In addition to decentralized controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the internal stability and scalability of homogeneous vehicular platoons moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and vehicle-to-vehicle (V2V) communications. Under linear feedback controllers, a unified internal stability theorem is proved by using the algebraic graph theory and Routh-Hurwitz stability criterion. The theorem explicitly establishes the stabilizing thresholds of linear controller gains for platoons, under a large class of different information flow topologies. Using matrix eigenvalue analysis, the scalability is investigated for platoons under two typical information flow topologies, i.e., 1) the stability margin of platoon decays to zero as 0(1/N 2 ) for bidirectional topology; and 2) the stability margin is always bounded and independent of the platoon size for bidirectional-leader topology. Numerical simulations are used to illustrate the results.

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet. Index Terms-Connected vehicles, energy management strategy (EMS), intelligent transportation systems (ITS), optimal control, plug-in hybrid electric vehicle (PHEV). I. INTRODUCTION A IR quality has become a serious concern in cities and urban areas in recent years. This has promoted new legislation, affecting the European automotive sector through Euro I-VI, which limits emissions of CO, HC, NO x , and particulate matter [1]. As Euro VI became into force, the spotlight is nowadays on CO 2 emissions. The European Commission has established a 130 g CO 2 /km target for 2015, which will be reduced to 95 g CO 2 /km in 2021 [2]. Similar policies have been imposed in other automotive markets, such as the USA, China, and Japan. This legislation has encouraged the introduction of Manuscript

This paper provides an overview of the latest advances in road vehicle suspension design, dynamics, and control, together with the authors' perspectives, in the context of vehicle ride, handling and stability. The general aspects of road vehicle suspension dynamics and design are discussed, followed by descriptions of road roughness excitations with a particular emphasis on road potholes. Passive suspension system designs and their effects on road vehicle dynamics and stability are presented in terms of in-plane and full-vehicle arrangements. Controlled suspensions are also reviewed and discussed. The paper concludes with some potential research topics, in particular those associated with development of hybrid and electric vehicles.

 Abstract-Battery health monitoring and management is of extreme importance for the performance and cost of electric vehicles. This paper is concerned with machine learning enabled battery State-of-Health (SOH) indication and prognosis. The sample entropy of short voltage sequence is used as an effective signature of capacity loss. Advanced sparse Bayesian predictive modeling (SBPM) methodology is employed to capture the underlying correspondence between the capacity loss and sample entropy. The SBPM-based SOH monitor is compared with a polynomial model developed in our prior work. The proposed approach allows for an analytical integration of temperature effects such that an explicitly temperature-perspective SOH estimator is established, whose performance and complexity is contrasted to the support vector machine (SVM) scheme. The forecast of remaining useful life (RUL) is also performed via a combination of SBPM and bootstrap sampling concepts. Large amounts of experimental data from multiple lithium-ion battery cells at three different temperatures are deployed for model construction, verification, and comparison. Such a multi-cell setting is more useful and valuable than only considering a single cell (a common scenario). This is the first known application of combined sample entropy and SBPM to battery health prognosis.

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