Long-term evaluation of the pIOL showed a persistent ECD decrease in some eyes that was numerically larger than the annual rate expected with aging. Endothelial cell loss resulted in explantation in 3.1% of all eyes with the pIOL. Patients had no permanent vision loss. The manufacturer recommends that patients continue to be monitored and their corneal endothelium evaluated semiannually.

The HVAC system represents the main auxiliary load in battery-powered electric vehicles (BEVs) and requires efficient control approaches that balance energy saving and thermal comfort. On the one hand, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle’s driving range, which constitutes a major concern in BEVs. In this paper, a thermal comfort management approach that optimizes the thermal comfort while preserving the driving range during a trip is proposed. The electric vehicle is first modeled together with the HVAC and the passengers’ thermo-physiological behavior. Then, the thermal comfort management issue is formulated as an optimization problem solved by dynamic programing. Two representative test-cases of hot climates and traffic situations are simulated. In the first one, the energetic cost and ratio of improved comfort is quantified for different meteorological and traffic conditions. The second one highlights the traffic situation in which a trade-off between the driving speed and thermal comfort is important. A large number of weather and traffic situations are simulated and results show the efficiency of the proposed approach in minimizing energy consumption while maintaining a good comfort.

The HVAC system represents the main auxiliary load in electric vehicles (EVs) and requires efficient control approaches that balance energy saving and thermal comfort. In fact, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle driving range, which constitutes the major concern in EVs. In this paper, dynamic programing is applied to develop an HVAC system supervisor that optimizes the thermal comfort on a given journey, for given climatic conditions and energy available. The electric vehicle model and the optimization approach are presented. Two test-cases, corresponding to hot climate, are simulated. In the first one, the energetic cost of improved comfort is quantified, while in the second one the trade-off between driving speed and thermal comfort is analyzed.

The HVAC system represents the main auxiliary load in electric vehicles, but passengers’ thermal comfort expectations are always increasing. Hence, a compromise is needed between energy consumption and thermal comfort. The present paper proposes a real-time thermal comfort management strategy that adapts the thermal comfort according to the energy available for operating the HVAC system. The thermal comfort is evaluated thanks to the “Predicted Mean Vote”, representative of passenger’s thermal sensations. Based on traffic and weather predictions for a given trip, the algorithm first estimates the energy required for the traction and the energy available for thermal comfort. Then, it determines the best thermal comfort that can be provided in these energetic conditions and controls the HVAC system accordingly. The algorithm is tested for a wide variety of meteorological and traffic scenarios. Results show that the energy estimators have a good accuracy. The absolute relative error is about 1.7% for the first one (traction), and almost 4.1% for the second one (thermal comfort). The effectiveness of the proposed thermal comfort management strategy is assessed by comparing it to an off-line optimal control approach based on dynamic programming. Simulation results show that the proposed approach is near-optimal, with a slight increase of discomfort by only 3%.