Energy-Optimal Adaptive Cruise Control based on Hybrid Model Predictive Control with Mixed-Integer Quadratic Programming

Author(s): Muehlegger, Erich; Rapson, David | Abstract: This research project explores the plug-in electric vehicle (PEV) market, including both Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), and the sociodemographic characteristics of purchasing households. The authors use detailed micro-level data on PEV purchase records to answer two primary research questions. Their results confirm that low-income households exhibit a lower share of PEV purchases than they do for conventional, internal combustion engine (ICE) vehicles. Households with annual income less than $50,000 comprise 33 percent of ICE purchases and only 14 percent of PEVS. By comparison, high-income households earning more than $150,000 annually comprise only 12 percent of ICE purchases and 35 percent of PEV purchases over their sample period. Similarly, unsurprising patterns can be seen across ethnicities. For example, non-Hispanic Whites represent 41 percent of ICE purchases but 55 percent of PEV purchases, as compared to Hispanics (38 percent of ICE and 10 percent of PEVs) and African Americans (3 percent of ICEs and 2 percent of PEVS). These differences naturally raise questions about barriers to PEV adoption among low-income and minority ethnic populations. By comparing outcomes in the ICE, hybrid, and PEV markets across income and ethnic groups, the authors are able to test whether price discrimination and barriers to market access are higher in PEV markets for low-income and minority ethnic groups. The authors find that, overall, they are not, although there are mixed results for the used PEV market. In general, non-white, low-income populations face higher prices in the used PEV market, relative to a baseline, than they do in the new PEV market. While some people travel farther to buy used PEVs than they do to buy used ICE vehicles, there is not a pattern that would indicate systematic discrimination (e.g. Hispanics travel farther to buy used PHEVs but less far to buy used BEVs). While the authors admit that their empirical approach cannot control for all potential vehicle composition effects, the authors view their results as being most consistent with a market that provides access to all ethnicities and income groups.View the NCST Project Webpage

Model predictive control (MPC) has been considered as one important feed-forward optimal control strategy for ocean wave energy converter (WEC) targeted on power maximization. The capability of MPC to handle system constraints (ex. stroke, velocity, actuator limitations), and the availability to provide optimal solution for linear system provide potential for the implementation of such algorithm in the WEC control. However, currently, only active MPC control has been introduced for single and two-body WECs. Such control strategy may introduce negative power during the optimization process, since the power take-off (PTO) damping has no constraint. In this paper, we proposed a hybrid MPC strategy in limiting both the PTO damping force and PTO damping to avoid negative power generation during cost function minimization (negative power minimization) for the two-body WEC. The problem is formulated into a quadratic programming (QP) problem targeted at power maximization. However, the standard QP problem formulation cannot be directly applied to the semi-active control problem due to the PTO damping constraints. Therefore, the problem is reformulated as a Mixed-integer Quadratic Programming (MIQP) problem, which contains logical switch to select constraint matrices based on the sign of the relative velocity between the buoy and submerged body. The optimal solution is compared with those of the active MPC control strategy and the passive model with the same irregular wave input.

Piecewise affine modeling and hybrid optimal control of intelligent vehicle longitudinal dynamics for velocity regulation

Li-ion battery safety in electric buses is critically affected by rollover accidents, which can cause severe deformation of the bus superstructure. Such deformation may cause internal short circuits and trigger thermal runaway. In this context, this work investigates how different propulsion modes affect the safety of a bus superstructure under a rollover scenario in accordance with UNECE Regulation No. R66.02. This work studied an internal combustion engine (ICE) and electric propulsion systems, using the ICE chassis as the base for the electric bus. The study defines worst-case scenarios for both vehicle types, determines the center of gravity (CoG), and performs numerical simulations using the finite element method. The ICE and electric buses have CoGs of 5397.5 × 13.0 × 1617.4 and 4474.1 × 20.9 × 1682.2 mm, respectively. The ICE vehicle had a total weight of 15,836 kg and a reference energy of 5.0580 × 10 5 kg·m 2 /s 2 , while the electric vehicle had a total weight of 18,118 kg and a reference energy of 5.1145 × 10 5 kg·m 2 /s. For the rollover simulations, the unstable positions before tilting were set to 33.7° and 35° for the ICE and electric vehicles, respectively, and the models were rotated to ground contact at angular velocities of 1.75 and 1.79 rad/s. The results indicate that the ICE vehicle satisfied the regulatory requirements. However, the electric vehicle exhibited intrusion of up to 32 mm into the residual space when the same ICE structural concept and passenger configuration were applied. Therefore, some modifications were proposed, including capacity adjustments and alternative battery systems, to preserve the residual space during the rollover scenario. These results highlight the need to account for electric bus specific characteristics rather than directly converting ICE chassis designs.

BnB-DAQP: A Mixed-Integer QP Solver for Embedded Applications

Three-phase distribution network optimal load flow (T-OLF) cannot be exactly linearized to mixed integer second order cone programming(MI-SOCP) like single-phase branch flow based OLF. Hence, interior point based OLF (IP-OLF) and mixed integer nonlinear programming based OLF (MINLP-OLF) are both tried out to cope with T-OLF in this paper. For IP-OLF, a quadratic three-phase transformer model is proposed to guarantee IP-OLF applying constant Hessian matrix during iterative process. For MINLP-OLF, to gain better optimization results, a Mixed-Integer Quadratically-Constrained Quadratic Program model (MIQCQP) of T-OLF is formulated via binary scheme and big-M method. Numerical tests implemented on simple 4-bus system, IEEE 13-bus and IEEE 34-bus distribution test feeders verify the validity of proposed models and algorithms.

Sustainable development requires inter al the reduction of energy consumption and of traffic-induced pollutant emissions. Hybrid electric vehicles (HEVs) are one of the most promising approaches to decrease emissions. This paper considers the influence of hybridization of transport on energy consumption and emissions on single lanes of road traffic. We have developed a micro-simulation tool which integrates instantaneous consumption and emission models. We have modelled microscopic behaviour of vehicles using a full velocity difference model for longitudinal moving. Then, we have used two macroscopic energy consumption models (COPERT and HBEFA) and an instantaneous energy-consumption model concerning the conventional Internal Combustion Engine (ICE) vehicle to illustrate the relevance of microscopic modelling of energy consumption. Furthermore, we have compared the energy consumption of the HEV Toyota Prius with that of the conventional ICE vehicle. An emission model emissions from traffic (EMIT) was also implemented and extended in order to estimate HEV emissions. The model is used to quantify CO2 and CO emissions for the HEV Toyota Prius and the conventional ICE vehicle. Moreover, we have studied the influence of fleet hybridization level on energy consumption for congested and uncongested traffic flow state. HEVs can offer major environmental improvements as well as substantial reductions of energy consumption and road traffic emissions. Hybridization is a relevant solution to reduce energy consumption and emissions.

<div class="htmlview paragraph">The purpose of this system study was to compare the performance and fuel consumption of a pure fuel cell vehicle (<i>i.e.</i> with no battery included) with an internal combustion engine (ICE) vehicle of similar weight in different drive cycles. Both light and heavy duty vehicles are studied.</div> <div class="htmlview paragraph">For light duty vehicles, the New European drive cycle, NEDC [70/220/EEC], the FTP75 [EPA] and a Swedish driving pattern from the city of Lund [<span class="xref">Ericsson, 2000</span>] are utilised. The fuel consumption for these drive cycles was compared with ICE vehicles of similar weight, an Ibiza Stella 1.4 (year 2000) from Seat and a Volvo 960 2.5 E sedan (year 1995). For heavy duty vehicles, urban buses in this study, two drive cycles were employed, the synthetic CBD14 and the real bus route 85 from Gothenburg, Sweden.</div> <div class="htmlview paragraph">It can be concluded that marked improvements in fuel economy can be achieved for hydrogen-fuelled light and heavy duty vehicles. The fuel consumption of a small fuel cell vehicle was 50% less than the corresponding ICE vehicle in both the NEDC and the FTP75. With proper dimensioning of the system components, e.g. the engine, further reductions in fuel consumption can be achieved. The range of more than 500 km with 5 kg of hydrogen in a 345 bar fuel tank was comparable to an ICE vehicle. If the pressure is raised to 690 bar, a driving range of 600 km could be achieved. As the auxiliary system counteracts the increase in fuel cell efficiency, raising the minimum operating voltage from 0.6 to 0.75 V in a 50 kW fuel cell system, provides only a 5% reduction in fuel consumption. A fuel cell bus operated in the CBD14 and the bus route 85, compared with diesel-fuelled urban bus of similar weight, demonstrates a reduction in fuel consumption of 33 and 37 % respectively.</div>

Nonlinear Model Predictive Control approach in design of Adaptive Cruise Control with automated switching to cruise control

A mixed-integer second order cone program (MISOCP) formulation is proposed for solving Markowitz’s asset portfolio construction problem under a cardinality constraint. Compared with a standard alternative big-M linearly constrained formulation, our reformulation is solved significantly faster using state-of-the-art integer programming solvers. We consider learning methods that are based on the MISCOP formulation: cardinality-constrained Markowitz (CCM) solves the MISCOP for a given cardinality k and training set data of asset returns. We also find reinforcing evidence for factor model theory in the selection of factors to form optimal CCM portfolios. For large datasets in the absence of a hard-cardinality constraint, we propose a method (CCM-R) that is based on the continuous relaxation of our MISCOP, where k selected by rolling time window validation. In predictive performance experiments, based on historical stock exchange data, our learning methods usually outperform a competing extension of the Markowitz model that penalizes the L1 norm of asset weights.

This paper considers the power minimization problem in downlink of cloud radio access networks with limited fronthaul capacity. A joint design of beamforming, remote radio head (RRH) selection and RRH-user association that explicitly takes into account per- fronthaul capacity constraints is considered. The problem of interest is in fact a combinatorial program which is generally NP- hard. We naturally write the considered problem as a mixed integer program by introducing binary selection variables. The challenge is that even if these binary selection variables are relaxed to be continuous, the resulting problem is still nonconvex. For such a problem, finding a high- quality solution, rather than an optimal one, is a more realistic goal. Towards this end we propose two iterative algorithms to deal with combinatorial nature of the joint design problem. In the first method, by novel transformations, we iteratively approximate the continuous nonconvex constraints by convex conic ones using successive convex approximation framework. More explicitly the problem arrived at each iteration of the first method is a mixed-integer second order cone program (MISOCP) for which dedicated solvers are available. The second method is a simplified variant of the first one where we further relax the binary variables in each iteration to be continuous. That is to say, the second method merely requires solving a sequence of SOCPs. After convergence, we then perform a postprocessing procedure on the relaxed selection variables to search for a high-performance solution. Numerical results are presented to demonstrate the superiority of the proposed algorithms over existing methods based on sparse-inducing norm.

Eco-driving is a key strategy for reducing energy consumption and emissions in electric vehicles (EVs) and internal combustion engine (ICE) vehicles. However, research gaps remain regarding its effectiveness across different driving environments, vehicle types, transmission systems, and contexts. This research evaluates eco-driving efficiency in urban and interurban settings, comparing small (Caceres) and large (Madrid) cities and assessing EVs ICE with direct, manual, and automatic transmissions. The authors conducted a large-scale driving experiment in Spain, with over 500 test runs across different road types. Results in the large city show that eco-driving reduces energy consumption by 30.4% in EVs on urban roads, benefiting from regenerative braking, compared to 10.75% in manual ICE vehicles. Automatic ICE vehicles also performed well, with 29.55% savings in local streets. In interurban settings, manual ICE vehicles achieved the highest savings (20.31%), while EVs showed more minor improvements (11.79%) due to already optimized efficiency at steady speeds. The small city showed higher savings due to smoother traffic flow, while single-speed transmissions in EVs enhanced efficiency across conditions. These findings provide valuable insights for optimizing eco-driving strategies and vehicle design. Future research should explore AI-driven eco-driving applications and real-time optimization to improve sustainable mobility.

Uncertainties from distributed generations (DGs) make conventional reactive power optimization (RPO) methods difficult to be applied in the distribution network (DN). To settle this issue, a novel two-stage RPO strategy for active distribution network (ADN) based on extreme scenarios is proposed in this paper. Random variables are handled through extreme scenarios, then flexible and inflexible variables are decided in two different stages to adapt to DG’s randomness. Firstly, based on second order cone relaxation (SOCR), power flow equations are convexly relaxed to transform original model into mixed integer second order cone programming (MISOCP). Then, extreme scenario method (ESM) is employed to determine DG’s deployment. Finally, numerical tests are performed on a modified IEEE 33-bus DN to demonstrate the effectiveness and feasibility of the proposed strategy.

To properly describe and solve complex decision problems, research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important. We establish in this paper different Lipschitz-type continuity results about the optimal value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of the linear constraints. The obtained results extend some existing results for continuous quadratic programs, and, more importantly, lay the foundation for further theoretical study and corresponding algorithm analysis on mixed-integer quadratic programs.

This paper presents a promising solution to the problem of the bad environmental impacts and the high operating cost of the Internal Combustion Engine (ICE) gasoline powered vehicles. In Toronto, the capital of Ontario, the traffic jam is becoming a regular flavor of every day's commute that make the driving pattern featured with low speed and a lot of stops and goes. This driving pattern increases the pollution problem that already exists due to the large number of vehicles in Toronto streets and reduces the lifetime of the engine and the brakes leading to a more running cost. This study investigates the performance of a hybrid Fuel Cell (FC)/battery vehicle configuration, which is considered as one of the most promising clean vehicles in comparison with the traditional ICE vehicles. In this study, a model of an ICE mid-size vehicle was developed and validated against experimental acceleration tests. The ICE vehicle model was modified by replacing the ICE power-train with a FC and battery power-train while keeping the other vehicle parameters the same. A comparison between ICE and hybrid FC/battery vehicle configurations was conducted using a representative alignment load cycle in Toronto. It was found that the hybrid FC/battery configuration is much better than the ICE version in terms of emission, fuel economy, efficiency and speed tracking error due to the faster response of the control system.