In electric vehicles, the Heating, Ventilation and Air-Conditioning (HVAC) function is often performed by a heat pump. Heating and cooling the cabin air drains energy directly from the vehicle's battery. In addition, these vehicles may operate in environments with high level of air pollution. In the cabin, passengers are confined to a small space where particles and harmful gases can accumulate. In addition, the ventilation system must also handle the air which does not enter the cabin through blower operation. This "infiltration" is a function of the vehicle speed and allows pollution to enter the cabin without being filtered or thermally treated. The objective of the study is to optimize the competing goals of the HVAC system: achieving the best air quality while maintaining good thermal comfort, at minimum energy costs. A system simulation tool is calibrated to represent the heating and cooling of an electric car. With this model, the influence of key factors is evaluated. Depending on ambient conditions and other parameters (number of occupants, vehicle speed, etc.), the blower flow rate and recirculation ratio can be adjusted to reach the objectives. The management of the proportion of fresh and recirculated air allows to regulate the humidity and carbon dioxide levels. Optimum controls are proposed as good trade-offs to reduce the power consumption, while maintaining a safe and comfortable environment for occupants. Compared to the full fresh air mode, the driving range gains are estimated in cold (-15 °C) and hot (30 °C) scenarios at 9 and 26 km respectively.

A car cabin’s small volume makes it vulnerable to discomfort if temperature, humidity, and carbon dioxide levels are poorly regulated. In electric vehicles, the HVAC system draws energy from the car battery, reducing the driving range by several dozen kilometres under extreme conditions. A 1D simulation model calibrated for the Renault ZOE was used to evaluate the effects of ventilation parameters on thermal comfort, humidity, and power consumption. The results highlighted the interdependence of factors such as the recirculation ratio and blower flow rate, showing that energy-efficient settings depend on ambient conditions and other factors (such as occupancy, vehicle speed, infiltration). Adjustments can reduce heat pump energy use, but no single setting optimally balances power consumption and thermal comfort across all scenarios. The opti-CO₂ mode is proposed as a trade-off, offering energy savings while maintaining safety and comfort. This mode quickly achieves the cabin temperature target, limits carbon dioxide concentration at a safe level (1100 ppm), minimises fogging risks, and reduces heat pump power consumption. Compared to fresh air mode, the opti-CO₂ mode extends the driving range by 9 km in cold conditions and 26 km in hot conditions, highlighting its potential for improving energy efficiency and occupant comfort in electric vehicles.

Efficient particle charging is a key step in electrostatic aerosol removal, yet optimizing corona ionizers remains challenging because geometry, flow conditions, and aerosol properties interact strongly. This study experimentally investigates particle charging downstream of a negative corona needle-to-ring ionizer operated in a controlled duct facility and compares the measurements with charging theory. Discharge current and particle charge were characterized using an ELPI+ while varying the inter-electrode gap (orthogonal distance 10, 20, and 40 mm), needle cone angle (15° to 180°), relative humidity (3% to 75%), air velocity (1 to 3 m/s), and aerosol nature (DEHS and NaCl). The results show that particle charging does not increase monotonically with applied voltage. Although smaller gaps and sharper needles promote earlier corona onset and higher discharge current, they may also reduce net particle charging at high voltage because ion drift becomes too fast for efficient charge transfer to the particles. A useful ion presence time in the charging volume is identified as a key parameter governing the existence of an optimal charging regime. Under the investigated geometry and operating conditions, the useful ion presence time associated with the onset of charge reduction was estimated to lie between 0.4 and 0.5ms. Intermediate needle cone angles (30°–60°) provide the best average charging performance by offering a better compromise between ion generation and ion distribution than a 15° needle. Relative humidity, air velocity, and aerosol properties also significantly affect charging through their influence on corona inception, ion mobility, flow transport, and particle electrical behaviour. Finally, an adapted charging model including a correction based on useful ion presence time improves agreement with experiments and provides practical guidance for the design and operation of corona-based pre-charging stage.

Microscopic Particulate Matter (PM) below 10 µm can enter the respiratory system and affect human health in the short and long term. Railway enclosures are sites with high concentrations of fine PM and technical solutions like mechanical filtration exist to increase the air quality. However, several crucial factors must be evaluated and optimized like energy consumption, maintenance cost/interval, design and control. A fast and adaptable evaluation of decontamination solutions is required to find the optimal solution. To answer this, a 1D multizone model based on station discretization aligned with the track direction is proposed to precisely place decontamination systems along the station. In each zone, a set of ordinary differential equations is used to forecast the daily progression of PM concentrations, based on physical parameters (air and train velocities, and train traffic) used to describe the different physical phenomena (resuspension, deposition, ventilation and generation). Three-dimensional CFD (Computational Fluid Dynamics) simulations are used to characterize the efficiency and range of decontamination products and reproduce their effect in the 1D model. This approach allows for flexible optimization of local and global decontamination efficiencies with multiple parameter changes. PM₁₀ and PM_2.5 (below 10 and 2.5 µm) are studied here as they are often monitored.