Convex modeling for optimal battery sizing and control of an electric variable transmission powertrain

Hybrid Electric Vehicles are being considered a convenient intermediate product in the conversion process from conventional to pure electric vehicles, due to their compromise on cost, fuel consumption, and driving range. Convex modeling steps for the problem of optimal battery sizing and energy management of a plug-in hybrid electric vehicle with an electric variable transmission are presented. Optimal energy management was achieved by a switched model control, with driving modes identified by the engine on/off state. In pure electric mode, convex optimization was employed to find the optimal torque split between two electric machines, to maximize powertrain efficiency. In hybrid mode, optimization was carried out in a bilevel program. One level optimizes speed of a compound unit that includes the engine and electric machines. Another level optimizes the power split between the compound unit and the battery. The proposed method is used to minimize the total cost of ownership of a passenger vehicle for a daily commuter, including costs for battery, fossil fuel and electricity

A Gauss-Seidel projection method with the minimal number of updates for stray field in micromagnetic simulations

Magnetization dynamics in magnetic materials is often modeled by the Landau-Lifshitz equation, which is solved numerically in general. In micromagnetic simulations, the computational cost relies heavily on the time-marching scheme and the evaluation of stray field. Explicit marching schemes are efficient but suffer from severe stability constraints, while nonlinear systems of equations have to be solved in implicit schemes though they are unconditionally stable. A better compromise between stability and efficiency is the semi-implicit scheme, such as the Gauss-Seidel projection method (GSPM) and the second-order backward differentiation formula scheme (BDF2). At each marching step, GSPM solves several linear systems of equations with constant coefficients and updates the stray field several times, while BDF2 updates the stray field only once but solves a larger linear system of equations with variable coefficients and a nonsymmetric structure. In this work, we propose a new method, dubbed as GSPM-BDF2, by combing the advantages of both GSPM and BDF2. Like GSPM, this method is first-order accurate in time and second-order accurate in space, and is unconditionally stable with respect to the damping parameter. However, GSPM-BDF2 updates the stray field only once per time step, leading to an efficiency improvement of about $60\%$ than the state-of-the-art GSPM for micromagnetic simulations. For Standard Problem \#4 and \#5 from National Institute of Standards and Technology, GSPM-BDF2 reduces the computational time over the popular software OOMMF by $82\%$ and $96\%$, respectively. Thus, the proposed method provides a more efficient choice for micromagnetic simulations

Convex modeling for optimal battery sizing and control of an electric variable transmission powertrain

This paper provides convex modeling steps for the problem of optimal battery sizing and energy management of a plug-in hybrid electric vehicle with an electric variable transmission. Optimal energy management is achieved by a switched model control, with driving modes identified by the engine on/off state. In pure electric mode, convex optimization is used to find the optimal torque split between two electric machines, in order to maximize powertrain efficiency. In hybrid mode, optimization is performed in a bilevel program. One level optimizes speed of a compound unit that includes the engine and electric machines. Another level optimizes the power split between the compound unit and the battery. The proposed method is used to minimize the total cost of ownership of a passenger vehicle for a daily commuter, including costs for battery, fossil fuel and electricity

Integrating Voice-Based Machine Learning Technology into Complex Home Environments

To demonstrate the value of machine learning based smart health technologies, researchers have to deploy their solutions into complex real-world environments with real participants. This gives rise to many, oftentimes unexpected, challenges for creating technology in a lab environment that will work when deployed in real home environments. In other words, like more mature disciplines, we need solutions for what can be done at development time to increase success at deployment time. To illustrate an approach and solutions, we use an example of an ongoing project that is a pipeline of voice based machine learning solutions that detects the anger and verbal conflicts of the participants. For anonymity, we call it the XYZ system. XYZ is a smart health technology because by notifying the participants of their anger, it encourages the participants to better manage their emotions. This is important because being able to recognize one's emotions is the first step to better managing one's anger. XYZ was deployed in 6 homes for 4 months each and monitors the emotion of the caregiver of a dementia patient. In this paper we demonstrate some of the necessary steps to be accomplished during the development stage to increase deployment time success, and show where continued work is still necessary. Note that the complex environments arise both from the physical world and from complex human behavior

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Predictive Energy Management for Electric Variable Transmission HEV

This paper introduces predictive energy management for a hybrid electric powertrain with an electric variable transmission. The optimization of the energy management in hybrid mode is formulated as a bi-level program, in which one static level optimizes the engine speed of a compound unit consists of the engine and electric variable transmission (EVT). The other layer optimizes a dynamic program by splitting power between the compound unit and the battery using equivalent consumption minimization strategy (ECMS). By combining ECMS with dynamic programming (ECMS-DP), the proposed strategy allows the engine on/off control to be also taken into account by iteratively updating the costate. Finally, the efficient ECMS-DP optimization is incorporated into the framework of model predictive control (MPC) and solved in a moving horizon fashion. Computation efficiency and optimization results are presented by simulation. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Comprehensive Analysis and Optimal Configurations of the EVT Powertrain

This paper investigates the design of a series-parallel hybrid electric powertrain that includes an electric variable transmission (EVT) and two mechanical gears that connect the EVT to the rest of the powertrain. The EVT is implemented as a dual rotor electric machine (EM), which from a design perspective can be studied as two separate EMs. The goal is to optimize the size of the two EMs and the two mechanical gears, while minimizing fuel consumption, subject to performance requirements on top vehicle speed and acceleration time. A practical optimization methodology is proposed that decouples the multi-objective problem into three different control subproblems: a slow dynamic subproblem for fuel economy, a fast dynamic subproblem for acceleration performance and a static optimization subproblem for the top speed performance. Efficient dynamic programming formulations are presented to solve the two dynamic subproblems. Two different scenarios are discussed and analyzed, where the first one presents the influence of the EMs\u27 sizes on the vehicle performance and the second one delivers near optimal configurations of the EVT powertrain for different gear ratios. A case study is also carried out to compare the sizing results between the proposed method and an existing benchmark method, showing that the two EMs could be reduced by 45% and 11.87% respectively, while powertrain performance could still be maintained at the same level

Design of an Electric Road-Rail Vehicle

In this paper, an electric road-rail vehicle and its control system are designed. The electric vehicle equipped a train couplers in front of it is used as a movable electric signal test bench for China Rail High-speed (CRH). Firstly, architecture of the vehicle is introduced. In the powertrain, hydraulic system is designed for the transition from road mode to rail mode, and vice versa. And in both road and rail modes, the vehicle is driven by an induction machine. Secondly, to improve the driving performance and satisfy different driving conditions, particular control strategy for the induction machine is put forward by using a load torque observer based on sliding mode control theory. The load observer based induction machine control system can improve the speed stability and robustness to the unexpected load disturbing. Finally, experiment is done and the results verify that the algorithm works well