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Range prediction and extension for automated electric vehicles with fail-operational powertrain: Optimal and safety based torque distribution for multiple traction motors

机译:用于故障操作动力驱动器的自动电动车辆的范围预测和延伸:多牵引电动机的最佳和安全的扭矩分布

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With the introduction of robocabs and automated shuttles new requirements for vehicle powertrain arise requiring fail-operational system behavior. Redundancy in powertrain components can be used for the increase of driving efficiency, but requires also new strategies for controlling powertrain topologies with independent traction batteries. This paper presents a new concept and algorithm for the optimal torque distribution based on multi-objective optimization problem. The first objective of the optimization problem is to increase the driving energy efficiency by torque distribution between front and rear axle as well as between left and right side motors. A symmetrical discharge of the independent batteries is considered as a second objective. Using the runtime powertrain diagnostics, the torque distribution strategy covers also possible failure case operation. An optimal torque distribution profile as well as required propulsion energy are predicted for the entire driving cycle online. This information is then used for safety based range extension allowing to arrive at the safest possible location for the passengers also in case of a failure. Simulation results for different normal and failure case scenarios show a significant increase of driving efficiency while ensuring symmetrical discharge of the independent traction batteries.
机译:随着Robocabs的引入和自动送叉车,车辆动力总成的新要求可能需要失效操作系统的行为。动力总成组件的冗余可用于提高驾驶效率,但还需要使用独立牵引电池控制动力总成拓扑的新策略。本文介绍了基于多目标优化问题的最优扭矩分布的新概念和算法。优化问题的第一个目的是通过前后轴之间的扭矩分布以及左侧和右侧电动机之间的扭矩分布增加驱动能效。独立电池的对称放电被认为是第二个目标。使用运行时动力系诊断,扭矩分布策略涵盖可能的故障案例操作。在线预测整个驾驶循环,预测了最佳扭矩分布曲线以及所需的推进能。然后,此信息用于基于安全的范围扩展,允许在发生故障时向乘客提供最安全的位置。不同正常和故障情况场景的仿真结果表明,在确保独立牵引电池的对称放电的同时,显着提高了驱动效率。

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