Raja Sangili Vadamalu

Online MPC based PHEV Energy Management using conic interior-point methods

Energy Management (EM) strategy relying on online optimization is proposed for Plug-in Hybrid Electric Vehicle (PHEV). The implementation is based on Model Predictive Control (MPC) and can account for varying driving conditions. EM problem is solved online by iterative optimization of an objective function over the constrainted feasible region, formulated as a Second Order Cone Problem (SOCP). The optimization relies on predictive information about future driving conditions within a limited time horizon. The EM strategy adapts its functionality based on situation-aware prediction and offers a possibility to tune online, the optimization process by heuristics on constraint-limits.

Explicit MPC PHEV energy management using Markov chain based predictor: Development and validation at Engine-In-The-Loop testbed

Model predictive control (MPC) based approaches are being increasingly employed for solving the Energy Management (EM) problem of Plug-In Hybrid Electric Vehicles (PHEV). Model uncertainty, un-modelled disturbances and uncertainties due to driver behavior in real-world driving necessitate approaches that extend nominal MPC. This paper proposes a MPC approach modeling the future power demand using Markov Chain (MC) and employing an Explicit MPC (EMPC) algorithm to solve the resulting finite-time horizon constrained optimal control problem. The stochastic power demand modeling approach does not require a-priori driving cycle knowledge explicitly. Further the MC transition probability matrices can be learnt and reconfigured to accommodate changing driving characteristics. EMPC based on multi-parametric programming ensures reduced computational complexity and provides guaranteed stability. The proposed method has been implemented for the EM of a PHEV powertrain with a downsized 2-cylinder combustion engine at an Engine-In-The-Loop (EiL) testbed. The results from the simulation study and EiL implementation show performance close to implicit or online MPC with knowledge of the future power request in standardized and representative real-world driving scenarios.

Online optimization based energy management of hybrid electric vehicles using direct optimal control

Hybrid Electric Vehicle Energy Management (HEV-EM) is being extended beyond the standard power/ torque splitting formulations to accommodate conflicting objectives such as fuel efficiency, battery aging and engine-out emissions. Pareto front based approaches are employed for Design of Experiments (DoE) -based calibration of deterministic rule based HEV-EM strategies using offline optimization [1]. Vehicle connectivity and Plug-In Hybrid Electric Vehicle (PHEV) have changed the system boundary conditions for the EM problem. Increased awareness of real driving efficiency, data availability during operation and advances in computation has motivated the application of online-optimization based strategies [2]. The focus had been on extension of Equivalent Consumption Minimization Strategy (ECMS) with additional dynamics, optimization criteria and constraints. This approach faces challenges such as availability and inaccuracy of the modeled dynamics as well as handling of adjoint state dynamics [3].

Identification of unbalance faults in rotors with unknown input observers using classical and LMI based approaches

Fault diagnosis in rotating machines have been attempted by researchers with many different techniques. The common fault occurring in a rotor system is the unbalance. Different techniques exist to determine their magnitude and location in circumferential and axial direction. In this paper, an attempt is made to identify and locate unbalance faults on a rotor system using an Unknown Input Observer (UIO). Its matrices are constructed by two methods, first is a classical method by solving the algebraic Riccatti equations and other by solving a set of Linear Matrix Inequality (LMI) equations. This has the advantage of designing a stable observer without facing the problem of pole placement. Both the methods have been simulated with the model of a rotor test bench. The results are found to be accurate in a simulation environment.