公开(公告)号：US06980894B1

公开(公告)日：2005-12-27

申请号：US10742714

申请日：2003-12-19

申请人： Susanna P. Gordon ,  John A. Evans

发明人： Susanna P. Gordon ,  John A. Evans

IPC分类号： B60L3/00 ,  G05D3/00 ,  G06F17/00

CPC分类号： B60L3/0015 ,  B60L2200/26 ,  B60L2260/54 ,  B61L3/006

摘要： The present invention provides methods for preventing low train voltages and managing interference, thereby improving the efficiency, reliability, and passenger comfort associated with commuter trains. An algorithm implementing neural network technology is used to predict low voltages before they occur. Once voltages are predicted, then multiple trains can be controlled to prevent low voltage events. Further, algorithms for managing inference are presented in the present invention. Different types of interference problems are addressed in the present invention such as “Interference During Acceleration”, “Interference Near Station Stops”, and “Interference During Delay Recovery.” Managing such interference avoids unnecessary brake/acceleration cycles during acceleration, immediately before station stops, and after substantial delays. Algorithms are demonstrated to avoid oscillatory brake/acceleration cycles due to interference and to smooth the trajectories of closely following trains. This is achieved by maintaining sufficient following distances to avoid unnecessary braking/accelerating. These methods generate smooth train trajectories, making for a more comfortable ride, and improve train motor reliability by avoiding unnecessary mode-changes between propulsion and braking. These algorithms can also have a favorable impact on traction power system requirements and energy consumption.

摘要翻译： 本发明提供了防止低列车电压和管理干扰的方法，从而提高与通勤列车相关联的效率，可靠性和乘客舒适性。 使用实现神经网络技术的算法来预测低电压发生之前。 一旦预测了电压，则可以控制多列以防止低电压事件。 此外，在本发明中给出了用于管理推理的算法。 在本发明中解决了不同类型的干扰问题，例如“加速时干扰”，“干扰近站停止”和“延迟恢复期间的干扰”。 管理此类干扰可避免在加速期间，车站停止之前，以及在实质性延迟之后不必要的制动/加速循环。 证明算法可以避免由于干扰而引起的振荡制动/加速周期，并使紧随其后的列车的轨迹平滑。 这通过保持足够的后续距离来实现，以避免不必要的制动/加速。 这些方法产生平稳的列车轨迹，使乘坐更舒适，并通过避免推进和制动之间的不必要的模式变化来提高列车的运行可靠性。 这些算法也可以对牵引力系统要求和能量消耗产生有利的影响。

公开(公告)号：US06332107B1

公开(公告)日：2001-12-18

申请号：US09291814

申请日：1999-04-14

申请人： Susanna P. Gordon ,  John A. Evans

发明人： Susanna P. Gordon ,  John A. Evans

IPC分类号： G06F1700

CPC分类号： B61L3/006

摘要： The present invention provides methods for preventing low train voltages and managing interference, thereby improving the efficiency, reliability, and passenger comfort associated with commuter trains. An algorithm implementing neural network technology is used to predict low voltages before they occur. Once voltages are predicted, then multiple trains can be controlled to prevent low voltage events. Further, algorithms for managing inference are presented in the present invention. Different types of interference problems are addressed in the present invention such as “Interference. During Acceleration”, “Interference Near Station Stops”, and “Interference During Delay Recovery.” Managing such interference avoids unnecessary brake/acceleration cycles during acceleration, immediately before station stops, and after substantial delays. Algorithms are demonstrated to avoid oscillatory brake/acceleration cycles due to interference and to smooth the trajectories of closely following trains. This is achieved by maintaining sufficient following distances to avoid unnecessary braking/accelerating. These methods generate smooth train trajectories, making for a more comfortable ride, and improve train motor reliability by avoiding unnecessary mode-changes between propulsion and braking. These algorithms can also have a favorable impact on traction power system requirements and energy consumption.

摘要翻译： 本发明提供了防止低列车电压和管理干扰的方法，从而提高与通勤列车相关联的效率，可靠性和乘客舒适性。 使用实现神经网络技术的算法来预测低电压发生之前。 一旦预测了电压，则可以控制多列以防止低电压事件。 此外，在本发明中给出了用于管理推理的算法。 不同类型的干扰问题在本发明中得到解决，例如“加速时的干扰”，“干扰近站停止”和“延迟恢复期间的干扰”。 管理此类干扰可避免在加速期间，车站停止之前，以及在实质性延迟之后不必要的制动/加速循环。 证明算法可以避免由于干扰而引起的振荡制动/加速周期，并使紧随其后的列车的轨迹平滑。 这通过保持足够的后续距离来实现，以避免不必要的制动/加速。 这些方法产生平稳的列车轨迹，使乘坐更舒适，并通过避免推进和制动之间的不必要的模式变化来提高列车的运行可靠性。 这些算法也可以对牵引力系统要求和能量消耗产生有利的影响。

公开(公告)号：US06681161B1

公开(公告)日：2004-01-20

申请号：US09975121

申请日：2001-10-09

申请人： Susanna P. Gordon ,  John A. Evans

发明人： Susanna P. Gordon ,  John A. Evans

IPC分类号： G05D100

CPC分类号： B61L3/006

摘要： The present invention provides methods for preventing low train voltages and managing interference, thereby improving the efficiency, reliability, and passenger comfort associated with commuter trains. An algorithm implementing neural network technology is used to predict low voltages before they occur. Once voltages are predicted, then multiple trains can be controlled to prevent low voltage events. Further, algorithms for managing inference are presented in the present invention. Different types of interference problems are addressed in the present invention such as “Interference During Acceleration”, “Interference Near Station Stops”, and “Interference During Delay Recovery.” Managing such interference avoids unnecessary brake/acceleration cycles during acceleration, immediately before station stops, and after substantial delays. Algorithms are demonstrated to avoid oscillatory brake/acceleration cycles due to interference and to smooth the trajectories of closely following trains. This is achieved by maintaining sufficient following distances to avoid unnecessary braking/accelerating. These methods generate smooth train trajectories, making for a more comfortable ride, and improve train motor reliability by avoiding unnecessary mode-changes between propulsion and braking. These algorithms can also have a favorable impact on traction power system requirements and energy consumption.

摘要翻译： 本发明提供了防止低列车电压和管理干扰的方法，从而提高与通勤列车相关联的效率，可靠性和乘客舒适性。 使用实现神经网络技术的算法来预测低电压发生之前。 一旦预测了电压，则可以控制多列以防止低电压事件。 此外，在本发明中给出了用于管理推理的算法。 在本发明中解决了不同类型的干扰问题，例如“加速时干扰”，“干扰近站停止”和“延迟恢复期间的干扰”。 管理此类干扰可避免在加速期间，车站停止之前，以及在实质性延迟之后不必要的制动/加速循环。 证明算法可以避免由于干扰而引起的振荡制动/加速周期，并使紧随其后的列车的轨迹平滑。 这通过保持足够的后续距离来实现，以避免不必要的制动/加速。 这些方法产生平稳的列车轨迹，使乘坐更舒适，并通过避免推进和制动之间的不必要的模式变化来提高列车的运行可靠性。 这些算法也可以对牵引力系统要求和能量消耗产生有利的影响。