Model numerical solver for system control

Abstract

A complete model numerical solver resides on an embedded processor for real time control of a system. The solver eliminates the need for custom embedded code, requiring only model equations, definition of the independent and dependent variables, parameters and input sources information as input to solve the model equations directly. Through elimination of the need for custom code, the solver speeds up the model deployment process and provides the control application sophisticated features such as Automatic Differentiation, sensitivity analysis, sparse linear algebra techniques and adaptive step size in solving the model concurrently.

1 . A method for controlling a physical battery system by means of model computations within an embedded processor, comprising:
 defining a model description, the model description comprising:
 a list of independent and dependent variables, parameters values and bounds, input sources, and model equations at least one of which is differential; 
   inputting the model description into a model numerical solver;   loading the model numerical solver into a memory of the embedded processor;   estimating an upper bound on the total memory requirement of computational tasks for the model numerical solver for the inputted model description;   allocating required amounts of memory during instantiation and initialization of a model simulation as determined by the estimation of the upper bound on the total memory requirement of the computational tasks for the model numerical solver for the inputted model description prior to commencement of solver steps;   receiving at the model numerical solver at least one value from at least one input source of the model;   solving numerically the model equations with the model numerical solver,   outputting at least one value of one dependent variable of the model equations to a control application;   receiving the at least one value of one dependent variable at the control application; and   effecting changes to the state of the physical battery system by the control application in response to receiving the at least one value of one dependent variable.   
     
     
         2 . The method of  claim 1 , wherein the inputting the model description step comprises a Functional Mock-up Interface (FMI) description of the model. 
     
     
         3 . The method of  claim 1 , wherein the inputting the model description step comprises a C++ source file containing at least the definition of a numerical routine to evaluate the dependent variables of the model as numerical functions for the independent variables, parameters and input sources. 
     
     
         4 . The method of  claim 1 , wherein the inputting the model description step comprises a XML/MathML file describing the model as a list of independent and dependent variables, parameters values and bounds, input sources and differential and algebraic equations. 
     
     
         5 . The method of  claim 1 , wherein the model numerical solver comprises numerical routines for an Automatic Differentiation (AD) feature, a complete Differential Algebraic Equation solver, sparse linear algebra techniques, sensitivity analysis, numerical model optimization and adaptive step-size. 
     
     
         6 . The method of  claim 1 , further comprising:
 providing user-controllable solver parameters and optimizer parameters.   
     
     
         7 . A method for controlling a physical battery system, comprising:
 embedding a processor into an electronic device;   defining a model description, the model description comprising:
 a list of independent and dependent variables, parameters values and bounds, input sources, and model equations at least one of which is differential; 
   inputting the model description into a model numerical solver;   loading the model numerical solver into a memory of the embedded processor;   estimating an upper bound on the total memory requirement of the computational tasks for the model numerical solver for the inputted model description;   allocating required amounts of memory during instantiation and initialization of a model simulation as determined by the estimation of the upper bound on the total memory requirement of the computational tasks for the model numerical solver for the inputted model description and prior to commencement of solver steps;   receiving at the model numerical solver at least one value from at least one input source of the model;   solving numerically the model equations with the model numerical solver;   outputting at least one value of one dependent variable of the model equations to a control application;   receiving the at least one value of one dependent variable at the control application; and   effecting changes to the state of the physical battery system by the control application in response to receiving the at least one value of one dependent variable.   
     
     
         8 . The method of  claim 7 , wherein the inputting the model description step comprises a Functional Mock-up Interface (FMI) description of the model. 
     
     
         9 . The method of  claim 7 , wherein the inputting the model description step comprises a C++ source file containing at least the definition of a numerical routine to evaluate the dependent variables of the model as numerical functions for the independent variables, parameters and input sources. 
     
     
         10 . The method of  claim 7 , wherein the inputting the model description step comprises a XML/MathML file describing the model as a list of independent and dependent variables,. 
     
     
         11 . The method of  claim 7 , wherein the model numerical solver comprises numerical routines for an Automatic Differentiation (AD) feature, a complete Differential Algebraic Equation solver, sparse linear algebra techniques, sensitivity analysis, numerical model opitimization and adaptive step-size. 
     
     
         12 . The method of  claim 7 , further comprising:
 providing user-controllable solver parameters and optimizer parameters.   
     
     
         13 . An embedded processor having a memory, comprising a model numerical solver in the memory of the embedded processor, the model numerical solver further comprising:
 a model description comprising: a list of independent and dependent variables, parameters values and bounds, input sources, and model differential and algebraic equations;   a memory structure wherein the model numerical solver estimates an upper bound on the total memory requirement of computational tasks for the model numerical solver for the inputted model description and wherein required amounts of memory are allocated during instantiation and initialization of a model simulation as determined by the upper bound estimation on the total memory requirement of the computational tasks of the model numerical solver for the inputted model description and prior to commencement of solver steps;   at least one input source value from at least one source; and   a software program for solving at least two equations, including at least one differential equation, numerically.   
     
     
         14 . The embedded processor of  claim 13 , wherein the at least one input signal is communicated using a Functional Mock-up Interface (FMI) description of the model. 
     
     
         15 . The embedded processor of  claim 13 , wherein the at least one input signal is communicated using a C++ source file containing at least the definition of a numerical routine to evaluate the dependent variables of the model as numerical functions for the independent variables, parameters and input sources. 
     
     
         16 . The embedded processor of  claim 13 , wherein the at least one input signal is communicated using an XML/MathML file describing the model as a list of independent and dependent variables, parameters values and bounds, input sources and differential and algebraic equations. 
     
     
         17 . The embedded processor of  claim 13 , further comprising numerical routines for an Automatic Differentiation (AD) feature, a complete Differential Algebraic Equation solver, sparse linear algebra techniques, sensitivity analysis, numerical model optimization and adaptive step-size.