Vehicle-to-Grid (V2G) enables electric vehicles to provide flexibility to the power system through bidirectional charging, but practical control of vehicle fleets remains challenging due to heterogeneous vehicle fleets, power capacity constraints and the need to regard battery degradation. This paper proposes a fleet-level charging and discharging strategy that combines Reinforcement Learning (RL) for strategic decision-making with Quadratic Programming (QP) for real-time power allocation under physical power capacity constraints. The RL agent determines high-level charging objectives, while the QP controller ensures feasible and coordinated power distribution among vehicles. Deep Q-Networks (DQN) are used as the RL method, and it is shown that training convergence is improved by employing n-step learning. A compact feature representation combined with radial basis functions enables training times on the order of one minute on a standard PC, making the approach suitable for practical use.The proposed framework supports parallel participation in energy and balancing markets without relying on explicit future information. The approach is evaluated through a simulation study based on a heavy-duty vehicle fleet use case and real electricity price data. The results demonstrate stable training behavior and economically viable operation under realistic conditions. In addition to real-world control, the simulation study shows that the RL–QP framework is also useful for techno-economic evaluation of V2G systems.

This paper presents a method for optimizing power allocation within a fleet of Electric Vehicles (EVs), addressing the challenge of achieving cost-effective fleet charging. The proposed Quadratic Programming (QP)–based power splitting approach can handle bidirectional charging (V2G). The QP formulation prioritizes EVs according to their required charging power, defined as the ratio between remaining energy demand and available charging time, while also enforcing power capability constraints at both the system and vehicle levels. As a case study, the method is applied to a fleet of electric city distribution trucks. The fleet is assumed to generate revenues from two Nordic electricity markets: the Day-Ahead (DA) energy market and the Frequency Containment Reserve (FCR) balancing market, where compensation is proportional to the power capacity offered. Revenues from DA and FCR are assessed in parallel. The results indicate that bidirectional charging offers significant economic potential compared to unidirectional charging.

In this paper, we present a framework for combined path and motion trajectory planning for the purpose of coordinating fully automated vehicles in confined sites. The path planning component utilizes a Monte-Carlo tree search approach for computing the vehicle paths and the motion trajectory component utilizes a two-stage optimization-based algorithm that optimizes the state and input trajectories for all vehicles while avoiding inter-vehicle conflicts. The motion trajectories are tracked by a low-level controller and both the path and motion trajectories are recomputed based on the feedback signals. The performance of the framework is validated through numerical simulations and results show both improved energy efficiency and productivity.

The placement of electric vehicle charging stations (EVCSs), which encourages the rapid development of electric vehicles (EVs), should be considered from not only operational perspective such as minimizing installation costs, but also user perspective so that their strategic and competitive charging behaviors can be reflected. This paper proposes a methodological framework to consider crowdedness and individual preferences of electric vehicle users (EVUs) in the selection of locations for fast-charging stations. The electric vehicle charging station placement problem (EVCSPP) is solved via a decentralized game theoretical decision-making algorithm and $k$ -means clustering algorithm. The proposed algorithm, referred to as $k$ -GRAPE, determines the locations of charging stations to maximize the sum of utilities of EVUs. In particular, we analytically present that 50% of suboptimality of the solution can be at least guaranteed, which is about 17% better than the existing game theoretical based framework. We show a few variants to describe the utility functions that may capture the difference in preferences of EVUs. Finally, we demonstrate the viability of the decision framework via three real-world data-based experiments. The results of the experiments, including a comparison with a baseline method are then discussed.

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading–unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. The primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing.

In road freight transport, the emerging technologies such as automated driving systems improve the mobility, productivity and fuel efficiency. However, the improved efficiency is not enough to meet environmental goals due to growing demands of transportation. Combining automated driving systems and electrified propulsion can substantially improve the road freight transport efficiency. However, the high cost of the battery electric heavy vehicles is a barrier hindering their adoption by the transportation companies. Automated driving systems, requiring no human driver on-board, make the battery electric heavy vehicles competitive to their conventional counterparts in a wider range of transportation tasks and use cases compared to the vehicles with human drivers. The presented data identify transportation tasks where the battery electric heavy vehicles driven by humans or by automated driving systems have lower cost of ownership than their conventional counterparts. The data were produced by optimizing the vehicle propulsion system together with the loading/unloading schemes and charging powers, with the objective of minimizing the total cost of ownership on 3072 different transportation scenarios, according to research article "Impact of automated driving systems on road freight transport and electrified propulsion of heavy vehicles" (Ghandriz et al., 2020) [2]. The data help understanding the effects of traveled distance, road hilliness and vehicle size on the total cost of ownership of the vehicles with different propulsion and driving systems. Data also include sensitivity tests on the uncertain parameters.

The technological barriers to automated driving systems (ADS) are being quickly overcome to deploy on-road vehicles that do not require a human driver on-board. ADS have opened up possibilities to improve mobility, productivity, logistics planning, and energy consumption. However, further enhancements in productivity and energy consumption are required to reach CO2-reduction goals, owing to increased demands on transportation. In particular, in the freight sector, incorporation of automation with electrification can meet necessities of sustainable transport. However, the profitability of battery electric heavy vehicles (BEHVs) remains a concern. This study found that ADS led to profitability of BEHVs, which remained profitable for increased travel ranges by a factor of four compared to that of BEHVs driven by humans. Up to 20% reduction in the total cost of ownership of BEHVs equipped with ADS could be achieved by optimizing the electric propulsion system along with the infrastructure for a given transportation task. In that case, the optimized propulsion system might not be similar to that of a BEHV with a human driver. To obtain the results, the total cost of ownership was minimized numerically for 3072 different transportation scenarios that showed the effects of travel distance, road hilliness, average reference speed, and vehicle size on the incorporated electrification and automation, and compared to that of conventional combustion-powered heavy vehicles.

Optimal energy management strategies of hybrid vehicles are computationally expensive when considering the entire trip ahead rather than a short upcoming horizon. Considering the entire representative trip is already needed in concept design stages of the vehicle. In order to come up with an appropriate design while minimizing the total ownership cost the energy management strategies must already be used together with early concept evaluations. To investigate the possibility of replacing the optimal energy management with simpler approaches, here, the sensitivity of optimal solution to some of vehicle parameters and traffic flow is studied. It is seen that a simpler approach, i.e. an instantaneous optimization, can be used, in case of smooth traffic flow, since the gain of optimal strategy in reduction of operational cost is less than 4% for different vehicle hardware setup and for selected representative driving cycle. Dynamic programming is used as a solution method for finding the optimal strategy.

Many researches have been focused on vehicle routing problem during past decades where subject vehicles are previously fully designed and ready to start operation. Further, extensive studies have been done on powertrain design irrespective of the routes where the vehicle is going to be employed. In the present paper, we try to define a new branch of problems where the vehicle design, in particular its propulsion system and loading capacity, is treated simultaneously with the routing problem. The focus is on optimization based design of heterogeneous electric truck fleet to perform a prescribed task with a lowest cost on an available set of routes. The approach is illustrated in a simple case study problem. It is shown that long heavy combination vehicles are energy-efficient but not cost-optimal on short routes.

This paper presents a novel predictive control scheme for energy management in hybrid trucks that drive autonomously on the highway. The proposed scheme uses information from GPS together with information about the speed limits along the planned route to schedule the charging and discharging of the battery, the vehicle speed, the gear, and when to turn off the engine and drive electrically. The proposed control scheme divides the predictive control problem into three layers that operate with different update frequencies and prediction horizons. The top layer plans the kinetic and electric energy in a convex optimization problem. In order to avoid a mixed-integer problem, the gear and the switching decision between hybrid and pure electric mode are optimized in a lower layer in a dynamic program whereas the lowest control layer only reacts on the current state and available references. The benefits of the proposed predictive control scheme are shown by simulations between Frankfurt and Koblenz. The simulations show that the predictive control scheme is able to significantly reduce the mechanical braking, resulting in fuel reductions of 4% when allowing an over and under speed of 5km/h.

This paper discusses the feasibility of electrifying medium to heavy urban goods distribution trucks. As a case study, an existing transport system in the Swedish city of Gothenburg is used. The project is a joint research effort between a vehicle OEM, an electric utility, a fleet operator, the Swedish Transport Administration and two research organizations.One main objective is to determine if and when different electrified powertrains are cost efficient to the end user. The results indicate that by 2015 conventional powertrains are still probably the most cost effective alternative in all applications studied. But in 2025, electrified powertrains are most cost efficient for most transport scenarios. These results indicate a transition in preferred powertrain technology for urban trucks within the coming ten years. It is important to point out that this result may not be general. Driving patterns, energy price developments and technology maturity of components such as batteries and motors greatly influence the total cost of ownership and large regional differences in when such a transition may occur are expected.In addition to the total cost of ownership, important issues for a successful deployment are policies (e.g. restricting access to urban areas for noisy and polluting vehicles), information and communication solutions (e.g. adapted route planning), access to a cost effective charging infrastructure (and low-carbon electricity production) and new business models. These must all be developed in parallel to the vehicle and powertrain technology. The large number of different stakeholders involved in this transition is also a challenge in itself.

This paper studies convex optimization and modelling for component sizing and optimal energy management control of hybrid electric vehicles. The novelty in the paper is the modeling steps required to include a battery wear model into the convex optimization problem. The convex modeling steps are described for the example of battery sizing and simultaneous optimal control of a series hybrid electric bus driving along a perfectly known bus line. Using the proposed convex optimization method and battery wear model, the city bus example is used to study a relevant question: is it better to choose one large battery that is sized to survive the entire lifespan of the bus, or is it beneficial with several smaller replaceable batteries which could be operated at higher c-rates?

With the topic of plug-in HEV city buses, this paper studies the highly coupled optimization problem of finding the most cost efficient compromise between investing in onboard electric powertrain components and installing a charging infrastructure along the bus line. The paper describes how convex optimization can be used to find the optimal battery sizing for a series HEV with fixed engine and generator unit and a fixed charging infrastructure along the bus line. The novelty of the proposed optimization approach is that both the battery sizing and the energy management strategy are optimized simultaneously by solving a convex problem. In the optimization approach the power characteristics of the engine-generator unit are approximated by a convex, second order polynomial, and the convex battery model assumes quadratic losses. The paper also presents an example for a specific bus line, showing the dependence between the optimal battery sizing and the number of charging stations on the bus line.

An essential part of Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) is the Energy Storage System (ESS). This paper describes a tool, named Tool for Energy Storage System Synthesis (TESSS), that automatically transforms ESS demands to cost-effective ESS design candidates. The following statements characterise the tool: 1) mathematical modelling and optimisation are essential ingredients; the handled ESS technologies are batteries of various types, supercapacitors and combined ESSs. Combined ESSs include both battery and supercapacitor cells. 2) the optimal design is pointed out by a cost function and requirements. The cost function includes investment cost, wear cost and costs due to energy losses. Examples of requirements are minimal power, maximum package volume and voltage. 3) TESSS is user friendly and extremely computation efficient owing to its compiled C code. To exemplify the practical use of the tool, two case studies are also presented in this paper.

This paper deals with the optimisation of the energy storage system of a parallel hybrid plug-in car. The objective is to study how the electric driving range is influenced by factors such as package volume and auxiliary power consumption. Simulation and optimisation are used to find the most cost effective battery arrangement for specified electric driving range, volume, power and lifetime requirements. Two battery technologies, NiMH and Li-ion, are included in the simplified case study. The result indicates that the battery cost and size are highly dependent on the required electric driving range. It is found that a 3-5km electric driving range is possible with a minor cost increase. An electric driving range of 15-25km is possible, but results in a more bulky, and much more costly, battery system. It is important to point out that the results are not general. The electric driving range can be significantly extended by decreasing the roll and/or the air resistance.

The international economy, in the beginning of the 20th century, is characterized by uncertainty about the supply and the price of oil. Together with the fast decrease of electrical propulsion component prices, it becomes more and more cost effective to develop vehicles with alternative powertrains. This paper focuses on two questions: Are alternative powertrains especially cost effective for specific applications?; How does an increased fossil fuel price influences the choose of powertrain? To assess these questions, a computer tool named THEPS, developed in a Ph.D. project, is used. Three applications and three scenarios are analysed. The applications, a car, a city bus and an intercity bus, are vehicles all assumed to operate in Sweden. One scenario represents year 2005, the other two year 2020. The two future scenarios are characterized by different fossil fuel prices. The study, presented in the paper, indicates that alternative powertrains can be competitive from a cost perspective, in some applications, already in year 2005. It is for example cost effective to equip a city bus, running in countries with a high fuel price, with a hybrid powertrain. The study also indicates that pure electric, hybrid and/or fuel cell cars will probably be a more cost effective choice than conventional cars in year 2020. Another indication is that it will not be clear which powertrain concept to choose. The reason is that many cost effective powertrain concepts will be offered. The best choice will depend on the application.

This paper deals with the effective brake energy regeneration of parallel hybrid electric vehicles. A computational procedure to maximise the regenerated brake energy during braking is presented. Mathematical modelling, optimisation and computer simulation are essential tools. In addition to the computational procedure, an extensive sensitivity analysis is carried out on a medium-size car. The relation between the regenerated brake energy and the following properties are surveyed: rear and front electric machine efficiency; vehicle stability restriction; ICE drag torque; brake system characteristic; battery power; transmission ratios and battery resistance. Two driving cycles are evaluated: SFTP SCO3 includes more aggressive decelerations than the other cycle, NEDC. By switching between front-wheel and all-wheel drive and changing clutch arrangements, four different powertrain configurations are derived. All previously presented properties are analysed for each driving cycle and powertrain configuration. The results show, for example, that electric all-wheel drives tend to regenerate more brake energy compared with front-wheel drives in aggressive driving cycles.

This paper presents a Modelica library made for the simulation of Hybrid Electrical Vehicles (HEVs). The library consists of vehicle models, component models and models of surrounding systems. An overview of the models within the library is given and some models are described more in detail. The major purpose of the vehicle models is to predict vehicle characteristics, especially fuel consumption, for a given vehicle and driving cycle. Modelica has been found useful for the simulation of HEVs.

Hybrid Vehicles (HVs) are proposed as a candidate to conventional vehicles. Characteristic for HVs is the use of a temporary energy buffer. But the control of the Primary Power Unit (PPU), e.g. an otto engine or a fuel cell, is much more complex than in conventional vehicles. This paper deals with a method to design a control algorithm for the PPU in a HV. Here, a Finite State Machine (FSM) is used as the control architecture and is trained using an evolutionary algorithm. A control sequence is derived from a simplified vehicle model and used to train the FSM. The evolved FSM is then used to control a much more accurate computer model of a vehicle. A special technique, which is introduced in this paper, has been developed, which considerably increases the speed of evolution. A realistic example of an implementation of a control algorithm in a virtual prototype is also presented. Key-words: Hybrid vehicle, Control strategy, Control algorithm, Propulsion control, Finite State Machine, Evolutionary algorithm.

Modelica, and previously Dymola, has been used at Chalmers University of Technology, at Machine & Vehicle Design (MVD) and at Control & Automation Laboratory (CAL), for automotive engineering research for some years, 1995-2000. Primarily it has been used for automotive powertrain engineering, both fuel consumption and emissions analyses and faster transients, like take-offs, gearshifts, shunt and shuffle etc. The paper presents the basic motives for equation oriented modelling at Chalmers, both in research projects as well as in undergraduate/graduate education. The paper also gives an introduction to some of the present research projects involving Modelica. The research projects includes mainly powertrain modelling of both conventional powertrains as well as hybrid powertrains. The paper tries to point out how Modelica have been used and what future use that could been foreseen. In undergraduate teaching Modelica/Dymola has been used for studying vehicle handling and will be introduced in modelling courses at CAL this winter.