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![Figure 4. Pareto Curve for the SC03 Cycle [10].](profile/Stephanie-Stockar/publication/271826588/figure/fig4/AS:295230952755204@1447399952900/Pareto-Curve-for-the-SC03-Cycle-10_Q320.jpg)
In a conventional passenger vehicle, the AC system is the largest ancillary load. This paper proposes a novel control strategy to reduce the energy consumption of the air conditioning system of a conventional passenger car. The problem of reducing the parasitic load of the AC system is first approached as a multi-objective optimization problem. Sta...
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... study is conducted on a conventional automotive air conditioning system based upon a simple vapor compression cycle with R-134a as the working fluid. A schematic of the experimental setup for the vapor compression cycle is shown in Figure 1, illustrating the thermodynamic states and measured signals. The system includes a screw compressor connected to the engine auxiliary belt through an electromagnetic clutch. ...
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... reference to the thermodynamic states and notation indicated in Figure 1, the two equations describing the dynamics of the system are given as follows: ...
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... limit the calibration effort of the rule-based control strategy, a regression analysis was performed to identify a correlation between the HVAC setpoints and the optimal control parameters. The results of thisanalysis are shown in Figure 10, where a linear relation is found between the control strategy parameter and the reference mean evaporator pressure. Compared to the tuning of the pressure threshold, the parameter for the rule on the engine torque are simpler to determine and were found to be invariant across the drive cycle considered, and not affected by the HVAC system settings. ...
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... the parameters were fine-tuned on the vehicle to achieve improved fuel economy, guarantee the comfort in the cabin and reduce the control effort. This procedure can be largely simplified if the correlation between the optimal control strategy parameters and the HVAC settings (shown in Figure 10) is exploited. ...
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... verify the validity of this approach, the rule-based controller is tested on the FTP drive cycle using the reference evaporator pressure setting calculated from the linear regressions shown in Figure 10 and the constant torque threshold settings determined previously. ...
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... rule-based control strategy was benchmarked against the production controller, and a comparison of the performance metrics is summarized in Table 3. A more detailed description of the clutch engagement strategy for the rule-based controller and a comparison of the vehicle fuel consumption against the production controller for the FTP cycle are illustrated in Figure 11. In this case, the rule-based controller is able to maintain the same tracking error on the evaporator pressure as the production controller, however it leads to a 2% reduction in the fuel consumption calculated for the given drive cycle. ...
Citations
... The experiences of different countries have proved that setting comprehensive development plans, facing challenges and solving problems cannot be achieved without the assistance of qualified human cadres with appropriate scientific qualifications and advanced technological training, in order to be able to keep pace with contemporary challenges [1]. With the trend of countries around the world to manufacture cars that are more fuel-efficient, this trend has brought about a tremendous development in the automotive industry in general and its air conditioning system in particular, as electronic circuits have been introduced to control all car systems, including the air conditioning system [2], [3] which led to the presence of new faults that did not exist before Cars also require special handling, unlike previous periods [4]. Since diagnosing car malfunctions, especially the air conditioning system, is a complex process that requires a high level of knowledge and skills. ...
... He also formulated the methods of dealing with various malfunctions in the form of a set of facts and rules in order to build a knowledge base. The researchers also designed the interface of the proposed system at this stage, and the following is a figure (2) showing the interface of the system. ...
Diagnosing car malfunctions is a complex process that requires a high level of knowledge and skills. Car users need a skilled technician to diagnose car faults. With the development in the automotive industry and the novelty of its components; There is a gap between the knowledge and skills of technicians and what the labor market demands. Therefore, it has become necessary to employ artificial intelligence technology to help technicians keep pace with the huge developments in the automotive industry. The aim of the research is to present a rule-based system for diagnosing various malfunctions of car conditioning; the system was applied to 22 technicians in the Arab Republic of Egypt. They were divided into a control group and an experimental group, and the results showed that the mean score of the experimental group technicians was 58.45, greater than the mean score of the technicians in the Control group 29.18 To confirm the results, the T-test was applied, and its value was 19.721, which indicates the effectiveness of the proposed system in improving technicians' knowledge and raising their skills in accurately diagnosing and fixing car air conditioning (AC) malfunctions, saving time and effort needed for the diagnosis process
... However, the RB controller does not ensure optimality in temperature regulation and energy minimization. Rostiti et al. 12 proposed an RB controller utilizing offline-solved dynamic programming in the process of rule calibration. This strategy offers control performance approaching the optimal temperature regulation and energy consumption of dynamic programming. ...
A cabin climate control system, often referred to as a heating, ventilation, and air conditioning (HVAC) system, is one of the largest auxiliary loads of an electric vehicle (EV), and the real‐time optimal control of HVAC brings a significant energy‐saving potential. In this article, a linear‐time‐varying (LTV) model predictive control (MPC)‐based approach is presented for energy‐efficient cabin climate control of EVs. A modification is made to the cost function in the considered MPC problem to simplify the Hessian matrix in utilizing quadratic programming for real‐time computation. A rigorous parametric study is conducted to determine optimal weighting factors that work robustly under various operating conditions. Then, the performance of the proposed LTV‐MPC controller is compared against a rule‐based (RB) controller and a nonlinear economic MPC (NEMPC) benchmark. Compared with the RB controller benchmark, the LTV‐MPC reaches the target cabin temperature at least 69 s faster with 3.2% to 15% less HVAC system energy consumption, and the averaged cabin temperature difference is 0.7°C at most. Compared with the NEMPC, the LTV‐MPC controller can achieve comparable performance in temperature regulation and energy consumption with fast computation time: the maximum differences in temperature and energy consumption are 0.4°C and 2.6%, respectively, and the computational time is reduced 72.4% on average with the LTV‐MPC.
... While extensive studies have been carried out on fuel economy optimization for electrified vehicles (see [3], [4] and the references therein), efficient thermal management of electrified connected and automated vehicles (CAVs) has not been fully explored. Most previous research efforts [5], [6] have addressed the thermal management problems associated with the AC system for ICE powered vehicles with belt-driven compressors. For electrified vehicles, however, the compressor of the AC system is electrically driven and directly draws power from high-voltage battery, thereby interacting with other power loads such as the traction power. ...
Incorporating traffic information in power management optimization process for electrified and connected vehicles offers opportunities for improving fuel economy. Integrating the management of thermal load (such as those used for heating, ventilation, & air conditioning (HVAC) of the passenger compartment, and for the battery cooling) with the power management process can provide even greater benefits for connected and automated vehicles (CAVs). However, given the relatively slow dynamics associated with the thermal subsystems, the lack of reliable power and thermal loads prediction over an extended prediction horizon is the main challenge for efficient thermal management using model predictive control (MPC). This paper presents a hierarchical two-layer MPC scheme which exploits vehicle speed and traffic preview predictions over short and long prediction horizons to schedule optimal thermal trajectories for the cabin and battery cooling in hybrid electric vehicles (HEVs) via a novel intelligent online constraint handling (IOCH) approach. These trajectories are next incorporated into the vehicle-level controller to determine the proper power split between electric motor and internal combustion engine (ICE). We present the development and experimental validation of control-oriented models used for prediction of the vehicle thermal dynamics and loads over a long planning horizon. Compared to a more traditional single-layer MPC approach, the proposed two-layer MPC shows that depending on the driving cycle and traffic conditions, 2.2% to 5.3% reductions in HEV fuel consumption can be achieved for urban driving and congested city driving cycles, respectively, in CAV operation scenario. This fuel economy improvement is a direct result of taking proactive actions through real-time prediction and optimization to avoid conservative and inefficient thermal responses, while enforcing cabin and battery operating constraints.
... Many of the CAV related research activities, such as eco-driving and platooning, have focused on reducing trac- tion power related losses, whereas the impact of thermal management has not been fully explored. Previous research has addressed the energy management of the A/C system for vehicles with traditional internal combustion engines (ICEs) [4] [5], where the A/C compressor is belt-driven by the ICE. The energy management problem considered in these references was solved by a dynamic programming (DP) algorithm. ...
This paper considers an application of model predictive control to managing cabin temperature in order to improve energy efficiency in connected and automated vehicles (CAVs) with battery electric or hybrid electric powertrain. A control-oriented prediction model for air conditioning (A/C) system is established, identified, and validated versus a higher fidelity simulation model (CoolSim). Based on this developed prediction model, an optimization-based nonlinear model predictive control (NMPC) problem is formulated and solved online to minimize the energy consumption of the air conditioning (A/C) system. Simulation results for a summer cooling scenario case study illustrate the desirable characteristics of the proposed NMPC solution on the higher fidelity model while enforcing physical constraints of the A/C system and maintaining temperature within a specified range. Moreover, it is shown that by utilizing the vehicle speed preview and exploiting the operating efficiency characteristics of the A/C system, energy efficiency improvements of up to 9\% can be effected compared to a conventional A/C control scheme which is operating with a constant temperature set-point.
... tion power related losses, whereas the impact of thermal management has not been fully explored. Previous research has addressed the energy management of the A/C system for vehicles with traditional internal combustion engines (ICEs) [4] [5], where the A/C compressor is belt-driven by the ICE. The energy management problem considered in these references was solved by a dynamic programming (DP) algorithm. ...
This paper considers an application of model predictive control to automotive air conditioning (A/C) system in future connected and automated vehicles (CAVs) with battery electric or hybrid electric powertrains. A control-oriented prediction model for A/C system is proposed, identified, and validated against a higher fidelity simulation model (CoolSim). Based on the developed prediction model, a nonlinear model predictive control (NMPC) problem is formulated and solved online to minimize the energy consumption of the A/C system. Simulation results illustrate the desirable characteristics of the proposed NMPC solution such as being able to enforce physical constraints of the A/C system and maintain cabin temperature within a specified range. Moreover, it is shown that by utilizing the vehicle speed preview and through coordinated adjustment of the cabin temperature constraints, energy efficiency improvements of up to 9% can be achieved.
... Attempts to develop vehicle energy management strategies for A/C systems in conventional vehicles have been made previously in [2,3,4]. In [2], a heuristic control is proposed to control the compressor clutch from the monitored acceleration pedal position. ...
... The vehicle kinetic energy is used to power the compressor during vehicle coasting and braking. An energy management strategy that trades off fuel consumption, tracking performance and the switching frequency was designed in [3], and a Pareto optimal solution is first obtained via dynamic programming to extract an off-line rule-based control strategy. [4] presents an integrated approach dealing with the trade-off between fuel economy and the switching frequency, resulting in simple online implementable energy management strategies. ...
... For practical purposes, we pick some important time instants to enforce the constraint, for example every 10 seconds for the first 100 seconds, and every 20 seconds for the rest of the SC03 drive cycle. (3) , where P evp,run is the cooling capacity at the design case, P evp,base is the baseline cooling capacity, and t i is the sampling time instants. ...
... Different duty cycle durations have been tested for the projection and a good compromise between number of clutching events, tracking performance, and fuel consumption has been found using the minimum time between two ON conditions. It is important to notice that the time constants of the A/C system are much faster than any of the vehicle breaking or deceleration events [34]. This allows one to assume that the optimal duty cycle duration is insensitive to the driving cycle. ...
... The results were verified in simulation and compared with the solution obtained from DP, resulting in a control policy that is only marginally suboptimal and significantly improves the baseline production control algorithm. The results presented in [34] show that the optimal control policy obtained from the DP algorithm does not depend on the driving cycle. Current work is focusing on extending this result to show that the optimal duration of the duty cycle is not affected by the specific driving cycle, but it depends only on the A/C characteristics. ...
The air conditioning (A/C) system is currently the largest ancillary load in passenger cars, with a significant impact on fuel economy and CO₂ emissions. Considerable energy savings could be attained by simply adopting supervisory energy management algorithms that operate the A/C system to reduce power consumption of the compressor, while maintaining the cabin comfort requirements. This paper proposes a model-based approach to the design of a supervisory energy management strategy for automotive A/C systems. Starting from an energy-based model of the A/C system that captures the complex dynamics of the refrigerant in the heat exchangers and the compressor power consumption, a constrained multiobjective optimal control problem is formulated to jointly account for fuel consumption, cabin comfort, and system durability. The tradeoff between fuel economy, performance, and durability is analyzed by performing a Pareto analysis of a family of solutions generated using dynamic programming. A forward-looking optimal compressor clutch policy is then obtained by developing an original formulation of Pontryagin's minimum principle for hybrid dynamical systems. The simulation results demonstrate that the proposed control strategy allows for fuel economy improvement while retaining system performance and driver comfort.
div class="section abstract"> Optimal control of battery electric vehicle thermal management systems is essential for maximizi ng the driving range in extreme weather conditions. Vehicles equipped with advanced heating, ventilation and air-conditioning (HVAC) systems based on heat pumps with secondary coolant loops are more challenging to control due to actuator redundancy and increased thermal inertia. This paper presents the dynamic programming (DP)-based offline control trajectory optimization of heat pump-based HVAC aimed at maximizing thermal comfort and energy efficiency. Besides deriving benchmark results, the goal of trajectory optimization is to gain insights for practical hierarchical control strategy modifications to further improve real-time controllers’ performance. DP optimizes cabin inlet air temperature and flow rate to set the trade-off between thermal comfort and energy efficiency while considering the nonlinear dynamics and operating limits of HVAC system in addition to typically considered cabin thermal dynamics. Detailed Dymola-based HVAC system and cabin models are used to parameterize low-order models for control trajectory optimization. Additionally, nonlinear regression models for HVAC power consumption and the Predicted Mean Vote thermal comfort index are developed to construct the optimization cost function. Repeating the optimization for multiple cost function settings yields the Pareto optimal frontiers expressed in terms of aggregated thermal comfort index and energy consumption. Dymola simulation results confirm that incorporating DP-derived insights into a hierarchical control strategy leads to significant improvements in both thermal comfort and energy efficiency compared to baseline hierarchical control strategy.
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