Discover our complete solutions portfolio covering Cathodic Protection and Corrosion management - PCB design and plating - Functional and decorative plating - Electrocoating - Acoustics
Elsyca V-PIMS
A revolution in digital PIMS combining Pipeline Corrosion Integrity Management System (PIMS) and computational modeling capabilities
Elsyca IRIS
Deep analysis of AC threats supporting efficient mitigation systems computer-aided design
Elsyca CatPro
Graphical simulation platform for cathodic protection and DC stray current analysis of pipeline networks
Elsyca CPManager
3D CAD-based software simulation platform for the computer-aided design and analysis of cathodic protection installations
Elsyca ACTA
Unique solution offering accurate, disambiguated, and tailored risk ranking report of pipeline networks
Elsyca PlatingManager
Leverage a digital twin of your plating line to predict plating performance and increase manufacturing capacity
Elsyca PCBBalance
The world’s only PCB DFM software that applies automated and optimized copper balancing to your PCB design and panel layout.
Elsyca PCBPlate
State-of-the-art graphical simulation platform for enhancing the plating performance of your PCB panel and pattern plating processes.
Elsyca ECoatMaster
CAD independent software platform for the simulation of the automotive electrocoating process of a body-in-white (BIW).
Elsyca EPOS
Simulate the performances of electropolishing processes based on a virtual mock-up of the electropolishing cell.
Elsyca AnodizingManager
State-of-the-art graphical simulation platform for analyzing the production performance and quality of anodizing processes.
Elsyca CorrosionMaster
CorrosionMaster identifies corrosion hot spots and predicts corrosion rates, enabling engineers to look at alternative material combinations and/or coating systems, or investigate corrosion-mitigating measures.
Elsyca LeakageMaster
Improve vehicles interior acoustic comfort by performing upfront virtual smoke tests.
Elsyca MeshingMaster
Automatically creates meshes for a variety of applications such as acoustics, CFD, thermal analysis, etc
Elsyca XPlorer
Interactive simulation results viewer for Finite Elements results
Elsyca XPlorer3D
Analyze, Understand and Get Immersed in your results

Revolutionizing EV battery tray protection

Unveiling the power of Computer Aided e-coating engineering

Revolutionizing EV battery tray protection

EV battery tray: A key component to protect

Regardless of their categorization as packs, boxes, or trays, these structures housing EV battery cells, alongside their electrical and thermal management components, are crucial in the EV industry. Enhancing battery packs involves various decisions in manufacturing and materials, ensuring safety, and addressing design challenges. One challenge is applying a durable acid-resistant coating to battery trays and power backup systems, serving to provide electrical insulation and resist acidic substances.

With the increasing EV numbers, there's a growing demand for specialized coatings and components to ensure functionality and safety without compromising performance. Since approximately 40% of an EV's cost is in its battery, safeguarding it with suitable coatings is crucial for longevity and efficiency.

Enhancing longevity and efficiency via electro-coating

To achieve this, dielectric coatings are applied via an electrophoretic process known as e-coating. E-coats, in liquid form, are contained in a bath where positively charged coating material adheres to negatively charged parts due to electrostatic attraction. E-coat systems are valued for their durability, ensuring consistent coverage of intricate parts with high productivity and minimal waste. They are particularly effective for large-scale production components like lids, trays, frame plates, and module racking. However, e-coating may encounter challenges such as uneven thickness and compatibility issues with substrate materials, as well as comparisons with more environmentally friendly alternatives. Understanding the process, tweaking parameters, and overcoming obstacles enables manufacturers to maximize e-coating's potential, enhancing production and product quality.

The Computer Aided Engineering (CAE) approach is invaluable for optimizing e-coating process performance. CAE utilizes predictive computer modeling to assess process quality before production, reducing the need for wet testing and streamlining iterations. It identifies process risks and limitations early in the design stage, ensuring superior coating quality. Overall, adopting CAE offers significant benefits in terms of cost, precision, innovation, and sustainability, making it an essential tool in modern engineering practices.

Fig. 1: Elsyca digital twin of an electrocoating bath used to e-coat rack of electric vehicle battery tray

The Elsyca ECoatMaster tool, an advanced graphical simulation platform developed in-house, conducts e-coating CAE. It utilizes a digital twin model of the e-coat tank setup, comprising a 3D representation of the processed part, tank configuration, process parameters, and paint solution. Using Finite Element Analysis (FEA), the Elsyca E-CoatMaster calculates current density distribution and paint layer thickness based on Faraday’s law. Detailed information on paint layer thickness distribution and quality, including under- and over-coated areas, is available for each part on the flight bar.

Getting relevant insights

These simulation results boast high accuracy, validated against field measurements. This accuracy stems from thorough electrochemical analysis of the paint solution, providing invaluable insights into its performance. Additionally, the optimal process operating parameters are determined, ensuring desired surface quality while preventing deposit defects like inadequate coverage.

Fig.2: Predicted paint deposited on ev battery tray

Improving the process

This technology has been utilized to evaluate the performance of the e-coating process for EV battery trays, focusing on current density and paint layer thickness distributions. Figure 2 depicts the paint layer thickness distribution over time, with color-coded mapping highlighting disparities in thickness—blue indicating under-deposition and red indicating over-deposition. Figure 3 showcases the distribution of current density, highlighting areas with varying densities on the e-coated parts. This analysis uncovers apparent quality issues in the e-coating, suggesting room for improvement. Tailored optimization strategies can be implemented by refining the process scenario and iterating simulations to address these issues effectively.

Fig. 3: Predicted current density during the e-coat process of battery tray

Further reading:

You might be interested reading the full case study: Revolutionizing EV Battery Tray Protection