The pursuit of sustainable transportation has propelled the rapid development of New Energy Vehicles (NEVs). While offering clear environmental benefits, a significant user pain point remains: the time required for conductive charging. Battery swap technology emerges as a compelling alternative, promising replenishment times comparable to refueling, alleviating range anxiety, enabling flexible battery upgrades, and facilitating better battery lifecycle management through centralized maintenance. Consequently, battery swap systems are gaining strategic importance. This paper delves into the critical engineering challenges associated with one such system, focusing on the dynamic performance of the EV battery pack within a snap-fit locking mechanism. My research and optimization efforts centered on mitigating the negative impact of the necessary mechanical tolerances and flexible connections on the vibrational characteristics of the swappable EV battery pack.
The core function of any quick-swap system is the secure, reliable, and repeatable mechanical connection between the vehicle chassis and the high-voltage EV battery pack. Among various locking mechanisms, the snap-fit or latch-type design, which often employs a pawl-and-ratchet principle, is noted for its simplicity and speed. However, this design inherently introduces a degree of compliance in the connection. To ensure successful engagement and disengagement across all locking points despite manufacturing and assembly variations, each lock must provide a certain tolerance in the primary attachment direction (typically the vertical, or Z-direction). This intentional clearance, while functionally crucial, creates a flexible interface that can significantly degrade the overall dynamic stiffness of the system, leading to low-frequency vibration modes. In vehicle applications, such low-frequency resonances can be easily excited by road inputs, potentially leading to accelerated fatigue damage, unwanted noise, and compromised durability of the EV battery pack and its internal components. Therefore, a key design challenge is to reconcile the functional requirement for locking tolerance with the performance requirement for high structural modal frequencies.
The primary performance metric for this study is the first-order natural frequency of the EV battery pack system in the Z-direction. For the vehicle platform in question, the target was set at ≥36 Hz. An initial Computer Aided Engineering (CAE) analysis of the baseline design revealed a significant shortfall. The system comprised a vehicle-mounted swap frame, the EV battery pack itself, 12 snap-fit lock units (6 per side), and initial locating blocks. The locks had a unilateral Z-tolerance travel of 3.0 mm, and no dedicated Z-direction restraint was present other than tensile engagement of the locks. When modeled with realistic stiffness values for the flexible lock interfaces, the first Z-direction modal frequency of the integrated system was found to be only 21.66 Hz, far below the target, despite the standalone EV battery pack structure having a healthy mode at 47.10 Hz. This confirmed that the lock compliance was the dominant factor limiting dynamic performance.
To understand the governing physics, we consider the system’s response to random vibration, a primary loading condition for a vehicle component. The equation of motion for a deterministic single-degree-of-freedom system under a random excitation force \( F_r(t) \) is given by:
$$
\ddot{X}(t) + 2\zeta\omega_n\dot{X}(t) + \omega_n^2 X(t) = F_r(t)
$$
where \( X(t) \) is the random displacement response, \( \zeta \) is the damping ratio, and \( \omega_n \) is the undamped natural frequency (related to the modal frequency \( f_n \) by \( \omega_n = 2\pi f_n \)). For a linear system starting from rest, the response can be expressed via the convolution integral with the unit impulse response function \( h(t) \):
$$
X(t) = \int_{0}^{t} h(t-s) F_r(s) ds
$$
The frequency response function \( H(\omega) \), which describes the system’s output response per unit harmonic input, is central to random vibration analysis. For our system:
$$
H(\omega) = \frac{1}{\omega_n^2 – \omega^2 + 2j\zeta\omega_n\omega}
$$
The power spectral density of the response \( S_{XX}(\omega) \) is related to the input excitation’s power spectral density \( S_{FF}(\omega) \) by:
$$
S_{XX}(\omega) = |H(\omega)|^2 S_{FF}(\omega)
$$
This relationship shows that a lower natural frequency \( \omega_n \) (or \( f_n \)) amplifies the response to low-frequency input energy, which is abundant in road profiles. The root-mean-square (RMS) stress \( \sigma_{RMS} \) and displacement \( X_{RMS} \), which are directly linked to fatigue life, can be derived from these spectral relationships. Increasing the system’s natural frequency is therefore paramount to reducing dynamic amplification and stress levels. Our optimization problem translates to maximizing \( f_n \) by modifying the system’s effective stiffness and boundary conditions, without compromising the lock’s functional travel.
The optimization process focused on two synergistic modifications: the lock engagement strategy and the implementation of a proactive Z-direction preload system. First, the lock’s tolerance travel was re-engineered. Instead of a single 3.0 mm gap, the pawl-and-ratchet profile was designed with two distinct engagement stages. This created three potential resting positions for the EV battery pack relative to the lock: a nominal position at 0 mm (first-stage engagement), a mid-position at 1.5 mm (second-stage engagement), and the full-tolerance position at 3.0 mm. In a real assembly, due to dimensional chains, different locks on the same EV battery pack might engage at different stages, but the *maximum possible* free travel before all locks are engaged is effectively reduced for a majority of the locks. This directly reduces the average unsupported amplitude.
Secondly, and more critically, a dedicated Z-direction preload and limiting mechanism was introduced. The previous X/Y locators were replaced with an integrated unit featuring an inverted “n”-shaped bracket on the vehicle frame and a “T”-shaped wedge on the EV battery pack. This design provided simultaneous X and Y location. For the Z-direction, a compressible elastomeric buffer block was installed at the top of the “n” bracket. Upon final installation of the EV battery pack, the “T” wedge contacts and compresses this buffer block before the lock’s ratchet fully seats. This compression creates a continuous restoring force acting upwards against the battery’s weight, effectively preloading the system and adding stiffness in the Z-direction. The force-displacement characteristic of this buffer block became a key tuning parameter. We tested blocks with different Shore A hardness levels to quantify this relationship, which is crucial for balancing preload force with swap engagement force.
| Buffer Block Hardness (Shore A) | Compression Displacement (mm) | Measured Reaction Force (N) |
|---|---|---|
| 30 | 3.0 | 460 |
| 40 | 3.0 | 1600 |
| 50 | 3.0 | 2700 |
Using CAE analysis, we evaluated multiple combinations of lock travel strategy and buffer block hardness. The model constraints included the 14 vehicle frame attachment points, and the lock stiffness was maintained. The integrated locator/preload mechanism was modeled with its appropriate stiffness based on the buffer block data. The results systematically showed the improvement in Z-direction modal frequency.
| Configuration ID | Lock Travel Scheme | Buffer Hardness (Shore A) | First Z-Directional Mode (Hz) | Meets Target (≥36 Hz)? |
|---|---|---|---|---|
| Baseline | 3.0 mm single stage | None | 21.7 | No |
| Opt-1 | 3.0 mm single stage | 30 | 28.0 | No |
| Opt-2 | 3.0 mm single stage | 40 | 32.1 | No |
| Opt-3 | 3.0 mm single stage | 50 | 36.6 | Yes |
| Opt-4 | Two-stage (0/1.5/3.0 mm) | 30 | 31.5 | No |
| Opt-5 | Two-stage (0/1.5/3.0 mm) | 40 | 38.4 | Yes |
| Opt-6 | 1.5 mm effective travel | 40 | 40.2 | Yes |
The analysis clearly indicates that introducing a Z-direction preload via the buffer block is essential. While a very hard buffer (50 HA) with the original 3.0 mm locks could meet the target (Opt-3, 36.6 Hz), this configuration posed a functional risk. The swap process relies on the vehicle’s weight to overcome resisting forces and fully seat the EV battery pack. The reaction force from a 50 HA block at full compression (2700 N) applied across multiple locators could sum to a force rivaling or exceeding the effective weight component of the EV battery pack, potentially causing engagement failure. Therefore, a balance between preload force and reliable locking is critical.
The optimal solution was found in a combination strategy. The two-stage lock design (Opt-5) inherently reduces the statistical average free travel. When paired with a 40 HA buffer block, it yields a modal frequency of 38.4 Hz, safely above the 36 Hz target. The reaction force from this block (1600 N) provides substantial preload for dynamic stiffness while remaining low enough to ensure reliable gravitational seating during the swap operation. This configuration was validated through physical prototype assembly and sweep-frequency vibration table tests, which confirmed the CAE-predicted modal behavior. The final design specification for the EV battery pack quick-swap system thus settled on the two-stage snap-fit lock with a nominal travel range of 1.5-3.0 mm and Z-direction preload buffers with a hardness of 40 Shore A.
In conclusion, enhancing the vibrational modal performance of a snap-fit quick-swap EV battery pack system requires a holistic approach that addresses the inherent flexibility of the connection. The study demonstrates that simply tightening manufacturing tolerances is insufficient and often impractical. Instead, the solution lies in intelligently managing the tolerance stack-up through multi-stage lock engagement and, most importantly, introducing a controlled, compliant preload in the vibration direction. This preload, achieved here via elastomeric buffers in an integrated locator, directly increases the system’s effective stiffness, thereby raising its natural frequency and improving its dynamic response. The success of this optimization hinges on meticulously balancing three factors: the magnitude of the functional mechanical tolerance (lock travel), the introduction of a restorative force in the direction of vibration, and the magnitude of that restorative force to ensure it does not inhibit the primary locking function. For engineers developing future swappable EV battery pack systems, this case underscores the necessity of co-optimizing the swap mechanism’s functional kinematics with the dynamic performance requirements from the earliest design stages, ensuring both operational reliability and long-term durability of the high-value EV battery pack.