As a researcher focused on vehicle dynamics, I have observed the rapid expansion of the electric vehicle market, particularly in regions like China where government support has driven widespread adoption. By the first half of 2024, the penetration rate of new energy vehicles in China exceeded 50%, marking a significant shift toward electrification in the automotive industry. This growth, however, has highlighted specific challenges unique to electric vehicles, such as longitudinal play during low-speed braking. This issue arises due to the inherent characteristics of electric vehicles, including their substantial mass and low center of gravity, which stem from the placement of battery packs under the vehicle floor. These factors exacerbate longitudinal oscillations when braking at low speeds, negatively impacting ride comfort and leading to customer dissatisfaction. In this article, I will delve into the mechanics of this phenomenon, propose optimization strategies based on suspension geometry, and validate these approaches through simulation and real-world testing, with a focus on enhancing the performance of China EV models.
The longitudinal play in electric vehicles during low-speed braking is a complex dynamic behavior that involves interactions between the vehicle’s mass, suspension system, and braking forces. When a driver applies the brakes at speeds between 5 km/h and 8 km/h, as commonly encountered in stop-and-go traffic scenarios like traffic lights, the vehicle experiences a series of longitudinal oscillations. These oscillations, often perceived as multiple cycles of forward and backward motion, result from the conversion of kinetic energy into elastic potential energy within the suspension and chassis components. In electric vehicles, the higher mass—typically around 2,500 kg for a mid-sized SUV—and lower center of gravity, often below 550 mm, amplify these effects compared to traditional internal combustion engine vehicles. This not only reduces subjective comfort scores but can also contribute to motion sickness, making it a critical area for improvement in the development of China EV platforms.
To quantify the severity of longitudinal play, subjective evaluation scales are employed, where professional drivers rate vehicle dynamics based on predefined criteria. For instance, a scale from 1 to 10 is used, with scores below 7 indicating unacceptable performance that requires immediate attention. In my experience with electric vehicle projects, vehicles exhibiting pronounced longitudinal play during low-speed braking often receive scores around 6.25, falling short of the target of 7.25 or higher. This underscores the need for a systematic approach to address the issue, combining experimental testing with advanced simulation techniques. The table below summarizes a typical subjective rating scale used in such evaluations, which helps in benchmarking vehicle comfort and guiding optimization efforts.
| Score | Description | Customer Impact |
|---|---|---|
| 1-3 | Poor to Unacceptable | Customers find it intolerable; must be fixed |
| 4-6 | Below Expectations | Noticeable and detracts from competitiveness |
| 7-8 | Acceptable | Minimal impact; most customers do not notice |
| 9-10 | Excellent | Imperceptible; only trained observers can detect |
In my analysis, I developed a detailed vehicle dynamics model using ADAMS software to simulate low-speed braking scenarios. The model incorporated key parameters of a typical electric SUV, such as a wheelbase of 3,020 mm, front and rear track widths of 1,700 mm and 1,680 mm respectively, and a mass of 2,500 kg. The tire model was derived from six-component force test data, and bushing stiffness values were obtained from physical measurements. This virtual model allowed me to replicate the longitudinal play phenomenon under controlled conditions, such as a braking input where the brake pedal travel reaches 70% within 0.2 seconds, simulating a sudden stop from low speeds. The simulation exit condition was set to a vehicle speed of -10 km/h to ensure comprehensive analysis of the oscillation cycles.
The validation of the ADAMS model involved comparing simulation results with real-world test data. As shown in the comparison, the model accurately captured the first three peaks of longitudinal acceleration, demonstrating a strong correlation with actual vehicle behavior. Although minor discrepancies occurred in later cycles due to unmodeled factors like tire-road friction and hydraulic bushing nonlinearities, the overall model fidelity was sufficient for investigating optimization strategies. This step was crucial for ensuring that any insights gained from simulations would translate to practical improvements in electric vehicle designs, particularly for the growing China EV market.
To understand the underlying mechanism of longitudinal play, I broke down the braking process into distinct phases. Initially, when brakes are applied, the vehicle experiences maximum deceleration, leading to load transfer and pitch motion. This is followed by energy conversion phases where kinetic energy is stored as elastic energy in the suspension system, causing the body to oscillate longitudinally. The key insight from this analysis is that the rear suspension’s anti-lift characteristics play a dominant role in mitigating these oscillations. By reducing the anti-lift rate, the vehicle can convert more longitudinal motion into pitch motion, which is then dissipated by the dampers. This approach leverages the human body’s lower sensitivity to pitch compared to longitudinal jerks, thereby enhancing comfort without compromising other dynamics. The equation below describes the pitch motion during braking, where ( I ) is the moment of inertia, ( \ddot{\theta} ) is the pitch acceleration, ( F_{b} ) is the braking force, and ( h ) is the height of the center of gravity:
$$ I \ddot{\theta} = F_{b} h $$
Further, the anti-lift rate ( A_{lr} ) for the rear suspension can be expressed as a function of the suspension geometry and hardpoint positions. For a five-link rear suspension common in electric vehicles, this rate influences how much the rear axle lifts under braking forces. Optimizing this parameter is essential for controlling longitudinal play. In my simulations, I varied hardpoint locations, such as the outer points of the lower control arms and inner points of the upper control arms, to achieve desired anti-lift rates. The table below summarizes the effects of different hardpoint adjustments on the anti-lift rate and the resulting impact on longitudinal oscillations.
| Adjustment | Anti-Lift Rate Change | Effect on Longitudinal Play |
|---|---|---|
| Lower control arm outer point raised by 100 mm | Decrease by ~69% | Significant improvement |
| Upper control arm inner point lowered by 100 mm | Decrease by ~88% | Substantial reduction |
| Baseline configuration | 82% | Pronounced oscillations |
Through extensive simulation studies, I evaluated multiple factors affecting longitudinal play, including center of gravity height and brake force distribution. For example, increasing the center of gravity height from 530 mm to 730 mm reduced the anti-lift rate from 82% to 62%, leading to a noticeable decrease in oscillation amplitude. Similarly, adjusting the brake force distribution coefficient ( k ), defined as the ratio of front to rear braking forces, showed that a higher proportion of rear braking (e.g., ( k = 0.4 )) intensified longitudinal play, whereas a more balanced distribution (e.g., ( k = 0.8 )) mitigated it. These findings highlight the interconnectedness of vehicle parameters and the importance of a holistic design approach for electric vehicles, especially in the context of China EV development where comfort is a key selling point.
The optimization strategy was applied to a next-generation electric vehicle model, where hardpoint designs were modified to achieve a front anti-dive rate of ≤48% and a rear anti-lift rate of ≤62%. Simulation results confirmed that this combination reduced the third peak of longitudinal acceleration to below 0.1g, effectively minimizing the perceptible oscillations. Subjective evaluations of prototype vehicles based on this design showed scores improving to 7.25, meeting the target for acceptable performance. This demonstrates the practical viability of the proposed method for enhancing the comfort of electric vehicles during low-speed braking. The following equation illustrates the relationship between suspension hardpoints and the anti-lift rate, where ( L ) represents the distance from the suspension pivot points to the center of gravity, and ( \alpha ) is the angle of the control arms:
$$ A_{lr} = \frac{L \cos(\alpha)}{\text{wheelbase}} \times 100\% $$
In conclusion, my research underscores the critical role of suspension geometry in addressing longitudinal play in electric vehicles. By focusing on the rear suspension’s anti-lift characteristics and ensuring that the front anti-dive rate is kept within optimal limits, manufacturers can significantly improve ride comfort without resorting to complex electronic controls that may compromise safety or braking performance. This approach has been validated in next-generation China EV models, providing a scalable solution for the industry. As the electric vehicle market continues to evolve, such insights will be invaluable for engineers striving to deliver superior driving experiences in urban environments where low-speed braking is frequent.
To further elaborate, the integration of these optimization strategies into the early stages of vehicle development can reduce reliance on post-production fixes, such as software-based braking adjustments, which often introduce trade-offs in braking distance or pedal feel. For instance, in one case study, modifying the rear suspension hardpoints alone led to a 30% reduction in longitudinal oscillation energy, as calculated using the formula for kinetic energy dissipation: ( E = \frac{1}{2} m v^2 ), where ( m ) is the vehicle mass and ( v ) is the velocity change during oscillations. This quantitative assessment reinforces the value of suspension tuning in achieving comfort goals for electric vehicles.
Moreover, the widespread adoption of electric vehicles in China and globally necessitates continuous innovation in dynamics control. My work emphasizes that a deep understanding of vehicle-specific parameters—such as mass distribution, suspension stiffness, and braking dynamics—is essential for tailoring solutions to different electric vehicle platforms. Future research could explore the integration of active suspension systems with these geometric optimizations to further enhance performance. However, for current production models, the proposed hardpoint-based approach offers a cost-effective and reliable method to address longitudinal play, ensuring that electric vehicles meet the high comfort standards expected by consumers in competitive markets like China EV.
In summary, through a combination of theoretical analysis, simulation, and practical validation, I have demonstrated that optimizing suspension hardpoints to control anti-lift and anti-dive rates is a highly effective strategy for mitigating longitudinal play in electric vehicles during low-speed braking. This not only improves subjective comfort scores but also contributes to the overall refinement and appeal of electric vehicles, supporting the continued growth of the China EV sector and beyond.