Jeffrey L. Stein

A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode power-split PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.

Analytical Target Cascading in Automotive Vehicle Design

Target cascading in product development is a systematic effort to propagate the desired top-level system design targets to appropriate specifications for subsystems and components in a consistent and efficient manner. If analysis models are available to represent the consequences of the relevant design decisions, analytical target cascading can he formalized as a hierarchical multilevel optimization problem. The article demonstrates this complex modeling and solution process in the chassis design of a sport-utility vehicle. Ride quality and handling targets are cascaded down to systems and subsystems utilizing suspension, tire, and spring analysis models. Potential incompatibilities among targets and constraints Throughout the entire system can he uncovered and the trade-offs involved in achieving system targets under different design scenarios can he quantified.

Combined optimisation of design and power management of the hydraulic hybrid propulsion system for the 6 × 6 medium truck

Hybrid propulsion systems are one of the critical technologies on the roadmap to future ultra-efficient trucks. While there is a significant body of work related to hybrid passenger cars and light ...

Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles via Electrochemical Modeling and Stochastic Control

This paper develops techniques to design plug-in hybrid electric vehicle (PHEV) power management algorithms that optimally balance lithium-ion battery pack health and energy consumption cost. As such, this research is the first to utilize electrochemical battery models to optimize the power management in PHEVs. Daily trip length distributions are integrated into the problem using Markov chains with absorbing states. We capture battery aging by integrating two example degradation models: solid-electrolyte interphase (SEI) film formation and the “Ah-processed” model. This enables us to optimally tradeoff energy cost versus battery-health. We analyze this tradeoff to explore how optimal control strategies and physical battery system properties are related. Specifically, we find that the slope and convexity properties of the health degradation model profoundly impact the optimal charge depletion strategy. For example, solutions that balance energy cost and SEI layer growth aggressively deplete battery charge at high states-of-charge (SoCs), then blend engine and battery power at lower SoCs.

Target cascading in vehicle redesign: a class VI truck study

The analytical target cascading process is applied to the redesign of a U.S. class VI truck. Necessary simulation and analysis models for predicting vehicle dynamics, powertrain, and suspension behaviour are developed. Vehicle design targets that include improved fuel economy, ride quality, driveability, and performance metrics are translated into system design specifications, and a consistent final design is obtained. Trade-offs between conflicting targets are identified. The study illustrates how the analytical target cascading process can reduce vehicle design cycle time while ensuring physical prototype matching, and how costly design iterations late in the development process can be avoided.

Reduction of an Electrochemistry-Based Li-Ion Battery Model via Quasi-Linearization and Padé Approximation

This paper examines an electrochemistry-based lithium-ion battery model developed by Doyle, Fuller, and Newman. The paper makes this model more tractable and conducive to control design by making two main contributions to the literature. First, we adaptively solve the models algebraic equations using quasi-linearization. This improves the models execution speed compared to solving the algebraic equations via optimization. Second, we reduce the models order by deriving a family of analytic Pade approximations to the models spherical diffusion equations. The paper carefully compares these Pade approximations to other published methods for reducing spherical diffusion equations. Finally, the paper concludes with battery simulations showing the significant impact of the proposed model reduction approach on the battery models overall accuracy and simulation speed.

Integrated, Feed-Forward Hybrid Electric Vehicle Simulation in SIMULINK and its Use for Power Management Studies

A hybrid electric vehicle simulation tool (HE-VESIM) has been developed at the Automotive Research Center of the University of Michigan to study the fuel economy potential of hybrid military/civilian trucks. In this paper, the fundamental architecture of the feed-forward parallel hybrid-electric vehicle system is described, together with dynamic equations and basic features of sub-system modules. Two vehicle-level power management control algorithms are assessed, a rule-based algorithm, which mainly explores engine efficiency in an intuitive manner, and a dynamic-programming optimization algorithm. Simulation results over the urban driving cycle demonstrate the potential of the selected hybrid system to significantly improve vehicle fuel economy, the improvement being greater when the dynamicprogramming power management algorithm is applied.

Review of hardware-in-the-loop simulation and its prospects in the automotive area

Hardware-in-the-loop (HIL) simulation is rapidly evolving from a control prototyping tool to a system modeling, simulation, and synthesis paradigm synergistically combining many advantages of both physical and virtual prototyping. This paper provides a brief overview of the key enablers and numerous applications of HIL simulation, focusing on its metamorphosis from a control validation tool into a system development paradigm. It then describes a state-of-the art engine-in-the-loop (EIL) simulation facility that highlights the use of HIL simulation for the system-level experimental evaluation of powertrain interactions and development of strategies for clean and efficient propulsion. The facility comprises a real diesel engine coupled to accurate real-time driver, driveline, and vehicle models through a highly responsive dynamometer. This enables the verification of both performance and fuel economy predictions of different conventional and hybrid powertrains. Furthermore, the facility can both replicate the highly dynamic interactions occurring within a real powertrain and measure their influence on transient emissions and visual signature through state-of-the-art instruments. The viability of this facility for integrated powertrain system development is demonstrated through a case study exploring the development of advanced High Mobility Multipurpose Wheeled Vehicle (HMMWV) powertrains.

Closed-loop, state and input observer for systems with unknown inputs

A closed-loop observer that can identify states and inputs simultaneously is developed for linear time-invariant systems with unknown inputs. The necessary and sufficient conditions for the existence of the observer are derived and proved. Parameter identification of a system with a few unknown or time-varying parameters is investigated by applying the observer technique developed in this paper. Potential usage in machine monitoring (machine diagnostics) appears promising.

A State-Space Model for Monitoring Thermally Induced Preload in Anti-Friction Spindle Bearings of High-Speed Machine Tools

Catastrophic and premature bearing failure caused by excessive thermally induced bearing preload is a major design problem for spindle bearings of highspeed machine tools. Due to a lack of a low cost and easy to maintain on-line preload measuring technique, the traditional solution is to limit the maximum spindle speed and the initial bearing preload. This solution is incompatible with the need to increase machining productivity, which requires increasing the spindle speed, and to increase product qualily (surface finish, dimensional accuracy), which requires increasing (or at least not decreasing) the preload to keep the spindle system stiff. This paper proposes a dynamic mathematical model of the spindle system, which can be used as part of a model-based monitoring system for estimating the spindle bearing preload