This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Model predictive control power management strategies for HEVs: A review

Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook

Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

/pdf/soc-estimation-for-lithium-ion-batteries-review-and-future-2mrvhj4xwd.pdf

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Cooperative energy management of automated vehicles

Robust recursive impedance estimation for automotive lithium-ion batteries

http://hybridfordonscentrum.se/wp-content/uploads/2015/02/presentation_1_ECOSM2015.pdf

Look-ahead Vehicle Energy Management with Traffic Predictions

Feedback error learning (FEL) is a proposed technique for reference-feedforward adaptive control. FEL in a linear and time-invariant (LTI) framework has been studied recently; the studies can be seen as proposed solutions to a feedforward MRAC problem. This paper reanalyzes two suggested schemes with new interpretations and conclusions. It motivates the suggestion of an alternative scheme for reference-feedforward adaptive control, based on a certainty-equivalence approach. The suggested scheme differs from the analyzed ones by a slight change in both the estimator and the control law. Boundedness and error convergence are then guaranteed when the estimator uses normalization combined with parameter projection onto a convex set where stability of the estimated closed-loop system holds.

A clarifying analysis of feedback error learning in an LTI framework

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/2296.pdf

Magnus Nilsson

Papers

Cooperative energy management of automated vehicles

Robust recursive impedance estimation for automotive lithium-ion batteries

Look-ahead Vehicle Energy Management with Traffic Predictions

A clarifying analysis of feedback error learning in an LTI framework

Robustness Comparison of Battery State of Charge Observers for Automotive Applications