Bo Egardt

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed. The predictive control was based on information supplied by the vehicle navigation system. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The results indicate that, for an urban route with varying topography, the use of predictive control can significantly reduce the fuel consumption.

Comparison of Three Electrochemical Energy Buffers Applied to a Hybrid Bus Powertrain With Simultaneous Optimal Sizing and Energy Management

This paper comparatively examines three different electrochemical energy storage systems (ESSs), i.e., a Li-ion battery pack, a supercapacitor pack, and a dual buffer, for a hybrid bus powertrain operated in Gothenburg, Sweden. Existing studies focus on comparing these ESSs, in terms of either general attributes (e.g., energy density and power density) or their implications to the fuel economy of hybrid vehicles with a heuristic/nonoptimal ESS size and power management strategy. This paper adds four original contributions to the related literature. First, the three ESSs are compared in a framework of simultaneous optimal ESS sizing and energy management, where the ESSs can serve the powertrain in the most cost-effective manner. Second, convex optimization is used to implement the framework, which allows the hybrid powertrain designers/integrators to rapidly and optimally perform integrated ESS selection, sizing, and power management. Third, both hybrid electric vehicle (HEV) and plug-in HEV (PHEV) scenarios for the powertrain are considered, in order to systematically examine how different the ESS requirements are for HEV and PHEV applications. Finally, a sensitivity analysis is carried out to evaluate how price variations of the onboard energy carriers affect the results and conclusions.

Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed on measured-drive data from an urban route with varying topography. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The lowest information case represents that the vehicle knows that it is being driven in a certain environment, e.g., city driving, and that the controller has been optimized for that type of environment. The second highest information level represents a vehicle equipped with a GPS combined with a traffic-flow information system. In the highest information level, the future power demand is completely known to the control system, hence, the corresponding optimal controller results in the minimal attainable fuel consumption. This paper showed that good performance (1%-3% from the minimal attainable fuel consumption) can be achieved with the lowest information case, with a time-invariant controller that is optimized to the environment. The second highest information level results in less than 0.2% higher consumption than the minimal attainable on the studied route. This means that it is possible to design a predictive controller based on information supplied by the vehicle-navigation system and traffic-flow-information systems that can come very close to the minimal attainable fuel consumption. A novel algorithm that uses information supplied by the vehicle-navigation system was presented. The proposed algorithm results in a consumption only 0.3% from the minimal attainable consumption on the studied route

Least squares type algorithm for adaptive implementation of Pisarenko's harmonic retrieval method

Pisarenkos harmonic retrieval method involves determining the minimum eigenvalue and the corresponding eigenvector of the covariance matrix of the observed random process. Recently, Thompson [9] suggested a constrained gradient search procedure for obtaining an adaptive version of Pisarenkos method, and his simulations have verified that the frequency estimates provided by his procedure were unbiased. However, the main cost of this technique was that the initial convergence rate could be very slow for certain poor initial conditions. Restating the constrained minimization as an unconstrained nonlinear problem, we derived an alternative Gauss-Newton type recursive algorithm, which also used the second derivative matrix (or Hessian); this algorithm may also be viewed as an approximate least squares algorithm. Simulations have been performed to compare this algorithm to (a slight variant of) Thompsons original algorithm. The most important conclusions are that the least squares type algorithm has faster convergence in the beginning, while its convergence rate close to the true parameters depends on the signal-to-noise ratio of the input signal. The approximate least squares algorithm resolves the sinusoids much faster than the gradient version.

Optimal Dimensioning and Power Management of a Fuel Cell/Battery Hybrid Bus via Convex Programming

This paper is concerned with the simultaneous optimal component sizing and power management of a fuel cell/battery hybrid bus. Existing studies solve the combined plant/controller optimization problem for fuel cell hybrid vehicles (FCHVs) by using methods with disadvantages of heavy computational burden and/or suboptimality, for which only a single driving profile was often considered. This paper adds three important contributions to the FCHVs-related literature. First, convex programming is extended to rapidly and efficiently optimize both the power management strategy and sizes of the fuel cell system (FCS) and the battery pack in the hybrid bus. The main purpose is to encourage more researchers and engineers in FCHVs field to utilize the new effective tool. Second, the influence of the driving pattern on the optimization result (both the component sizes and hydrogen economy) of the bus is systematically investigated by considering three different bus driving routes, including two standard testing cycles and a realistic bus line cycle with slope information in Gothenburg, Sweden. Finally, the sensitivity of the optimization outcome to the potential price decreases of the FCS and the battery is quantitatively examined.

Hybrid systems in process control

Modeling and control of hybrid systems, with particular emphasis on process control applications, are considered in this article. Based on a number of observations on typical mixed discrete and continuous features for such applications, a fairly general model structure for hybrid systems is proposed. This model structure, which clearly separates the open-loop plant from the closed-loop system, is suitable for analysis and synthesis of hybrid control systems. To illustrate this, three different approaches for control-law synthesis based on continuous and discrete specifications are discussed. In the first one, the hybrid plant model is replaced by a purely discrete event model, related to the continuous specification, and a supervisor is synthesized applying supervisory control theory suggested by Ramadge and Wonham (1987). The other two methods directly utilize the continuous specification for determination of a control event generator, where time-optimal aspects are introduced as an option in the last approach.

Integrated Optimization of Battery Sizing, Charging, and Power Management in Plug-In Hybrid Electric Vehicles

This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO2) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.

Stability analysis of discrete-time adaptive control schemes

The stability properties of a fairly general diserete-time adaptive control scheme are analyzed. Sufficient conditions for L^{\infty} stability in the presence of disturbances are given. The stability results are used to prove convergence of the process outputs in the disturbance-free case, without requiring any a priori stability assumption.

Backlash Estimation With Application to Automotive Powertrains

In automotive powertrains, backlash imposes well-known limitations on the quality of control and, hence, on vehicle driveability. High-performance controllers for backlash compensation require high-quality measurements of the current state of the powertrain. Information about the size of the backlash is also needed. In this paper, nonlinear estimators for backlash size and state are developed, using the Kalman filtering theory. A linear estimator for fast and accurate estimation of the angular position of a wheel and the engine is also described. It utilizes standard engine speed sensors and the antilock brake system speed sensors and event-based sampling at each pulse from these sensors. The estimators are validated through experiments on a real vehicle and the results show that the estimates are of high quality, and hence, useful for improving backlash compensation functions in the powertrain control system

Nonlinear control applied to gearshifting in automated manual transmissions

In this paper the problem of automating the gearshift process of a manual transmission without synchronizers is investigated. The application is very interesting from an integrated powertrain control point of view, since it includes many different control tasks and encourages the use of the engine as an actuator to the rest of the powertrain. A model-based control law for the task of gearshifting is presented. The controller is designed based on the backstepping methodology. It includes control laws for transmission torque control as well as for engine speed control. Simulations have shown good results for the gearshift controller.