A series hybrid powertrain provides ultimate freedom in controlling the engine. The flexibility enabled with hybridization creates chances for a synergistic approach, in which the hybrid supervisory control can be augmented to address both the emissions and the efficiency. In this paper, two policy optimization techniques are proposed, namely stochastic dynamic programming and neurodynamic programming, for designing power management controllers. These controllers are then compared with a baseline rule-based controller. The intention is to investigate the additional benefits possible through application of policy optimization algorithms and a systematic framework capable of representing complex system-level effects. The power management of a series hydraulic hybrid vehicle is pursued as a sequential decision-making problem under uncertainty (stochastic control). The low energy density of the hydraulic accumulator adds to the control challenge. First, stochastic dynamic programming and neurodynamic programming are applied to design a controller based on the fuel economy objective. The problem is subsequently expanded to include minimization of transient diesel engine emissions. This poses additional challenges due to the increased state space. The problem is computationally intractable by stochastic dynamic programming and is solved using the newly proposed neurodynamic programming framework. Finally, the supervisory controllers are implemented and evaluated using simulations and an engine-in-the-loop facility. It is shown that, by designing an intelligent multi-objective controller, significant reduction in both the fuel consumption and the emissions can be achieved compared with strategies which focus solely on the fuel consumption.

A series hydraulic hybrid concept (SHHV) has been explored as a potential pathway to an ultra-efficient city vehicle. Intended markets would be congested metropolitan areas, particularly in developing countries. The target fuel economy was ~100 mpg or 2.4 l/100km in city driving. Such an ambitious target requires multiple measures, i.e. low mass, favorable aerodynamics and ultra-efficient powertrain. The series hydraulic hybrid powertrain has been designed and analyzed for the selected light and aerodynamic platform with the expectation that (i) series configuration will maximize opportunities for regeneration and optimization of engine operation, (ii) inherent high power density of hydraulic propulsion and storage components will yield small, lowcost components, and (iii) high efficiency and high power limits for accumulator charging/discharging will enable very effective regeneration. The simulation study focused on the SHHV supervisory control development, to address the challenge of the low storage capacity of the accumulator. Two approaches were pursued, i.e. the thermostatic SOC control, and Stochastic Dynamic Programming for horizon optimization. The stochastic dynamic programming was setup using a set of naturalistic driving schedules, recorded in normal traffic. The analysis included additional degree of freedom, as the engine power demand was split into two variables, namely engine torque and speed. The results represent a significant departure from the conventional wisdom of operating the engine near its "sweet spot" and indicate what is preferred from the system standpoint. Predicted fuel economy over the EPA city schedule is ~93 mpg with engine idling, and ~110 mpg with engine shutdowns.

Diesel engine combustion and emission formation is highly nonlinear and thus creates a challenge related to engine diagnostics and engine control with emission feedback. This paper presents a novel methodology to address the challenge and develop virtual sensing models for engine exhaust emission. These models are capable of predicting transient emissions accurately and are computationally efficient for control and optimization studies. The emission models developed in this paper belong to the family of hierarchical models, namely the “neuro-fuzzy model tree.” The approach is based on divide-and-conquer strategy, i.e., to divide a complex problem into multiple simpler subproblems, which can then be identified using a simpler class of models. Advanced experimental setup incorporating a medium duty diesel engine is used to generate training data. Fast emission analyzers for soot and NOx provide instantaneous engine-out emissions. Finally, the engine-in-the-loop is used to validate the models for predicting transient particulate mass and NOx.

This paper proposes a self-learning approach to develop optimal power management with multiple objectives, e.g. to minimize fuel consumption and transient engine-out NOx and particulate matter emission for a series hydraulic hybrid vehicle. Addressing multiple objectives is particularly relevant in the case of a diesel powered hydraulic hybrid since it has been shown that managing engine transients can significantly reduce real-world emissions. The problem is formulated as an infinite time horizon stochastic sequential decision making/markovian problem. The problem is computationally intractable by conventional Dynamic programming due to large number of states and complex modeling issues. Therefore, the paper proposes an online self-learning neural controller based on the fundamental principles of Neuro-Dynamic Programming (NDP) and reinforcement learning. The controller learns from its interactions with the environment and improves its performance over time. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. The control law is a stationary full state feedback based on 5 states and can be directly implemented. The controller performance is then evaluated in the Engine-in-the-Loop (EIL) facility.

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space. We propose developing a supervisory controller for hybrid vehicles based on the principles of reinforcement learning and neuro-dynamic programming, whereby the costto-go function is approximated using a neural network. The controller learns and improves its performance over time. The simulation results obtained for a series hydraulic hybrid vehicle over a driving schedule demonstrate the effectiveness of the proposed technique.

Diesel engine combustion and emission formation are highly nonlinear and thus create a challenge related to engine diagnostics and engine control with emission feedback. This article describes the development of neuro-fuzzy models for prediction of transient NOX and soot emission from a diesel engine. The modeling techniques are motivated by the idea of divide and conquer the input–output space. The complex problem is divided into multiple simpler subproblems, which are then identified using simpler class of models. This article explores two different choices of local models, specifically polynomial and neural networks. The modeling technique is augmented with input relevance algorithm to select the most relevant input regressors. Two algorithms, namely, orthogonal least square and automatic relevance determination, are introduced. The models are data driven, and an advanced experimental setup incorporating a medium duty diesel engine and fast emission analyzers for soot and NOX is used to generate training data. The choice of local models and input relevance algorithm is validated with instantaneous emission recorded during transient schedules different from those used in development. High prediction accuracy, both qualitatively and quantitatively, is demonstrated with low computational cost.

Wireless communication is a basis of the vision of connected and automated vehicles (CAVs). Given the heterogeneity of both wireless communication technologies and CAV applications, one question that is critical to technology road-mapping and policy making is which communication technology is more suitable for a specific CAV application. Focusing on the technical aspect of this question, we present a multi-scale spatiotemporal perspective of wireless communication technologies as well as canonical CAV applications in active safety, fuel economy and emission control, vehicle automation, and vehicular infotainment. Our analysis shows that CAV applications in the regime of small spatiotemporal scale communication requirements are best supported by V2V communications, applications in the regime of large spatiotemporal scale communication requirements are better supported by cellular communications, and applications in the regime of small spatial scale but medium-to-large temporal scale can be supported by both V2V and cellular communications and provide the opportunity of leveraging heterogeneous communication resources. I. INTRODUCTIONTransforming the traditional paradigm of single-vehicle-oriented optimization and operation, next-generation vehicles are expected to coordinate with one another, transportation infrastructures, Internet clouds, and people in maximizing the safety, sustainability, and comfort of road transportation. One basic enabler of this vision of connected and automated vehicle (CAV) operation is for vehicles to wirelessly communicate with one another, transportation infrastructures, Internet clouds, and people. Given the wide spectrum of available wireless communication technologies (e.g., cellular and DSRC) and the heterogeneity of CAV applications envisioned, there has been heated debate on the exact wireless communication technologies to be used for CAVs, and complex factors such as market penetration of new technologies have complicated this debate. Towards a thorough technical examination of the debate and for enabling strategic planning of technology roadmap, we analyze different categories of CAV applications by examining their spatiotemporal scales of communication requirements, and we analyze capacity limits of different wireless communication alternatives. Based on this analysis, we make recommendations on technology planning.The rest of this article is organized as follows. In Section II, we review representative CAV applications and their communication requirements. In Section III, we comparatively analyze cellular communication and V2V communication in supporting CAV applications. We make concluding remarks in Section IV.

The paper describes the approach, addresses integration challenges and discusses capabilities of the Hybrid Powertrain-in-the-Loop (H-PIL) facility for the series/hydrostatic hydraulic hybrid system. We describe the simulation of the open-loop and closed-loop hydraulic hybrid systems in H-PIL and its use for concurrent engineering and development of advanced supervisory strategies. The configuration of the hydraulic-hybrid system and details of the hydraulic circuit developed for the H-PIL integration are presented. Next, software and hardware interfaces between the real components and virtual systems are developed, and special attention is given to linking component-level controllers and system-level supervisory control. The H-PIL setup allows imposing realistic dynamic loads on hydraulic pump/motors and accumulator based on vehicle driving schedule. Application of fast analyzers allows characterization of the impact of dynamic interactions in the propulsion system on engine-out emissions. Therefore, the H-PIL facility allows optimization of the hybrid system for both high-efficiency and low emissions. The impetus is provided by previous work showing that more than half of the soot emissions from a conventional diesel powertrain over the urban driving schedule can be attributed to transients. The setup includes a 6.4L V-8 International diesel engine, highly dynamic dynamometer, Radial piston pump/motors supplied by Bosch-Rexroth and dSPACE real-time environment with in-house developed simulation of the virtual vehicle.