more that when fed by the main battery can reduce electric drive range by 15%, especially in city drive. On the other hand, machine driving is expected to deliver a smoother drive pattern compared to human driving behavior. Smoother drive cycles allow more efficient use of battery energy that can help offset some of the impact on electric drive range. The change in driving profile that can be achieved with machine driving and with the aid of connected vehicle and smart city infrastructure can also reduce overall heat generation for the battery, electric motors and power electronics. Since heat is a key life-limiting stressor for these components, change in overall driving behavior can be exploited by battery pack developers, powertrain manufacturers and automakers to enhance battery life and optimize thermal management strategies.
To account for these challenges and exploit new optimization opportunities for batteries that come with autonomous electric vehicles, companies need to include co-dependencies in the engineering of their electric powertrain and autonomous driving functionality development, verification and validation. As auto OEMs are increasingly relying on virtual traffic scenarios and vehicle driving simulations for autonomous vehicle testing, simulation tools such as Simcenter Prescan and its connection with DRS360 can be used to determine what level of drive profile smoothing can be achieved. Integration of Simcenter Prescan with Simcenter Amesim can then allow OEMs to evaluate the impact of autonomous driving on electric drive range and optimize vehicle energy efficiency and thermal management. These upfront system simulations can accurately size battery packs for specific configurations of autonomous driving, and help develop requirements and foster collaborations with battery suppliers for autonomous electric vehicles.
Simulated impact of autonomous driving on electric drive range.
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