In my extensive research and practical experience within the automotive industry, particularly focusing on the electrification wave, I have dedicated significant effort to understanding and optimizing the mounting systems of pure electric cars. The transition to electric vehicles (EVs) has intensified market competition, compelling manufacturers to adopt aggressive cost-reduction strategies without compromising performance. The mounting system, a critical component that isolates vibrations, ensures drivetrain stability, and impacts overall vehicle comfort and safety, presents a substantial opportunity for design-led cost savings. Through a forward development process, I have systematically explored how strategic decisions in system layout, performance setting, optimization, and structural design can dramatically reduce weight and cost. This article shares my insights and methodologies, emphasizing the pivotal role of early-phase design in achieving cost efficiency for electric car mounting systems. The term ‘electric car’ will be frequently referenced to underscore the specific context of this study.
The overarching goal is to establish a framework that enables automotive engineers to inherently design cost-effective mounting systems for electric cars from the outset. Traditional after-the-fact cost-cutting through supplier negotiations is unsustainable and can degrade quality. Instead, I advocate for a proactive approach where approximately 80% of the cost is locked in during the design phase. My analysis begins with a thorough dissection of the cost structure inherent to electric car mounting systems.
The cost of an electric car mounting system, as derived from supplier quotations and my own project engagements, can be categorized into four primary segments: Direct Material Cost, Tooling and Gauge Cost, Processing and Assembly Cost, and Logistics and Management Cost. Each segment is influenced by specific design factors, most of which are directly controlled within the research and development (R&D) domain. The following table summarizes this cost constitution and its key influencers.
| Cost Segment | Primary Components | Key Design-Influencing Factors | Typical Cost Share |
|---|---|---|---|
| Direct Material Cost | Raw materials (e.g., rubber, metals, plastics) | Material grade/specification, part weight, 3D dimensions, part commonality | 50% – 70% |
| Tooling and Gauge Cost | Molds, inspection gauges, fixtures | Part 3D dimensions, part count, production process, precision requirements | 10% – 30% |
| Processing and Assembly Cost | Machining, surface treatment, assembly labor | Material grade, precision requirements, assembly complexity, welding/painting specs | 5% – 10% |
| Logistics and Management | Packaging, transportation, warehousing, profit margin | Packaging size, shipping distance (less directly tied to R&D) | 10% – 15% |
As evident, factors tied to R&D account for over 80% of the total cost influence, making the design phase the paramount arena for cost reduction in an electric car mounting system. Let’s delve deeper into each factor. Direct material cost is dominantly driven by part weight and material choice. For instance, selecting a domestic steel grade like SAPH440 over a premium Japanese grade like JSH440W can save approximately $1.4 per kg. Furthermore, the geometry of a part influences material utilization; complex shapes generate more scrap during cutting or stamping. The formula for approximating raw material cost for a metal part can be expressed as:
$$C_{material} = \rho \cdot V \cdot P_m \cdot (1 + s)$$
where \(C_{material}\) is the material cost, \(\rho\) is the material density, \(V\) is the part volume, \(P_m\) is the price per unit mass of the material, and \(s\) is the scrap rate factor. Minimizing \(V\) and \(s\) through compact design is crucial for the electric car’s cost targets.
Tooling cost is highly sensitive to part size and count. Larger parts require larger, more expensive molds. Increasing part variety directly multiplies the number of required molds and gauges. For high-volume production of an electric car, a single set of production molds can cost 3-5 times more than those for low-volume runs. The processing cost is affected by tolerances and assembly steps. Tighter tolerances necessitate additional finishing operations like grinding and polishing. A complex mounting assembly requiring multiple sub-components and assembly stages (e.g., vulcanization, press-fitting, riveting) incurs higher labor and overhead costs. Therefore, simplifying the assembly process is a key design lever for cost reduction in electric car components.
Building upon this cost understanding, I have structured the forward development process for an electric car mounting system into four integral stages: System Layout, Performance Setting & Optimization Calculation, Structural Design, and finally, Test Validation. The first three stages are where design-centric cost strategies must be rigorously applied. The relationship between these design stages and the cost factors is mapped below.
| Design Stage | Primary Cost Factors Addressed | Key Cost-Reduction Levers |
|---|---|---|
| System Layout | Part count, 3D dimensions, commonality | Integration with subframe, compact arrangement, layout type selection |
| Performance Setting & Optimization | Part weight, material grade (indirectly) | Target setting, robustness design, stiffness parameter optimization |
| Structural Design | All factors: weight, material, dimensions, count, precision, assembly, etc. | Material selection, commonality design, process simplification, tolerance relaxation |
The system layout for an electric car’s powertrain mounting is foundational. Most modern electric cars utilize a Center of Gravity (COG) layout with three mounts attached to a subframe. Based on the orientation of the mount’s principal axis (often a rubber bushing), I classify layouts into four types: 3X0Y, 2X1Y, 1X2Y, and 0X3Y, where X is the longitudinal direction, Y is the lateral direction, and the notation indicates how many mounts have their principal stiffness aligned in each direction. The choice significantly impacts cost and performance. For a cost-priority electric car project, layouts that enable high integration of the mount bracket or bushing directly into the subframe (like 0X3Y or certain 2X1Y configurations) are advantageous as they reduce part count and assembly steps. However, this can sometimes trade off against dynamic stiffness at the passive side. The layout must also minimize dynamic clearances (typically 5-10mm) to shrink the size of brackets and arms, directly reducing material usage. The optimal layout balances cost, assembly feasibility, and NVH performance for the specific electric car platform.
Performance setting and optimization is where engineering calculations directly interface with cost. Following the V-model development framework, system-level targets (e.g., decoupling rate, frequency distribution, engine roll displacement) are decomposed into component-level specs (e.g., bushing stiffness, bracket natural frequency). Overly conservative targets lead to over-design and cost inflation. For instance, setting a bracket dynamic stiffness target above 20,000 N/mm for a mainstream electric car, when 8,000-10,000 N/mm often suffices, unnecessarily increases weight and volume. The natural frequency of a bracket, often set by experience, can be more economically targeted using fundamental beam or plate theory formulas. For a simplified bracket model, the first natural frequency can be estimated by:
$$f_n = \frac{1}{2\pi} \sqrt{\frac{K_{eq}}{M_{eq}}}$$
where \(f_n\) is the natural frequency, \(K_{eq}\) is the equivalent stiffness, and \(M_{eq}\) is the equivalent mass. Properly setting \(f_n\) targets avoids over-engineering.
The core of optimization lies in tuning the stiffness parameters of the left, right, and rear mounts. My strategy is to maximize bushing commonality to minimize part variety and cost. The optimization problem can be formulated to find stiffness values \(k_x, k_y, k_z\) for each mount that satisfy system performance constraints (like decoupling rates >80% and frequency separation) while minimizing a cost function \(J\). A simplified cost function could be the total weight or the number of unique bushing specs. The decoupling rate for a specific mode (e.g., vertical translation) is calculated from the system’s mass and stiffness matrices \([M]\) and \([K]\). After solving the eigenvalue problem \([K] – \omega^2[M] = 0\), the modal matrix \([\Phi]\) is obtained. The decoupling rate for mode \(i\) in direction \(j\) is:
$$DR_{ij} = \frac{(\Phi_{ij})^2}{\sum_{k=1}^{6} (\Phi_{ik})^2} \times 100\%$$
where \(\Phi_{ij}\) is the element of the eigenvector. The optimization loop adjusts stiffness parameters to achieve high \(DR_{ij}\) values across all critical modes, first attempting with fully common bushings, then relaxing commonality if targets are not met. This method ensures cost-efficient performance for the electric car.
Structural design is the stage where all cost factors converge. For part weight reduction, I adhere to cascading targets from vehicle to system to component. Material selection is critical: for cost-sensitive electric car models, stamped steel (e.g., SAPH440) is preferred for brackets; for performance or lightweight-focused models, aluminum die-casting (e.g., A380) or engineering plastics (e.g., PA66) are chosen despite higher material cost per kg, as they enable significant weight savings that benefit overall electric car range. Commonality design is aggressively pursued. For example, in a 3X0Y layout, the left and right mount brackets can be designed as mirror-image common parts. Integration is key; designing the rear mount bracket as a welded part of the subframe eliminates a separate component. The table below contrasts two common rubber bushing structures, highlighting the cost and weight benefits of a simpler cylindrical design for an electric car application.
| Parameter | Tapered Bushing | Cylindrical Bushing |
|---|---|---|
| Typical Dimensions (Ø x Height mm) | ~90 x 75 | ~80 x 60 |
| Approx. Weight (kg) | 0.38 | 0.28 |
| Relative Cost | Higher (+30% or more) | Lower (Baseline) |
| Stiffness Ratio Flexibility (X:Z) | Limited (0.6-1.0) | Wide (0.5-2.0) |
| Assembly Process | Complex (vulcanize, assemble, rivet) | Simple (vulcanize, press-fit) |
Assembly simplification is a major cost saver. Replacing bolted connections with press-fit or riveted joints reduces parts and labor. Direct vulcanization of rubber onto metal parts eliminates secondary pressing operations. For painted or welded steel brackets, minimizing coating thickness (15-20µm is often adequate) and weld seam length directly cuts processing cost. Furthermore, I advocate for a robustness-based approach to tolerance design. If the system performance of the electric car mounting system is relatively insensitive to bushing stiffness variation, the tolerance can be relaxed from ±10% to ±15%, reducing supplier tuning efforts and scrap rates without affecting vehicle-level NVH. This holistic design-for-cost mindset must be embedded in a structured process, where each design iteration is reviewed against both performance and cost targets until both are satisfied.
To validate these strategies, I led their comprehensive application in a forward development project for a battery-electric passenger car. The previous model used a Torque Roll Axis (TRA) layout with multiple discrete brackets and a complex assembly. For the new electric car model, we adopted a COG layout with a 2X1Y configuration. This allowed deep integration: the left and right mount brackets were designed as common aluminum castings attached directly to the subframe, and the rear mount function was integrated into a welded bracket on the subframe. This eliminated several components like separate motor brackets and a rear mount bracket, drastically reducing part count. The layout was optimized for compactness, minimizing all clearances to the powertrain package, which allowed us to design shorter, lighter mount arms.
In performance setting, we established aggressive but realistic targets based on benchmarking mainstream electric cars. The optimization calculation was performed with the goal of part commonality. We successfully achieved a design where the left and right bushings were identical, while the rear bushing had different stiffness. The system met all key performance targets. For instance, the calculated decoupling rates for the six rigid-body modes all exceeded 90%, and the shock isolation over bumps, simulated at the driver’s seat rail, achieved -24.8 dB, well within the required -20 dB limit. The formulas used for these assessments were integral to our decision-making.
In structural design, aluminum alloys A380 (for brackets) and A356 (for arms) were selected for an optimal balance of weight and cost for this electric car. The bushing design shifted from a tapered type to a cylindrical type, simplifying production to just two main steps: vulcanization and press-fitting. Tolerances for bushing stiffness were set at ±15%, leveraging the system’s robustness. The results were substantial. Compared to the previous-generation electric car model, the new mounting system achieved a 77.1% reduction in weight and a 68.3% reduction in total cost. Even when comparing only equivalent part numbers (excluding parts eliminated by system integration), the new design showed a 37.8% weight reduction and a 44.1% cost reduction. These gains were realized while maintaining or improving key NVH performance metrics, proving the efficacy of the design-led approach for electric car component development.
The mathematical underpinning of the performance validation can be summarized through key metrics. The frequency distribution of the powertrain rigid-body modes is a critical outcome. For a 6-degree-of-freedom system, the natural frequencies \(\omega_i\) are found from the eigenvalues of \([M]^{-1}[K]\). Our design yielded frequencies in the range of 15-50 Hz, effectively separated from major excitation sources in an electric car, such as road input (1-20 Hz) and motor torque ripple (multiples of base frequency). The static displacement under maximum torque \(T_{max}\) is another check, calculated for the longitudinal direction (X) as:
$$\delta_x \approx \frac{T_{max} \cdot r}{K_{x,eff}}$$
where \(r\) is a moment arm and \(K_{x,eff}\) is the effective system stiffness in X. Our design kept \(\delta_x\) under 10 mm, ensuring acceptable powertrain motion. These calculations, combined with cost models, formed the basis of our design iterations.
In conclusion, my research and practical application demonstrate that substantial cost reduction in electric car mounting systems is achievable through meticulous design strategies embedded in the forward development process. The key lies in understanding the cost structure dominated by R&D-influenced factors and systematically addressing them at each stage: through integrated and compact system layouts, rational and robustness-focused performance targets, optimization algorithms that prioritize component commonality, and structural designs that emphasize material efficiency, process simplicity, and assembly ease. The successful project case, where weight and cost were nearly halved while performance was enhanced, serves as a compelling blueprint. As the electric car market continues to evolve under intense cost pressure, this holistic design-for-cost methodology provides a sustainable competitive advantage, moving beyond mere supplier price negotiations to inherent, intelligent design that benefits the entire electric vehicle architecture. Future work may involve applying machine learning algorithms to further refine the multi-objective optimization between cost, weight, and performance for the next generation of electric cars.