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In an authors' previous SAE publication, an energy management control strategy has been proposed for the basic, charge-depleting/charge-sustaining (CD/CS) regime of an Extended Range Electric Vehicle (EREV). The strategy is based on combining a rule-based controller, including a state-of-charge regulator, with an equivalent consumption minimization strategy. This paper presents an extension of the control strategy, which can provide a favorable vehicle behavior in the more general blended (BLND) operating regime, as well. Dynamic programming-based control variables optimization is first conducted to gain an insight into the vehicle optimal behavior in the BLND regime, facilitate the feedback control strategy development/extension, and serve as a benchmark for the control strategy verification. Next, a parameter optimization method is applied to find optimal values of critical engine on/off thresholds.

Driving cycles are typically used for estimation of vehicle fuel/energy consumption and CO2 emissions. In most of applications only the vehicle velocity vs. time profile is considered as a driving cycle, while a road slope is typically omitted. Since the road slope significantly impacts the fuel consumption, it should be included into realistic driving cycles for hilly roads. As a part of wider research of multidimensional driving cycle synthesis, this paper focuses on analysis of a broad city bus driving cycle dataset recorded in the city of Dubrovnik. The analysis is aimed at revealing the impact of road slope on velocity and acceleration distributions, and clustering the recorded data into several groups reflecting various driving and traffic congestion characteristics. Finally, the Markov chain method is employed to synthesize 3D driving cycles for the selected data clusters, where the Markov chain states include vehicle velocity, vehicle acceleration, and road slope.

The paper deals with the design of shift scheduling maps based on dynamic programing (DP) optimization algorithm. The recorded data related to a delivery vehicle fleet are used, along with a model of delivery truck equipped with a 12-gear automated manual transmission, for an analysis and reconstruction of the truck-implemented shift scheduling patterns. The same map reconstruction procedure has been applied to a set of DP optimization-based operating points. The cost function of DP optimization is extended by realistic clutch energy losses dissipated during shift transients, in order to implicitly introduce hysteresis in the shift scheduling maps for improved drivability. The different reconstructed shift scheduling maps are incorporated within the truck model and validated by computer simulations for different driving cycles.

This paper deals with a thorough analysis of using two fundamentally different algorithms for optimization of electric vehicle (EV) fleet charging. The first one is linear programming (LP) algorithm which is particularly suitable for solving linear optimization problems, and the second one is dynamic programming (DP) which can guarantee the global optimality of a solution for a general nonlinear optimization problem with non-convex constraints. Functionality of the considered algorithms is demonstrated through a case study related to a delivery EV fleet, which is modelled through the aggregate battery modeling approach, and for which realistic driving data are available. The algorithms are compared in terms of execution time and charging cost achieved, thus potentially revealing more appropriate algorithm for real-time charging applications.

The paper proposes an energy management control strategy for a Extended Range Electric Vehicle comprising an internal combustion engine, two electrical machines, and three clutches. The control strategy smoothly combines a rule-based strategy, extended with a battery state-of-charge (SoC) controller, with an instantaneous optimization algorithm based on equivalent consumption minimization strategy (ECMS). In addition to engine on/off logic, the rule based controller includes rules which are extracted from the global dynamic programming-based off-line optimization results. The control strategy is verified by means of computer simulation for different operating modes and certification driving cycles, and the simulation results are compared with the dynamic programming optimization results which are considered as globally optimal.

A dynamic programming-based algorithm is developed and used for off-line optimization of range extended electric vehicle power train control variables over standardized certification driving cycles. The aim is to minimize the fuel consumption subject to battery state-of-charge constraints and physical limits of different power train variables. The control variables to be optimized include engine torque and electric machine speed, as well as a variable that selects the power train operating mode. The optimization results are presented for four characteristic certification driving cycles and characteristic vehicle operating regimes including electric driving during charge depleting mode, hybrid driving during charge sustaining mode, and combined/blended regime.

The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent.

To enable smooth and low-risk autonomous driving in the presence of other road users, such as cyclists and pedestrians, appropriate predictive safe speed control strategies relying on accurate and robust prediction models should be employed. However, difficulties related to driving scene understanding and a wide variety of features influencing decisions of other road users significantly complexifies prediction tasks and related controls. This paper proposes a hierarchical neural network (NN)-based prediction model of pedestrian crossing behavior, which is aimed to be applied within an autonomous vehicle (AV) safe speed control strategy. Additionally, different single-level prediction models are presented and analyzed as well, to serve as baseline approaches.

In order to reduce air and noise pollution in urban environments, low emission zones (LEZ) are being introduced in many cities worldwide. This paper deals with design of a LEZ-anticipating control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) given in a P2-type parallel powertrain configuration. A control-oriented backward-looking model of the PHEV powertrain is used as a design basis. The core control strategy is based on combining a rule-based (RB) controller including an explicit battery state-of-charge (SoC) controller and an equivalent consumption minimization strategy (ECMS), and it is superimposed by generating an optimal SoC reference trajectory aimed at enabling pure electric driving through forthcoming LEZs and minimizing the overall fuel consumption. The optimal SoC reference trajectory is generated by minimizing its length over travelled distance.

Driving cycles are usually defined by vehicle speed as a function of time and they are typically used to estimate fuel consumption and pollutant emissions. Currently, certification driving cycles are mainly used for this purpose. Since they are artificially generated, the resulting estimates and analyzes can generally be biased. In order to address these shortcomings, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded real-world data. To this end, this paper focuses on synthesis of multidimensional driving cycles using the Markov chain-based method and particularly on their validation. The synthesis is based on Markov chain of fourth order, where the road slope is accounted, as well. The corresponding transition probability matrix is implemented in the form of a sparse matrix parameterized with a rich set of recorded city bus driving cycles.

Since the Extended range electric vehicle (EREV) powertrain structure is based on different power sources, a key vehicle design activity is related to development of an optimal control strategy for achieving a high fuel economy potential. The central role in developing an optimized energy management strategy is related to availability of computationally-efficient, high-fidelity EREV powertrain model. This paper proposes a method for developing an extended quasi-static backward-looking EREV powertrain model, which when compared to traditional backward model accounts for powertrain transient effects through additional fuel and battery state-of-charge consumptions. The effects of powertrain transients are characterized by means of extensive simulations of dynamic forward-looking EREV powertrain model covering a wide array of possible powertrain transient scenarios.