The driving range stands as a pivotal performance indicator for electric vehicle cars, with its prediction accuracy being directly linked to alleviating user range anxiety and optimizing vehicle performance. Research consistently shows that the dynamic coupling between the total energy of the power battery and the overall driving energy consumption of the electric vehicle car is the decisive factor determining the achievable range. This relationship is governed by a multidimensional system of influencing factors, including the energy attenuation characteristics of the power battery, the efficiency profile of the drivetrain, the distribution of the ambient temperature field, and the features of the driving cycle. Among these, driving behavior represents the primary variable under direct user control, exerting a significant and immediate “elastic impact” on energy consumption and, consequently, on the practical range of an electric vehicle car. To quantify this differential impact, we constructed a dynamic test platform based on a shortened World Harmonized Light Vehicles Test Cycle (WLTC) methodology. Utilizing a typical compact electric vehicle car as the test subject under controlled ambient temperature, we designed a three-dimensional driving habit matrix encompassing aggressive, steady, and economical styles. Key operational parameters, including vehicle bus current, power battery terminal voltage, and State of Charge (SOC), were collected in real-time. The experimental findings reveal that differences in instantaneous power demand under varying driving habits can reach up to 50.9%, leading to a WLTC cycle range deviation of 8.9%. Through multi-physics coupling analysis, the correlation mechanism between the nonlinear characteristics of accelerator pedal actuation and the energy recovery efficiency of regenerative braking was elucidated, providing a theoretical foundation for the adaptive optimization of energy management strategies tailored to individual driving habits in electric vehicle cars.
1. Introduction and Problem Statement
The global shift towards sustainable transportation, accelerated by “Dual Carbon” strategies, has positioned the electric vehicle car at the forefront of the automotive industry’s evolution. With advantages in zero tailpipe emissions, lower operational energy consumption, and integration with smart technologies, electric vehicle cars are transitioning from a policy-driven initiative to a market-driven phase of high-quality development. Market penetration rates continue to climb globally. However, despite continuous improvements in battery energy density and fast-charging technology, “range anxiety” persists as a critical barrier affecting consumer purchase decisions and overall ownership experience for an electric vehicle car. A significant contributor to this anxiety is the often-observed discrepancy between the certified or advertised range and the real-world range experienced by drivers. This discrepancy can be substantially amplified by various factors, with dynamic driving behavior being one of the most prominent and variable.
The range capability of an electric vehicle car is fundamentally a dynamic equilibrium between the total usable energy stored in the power battery pack and the energy consumed by the vehicle to overcome driving resistances and power auxiliary systems. This equilibrium is influenced by a complex, coupled system of factors. From a technical perspective, the energy attenuation characteristics of the lithium-ion power battery (influenced by cycle life, temperature sensitivity, and discharge rate) and the efficiency map of the electric drivetrain (encompassing motor efficiency, power electronic converter losses, and gearbox efficiency) form the foundational basis for energy consumption. Environmental conditions introduce another layer of complexity; for instance, low ambient temperatures can reduce range by over 30% due to increased battery internal resistance and cabin heating demands, while aerodynamic drag becomes the dominant energy consumer at high speeds, accounting for more than 50% of the total tractive force. Crucially, from the user interaction layer, driving behavior emerges as the sole major variable that can be actively modulated by the driver. Its impact is both immediate and highly differentiated: aggressive maneuvers like hard acceleration and frequent braking significantly elevate instantaneous power demand, whereas anticipatory, smooth driving techniques such as coasting and gentle acceleration can effectively minimize energy drain.
While advancements in battery technology and vehicle hardware design are steadily progressing, the quantification of this “elastic influence” of driving behavior on the practical range of an electric vehicle car remains an area requiring deeper investigation. Empirical observations suggest that the same electric vehicle car model can exhibit real-world range variations exceeding 10% solely due to differences in driving style. This variability not only exacerbates user uncertainty regarding range prediction but also highlights a significant opportunity for optimizing vehicle energy management systems. Therefore, scientifically quantifying the impact mechanism of driving behavior, elucidating the relationship between accelerator pedal control patterns, braking strategies, and regenerative energy recovery efficiency, is of paramount importance. Such research not only empowers users with data-driven “eco-driving” guidance to extend the range of their electric vehicle car but also provides essential data support and theoretical underpinnings for developing next-generation, adaptive energy management systems that can learn and respond to individual driver habits.
Accordingly, this study was designed to isolate and measure the effect of driving behavior. Using a representative compact electric vehicle car as the test platform and strictly controlling variables such as ambient temperature and initial vehicle state, we constructed a repeatable test protocol based on a modified WLTC cycle. By defining and executing three distinct driving profiles—Economical, Steady, and Aggressive—we collected high-fidelity, time-synchronized data on key energy consumption parameters. The objective is to provide a clear, quantified analysis of how different driving habits influence the range capability of an electric vehicle car, offering scientific insights to enhance the practical utility and user confidence in electric vehicle cars.
2. Methodology and Experimental Design
2.1 Test Methodology and Vehicle
The chassis dynamometer testing was designed in accordance with the core principles of global harmonized test procedures for measuring the energy consumption and range of electric vehicle cars, such as those outlined in WLTP. The fundamental approach involved consecutive test runs on a chassis dynamometer, where three drivers, each adhering to a pre-defined and distinctly different driving style profile, executed the shortened WLTC combined cycle. The test process was strictly controlled: the electric vehicle car was prepared from a full charge state, following a standardized soak and preconditioning routine. Testing commenced and continued until the vehicle’s powertrain control system determined that the usable battery energy was depleted and the vehicle could no longer follow the target dynamometer speed curve. At this endpoint, the test was terminated, and key data was recorded. This sequence was repeated for each of the three driving styles. The selected test vehicle was a mainstream compact-class electric vehicle car with mileage over 3,000 km, ensuring it was properly run-in. Its key specifications are summarized in Table 1.
| Parameter | Value | Unit |
|---|---|---|
| Curb Mass | 2640 | kg |
| Test Mass (including driver & equipment) | 2841 | kg |
| Maximum Speed | 180 | km/h |
| Drive Configuration | All-Wheel Drive (AWD) | – |
| Selected Driving Mode | ECO | – |
2.2 Driving Behavior Habit Definition
To systematically evaluate the impact of driving style, three distinct driving behavior profiles were formally defined and assigned to experienced test drivers. These profiles were characterized primarily by their acceleration and braking habits, as detailed in Table 2.
- Economical Driving: This style aims to minimize energy consumption. It is characterized by maintaining accelerator pedal actuation below a threshold that corresponds to a vehicle acceleration of approximately 2.5 m/s². The driver employs extensive anticipatory driving, utilizing coasting well in advance of required decelerations to maximize regenerative energy recovery and minimize friction brake use.
- Steady Driving: This style represents a balanced, moderate approach typical of everyday driving. Acceleration is more assertive than in the Economical mode but remains controlled, with a target acceleration typically between 2.5 and 3.5 m/s². Braking is applied smoothly and in a timely manner, blending regeneration and friction braking as needed to accurately follow the speed trace.
- Aggressive Driving: This style prioritizes immediate power response and minimizes travel time within the constraints of the cycle. It involves frequent and high-rate acceleration events, often exceeding 3.5 m/s², and correspondingly late and harder braking. This results in significant speed fluctuations around the target cycle trace and high instantaneous power demands from the electric vehicle car’s powertrain.
| Driving Behavior Profile | Acceleration Habit | Braking Habit |
|---|---|---|
| Economical | Acceleration threshold < ~2.5 m/s² Very smooth pedal application |
Strong reliance on anticipatory coasting and regenerative braking; minimal friction brake use. |
| Steady | Acceleration threshold ~2.5 – 3.5 m/s² Moderate, controlled pedal application. |
Timely, smooth braking; balanced use of regeneration and friction brakes. |
| Aggressive | Acceleration threshold > ~3.5 m/s² Frequent, sharp pedal application. |
Late, frequent, and harder braking; higher proportion of friction braking. |
2.3 Test Cycle: WLTC Shortened Method Combined Cycle
The testing was conducted using a WLTC Shortened Method Combined Cycle, which is designed to determine range and consumption while reducing overall test duration. This cycle is constructed from segments of the full WLTC Class 3b cycle (applicable to higher-power vehicles). The combined cycle structure is as follows:
- Dynamic Segment 1 (DS1): Begins with a full WLTC cycle (Low, Medium, High, Extra-High speed phases), followed immediately by an Urban WLTC cycle (Low and Medium speed phases only).
- Constant Speed Segment 1 (CSS1 or CSSM): A prolonged constant-speed driving segment at a moderate speed (e.g., ~80 km/h). The distance for this segment is calculated based on the vehicle’s energy consumption characteristics.
- Dynamic Segment 2 (DS2): Another full WLTC cycle followed by an Urban WLTC cycle.
- Constant Speed Segment 2 (CSS2 or CSSE): A final constant-speed driving segment at a moderate speed until the electric vehicle car reaches the discharge termination criteria.
This structure subjects the electric vehicle car’s powertrain and battery to a mix of dynamic urban/motorway driving and sustained high-load cruising, providing a comprehensive assessment of range under varied conditions influenced by driving style.
2.4 Data Acquisition and Key Metrics
The test system integrated a climate-controlled chamber, a chassis dynamometer, and high-precision electrical measurement equipment. A Yokogawa WT1806 precision power analyzer was used to sample the instantaneous current and voltage at the main terminals of the electric vehicle car’s traction battery pack at a high frequency. This allows for direct calculation of instantaneous power and cumulative energy flow. The dynamometer controller recorded the actual vehicle speed trace. Key metrics analyzed included:
- Cumulative Discharge Energy (Wh): Total energy delivered from the battery to the powertrain.
- Cumulative Regenerative Energy (Wh): Total energy recovered to the battery during braking/coasting.
- Net Energy Consumed (Wh): Discharge Energy minus Regenerative Energy.
- Total Distance Traveled (km): Measured by the dynamometer.
- Net Energy Consumption (Wh/km): Net Energy Consumed / Distance Traveled.
- Instantaneous Current (A): A direct indicator of powertrain load and driving behavior intensity.
3. Results and Data Analysis
3.1 Overall Range and Energy Consumption Results
The primary test results unequivocally demonstrate the profound impact of driving behavior on the range of an electric vehicle car. The aggregated data for the complete WLTC Shortened Method test under the three driving styles is presented in Table 3.
| Driving Behavior | Actual Distance (km) | Actual Discharge Energy (Wh) | Actual Net Energy Consumption (Wh/km) | Range vs. Economical |
|---|---|---|---|---|
| Economical | 570.0 | 110,706 | 194.2 | 0.0% (Baseline) |
| Steady | 559.0 | 111,601 | 200.4 | -1.9% (-11 km) |
| Aggressive | 529.0 | 109,985 | 208.3 | -7.2% (-41 km) |
Analysis: The Economical driving style achieved the longest range (570 km) with the lowest net energy consumption (194.2 Wh/km). The Steady style resulted in a 1.9% range reduction, equating to 11 fewer kilometers, with a 3.2% increase in energy consumption. Most strikingly, the Aggressive driving style caused a 7.2% reduction in total range—a loss of 41 kilometers on a single charge—alongside a 7.3% increase in net energy consumption compared to the Economical baseline. This provides clear, quantitative evidence that how an electric vehicle car is driven is a major determinant of its practical utility. The relationship between increased consumption and reduced range is direct and significant.
3.2 Instantaneous Current Analysis: Mapping Driving Behavior
The instantaneous current drawn from (or supplied to) the battery is a highly sensitive real-time proxy for driving behavior in an electric vehicle car. The fundamental relationship can be simplified as the driver’s accelerator or brake pedal command translating into a torque request to the electric motor, which in turn demands a specific current from the battery. During acceleration, a quasi-linear relationship often exists between pedal position and motor current demand:
$$I_{drive}(t) = k \cdot \alpha(t) + I_0$$
where $I_{drive}(t)$ is the drive current, $k$ is a vehicle-specific constant relating pedal input to torque/current, $\alpha(t)$ represents the accelerator pedal command, and $I_0$ is a base current. Analyzing the current waveforms across different WLTC cycle phases reveals the distinct signatures of each driving style.
Low-Speed Phase Analysis: In the low-speed urban part of the WLTC, the differences in current peaks are moderate but revealing. The Aggressive style showed the highest discharge current peak at 37.75 A, while its regenerative current peak was also the highest at 6.16 A, indicating sharp braking events. The Economical style had a slightly lower discharge peak (35.52 A) but a significantly lower regenerative peak (4.16 A), consistent with smoother, coasting-based deceleration. The Steady style values fell between the two.
High-Speed Phase Analysis: The differences magnify in the high-speed phase. The Aggressive style’s discharge current peaked at 51.66 A, substantially higher than the Economical style’s 39.43 A—a difference of over 30%. This highlights the dramatic impact of aggressive high-speed acceleration on the power demand of an electric vehicle car. Notably, the regenerative current peaks across all styles converged in this phase (Economical: 16.58 A, Aggressive: 17.54 A), suggesting that high-speed braking strategies are more constrained by system limits, reducing the behavioral differential in regeneration during these events.
The current analysis confirms that aggressive driving imposes high peak loads on the electric vehicle car’s battery and powertrain during acceleration across all speed ranges, while economical driving effectively curtails these peaks, especially in high-power demand situations.
3.3 Regenerative Braking Efficiency: A Key Differentiator
The efficiency of regenerative braking—the ability to recapture kinetic energy during deceleration and return it to the battery—is critically dependent on driving behavior. Our data quantifies this dependency. Table 4 breaks down the cumulative regenerative energy recovered during the two dynamic WLTC segments (DS1 & DS2) of the combined cycle.
| Driving Behavior | Regen Energy in WLTC Segments (Wh) | Total Regen Energy (Wh) | Regen as % of Discharge |
|---|---|---|---|
| Economical | 591.52 (DS1) + 1,369.20 (DS2) = 1,960.72 | ~1,961 | ~1.77% |
| Steady | 491.77 (DS1) + 1,321.40 (DS2) = 1,813.17 | ~1,813 | ~1.62% |
| Aggressive | 284.93 (DS1) + 1,404.80 (DS2) = 1,689.73 | ~1,690 | ~1.54% |
Analysis: The Economical driver recovered the most energy overall (~1,961 Wh), approximately 16% more than the Aggressive driver (~1,690 Wh). This is primarily due to the strategy of anticipatory coasting and gentle braking, which allows the regenerative system to operate at optimal efficiency for longer durations, capturing more energy per deceleration event. The Aggressive style, characterized by late and hard braking, often exceeds the maximum regenerative capacity of the electric vehicle car, forcing the use of friction brakes and wasting kinetic energy as heat. The Steady style’s recovery is intermediate. The efficiency $\eta_{regen}$ of the regenerative process relative to the available kinetic energy can be conceptually represented as a function of braking harshness:
$$\eta_{regen} = f(|\dot{v}|) \quad \text{where} \quad \frac{\partial \eta_{regen}}{\partial |\dot{v}|} < 0 \quad \text{for high deceleration rates}$$
This indicates that regeneration efficiency tends to decrease with increasing deceleration severity, a regime frequently entered by aggressive drivers.
3.4 Impact on Constant Speed (Cruising) Performance
Analyzing the two Constant Speed Segments (CSSM and CSSE) isolates the impact of driving style on sustained, steady-state efficiency of the electric vehicle car, which is less about transient behavior and more about the residual energy state and system losses. The results, summarized in Table 5, show interesting trends.
| Segment & Driving Behavior | Distance (km) | Avg. Net Consumption (Wh/km) | Note |
|---|---|---|---|
| CSSM (Economical) | 474.86 | 192.40 | Highest range, lowest consumption. |
| CSSM (Aggressive) | 455.26 | 204.51 | Range 4.1% lower than Economical. |
| CSSE (Economical) | 49.33 | 189.56 | Consumption improved from CSSM. |
| CSSE (Aggressive) | 28.34 | 203.17 | Drastic 42.6% range reduction vs. Econ. |
Analysis: During the first constant-speed segment (CSSM), the trends are consistent: Economical driving yields the best efficiency. However, the second constant-speed segment (CSSE) reveals a critical finding. The Aggressive driver’s range in CSSE collapsed to only 28.34 km—a 42.6% reduction compared to the Economical driver’s 49.33 km in the same segment type. This cannot be explained by differences in steady-state cruising efficiency alone, as the average net consumption values remained similar to CSSM. The primary cause is the state of the battery at the start of CSSE. The aggressive driving during the preceding dynamic segments (DS1 & DS2) consumed more net energy and likely incurred higher losses, leaving the battery at a lower effective State of Energy (SOE) and potentially at a less efficient point in its discharge curve or at a slightly elevated temperature. This “carry-over” effect demonstrates that aggressive driving not only wastes energy in the moment but also degrades the subsequent performance of the electric vehicle car, even under ostensibly efficient constant-speed conditions. The energy balance can be modeled as:
$$E_{CSSE}^{avail} = E_{total} – (E_{net, DS1+DS2} + E_{loss, DS1+DS2})$$
where $E_{loss, DS1+DS2}$ is higher for the Aggressive style, thereby reducing $E_{CSSE}^{avail}$, the energy available for the final constant speed segment.
4. Conclusion and Implications
This study, through a meticulously designed WLTC-based test protocol, successfully quantified the significant and multi-faceted influence of driving behavior characteristics on the real-world range of an electric vehicle car. The experimental matrix isolating Economical, Steady, and Aggressive driving styles provided clear, data-driven conclusions with important implications for users, manufacturers, and researchers focused on electric vehicle car technology.
1. Driving Behavior is a Primary Determinant of Electric Vehicle Car Range: The core finding is that driving style alone can cause a range variation exceeding 8% under a standardized test cycle, translating to a difference of over 40 kilometers on a single charge for the tested electric vehicle car. The instantaneous power demand differential between aggressive and economical driving exceeded 50%, highlighting the dramatic effect of pedal command dynamics on the energy draw from the battery.
2. Regenerative Braking Efficiency is Highly Driving-Style Dependent: The study quantified the direct link between smooth, anticipatory deceleration and efficient energy recovery. The Economical driving style recovered approximately 16% more regenerative energy than the Aggressive style. This underscores that maximizing the range of an electric vehicle car is not just about gentle acceleration but equally about mastering regenerative braking through foresight and smooth control inputs.
3. The Impact Propagates Beyond Transient Events: The analysis of constant-speed segments revealed a crucial “carry-over” effect. Aggressive driving during dynamic phases degraded the subsequent cruising range of the electric vehicle car by over 40% in one segment, not due to worse steady-state efficiency, but because it left the battery system in a less favorable energy state. This means the penalty for aggressive driving compounds throughout a journey.
4. Implications for Adaptive Energy Management: The clear signatures of different driving behaviors in current and power data suggest the feasibility of real-time driving style recognition algorithms. An intelligent electric vehicle car could use such an algorithm to adapt its energy management strategy—for example, by providing more assertive regenerative braking feedback in “Sport” mode or offering enhanced eco-coaching guidance in a default mode. Furthermore, range prediction algorithms could be significantly improved by incorporating a real-time assessment of driver behavior, rather than relying solely on historical average consumption data.
In summary, for consumers, this research reinforces the tangible benefits of eco-driving techniques for extending the range and reducing the operating cost of an electric vehicle car. For the automotive industry, it provides a compelling data foundation and a clear target for developing the next generation of smart, adaptive, and user-centric energy management systems that can help bridge the gap between certified and real-world range, thereby enhancing the ownership experience and accelerating the adoption of electric vehicle cars.