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. INDEX TERMS Dynamic programming, eco-driving, electric vehicles, speed profile, optimization, pulse-and-glide.

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V2X connectivity and powertrain electrification are emerging trends in the automotive sector, which enable the implementation of new control solutions. Most of the production electric vehicles have centralized powertrain architectures consisting of a single central on-board motor, a single-speed transmission, an open differential, half-shafts, and constant velocity joints. The torsional drivetrain dynamics and wheel dynamics are influenced by the open differential, especially in split-𝝁 scenarios, i.e., with different tire-road friction coefficients on the two wheels of the same axle, and are attenuated by the socalled anti-jerk controllers. Although a rather extensive literature discusses traction control formulations for individual wheel slip control, there is a knowledge gap on: a) model based traction controllers for centralized powertrains; and b) traction controllers using the preview of the expected tire-road friction condition ahead, e.g., obtained through V2X, for enhancing the wheel slip tracking performance. This study presents nonlinear model predictive control formulations for traction control and anti-jerk control in electric powertrains with central motor and open differential, and benefitting from the preview of the tire-road friction level. The simulation results in straight line and cornering conditions, obtained with an experimentally validated vehicle model, as well as the proof-of-concept experiments on an electric quadricycle prototype, highlight the benefits of the novel controllers.

Battery/Ultracapacitor (UC) Hybrid Energy Storage Systems (HESS) for Electric Vehicles (EVs) have been frequently proposed in the literature to increase battery cycle life. The HESS consists of a Power Management Strategy (PMS) and an Energy Management Strategy (EMS). Existing EMS are quite empirical, such as setting constant target UC energy levels regardless of load. This work presents an improved complete HESS management strategy. The EMS involves a more comprehensive method of setting the target UC energy level using a speed-dependent band. This allows the UC to achieve two goals – contain sufficient energy for future accelerations and have sufficient space for capturing energy from future regenerative braking – without knowledge of the future drive profile. The PMS involves a speed-dependent battery power limit, which also achieves two goals – better UC utilization and allowing the battery to supply the steady state power. Simulations show existing works cannot achieve the four goals simultaneously unless their UCs are sized twice as large compared to the proposed rule-based HESS. In addition, the proposed HESS extends battery cycle life by up to 42% compared to a battery-only system. Lastly, a reduced-scale experiment was built to show that the proposed HESS is able to run in real-time.

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