Abstract:
Optimized control algorithms for a vehicle automatic climate control system (ACCS) are developed using math-based models of the vehicle, the ACCS and a vehicle occupant. The models are cross-coupled in closed-loop fashion with feedback from both vehicle systems and occupant. A first feedback loop including the vehicle and the ACCS, simulates how the ACCS interacts with the cabin environment; and a second feedback loop including the vehicle, the ACCS and the occupant, simulates how the occupant will adjust the ACCS to optimize comfort. When the system arrives at a control algorithm that satisfies control objectives and optimizes occupant comfort, an auto-code generation tool is used to create program code directly from the control model, which may be downloaded into a test vehicle for final system confirmation and calibration.

Description:
PRIOR APPLICATION  
       [0001]    This application claims the benefit of prior Provisional Patent Application Serial No. 60/354,110 filed Feb. 4, 2002. 
     
    
     
       TECHNICAL FIELD  
         [0002]    This invention relates to automatic climate control systems for vehicles, and more particularly to a method of generating control system algorithms that optimize occupant comfort.  
         BACKGROUND OF THE INVENTION  
         [0003]    In an automotive automatic climate control system (ACCS), the driver generally selects a desired cabin temperature, and a microprocessor-based system controller responds in a pre-programmed way to control the blower speed, the air discharge temperature and the air delivery mode. While the driver has the option of overriding the pre-programmed settings, the objective is to design the control algorithms so that the pre-programmed settings sufficiently satisfy the occupants that little or no overriding is necessary. This presents a very difficult challenge to system and calibration engineers because control settings that satisfy the engineers may only satisfy a small subset of the overall population of vehicle occupants. For this reason, and in order to reduce development time, there has been a trend toward increased usage of math-based tools to simulate and analyze system operation, and to compare the performance achieved with different system designs and control approaches. See, for example, the U.S. Pat. No. to Webster et al. 6,209,794, where mathematical models of a vehicle and thermal management system are utilized to evaluate the impact of different system designs on the time required for the cabin to reach a comfortable temperature.  
           [0004]    While math-based tools have the capability of accelerating the validation process and significantly reducing product development time, the fact remains that it is difficult to develop control strategies that satisfactorily address occupant comfort. Even in cases where occupant comfort standards are reasonably well defined, many design iterations are required to develop a control algorithm that will satisfy the defined comfort standards. Accordingly, what is needed is an improved method of applying math-based tools to the control algorithm design process that minimizes the number of design iterations required to arrive at a solution that optimizes occupant comfort.  
         SUMMARY OF THE INVENTION  
         [0005]    The present invention is directed to an improved method of developing optimized control algorithms for a vehicular automatic climate control system (ACCS). According to the invention, math-based models are utilized to simulate the vehicle, the ACCS and the occupant, and the models are cross-coupled in closed-loop fashion with feedback from both vehicle and occupant. A first feedback loop including the vehicle and the ACCS simulates how the ACCS interacts with the cabin environment; and a second feedback loop including the vehicle, the ACCS and the occupant simulates how the occupant will adjust the ACCS to optimize comfort. When the control algorithm satisfies the control objectives and optimizes occupant comfort, an auto-code generation tool is used to create program code directly from the control model, which may be downloaded into a test vehicle for final system confirmation and calibration. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0006]    [0006]FIG. 1 is a block diagram illustrating the simulation of a vehicle, its automatic climate system and its occupants according to this invention, along with automatic code generation tools for transferring simulated control algorithms to a physical automatic climate control system in an actual vehicle.  
         [0007]    [0007]FIG. 2 depicts a visual interface of a control head model of a simulated automatic climate control system according to this invention.  
         [0008]    [0008]FIG. 3 is a block diagram of a simulated automatic climate control algorithm according to this invention.  
         [0009]    [0009]FIG. 4 is a block diagram of a simulated vehicle climate control plant according to this invention.  
         [0010]    [0010]FIG. 5 is a block diagram of a human comfort reaction model according to this invention. 
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0011]    [0011]FIG. 1 illustrates the method of the present invention in the context of a conventional motor vehicle automatic climate control system (ACCS)  10  including two electronic control units (ECUs): a climate control system (CCS) ECU  12 , and a control head (CH) ECU  14 . In vehicle operation, the CCS ECU  12  interacts with the CH ECU  14 , receives various inputs  16  pertaining to ambient conditions and actual cabin temperature, and produces various outputs, including command signals for a compressor clutch (CL)  18 , a condenser fan (CF)  20 , a blower motor (BM)  22 , and a number of air control doors actuators (ACDA)  24 . The CH ECU  14  resides in a user interface, generally referred to as a control head, whereby the driver or other occupant can set the desired cabin temperature and manually override the control settings of the blower motor  22  and air control doors  24 . The CH ECU  14  can also display data received from CCS ECU  12 , such as the outside temperature. Of course, the CCS ECU  12  and the CH ECU  14  may be combined into a single ECU if desired.  
         [0012]    In carrying out the above-mentioned control functions, the CCS ECU  12  and the CH ECU  14  each have embedded control algorithms that are periodically executed by an internal microprocessor. Such algorithms are ordinarily developed by systems engineers, and converted into suitable program code for storage in non-volatile memory within the respective ECU. The vehicle is then subjected to a fairly rigorous testing regimen, during which the control algorithms are adjusted and calibrated to optimize system performance, which may be defined in terms of transient performance, steady-state temperature regulation, and occupant comfort, for example. However, the present invention contemplates a totally different control algorithm development methodology in which the control algorithms for CCS ECU  12  and CH ECU  14  are generated off-line in a simulation environment defined by various interlinked mathematical models, designated generally in FIG. 1 by the reference numeral  26 . These models include a control head model (CHM)  28 , a climate control system model (CCSM)  30 , a vehicle plant model (VPM)  36 , a thermal comfort model (TCM)  40 , and a comfort reaction model (CRM)  42 . The automatic code generation (ACG) units  32  and  34  link the simulation environment models  26  to ACCS  10  by generating program code for CH ECU  14  and CCS ECU  12  based on the functionality of CHM  28  and CCSM  30 , respectively. The simulation environment models  26  are implemented with a mixture of commercially available software tools and custom developed applications. The ACG units  32  and  34  produce C code from the transfer functions of CHM  28  and CCSM  30 ; the ACG unit  32  can be implemented using the DeepScreen tool developed and marketed by Altia Inc., and the ACG unit  34  can be implemented with the Real Time Workshop Embedded Coder developed and marketed by The MathWorks Inc.  
         [0013]    In general, CCSM  30  interacts bi-directionally with VPM  36 , which simulates the mechanical and thermal response of the vehicle to ambient conditions and the outputs of CCSM  30 . For example, VPM  36  supplies information concerning the simulated compressor speed, cabin air temperature and engine coolant temperature to CCSM  30 , and CCSM  30  supplies information concerning the simulated air control door positions, blower motor speed, and compressor clutch state to VPM  36 . The VPM  36  supplies simulated cabin environment information (such as air discharge temperature, air velocity, and air delivery locations) to an occupant model  38  that comprises TCM  40  and CRM  42 . The TCM  40  simulates comfort levels for various body segments (torso, arms, legs, head, etc.) of the occupants, and the CRM  42 , in turn, simulates how the occupants will adjust the user inputs (desired temperature, blower motor speed, and air delivery mode) of the CHM  28  to maximize comfort. Thus, there is a first feedback loop including VPM  36  and CCSM  30  simulating how the climate control system interacts with the cabin environment, and a second feedback loop including CCSM  30 , CHM  28 , VPM  36 , TCM  40 , CRM  42  simulating how the occupant will adjust the climate control system to optimize comfort. Additionally, the comfort optimization module (COM)  44  adjusts the calibration parameters of CCSM  30 , as shown. The simulated adjustments supplied to CHM  28  and CCSM  30  produce corresponding adjustment of the simulated control algorithms for CHM  28  and CCSM  30  until the control algorithms produce a simulated vehicle environment that satisfies the occupant model  38 , obviating further adjustment of the user inputs of CHM  28 . At such point, the ACG units  32 ,  34  create program code corresponding to the CCSM and CHM control functions, which is compiled and downloaded into CCS ECU  12  and CH ECU  14  for final in-vehicle validation and calibration.  
         [0014]    The CHM  28  is implemented with the Altia Design/FacePlate software package developed and marketed by Altia Inc., and includes a visual interface, generally designated by the reference numeral  50  in FIG. 2. Referring to FIG. 2, the button pair  52  controls the driver set temperature, the button pair  54  controls the blower speed, the button pair  56  control the air discharge mode, and the buttons  58  and  60  activate full cold and hot settings with cabin air recirculation. Additionally, the buttons  62  and  64  activate defrost and rear defog functions, and the display panel  66  provides visual feedback to the occupants. Behind the graphical interface is logic that decodes the activation of the buttons  52 - 64  into commands for CCSM  30  and occupant feedback via indicators on display panel  66 . In many cases, the decode logic may affect several system operations; for example, when the Defrost button  62  is activated, the mode override is set to deliver air to the windshield, the air inlet door commanded to a position for introducing outside air, and the refrigerant compressor is activated to de-humidify the discharge air.  
         [0015]    The CCSM  30  is implemented with the MatLab software package (MatLab, Simulink, StateFlow) developed and marketed by The MathWorks Inc. Essentially, the MatLab software package acts as a backplane, providing easy interfacing with the VPM  30  and the occupant model  38 . The model describes a desired transfer function, and becomes an executable specification which ACG  34  converts into C program code. Functionally, the control algorithm carried out by CCSM  30  includes a transient phase during which the initial cabin air temperature transitions to a set temperature TSET, and a steady-state phase during which the cabin air temperature is maintained at TSET while the vehicle is subjected to various ambient temperature and solar conditions. FIG. 3 depicts a high level block diagram of CCSM  30 ; in practice, each of the depicted blocks is further defined by a set of sub-blocks, which can be further defined by another set of sub-blocks until the function is completely described using the primitive blocks of Simulink or custom defined blocks.  
         [0016]    Referring to FIG. 3, CCSM  30  includes a temperature controller (TC)  70 , an inlet air controller (IAC)  72 , a mode controller (MC)  74 , and a blower controller (BC)  76  for implementing an automatic climate control algorithm. Interaction between the blocks  70 - 76  can be seen via the various connecting signals. For example, TC  70  develops a temperature blower speed TBS which is provided to BC  76  along with a blower speed request (BSR) from CHM  28 , and BC  76  selects a blower speed target BSTAR based on the two inputs. The TC  70  also develops a temperature related inlet air request IATRQ, which is provided to IAC  72  along with a RECIRC request from CHM  28 , and IAC  72  selects an inlet air door position delta based on the two inputs. A vehicle communications block (VC)  78  simulates interaction with other vehicle controllers, allowing CCSM  30  to control the air conditioning compressor (CRQ) and shared devices such as engine cooling fans, and to receive shared sensor data such as engine speed, vehicle speed, battery voltage, and coolant temperature CT. The user interface block (UI)  80  permits data sharing between CCSM  30  and CHM  28 , and the input and output processing blocks (IP, OP)  82 ,  84  permit data sharing between CCSM  30  and VPM  36 . For example, UI  80  receives inputs concerning rear defogger RDef, air conditioning enable/disable ACRQ, occupant set temperature requests OSTR, cabin air recirculation RECIRC, occupant air delivery mode requests OMR, and occupant blower speed requests OBSR. The input processing block  82  receives data from VPM  36  concerning the discharge air temperature Tair, the evaporator outlet air temperature Tevap, the cabin air temperature Tcabin, the temperature door position TDP, the mode door position MDP, and the air inlet door position IADP. Similarly, the output processing block  84  provides data to VPM  36  concerning the target blower speed BSTAR, and position deltas TDD, IADD, MDD for the temperature, air inlet and mode doors.  
         [0017]    In general, the VPM  36  simulates the performance of the air conditioning system, and develops data pertaining to the discharge air velocity, delivery locations, and temperatures. The VPM  36  is implemented using the EASY5 Simulation package developed by Boeing Corporation and the computational fluid dynamics (CFD) package developed by Fluent Inc., and includes a model of the transient behavior of an air conditioning (AC) system. The transient AC model is illustrated by the block diagram of FIG. 4, and includes five main components: a refrigerant compressor  100 , a condenser  102 , and orifice tube  104 , an evaporator  106 , and an accumulator  108 .  
         [0018]    The compressor model  100  receives inputs pertaining to accumulator output vapor flow on line  110  and the compressor drive speed (CS) on line  112 , and implements empirically determined isentropic efficiency and volumetric efficiency maps characterizing a particular compressor design. The refrigerant flow rate output RFRcomp is calculated according to:  
         
       RFRcomp=Vd*CS*VE*Ds  
     
         [0019]    where Vd is the compressor displacement, VE is the volumetric efficiency, and Ds is the density of the inlet refrigerant. The compressor work is calculated based on the outlet pressure, the state point of the inlet refrigerant, and the isentropic efficiency (which can be empirically determined).  
         [0020]    The condenser and evaporator models  102 ,  106  each receive inputs pertaining to refrigerant flow and the respective airflows (COND_AIRFLOW, EVAP_AIRFLOW), and describe the refrigerant outlet state. The models comprehend the geometries of the respective devices (tube lengths, heat transfer areas, etc.), and the refrigerant-side and the air-side heat transfer coefficients, and maintain a transient energy balance between the refrigerant-side and the air-side. The evaporator model  106  additionally comprehends the formation of condensate and its impact on heat transfer.  
         [0021]    The orifice tube model  104  predicts the refrigerant flow rate m_dot given the upstream state and the downstream pressure, and can be implemented as follows:  
           m _dot= C   tp   *A   s [2 *g*D   i  ( P   up   −P   f )] 1/2    
         [0022]    where C tp  is a two-phase quality correction factor, A s  is the cross-sectional area, D i  is the inlet refrigerant density, P up  is inlet refrigerant pressure, and P f  is the adjusted downstream refrigerant pressure.  
         [0023]    The TCM  40  is implemented by custom application software, and includes sub-models that simulate the occupant thermal environment and human physiology. The occupant thermal environment sub-model is implemented with Fluent&#39;s CFD software, and simulates the vehicle cabin, taking into account solar loading and radiation heat exchange between the cabin and the occupant. Solar loading increases occupant and cabin temperatures, and varies with the transmission properties of the cabin glass, the solar angle and intensity and the solar spectrum. The heat flux due to solar radiation is modeled by separately considering the short-wave radiation which is absorbed based on skin or clothing absorptance, and long-wave radiation which is absorbed based on skin or clothing emittance. Radiation heat transfer between the cabin and the occupant is calculated using an explicit 3-D occupant model defined by the Stefan-Boltzmann law. The CFD software computes view factors characterizing the radiation heat transfer between the cabin surfaces and the various body segments of the occupant. The occupant thermal environment sub-model divides the cabin into finite volumes, and Reynolds-averaged Navier-Stokes equations for the various volumes are solved simultaneously with a conservation of energy equation to predict airflow, temperature and humidity distribution around the occupants. The human physiology sub-model, in turn, calculates the thermal responses of various body segments in terms of skin and core temperatures. In the illustrated embodiment, the simulated occupant is divided into sixteen body segments consisting of clothing and defined layers (core, muscle, fat and skin tissue), and a vascular model dictates convective heat transfer among the various segments. The portion of each segment that is in contact with an interior surface of the cabin is specified, and as mentioned above, radiative heat transfer between the cabin surfaces and the various body segments is computed by the CFD view factors. The output is in the form of Equivalent Homogeneous Temperature (EHT) data for each of the sixteen body segments, and if desired, the model may be expanded to include the effects of humidity on occupant comfort. A more detailed description of the modeling techniques is set forth, for example, in the SAE Paper No. 2001-01-0588 authored by Han, Huang, Kelly, Huizenga and Hui, and entitled Virtual Thermal Comfort Engineering.  
         [0024]    The CRM  42  receives the EHT data developed by TCM  40 , as well as the air discharge location and velocity data, and creates a discomfort function (DF) based on deviations in the EHT data from optimal EHT values. When the discomfort function reaches at least a certain level, CRM  42  reacts by proportionately adjusting one or more of the manual override settings of the CHM  28 . While the blower speed or mode overrides occur without delay, some time is required to change the temperature of the cabin, and the CRM  42  models human patience so that the controls are not adjusted too frequently.  
         [0025]    In general, the functionality of CRM  42  is illustrated by the block diagram of FIG. 5, where the blocks  120 - 126  cooperate to determine the occupant requests (OMR, OSTR, OBSR) for air delivery mode, set temperature, and blower speed. The block  120  is responsive to the EHT data developed by TCM  40 , and determines an overall or cumulative discomfort indication according to the deviation of the EHT data from optimal EHT values. The block  122  evaluates the overall occupant discomfort data, along with the air discharge location data (AD_LOC) developed by VPM  36 , and determines if the air delivery mode could be adjusted to improve the comfort at one or more of the predefined body segments for which EHT data is available. Similarly, the block  124  evaluates the overall occupant discomfort data, and determines if the set temperature could be adjusted to improve the comfort at one or more of the predefined body segments for which EHT data is available. And finally, the block  126  evaluates the overall occupant discomfort data, along with the air velocity data (AIR_VEL) developed by VPM  36 , and determines if the blower speed could be adjusted to improve the comfort at one or more of the predefined body segments for which EHT data is available. Also, the CRM  42  could be expanded to model reaction to windshield fogging, system noise (due to blower speed and air discharge location, for example), and so on, to enhance its simulation of human system overrides.  
         [0026]    At the same time, COM  44  reacts to the discomfort function DF by adjusting one or more calibration parameters of the climate control algorithm modeled by CCSM  30 . These parameters may include both transient phase parameters (i.e., those parameters that govern the transient response of the system) and steady-state parameters (i.e., those parameters that govern the steady-state response of the system). The COM  44  averages the discomfort function DF over both the transient and steady-state phases of a simulation run, so that the averaged discomfort function (DF_AVG) can be considered as a function of both the transient and steady-state calibration parameters. A multi-dimensional optimization method (such as the Conjugate Gradient method) is then used to find a set of calibration values that will optimize (minimize) DF_AVG, and COM  44  applies such set of calibration values to CCSM  30 .  
         [0027]    While the process of adjusting the algorithm calibration parameters has been described above as an automatic function performed by COM  44 , it will be recognized that the adjustments can alternatively be carried out manually by a calibration engineer, if desired. For example, if the transient response of the simulation is unsatisfactory, the calibration engineer can manually adjust the transient calibration parameters and re-start the simulation to see if the transient performance is improved. However, it should also be recognized the ability of the optimization method (whether manual or automatic) to minimize occupant discomfort is constrained by the control strategy of the climate control algorithm modeled by CCSM  30 . In other words, if the control strategy is flawed, optimization of its calibration parameters may still fail to produce the desired occupant comfort levels. In such case, the control algorithm strategy must be re-visited and modified by system engineers, after which the above-described methods can be utilized to optimize the modified algorithm.  
         [0028]    In summary, the present invention provides a radically new methodology for generating improved automatic climate control system algorithms on a significantly abbreviated timetable and with significantly reduced cost, compared to conventional approaches. While described in reference to the illustrated embodiment, it is expected that various modifications in addition to those mentioned above will occur to those skilled in the art. For example, a greater or lesser number of factors can be modeled, different software tools can be utilized to model the various functional blocks, and so on. Thus, it will be understood that methods incorporating these and other modifications may fall within the scope of this invention, which is defined by the appended claims.