The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark a...The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.展开更多
建立了包括燃料电池发动机、电机及其控制器、动力蓄电池组在内的燃料电池轿车动力系统的动态数学模型.在该模型基础上,设计了基于状态反馈的闭环功率平衡算法.针对状态变量蓄电池开路电压在运行过程中无法测量的问题,构造了相应的渐进...建立了包括燃料电池发动机、电机及其控制器、动力蓄电池组在内的燃料电池轿车动力系统的动态数学模型.在该模型基础上,设计了基于状态反馈的闭环功率平衡算法.针对状态变量蓄电池开路电压在运行过程中无法测量的问题,构造了相应的渐进状态观测器.以蓄电池开路电压观测值反馈实现了蓄电池最佳荷电状态的控制,使算法克服了对蓄电池SOC(state of charge,荷电状态)估计值的依赖,实现了解析冗余,较好地解决了SOC估计过程中存在的初始值不易确定和累计误差的问题.离线仿真和实车转鼓实验的结果证明,所建立的动力控制算法达到既定的控制目标,并且能够充分考虑动力系统主要部件的动力性和经济性,具有一定的实用价值.展开更多
文摘The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.
文摘建立了包括燃料电池发动机、电机及其控制器、动力蓄电池组在内的燃料电池轿车动力系统的动态数学模型.在该模型基础上,设计了基于状态反馈的闭环功率平衡算法.针对状态变量蓄电池开路电压在运行过程中无法测量的问题,构造了相应的渐进状态观测器.以蓄电池开路电压观测值反馈实现了蓄电池最佳荷电状态的控制,使算法克服了对蓄电池SOC(state of charge,荷电状态)估计值的依赖,实现了解析冗余,较好地解决了SOC估计过程中存在的初始值不易确定和累计误差的问题.离线仿真和实车转鼓实验的结果证明,所建立的动力控制算法达到既定的控制目标,并且能够充分考虑动力系统主要部件的动力性和经济性,具有一定的实用价值.
