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The effectiveness of regenerative braking strategies plays an important role in extending the driving range of electric vehicles. Since the driver is still an essential factor in levels 3 and 4 of intelligent electric vehicles, improving user acceptance and adoption of the braking control strategy is crucial. This paper puts forward a new regenerative braking strategy to find a compromise between optimal braking control performance and naturalistic regeneration performance while satisfying the maximum speed preference when driving between two-stop events. Unlike other similar works that only maximize regenerative braking energy while satisfying the physical limits of an electrified powertrain, this paper considers naturalistic regeneration performance. To achieve this, firstly, the power regenerated by three drivers is predicted with a long-horizon (30 seconds), using long-short-term memory networks (LSTM) and non-linear autoregressive exogenous model (NARX). Subsequently, an estimation of the energy recovery maximization rate is performed to give a perception of the naturalistic regeneration performance. As this performance varies, the deceleration planning employs three horizon scales of long, medium, and short, determined by the energy recovery maximization rate. Finally, dynamic programming (DP) is utilized to optimize a deceleration profile. The study utilizes real data of inverter efficiency, transmission efficiency, and motor-to-battery efficiency map. The outcome of this study shows that the proposed regeneration braking strategy is adaptive, improving regeneration efficiency by 39,6% for driver 1, 16% for driver 2, and 26% for driver 3, and forecasting the optimality of some deceleration behaviors.

This paper presents a control strategy to optimize both the vehicle speed and gearbox position on curved roads with the aim of maximizing fuel economy. Previous studies have focused only on the vehicle speed optimization during cornering. Combining vehicle speed and gearbox position optimization can significantly maximize the energy-saving for an Internal combustion engine (ICE) vehicle. The problem is formulated as two successive optimization problems. In the first one, based on the road map and traffic data, the vehicle’s optimal speed profile is calculated by minimizing the energy spent for the entire curve. In the second one, the gearbox position is optimized for fuel consumption minimization using realistic engine map. The presented control structure’s effectiveness was evaluated through co-simulation of Matlab/Simulink and CarSim in various driving scenarios. The results show that approximately 5.25% to 11.44 % of fuel savings can be achieved compared with a typical driver model.

This paper presents a vehicle speed planning system called the energy-optimal deceleration planning system (EDPS), which aims to maximize energy recuperation of regenerative braking of connected and autonomous electrified vehicles. A recuperation energy-optimal speed profile is computed based on the impending deceleration requirements for turning or stopping at an intersection. This is computed to maximize the regenerative braking energy while satisfying the physical limits of an electrified powertrain. To obtain smooth optimal deceleration speed profiles, optimal deceleration commands are determined by a parameterized polynomial-based deceleration model that is obtained by regression analyses with real vehicle driving test data. The design parameters are dependent on preview information such as residual time and distance as well as target speed. The key design parameter is deceleration time, which determines the deceleration speed profile to satisfy the residual time and distance constraints as well as the target speed requirement. The time-varying bounds of deceleration commands corresponding to the physical limits of the powertrain are deduced from realistic deceleration test driving. For validation and comparisons of the EDPS with different preview distances, driving simulation tests with a virtual road environment and vehicle-to-infrastructure connectivity are presented. It is shown that the longer preview distance in the EDPS, the more energy-recuperation. In comparison with driver-in-the-loop simulation tests, EDPS-based autonomous driving shows improvements in energy recuperation and reduction in trip time.

Driving vehicles according to eco-driving principles and techniques have significant impact on decreasing both fuel consumption and carbon dioxide (CO2) emissions. In addition to some kind of technical and/or mechanical features brought by today's new generation vehicles, driver behavior is also one of the greatest factors affecting the fuel consumption. Many studies show that the effect of eco-driving education on the drivers loses its impact in long term and there needs some sort of continuous monitoring and/or feedback mechanisms. This kind of driver monitoring becomes very critical especially in fleets composed of heavy-duty vehicles, such as municipality buses, truck fleets, etc. Moreover, in order to adapt behavior to drive more economically, information about instant fuel consumption has to be provided to the driver. Hence, in this paper, we introduce an eco-driving system in which data gathered from the controller area network (CANBus) of public transport vehicles are processed for both comparative and fair evaluation of bus drivers' eco-driving performance. Moreover, in-vehicle components of the system guide the drivers during their trips; provide feedbacks and real-time warnings considering the fuel consumption. Developed system was successfully deployed and evaluated in one of the public metrobus systems used by approximately 250000 passengers every day. Based on the 15-months evaluation period, the results are very promising in the sense that both drivers and operators found the system useful and the system provided fuel saving up to approximately 5% even in the short term of monthly comparisons.

The main question in eco-driving is - what speed or torque profile should the vehicle follow to minimize its energy consumption over a certain distance within a desired trip time? Various techniques to obtain globally optimal energy-efficient driving profiles have been proposed in the literature, involving optimization algorithms such as dynamic programming (DP) or sequential quadratic programming. However, these methods are difficult to implement on real vehicles due to their significant computational requirements and the need for precise a-priori knowledge of the scenario ahead. Although many predictions state that electric vehicles (EVs) represent the future of mobility, the literature lacks a realistic analysis of optimal driving profiles for EVs. This paper attempts to address the gap by providing optimal solutions obtained from DP for a variety of trip times, which are compared with simple intuitive speed profiles. For a case study EV, the results show that the DP solutions involve forms of Pulse-and-Glide (PnG) at high frequency. Hence, detailed investigations are performed to: i) prove the optimality conditions of PnG for EVs; ii) show its practical use, based on realistic electric powertrain efficiency maps; iii) propose rules for lower frequency PnG operation; and iv) use PnG to track generic speed profiles.

Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.

This paper proposed an eco-driving model for divisible electric platoons at a signalized intersection in the environment of cooperative vehicle-infrastructure systems (CVIS). Platoon releasing plan and signal control scheme are obtained through platoon splitting and speed guidance. One main contribution of the method is the proposal of a new multi-objective optimization model with the objective functions aiming at minimizing platoon passing time and energy consumption, as well as the optimal solution is obtained by Matlab programs. According to the optimal scheme, the platoons are split up, and speed guidance is conducted. Combining with the phase plan, the platoons are released at the signalized intersection. The simulation of the proposed eco-driving platoon control model (EPCM) and actuated platoon control model (APCM) are conducted with Matlab and Simulation of Urban Mobility (SUMO) invoked by Python. The simulation results show that the average waiting time of vehicles decreased by 8.97%, the maximum queuing time of vehicles in four directions decreased from 38.1% to 54.3%, respectively, and the total energy consumption decreased by 21.3%, which indicate that the EPCM can effectively reduce the energy consumption of electric platoons while through the intersection. The passing efficiency can be ensured, and the average waiting time at the stop line can be reduced in the meantime.

Intelligent planning and accurate execution of connected automated vehicles (CAVs) enable not only improved traffic safety but also better fuel economy. This paper presents two longitudinal speed planning algorithms for fuel-saving driving on highways with varying road slopes. One is designed on the top of the model predictive control (MPC) and the other is called equivalent kinetic-energy and fuel conversion method. The MPC algorithm solves the optimal speed profile in a receding finite horizon with repeated optimization, which is numerically solved by the Legendre pseudospectral method. The latter is designed based on an instantaneous optimization, which considers vehicle kinetic energy an admissible power source, and then minimizes a weighted sum of fuel energy and kinetic energy. This strategy is capable of generating analytical rules to get the economical speed as well as the corresponding commands of the engine, transmission, and brake. The two algorithms are featured by near-global optimization and local optimization, respectively. Their performances in fuel economy and computational load are quantitatively explored and compared in order to distinguish the potential of real implementation in CAVs.