On optimal mission planning for conventional and electric heavy duty vehicles

Ever-growing energy consumption and CO2 emissions due to the increase in road transport are major challenges that attract international attention, especially policy makers, logistic service providers and customers considering environmental, ecological and economic issues. Other negative side-effects caused by the growth of the road transport are the extensive economic and social costs because of traffic congestion. Thus, there is a strong motivation to investigate possible ways of improving transport efficiency aiming at achieving a sustainable transport, e.g. by finding the best compromise between resource consumption and logistics performance. The transport efficiency can be improved by optimal planning of the transport mission, which can be interpreted as optimising mission start and/or finish time, and velocity profile of the driving vehicle. This thesis proposes a bi-layer mission planner for long look-ahead horizons stretched up to hundreds of kilometers. The mission planner consists of logistics planner as its top level and eco-driving supervisor as its bottom level. The logistics planner aims at optimising the mission start and/or finish time by optimising energy consumption and travel time, subject to road and traffic information, e.g. legal and dynamic speed limits. The eco-driving supervisor computes the velocity profile of the driving vehicle by optimising the energy consumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in MPC framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. The mission planner has been applied to conventional and fully-electric powertrains. It is observed that total travel timeis reduced up to 5.5 % by optimising the mission start time, when keeping anaverage cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %

On Optimal Mission Planning for Vehicles over Long-distance Trips

This thesis proposes a mission planner for vehicles over long-distance trips, for finding the optimal trade-off between trip time, energy efficiency, anddriver comfort, subject to road information, traffic situations, and weather conditions. The mission planner consists of three components, i.e. logisticsplanner, eco-driving supervisor, and thermal and charging supervisor. The logistics planner aims at optimising the mission start and/or finish time byminimising energy consumption and trip time. The eco-driving supervisor computes the velocity profile of the driving vehicle, by optimising the energyconsumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in a model predictive control framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. Eco-driving has also been achieved for a vehicleconfronted with wind, by applying stochastic dynamic programming method. The thermal and charging supervisor regulates battery temperature and state of charge by coordinating the energy use of different thermal components. Within the thermal and charging supervisor design, a heat pump has been included for waste heat recovery purposes. Also, the charging stops have been optimally planned, in favour of energy efficiency and trip time. The performance of the proposed algorithms over a road with a hilly terrain is assessed using simulations. According to the simulation results, it is observed that total travel time is reduced up to 5.5 % by optimising the mission start time, when keeping an average cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %. Furthermore, total charging time and energy consumption are reduced by up to 19.4 % and 30.6 %, respectively by developing the thermal and charging supervisor, compared to a case without the heat pump activated and without charge point optimisation

Data preprocessing for machine-learning-based adaptive data center transmission

To enable optical interconnect fluidity in next-generation data centers, we propose adaptive transmission based on machine learning in a wavelength-routing network. We consider programmable transmitters that can apply N possible code rates to connections based on predicted bit error rate (BER) values. To classify the BER, we employ a preprocessing algorithm to feed the traffic data to a neural network classifier. We demonstrate the significance of our proposed preprocessing algorithm and the classifier performance for different values of N and switch port count

Computationally Efficient Approach for Preheating of Battery Electric Vehicles before Fast Charging in Cold Climates

This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation at low temperatures, a thermal management (TM) system has been considered, including a high-voltage coolant heater (HVCH) for the battery and cabin compartment heating. Accordingly, an optimal control problem (OCP) has been formulated in the form of a nonlinear program (NLP), aiming at minimising the total energy consumption of the battery. The main focus here is to develop a computationally efficient approach, mimicking the optimal preheating behavior without a noticeable increase in the total energy consumption. The proposed algorithm is simple enough to be implemented in a low-level electronic control unit of the vehicle, by eliminating the need for solving the full NLP in the cost of only 1Wh increase in the total energy consumption

Predictive velocity control in a hilly terrain over a long look-ahead horizon

This paper presents a computationally efficient velocity control of vehicles drivingin a possibly hilly terrain and over long look-ahead horizons that may stretch to hundreds ofkilometers. The controller decouples gear scheduling into an online optimization problem, fromthe remaining optimization problem that governs two real-valued states. One of the states, thetravel time, is adjoined to the objective by applying the necessary optimality conditions, whichresults into an online optimization problem that has kinetic energy as the single state. Finallyan inner approximation is proposed for the online problem to obtain a quadratic program thatcan be solved efficiently. The efficiency of the proposed controller is shown for different horizonlengths

Optimal Thermal Management and Charging of Battery Electric Vehicles over Long Trips

This paper studies optimal thermal management and charging of a battery electric vehicle driving over long distance trips. The focus is on the potential benefits of including a heat pump in the thermal management system for waste heat recovery, and charging point planning, in a way to achieve optimality in time, energy, or their trade-off. An optimal control problem is formulated, in which the objective function includes the energy delivered by the charger(s), and the total charging time including the actual charging time and the detour time to and from the charging stop. To reduce the computational complexity, the formulated problem is then transformed into a hybrid dynamical system, where charging dynamics are modelled in the domain of normalized charging time. Driving dynamics can be modelled in either of the trip time or travel distance domains, as the vehicle speed is assumed to be known a priori, and the vehicle is only stopping at charging locations. Within the hybrid dynamical system, a binary variable is introduced for each charging location, in order to decide to use or skip a charger. This problem is solved numerically, and simulations are performed to evaluate the performance in terms of energy efficiency and time. The simulation results indicate that the time required for charging and total energy consumption are reduced up to 30.6% and 19.4%, respectively, by applying the proposed algorithm

Time Optimal and Eco-Driving Mission Planning under Traffic Constraints

This paper addresses optimising a transport mission by controlling the mission start time and velocity profile of an electric vehicle (EV) driving in a hilly terrain, subject to legal and dynamic speed limits imposed by traffic congestion. To this end, a nonlinear program (NLP) is formulated, where the mission start time is allowed to vary within an interval and final time is kept free. The goal is to find the optimal trade-off between energy consumption and travel time, while allowing a flexibility in starting time and a certain variation of vehicle speed around an average. It is observed that total travel time is reduced up to 5.5% by adjusting the mission start time, when keeping an average cruising speed of about 75 km/h

Electric Vehicle Eco-driving under Wind Uncertainty

This paper addresses eco-driving of an electric vehicle driving in a hilly terrain under stochastic wind speed uncertainty. The eco-driving problem has been formulated as an optimisation problem, subject to road and traffic information. To enhance the computational efficiency, the dimension of the formulated problem has been reduced by appending trip time dynamics to the problem objective, which is facilitated by necessary Pontryagin\u27s Maximum Principle conditions. To cope with the wind speed uncertainty, stochastic dynamic programming has been applied to solve the problem. Moreover, soft constraints on speed limits (kinetic energy) have been considered in the problem by enforcing sharp penalties in the objective. To benchmark the results, a deterministic controller has also been obtained with the aim of investigating possible constraints violations due to the wind speed uncertainty. For the proposed stochastic controller the optimised speed trajectories always remain within the limits and the violation on the trip time limit is only 8%. On the other hand, the speed and trip time constraints violations for the deterministic controller are 21% and 25%, respectively

Computationally Efficient Algorithm for Eco-Driving Over Long Look-Ahead Horizons

This paper presents a computationally efficient algorithm for eco-driving along horizons of over 100km. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity (kinetic energy) and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables. To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin\u27s Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to average driver\u27s driving cycle, the energy savings of using the proposed algorithm is up to 11.60%

Optimal eco-driving of a heavy-duty vehicle behind a leading heavy-duty vehicle

We propose an eco-driving technique for a heavyduty ego vehicle that drives behind a leading heavy-duty vehicle. By observing a decrease in speed of the leading vehicle when driving uphill, its power capability is estimated and its future speed is predicted within a look-ahead horizon. The predicted speed is utilised in a model predictive controller (MPC) to plan the optimal speed of the ego vehicle such that its fuel consumption is minimised, while keeping a safe distance to the leading vehicle and reducing the need for braking. The effectiveness of the proposed technique is analysed in two case studies on real road topographies. By using the leading vehicle observer, fuel savings are achieved up to 8% compared to the case where the preceding vehicle is assumed to have a constant speed within the look-ahead horizon