Yue-Yun Wang

Two Time-Scaled Battery Model Identification With Application to Battery State Estimation

Electrified propulsion systems have now become an increasingly popular option for automotive companies to meet the more stringent emissions standards. A well-designed battery state estimation (BSE) system, which includes state-of-charge and state-of-health estimation, is one of the most important aspects of a successful electrified propulsion system design. Among different methods, model-based state estimation has proven to be very successful in their accuracy and implementability. A relatively newer approach to model-based BSE is to identify the battery model parameters (typically a low-order control-oriented model) in real time. This allows the battery model parameter to adjust to changing characteristics of the battery, and thus further improving the robustness of the design. However, standard identification algorithms used have very limited capability in performing this identification successfully due to the frequency response characteristics of the battery. In this brief, we describe a two time-scaled battery model parameter identification method, where the slower and faster battery dynamics are identified separately. Compared with standard approach to real-time battery model identification, where no such separation is made, this method can generate a model whose frequency response is much closer to that of the actual battery. Furthermore, this method uses the standard least squares regression method, which can be easily implemented in real time in the form of recursive least squares. Using this identification method, we show how battery SoC can be estimated. Laboratory battery cell data is used to illustrate the difference between this method and the more standard approach. Then, battery pack collected from a test vehicle is used to demonstrate the SoC estimation capability.

Optimal Air-to-Fuel Ratio Tracking Control With Adaptive Biofuel Content Estimation for LNT Regeneration

This paper presents an optimal control method of tracking the desired air-to-fuel ratio (AFR) based upon the adaptively estimated biofuel content for internal combustion engines equipped with the lean NOx trap (LNT) aftertreatment system. This biofuel content (or percentage of biofuel) is adaptively estimated based upon the exhaust oxygen AFR sensor signal under both the normal engine operations with lean combustion and the LNT regeneration operations with the closed-loop AFR control. The engine system is approximated by a third-order linear system in this paper. A linear quadratic optimal tracking controller is used to track the desired engine AFR during the LNT regeneration period. The robust stability of the closed-loop tracking control system with the adaptive biofuel content estimation is guaranteed over the entire biofuel range and engine speed between 600 and 5500 rpm by using the robust stability criteria for linear parameter variation systems, where the biofuel gain and engine speed are considered as the variable parameter. Several adaptive control schemes are studied through simulations, and then the selected control strategies are evaluated through dynamometer tests for a lean burn spark ignition engine. The best performance is achieved by the gain-scheduled adaptive scheme.

Model-based quantitative feedback control of EGR rate and boost pressure for turbocharged diesel engines

This paper proposes a closed loop multivariable VGT/EGR control system for a turbocharged diesel engine. The control system is synthesized based on quantitative feedback theory to maintain robust stability and performance in the presence of model variations via sequential MIMO loop-shaping. Simulation results from a turbocharged diesel engine are included to illustrate the effectiveness of the proposed control design.

Cylinder Pressure-Based Combustion Controls for Advanced Diesel Combustion With Multiple-Pulse Fuel Injection

For current diesel engines, multiple fuel injection mechanisms enabled by high rail-pressure systems is a key lever that can help to achieve further reduction in engine-out emissions and improvements in performance. In the case of multiple fuel injections, timing and fuel pulse-width for each pulse (or, equivalently, fuel amount) need to be optimized and maintained for low emissions, fuel economy, noise, and exhaust thermal management over different operating ranges. This paper presents a research study on the application of pressure-based controls for management of the multiple-pulse fuel injection, particularly main and post injections, to maintain a robust combustion behavior against disturbances and variations in the field. Several control features for simultaneous management of main and post injections are proposed and experimentally validated on a 6.6 L V8 diesel engine in an engine dynamometer, both at steady-state and during federal test procedure transients.

Exhaust pressure estimation and its application to variable geometry turbine and wastegate diagnostics

Exhaust pressure is a critical engine parameter used to calculate engine volumetric efficiency and EGR flow rate. In this paper, exhaust pressure is estimated for an internal combustion engine equipped with a variable geometry turbocharger. A coordinate transformation is applied to generate a turbine map for estimation of the exhaust pressure. This estimation can be used to replace an expensive pressure sensor for cost saving. On the other hand, for internal combustion engines that have already installed exhaust pressure sensors, this estimation can be used to generate residual signals for model-based diagnostics. Based on the residual signals, two diagnostic methods are proposed: one based on cumulative sum algorithms and the other based on pattern recognition and neural networks. The algorithms are able to detect and isolate different failure modes for a turbocharger system.

Air-to-fuel ratio control with adaptive estimation of biofuel content for diesel engine LNT regeneration

This paper presents a method of controlling the air-to-fuel ratio (AFR) based upon an adaptively estimated biodiesel fuel content for a diesel engine equipped with the lean NOx trap (LNT) aftertreatment system. The fuel content (or percentage of biodiesel fuel) is estimated by an adaptive estimation scheme based upon the exhaust oxygen sensor signal during the normal engine operations and during the LNT regeneration when the AFR is controlled in a closed loop. The engine system was modeled by a third order linear system in this study. A Linear Quadratic optimal tracking controller was used to regulate the engine AFR to the desired level during the LNT regeneration period. The closed loop system robustness with respect to the air flow measurement error and the fuel content estimation error is also analyzed. Three adaptive control schemes were studied through simulations, and the best performance was obtained for the dual-gain scheme, where the low adaptive estimation gain is used during normal engine operations and high gain is used during the LNT regeneration.