Adopting energy-efficient driving practices can harness the full benefits of EVs. This work uses a multi-objective optimization strategy to perform eco-driving to reduce the energy consumption of EVs and to prolong the health of batteries. The problem jointly considers constraints of conflicting nature; such as traffic signals, preceding vehicles, limitations on speed and acceleration, checks on input torque and its rate of change and bounds on battery's SoC and charging/discharging rates. This research also explores how adhering strictly to one constraint may compromise other constraints. A comprehensive control strategy using MPC is adopted to formulate eco-driving as nonlinear programming and to achieve a realistic and optimal solution. The proposed strategy has successfully achieved eco-driving along with satisfying all the conflicting constraints in uncertain environmental conditions. Furthermore, results are compared with PMP to validate the optimal solution. SoH analysis indicates that the inclusion of battery-related constraints improves the battery's health. Finally, Lyapunov stability analysis is conducted to check the systems' stability with parametric uncertainty.

Vehicle with electrified powertrains exhibit degraded performance when operated in hot environments. When the operating and surrounding temperatures rise, an electric drive suffers from torque derating as its parameters change. This paper proposes a Linear Parameter Varying (LPV) based observer controller pair to address this problem. The feedback Field Oriented Control (FOC) is the most commonly adopted instantaneous torque control method for an electrified powertrain drive system. The flux and torque performance of a conventional feedback FOC deteriorates under the vast uncertainties in rotor and stator resistance due to temperature variations during Electric Vehicle (EV) operation. To cater for these uncertain scenarios, a robust closed-loop observer is designed to estimate the thermally derated torque and flux. The stability of the whole LPV scheme is established. The efficacy of the proposed algorithm is demonstrated for an EV operating in Federal Urban Driving Schedule (FUDS) with a dynamic temperature profile. The nonlinear simulation results confirm the LPV observer capability to successfully estimate the flux and derated torque in an EV drive system. The proposed technique, after validating in simulation environment, is verified experimentally on an Induction Machine (IM) drive controlled by NI myRIO-1900.

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS.

This letter studies the problem of safe control of sampled-data systems under bounded disturbance and measurement errors with piecewise-constant controllers. To this end, we first propose the High-Order Doubly Robust Control Barrier Function (HO-DRCBF) for continuous-time systems where the safety enforcing constraint is of relative degree 1 or higher. We then extend this formulation to sampled-data systems with piecewise-constant controllers by bounding the evolution of the system state over the sampling period given a state estimate at the beginning of the sampling period. We demonstrate the proposed approach on a kinematic obstacle avoidance problem for wheeled robots using a unicycle model. We verify that with the proposed approach, the system does not violate the safety constraints while accounting for robustness against both bounded disturbance and measurement errors.

Diesel engines are widely used for the propulsion of marine vehicles and are exposed to uncertain working environment. In this paper, a robust nonlinear controller is proposed for a marine diesel engine that employees sliding mode control theory. The robust controller aims to maintain the desired diesel engine speed performance under harsh sea environment. The sliding surface has been carefully chosen that minimises the error in both angular velocity and acceleration. Robust control algorithm development and its tuning are also discussed. The performance of the proposed nonlinear robust controller is investigated thoroughly and is compared with a classical Proportional–Integral–Differentiation controller with integral windup scheme. Simulation results show that the proposed super-twisting-algorithm-based sliding mode controller can effectively improve the speed performance of the marine diesel engine in transient and steady operating conditions.

Energy economizing using Connectivity-based eco-Routing and Driving (CeRD) is a key to conserve the resources of a fleet. This work takes maximum advantage by advising optimal energy consumption based route and velocity. Product delivery to several universities by group of Battery Electric Vehicles (BEVs), is considered. Problem contains certain constraints i.e., BEV load capacity, time window, resources restriction, speed bounds, battery charging/discharging limits and traffic. Simulation of Urban Mobility (SUMO) is used for the estimation of traffic and routes. Selection of route is based on energy consumption of traffic’s velocity. Once the traffic based energy consumptions are available, Dijkstra’s algorithm is then applied to select the route with least energy consumption. Clarke and Wright (C&W) algorithm is then applied to find the optimal sequence of visiting universities. Pontryagin’s Minimum Principle (PMP) is then applied to find the optimal velocity profiles for BEVs on the optimized routes. Results have shown 60-70% reduction in energy consumption along with improvement in charging rates as compared to the vehicles without CeRD assistance in different scenarios. Furthermore, results are validated for distance and time using online route planner, for energy efficiency using electric vehicles database and for energy consumptions using driver model based forward simulator.