Soft-computing method for establishing the heat dissipation law in a diesel common rail engine
AssigneeSTMicroelectronics S.r.l.
InventorsNicola Cesario, Claudio Muscio, Marco Farina, Paolo Amato
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Abstract
A soft-computing method for establishing the dissipation law of the heat in a diesel Common Rail engine, in particular for establishing the dissipation mean speed (HRR) of the heat, includes the following steps: choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed; applying a Transform Ψ to the dissipation speed signal (HRR) of the heat; carrying out analysis of homogeneity of the Transform Ψ output; realizing a corresponding neural network MLP wherein the design is guided by an evolutive algorithm; and training and testing the neural network MLP.
Claims20
A soft-computing method for establishing the dissipation law of the heat in a diesel Common Rail engine, in particular for establishing the dissipation mean speed (HRR) of the heat, wherein the system set-up comprises the following steps:
- choosing a number of Wiebe functions whereon a dissipation speed signal (HRR) of the heat is decomposed;
- applying the Transform Ψ to said signal;
- realizing a corresponding neural network MLP by means of an evolutive algorithm;
- training and testing said neural network MLP.
A method for modeling a parameter of an engine having an operating cycle, the method comprising:
- selecting a first number of first functions of a first variable that together represent the values of the parameter over a portion of the operating cycle;
- transforming the selected first functions into a second number of second functions of a second variable, each of the second functions having a corresponding coefficient;
- forming a neural network by applying an evolutive algorithm to the second functions;
- and
- training the neural network by determining values for the coefficients.
A vehicle, comprising:
- an engine having a first operating parameter that is dependent on a control parameter;
- a controller coupled to the engine and operable to,
- receive a value of the first operating parameter,
- generate a value of the control parameter in response to the received value of the first operating parameter, and
- provide the generated value of the control parameter to the engine;
- and
- a neural network coupled to the controller and operable to,
- receive the generated value of the control parameter from the controller,
- generate the value of the first operating parameter in response to the received value of the control parameter, and
- provide the value of the first operating parameter to the controller.
Dependencies are inferred from the claim text.