Toyota Jidosha Kabushiki Kaisha

Kabushiki Kaisha Toyota Chuo Kenkyusho

Masuji Oshima

Hiroyuki Yoshida

Tatsuya Hattori

Motomi Iyoda

Masataka Osawa

Katsuji Imai

Kisaburo Hayakawa

Kagenori Fukumura

Takashi Ohta

Hideo Tomomatsu

Hiroji Taniguchi

Yoshio Ito

Yasunari Nakamura

Makoto Nishida

Syoichi Sayo

Tamotsu Hasegawa

Katsuhito Yamada

Norihiro Amano

A collision type identifying device is disposed in a central portion of a vehicle main body and has first deceleration detecting device (), peak time detecting device (), required time detecting device (), and type identifying device (). The deceleration detecting device () detects a vehicle deceleration in the longitudinal direction. The peak time detecting device () detects, as a first peak time (tp), a time from the excess of a preset threshold (GTH) by a waveform of the vehicle deceleration (G) detected by the deceleration detecting device () to a first peak. The required time detecting device () detects, as a required time (tn), a time when an integrated deceleration (VG) obtained through time quadrature of the vehicle deceleration (G) becomes equal to a predetermined integrated value set in advance. The type identifying device () identifies a vehicle collision type on the basis of the first peak time (tp) and the required time (tn). The collision type identifying device can identify a vehicle collision as one of a plurality of collision types at once.

Collision type identifying device

A product-sum operation portion for performing a product-sum operation (wavelet transformation) with respect to an input time-series signal by using as a base of integral a complex function in which the imaginary number portion is &pgr;/2 shifted in phase from the real number portion, a phase calculation portion for calculating a phase &thgr; from the ratio between the real number portion and the imaginary number portion of a result of the product-sum operation, a peak time detection portion for detecting a time point at which the calculated phase &thgr; changes from 2&pgr; to zero, as a peak time, are provided. Since the wavelet transformation is performed by using a basic wavelet function that is localized in terms of time and frequency, a peak time can be promptly detected. Furthermore, since a differential operation is not employed but the product-sum operation is performed, false detection caused by noises or the like can be prevented.

Peak time detecting apparatus and method

To calculate a vehicle mass based on a driving force caused by an engine, running resistance, and vehicle acceleration, influence of gradient resistance is removed. A gross driving force calculating section calculates a gross driving force F of a vehicle by deducting running resistance from a driving force of a vehicle caused by an engine. An acceleration sensor calculates a longitudinal acceleration &agr; of the vehicle. Relationship among a gross driving force F, a longitudinal acceleration &agr;, a vehicle mass M, and road gradient &THgr; can be expressed as (&agr;&equals;F/M&minus;g sin &THgr;). Because the change of gradient contains only a low frequency component, by processing a gross driving force F and an acceleration &agr;, using a high-pass filter with a predetermined cut-off frequency, the influence of the gradient &THgr; can be removed. Based on the resultant processed gross driving force F and the processed acceleration &agr;, a vehicle mass can be obtained from the above expression without being affected by the influence of the gradient

Vehicle mass calculation device

Apparatus for estimating a drive mode of a motor vehicle desired by an operator of the motor vehicle, including a variable calculating device and a drive mode estimating device, wherein the variable calculating device calculates at least one of drive mode indicating variables such as a drive force of the vehicle desired by the operator upon starting of the vehicle, a maximum rate of increase of the drive force, a maximum deceleration of the vehicle upon operation of a manually operated member for brake application to the vehicle, a coasting run time of the vehicle and a steady run time of the vehicle, and wherein the drive mode estimating device includes a neural network which receives the drive mode indicating variable or variables calculated by the variable calculating device, so that the drive mode estimating device estimates the drive mode of the motor vehicle desired by the operator on the basis of an output of the neural network. The output of the neural network may be used to control a desired controllable system of the vehicle such as an automatic transmission and a vehicle drive force control device.

Vehicle drive mode estimating device, and vehicle control apparatus,

In an electric furnace including a heating device which includes a cooling water passage for cooling the heating device to prevent the heating device from melting, estimation of a molten metal temperature in the electric furnace using a heat equilibrium model is conducted taking into account a thermal loss by the cooling water, i.e., the amount of a heat taken away by the cooling water. Further, the amount of the cooling water flowing through the heating device is adjusted so that the thermal loss by the cooling water is in a predetermined optimum thermal loss range.

Temperature control method and apparatus for an electric furnace

An engine controller includes a accelerator stroke detector, vehicle speed and vehicle acceleration sensors, first and second model learning units and a drive control unit. The first model learning unit constructs a vehicle acceleration model that exhibits and updates vehicle acceleration characteristics as required by a driver. The first unit includes a first learning program for updating acceleration model, and generates a first output (Gx) in response to the detected accelerator stroke and vehicle speed according to the first program. The first unit calculates a deviation (.DELTA.G) between the detected vehicle acceleration (G) and the first output (Gx), and modifies the first output (Gx) to decrease the deviation (.DELTA.G) to provide an updated acceleration model as required by the driver. The second model learning unit is for constructing a sensitivity model used to regulate the opening angle of an engine throttle valve. The second unit includes a second learning program for updating the sensitivity model and generating a second output (Thx) in response to the detected accelerator stroke and vehicle speed. As a consequence, the output (Thx) is modified to decrease the deviation (.DELTA.G) which provides for an update to the sensitivity model. The drive control unit controls the throttle angle referring to a target value determined on the basis of the second output (Thx).

Apparatus for controlling vehicle driving power

Disclosed is a driving power control apparatus for a vehicle driven by an engine. A throttle valve which is openable and closable by a DC motor is provided in an air intake passage of the engine. The vehicle is provided with an acceleration sensor and a vehicle speed sensor. The vehicle is further provided with an accelerator pedal sensor for detecting the manipulation amount of the accelerator pedal that is manipulated by a driver. The vehicle is also provided with a neuro computer and a throttle computer for controlling the angle of the throttle valve based on the result of the detection by various sensors. With the acceleration detected by the acceleration sensor used as teaching data to be compared, the neuro computer learns the relationship among the manipulation amount detected by the accelerator pedal sensor, the speed detected by the vehicle speed sensor and the acceleration, as the output of a 'requested-acceleration model'. The neuro computer computes a throttle sensitivity from the difference between the output of the 'requested-acceleration model' and the output of a standard 'reference acceleration model' previously learned. Based on the throttle sensitivity output from the neuro computer, the throttle computer controls the DC motor in accordance with the manipulation of the accelerator pedal. Accordingly, the angle of the throttle valve is controlled and the output of the engine is controlled, thus controlling the driving power of the vehicle.

Driving power control apparatus for vehicle

A neural network of a neuro computer has learned a prediction pattern based on the vehicle speed and time series data of the acceleration stroke received before the shipment of a vehicle. Based on the vehicle speed and time series data of the acceleration stroke, a request on the acceleration is predicted by a neural network computer during driving. At this time, an acceleration sensor or the like is not used, and the acceleration request is predicted more directly. Based on the prediction result, the throttle sensitivity is computed and set. A throttle computer refers to the throttle sensitivity to open or close the throttle valve to control the output of the engine.

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Kariya, Japan

Masuji Oshima