As a researcher in the field of automotive engineering, I have observed the rapid growth in the adoption of EV cars worldwide, with penetration rates exceeding 18% globally. However, range anxiety remains a critical barrier for many consumers, with over 72% expressing concerns about the limited driving distance of EV cars. Traditional approaches to improving the range of EV cars have focused on incremental advancements in battery materials and lightweight designs, but these methods often fall short in addressing the dynamic energy consumption fluctuations caused by real-world traffic conditions. For instance, frequent start-stop cycles in urban environments can lead to up to 27% of energy being wasted in EV cars. The emergence of intelligent connected technology, with Vehicle-to-Everything (V2X) communication latency below 50 ms and roadside perception coverage reaching 40%, presents a transformative opportunity to optimize energy usage in EV cars in real-time. In this study, I propose a multi-scale range optimization system driven by intelligent connected technology, which innovatively integrates digital twin simulations, multi-source data fusion, and cooperative vehicle-infrastructure frameworks to enhance the efficiency of EV cars. My approach establishes quantitative models linking V2I communication frequency (2–10 Hz) to energy recovery efficiency, moving beyond traditional qualitative analyses, and employs a federated learning distributed architecture to offload 85% of computational load to the cloud. Through deep coordination between vehicles, roads, and cloud systems, this research achieves robust range improvements under communication constraints, offering a new paradigm for energy management in intelligent EV cars.
The technical foundation of my research is built upon an “end-edge-cloud” three-tier cooperative architecture, which enables seamless data exchange and processing for EV cars. This system comprises several key components: the onboard perception system, vehicle-road cooperative communication, and a cloud-based big data platform. The onboard perception system for EV cars incorporates a multi-modal sensor cluster, including environmental perception layers with 4D millimeter-wave radar (detection range of 300 m, accuracy of ±0.1°), solid-state LiDAR (128 lines, field of view of 120°×25°), and vehicle state layers with a Battery Management System (BMS) sampling at 1 kHz and wheel speed sensors with 0.1 km/h accuracy. Additionally, a Driver Monitoring System (DMS) tracks facial features at 30 Hz to assess driving behavior. For vehicle-road cooperative communication, I utilize a C-V2X hybrid mode, combining DSRC based on IEEE 802.11p standards (latency ≤100 ms, communication range of 300 m at 60 km/h) and LTE-V2X supporting 3GPP R14 protocols (peak data rate of 50 Mbit/s, latency ≤50 ms). The communication quality is evaluated using a function that accounts for signal strength and latency, with weights determined through entropy methods. The cloud-based big data platform employs a Hadoop+Spark hybrid architecture, storing second-level operational data from over 100,000 EV cars, with daily increments of 2 TB. This platform uses LightGBM algorithms for energy consumption feature mining across more than 85 dimensions and provides API services with response times under 500 ms, supporting concurrent requests of at least 1000 per second.
To quantify the factors affecting the range of EV cars, I developed a comprehensive energy consumption decomposition model. The total energy consumption in EV cars can be expressed as the sum of various components, including motor energy, auxiliary system energy, and losses. A detailed equation is used to describe the energy consumption dynamics:
$$E_{total} = E_{motor} + E_{aux} + E_{loss}$$
where \( E_{motor} \) represents the energy used by the motor, \( E_{aux} \) accounts for auxiliary systems like climate control, and \( E_{loss} \) includes losses from factors such as aerodynamic drag and rolling resistance. The aerodynamic drag energy, for instance, is calculated using the formula:
$$E_{drag} = \frac{1}{2} \rho C_d A v^2$$
Here, \( \rho \) is the air density (1.225 kg/m³), \( C_d \) is the drag coefficient, \( A \) is the frontal area, and \( v \) is the velocity of the EV car. Rolling resistance energy is given by \( E_{roll} = C_r m g v \), where \( C_r \) is the rolling resistance coefficient (0.015), \( m \) is the mass, and \( g \) is gravitational acceleration. The rotational mass coefficient \( \delta \) (1.05) is also considered to account for inertial effects. Environmental parameters significantly impact the range of EV cars; for example, lithium-ion batteries experience capacity degradation in low temperatures, with a decay function showing up to 38% reduction at -20°C. Slope and wind effects are modeled through orthogonal experiments, revealing that energy consumption increases by 2.1% per degree of slope and by 12% for every 5 m/s increase in crosswind speed, as validated by CFD simulations. Driving behavior is quantified using an aggressiveness index \( K_d \), defined as:
$$K_d = \frac{1}{n} \sum_{i=1}^{n} |a_i| + \sigma_v$$
where \( a_i \) is the instantaneous acceleration and \( \sigma_v \) is the standard deviation of velocity. Through K-means clustering, driving styles are categorized into three types: conservative (\( K_d < 0.4 \)), standard (\( 0.4 \leq K_d \leq 0.7 \)), and aggressive (\( K_d > 0.7 \)), which helps in tailoring energy management strategies for EV cars.
The mechanisms through which intelligent connected technology enhances the range of EV cars are multifaceted. Dynamic energy management optimization leverages V2X communication to acquire real-time traffic events up to 500 meters ahead, such as congestion or traffic light phases. By integrating this data with vehicle parameters like speed, acceleration, and battery temperature, I dynamically adjust the State of Charge (SOC) allocation in EV cars. A Kalman filter algorithm fused with roadside unit data reduces prediction errors to within 8%, outperforming traditional LSTM models that average 15% error. This results in more efficient energy use, particularly in urban settings where stop-and-go traffic is common. Intelligent path planning systems further contribute by utilizing high-definition maps—including slope, curvature, and wind data—along with real-time traffic flow information. A multi-objective optimization algorithm, based on an improved NSGA-II approach, generates Pareto-optimal solutions that balance time, safety, and energy consumption. In validation tests, this system demonstrated a 14.7% reduction in energy usage compared to conventional navigation for EV cars, with dynamic path updates at 5 Hz minimizing mileage loss by 8.2% in sudden congestion scenarios.
Environmental adaptive control plays a crucial role in optimizing the range of EV cars under varying conditions. For battery thermal management, I employ a fuzzy PID control strategy that adjusts preheating parameters based on weather forecasts and vehicular data. This model adapts PID parameters according to temperature change rates, avoiding overshoot issues common in fixed-parameter strategies. In动力 system matching, pre-reading slope information from high-definition maps for up to 1 km ahead allows for dynamic optimization of motor torque output curves. Field tests on challenging routes like the Sichuan-Tibet highway showed a 63% reduction in energy consumption fluctuations during continuous climbs, with the standard deviation of range decreasing from 12.8 km to 4.7 km in high-altitude environments for EV cars. Group cooperative energy saving mechanisms, such as vehicle platooning, utilize V2V communication to maintain optimal following distances of 0.8 times the vehicle length, leveraging aerodynamic drafting to reduce drag. Tests with a 10-vehicle platoon of EV cars on highways revealed an average energy reduction of 11.3% at 100 km/h, with lead cars saving 7.2% and middle vehicles achieving up to 14.8% savings. Regional energy scheduling, based on game theory models, intelligently allocates charging station resources by considering factors like charging power, wait times, and user preferences, resulting in a 44% decrease in average wait times and a 29% increase in charger utilization for EV cars.
To validate the proposed systems, I constructed a comprehensive simulation framework using a “digital twin-VISSIM-MATLAB/Simulink” triple-loop model. This includes a traffic environment layer replicating typical commuter corridors with 12 signalized intersections, a vehicle dynamics layer with high-fidelity models (battery parameter errors <1.5%, motor MAP diagrams from bench tests), and a control algorithm layer deploying an improved Model Predictive Controller (MPC) with a prediction horizon of 10 s and control horizon of 2 s. Credibility was ensured through cross-validation: static checks against battery discharge curves showed errors ≤0.8% in SOC-voltage relationships at 25°C, dynamic validation using real driving datasets resulted in speed tracking RMS errors ≤0.3 m/s, and extreme condition tests aligned with reference data on low-temperature battery performance. The simulation results demonstrate significant improvements in the range and efficiency of EV cars across various scenarios, as summarized in the tables below.
| Evaluation Metric | Traditional Strategy | Proposed Strategy | Improvement Rate |
|---|---|---|---|
| Braking Energy Recovery Rate | 42.3% | 59.7% | 41% |
| Start-Stop Energy Consumption Share | 31.5% | 24.1% | -23% |
| Equivalent Range | 427 km | 503 km | 17.8% |
In urban high-congestion scenarios with average speeds of 18 km/h, the proposed SOC dynamic allocation strategy based on V2X predictions enhanced the range of EV cars by 17.8% and increased braking energy recovery to 59.7%. For extreme temperature conditions, COMSOL Multiphysics coupled simulations showed that in -20°C environments, battery preheating energy consumption was reduced by 38% compared to traditional PID control, while in 45°C heat, liquid cooling system energy use decreased by 21% while maintaining cell temperature differences below 2°C. High-speed platooning simulations in PreScan with 10 EV cars at 100 km/h and 0.8 vehicle-length spacing achieved an overall energy reduction of 12.8%, with aerodynamic drag coefficients decreasing by 17.5% for lead cars and 25.3% for trailing vehicles. Sensitivity analyses using OPNET network simulations indicated that communication reliability is critical; delays over 120 ms or packet loss rates exceeding 15% reduce optimization efficiency by more than 50%, but Kalman prediction compensation maintains 83% efficacy even at 20% packet loss. Monte Carlo methods with random disturbances (e.g., wind speed ±5 m/s, slope ±2°) showed that the range stability of EV cars improved, with standard deviation dropping from 9.2 km to 3.7 km, and MPC controllers kept speed tracking errors below 0.5 m/s. Testing with various driver models revealed that aggressive drivers saw 29% lower energy savings, but compensation algorithms restored 85% of the benefit, while conservative drivers achieved up to 19.3% range improvement, highlighting the adaptability of the approach for diverse EV car users.
| Simulation Scenario | Parameter | Value | Impact on EV Cars |
|---|---|---|---|
| Extreme Low Temperature (-20°C) | Battery Preheating Energy Reduction | 38% | Improved cold-weather range |
| High-Speed Platooning | Overall Energy Reduction | 12.8% | Enhanced efficiency for fleet EV cars |
| Urban Congestion | Range Increase | 17.8% | Reduced anxiety for city EV car drivers |
In conclusion, my research establishes an intelligent connected technology-driven system for optimizing the range of EV cars, validated through multi-scale joint simulations that confirm the feasibility and effectiveness of the approach. The results indicate substantial improvements: in urban high-congestion scenarios, dynamic SOC allocation based on V2X predictions increases the range of EV cars by 17.8% and boosts braking energy recovery to 59.7%; high-speed cooperative control in platoons achieves 12.8% energy savings for EV cars; and in extreme cold conditions, battery preheating energy consumption is reduced by 38%. Future work should focus on developing lightweight digital twin models, promoting the deployment of 5G-Advanced communication standards and V2I infrastructure, and establishing cross-domain data fusion interface standards to accelerate the integration of vehicle-road-cloud systems. This study provides theoretical support and a technical framework for enhancing the energy efficiency of intelligent connected EV cars, contributing to the high-quality development of the electric vehicle industry. The insights gained here underscore the potential of connected technologies to address range limitations, making EV cars more practical and appealing for widespread adoption.