Kasemsak Uthaichana

Development and Validation of a Battery Model Useful for Discharging and Charging Power Control and Lifetime Estimation

Accurate information on battery state-of-charge, expected battery lifetime, and expected battery cycle life is essential for many practical applications. In this paper, we develop a nonchemically based partially linearized (in battery power) input-output battery model, initially developed for lead-acid batteries in a hybrid electric vehicle. We show that with properly tuned parameter values, the model can be extended to different battery types, such as lithium-ion, nickel-metal hydride, and alkaline. The validation results of the model against measured data in terms of power and efficiency at different temperatures are then presented. The model is incorporated with the recovery effect for accurate lifetime estimation. The obtained lifetime estimation results using the proposed model are similar to the ones predicted by the Rakhmatov and Virudhula battery model on a given set of typical loads at room temperature. A possible incorporation of the cycling effect, which determines the battery cycle life, in terms of the maximum available energy approximated at charge/discharge nominal power level is also suggested. The usage of the proposed model is computationally inexpensive, hence implementable in many applications, such as low-power system design, real-time energy management in distributed sensor network, etc.

MPC of Switching in a Boost Converter Using a Hybrid State Model With a Sliding Mode Observer

In this paper, a model of a DC-DC (boost) converter is first expressed as a hybrid/switched/variable-structure system state model for the purpose of applying recently developed hybrid optimal control theory to control switching in a boost converter. Switching control is achieved by forming the embedded form of the hybrid state model, which enables the derivation of a control that solves for the switching function that minimizes a user-defined performance index. This approach eliminates the need to form average-value models and provides flexibility to balance competing objectives through appropriate weighting of individual terms in the performance index. Since, in practical situations, both the source voltage and the load resistance vary with time in unknown and unmeasurable ways, we introduce a sliding mode observer based on an enlarged state model which allows implicit estimation of the unknown variables. The combined optimal switching control and sliding mode observer are applied to a boost converter in which several nonidealities and losses are represented. The results of time-domain simulation and hardware experiments are used to validate and compare the response of the hybrid optimal control-sliding mode observer to that of a traditional current-mode control strategy.

Hybrid Model Predictive Control for Stabilization of Wheeled Mobile Robots Subject to Wheel Slippage

This paper studies the problem of traction control, i.e., how to stabilize a wheeled mobile robot (WMR) subject to wheel slippage to a desired configuration. The WMR is equipped with a rechargeable battery pack which powers electric drives on each wheel. The drives propel the WMR in one mode of operation or recharge the battery pack (recover energy) in a second mode. These modes of operation are controlled, whereas wheel slippage, e.g., due to ice, is an autonomous mode change. The WMR can be thus modeled as a hybrid system with both controlled and autonomous switches. Model predictive control (MPC) for such systems, although robust, typically results in numerical methods of combinatorial complexity. We show that recently developed embedding techniques can be used to formulate numerical algorithms for the hybrid MPC problem that have the same complexity as MPC for smooth systems. We also discuss the numerical techniques that lead to efficient and robust MPC algorithms in detail. Simulations illustrate the effectiveness of the approach.

Hybrid Model Predictive Control for the Stabilization of Wheeled Mobile Robots Subject to Wheel Slippage

This paper studies the problem of stabilizing wheeled mobile robots (WMRs) subject to wheel slippage to a predefined set. When slippage of the wheels can occur, WMRs can be modeled as hybrid systems. Model predictive control for such systems typically results in numerical methods of combinatorial complexity. We show that recently developed embedding techniques can be used to formulate numerical algorithms for the hybrid model predictive control (MPC) problem that have the same complexity as the MPC for smooth systems. We also discuss in detail the numerical techniques that lead to efficient and robust MPC algorithms. Examples are given to illustrate the effectiveness of the approach.

Hybrid model predictive control tracking of a sawtooth driving profile for an HEV

Based on a two-mode operation model of a parallel hybrid electric vehicle (PHEV) developed in previous work, this paper presents a hybrid optimal control solution for the power management problem of a PHEV. The optimal power flows between the vehicles main subsystems are computed as solutions of a switching-system optimization (nonlinear programming problem) formulated at the supervisory level assuming a hierarchical vehicle control structure. The resulting equations are numerically solved using collocation methods. The approach is illustrated using the optimal and MPC tracking of a sawtooth velocity driving profile. Unlike other work, the assumption that there is always sufficient power to achieve perfect tracking is not made in this work.

Comparative study on friction compensation using Coulomb and Dahl models with extended and unscented Kalman filters

In this paper, the Coulomb and the Dahl friction models have been simulated and estimated using extended Kalman filter (EKF) and unscented Kalman filter (UKF). The estimated friction is used by a controller to compensate and track a desired sinusoidal position profile. In the simulation, the real friction is estimated using EKF and UKF with the Coulomb and the Dahl friction models. The designed controller consists of both feedback and feedforward for position tracking. The proportional and derivative (PD) output feedback is selected. The desired trajectory inertia and the estimated friction are compensated as the feedforward. The tracking performance is analyzed in terms of the normalized root mean square error. It is found that the UKF based controller using Dahl friction compensation gives the lowest sinusoidal position tracking error, outperforming the EKF based controller counterpart.

HEV MODELING FOR A SUPERVISORY LEVEL POWER FLOW CONTROL PROBLEM

Abstract This paper explores the modeling equations underlying a supervisory level power flow control problem for a hybrid electric vehicle (HEV). For a given driving profile, a supervisory controller decides on the power split between the ICE and the battery-electric-motor-generator to achieve optimal performance, e.g., a trade-off between energy usage, driving profile tracking, and drivability constraints. Formulation and solution of such a problem require a supervisory level power flow control model amenable to hybrid optimal control techniques. This paper develops constrained power flow control models for the various HEV subsystems and their interactions, along with a differential equation modeling the HEVs longitudinal dynamics amenable to recent advances in hybrid optimal control theory.

SUBOPTIMAL SUPERVISORY LEVEL POWER FLOW CONTROL OF A HYBRID ELECTRIC VEHICLE

Abstract This paper develops a supervisory level power flow suboptimal controller for a hybrid electric vehicle. The power flow model has two modes of operation determined by whether or not the electric motor is motoring or generating. The solution to the hybrid optimal control problem uses the recently developed embedding technique which places the original problem into a parameterized family of problems. The parameterized family of problems, amenable to the application of classical optimal control theory, is then solved. Because of the usual numerical difficulty in solving the state and adjoint equations simultaneously combined with nonlinearities in the model, a suboptimal piecewise solution is obtained for tracking a trapezoidal driving profile. Results are reasonable and encouraging.

Modeling of an electric drive for a HEV supervisory level power flow control problem

In this paper, a supervisory level power flow control model for a parallel hybrid electric vehicle (HEV) is developed. The HEV studied utilizes power flow from an internal combustion engine ICE and from an electric-motor-generator, EM-GEN, (30 kW) and battery pack consisting of thirty 25 Ah 12 V lead acid batteries connected in series. The EM-GEN unit is an induction machine operated under maximum torque/amp control. This paper summarizes the non EM-GEN aspects of the HEV power flow control model developed earlier but focuses primarily on the development of a viable EM-GEN model amenable to a supervisory level power flow control problem.

Optimal Li-Ion battery sizing on PEMFC hybrid powertrain using dynamic programming

This investigation studies the effect of the number of Li-Ion battery modules on the fuel consumption and the 10-year operating cost for optimal powertrain design in a Proton Exchange Membrane fuel cell (PEMFC) hybrid vehicle. A 30kW PEMFC stack is in parallel with a number of 334Wh-LiFePO4 battery modules to deliver its energy to a 77 kW electric drive (ED). The ED output is connected to the gear box and the lower powertrain. For a given road/load mechanical power demand on the vehicle, the ED power profile can be computed. The electrical power-split strategy between the PEMFC and the battery pack plays a great role on the hydrogen fuel consumption and cost. The dynamic programming (DP) approach is adopted to compute the optimal power management strategy and to evaluate the vehicle performance and the average fuel consumption over five different standard driving profiles, i.e. Japan 10/15 mode, UN/ECE, UDDS, HWFET, and SFTP. The objective function to be minimized consists of the fuel cost and the Li-Ion battery cost. Since the Li-Ion battery is expensive, the batterys state of charge (SOC) operating range is limited to 0.5 and 0.7 to prolong the battery lifetime. From the simulation results, it is found that for average driving distance 10,000 km/year, the set of 5 battery modules is the most appropriate option. The set of 8 battery modules is best for average driving distance more than 50,000 km/y.