Application of an Optimal Control Algorithm on ABS for an Electric Vehicle

<div class="section abstract"><div class="htmlview paragraph">Autonomous driving related technologies have become a hot topic in academia and industry. Planning control is one of the core technologies of autonomous driving, which is conducive to vehicles safe and efficient driving. This paper proposes a novel optimal speed control algorithm, which considers the power system's energy consumption, the speed limit on the road, and the safe distance of the vehicle in front. An optimal speed control model of “From battery to wheel” energy consumption is established by constructing a performance index function based on the best-fitting formula of motor power, motor speed and torque. Based on the optimal control principle, the fourth-order ordinary differential equation of the speed control model is established, based on the indirect adjoining approach, the speed control model under the restriction of the road speed limit and safe distance of the preceding vehicle is derived and the analytical expression is obtained. Under the restriction of the road speed limit and safe distance of the preceding vehicle, the model predictive control method is introduced, the urban working conditions in the NEDC cycle are selected for simulation, and the optimal control input is solved in real-time, compared with the intelligent driver driving strategy, this algorithm is proved to be superior in the aspect of vehicle speed optimization control. Finally, the influence mechanism of the road speed limit constraint on the vehicle speed control is analyzed. This paper realizes the design of the optimal speed control algorithm for energy consumption under various constraints. The results can be used to guide the energy-saving driving of vehicles, and provide theoretical guidance for ecological driving technology in the context of intelligent transportation.</div></div>

Mobile green billboards on the road? The impact of electric ride-hailing vehicles on electric vehicle promotion and air quality

1 A Survey on Computational Optimal Control.- Issues in the Direct Transcription of Optimal Control Problems to Sparse Nonlinear Programs.- Optimization in Control of Robots.- Large-scale SQP Methods and their Application in Trajectory Optimization.- Solving Optimal Control and Pursuit-Evasion Game Problems of High Complexity.- 2 Theoretical Aspects of Optimal Control and Nonlinear Programming.- Continuation Methods In Boundary Value Problems.- Second Order Optimality Conditions for Singular Extremals.- Synthesis of Adaptive Optimal Controls for Linear Dynamic Systems.- Control Applications of Reduced SQP Methods.- Time Optimal Control of Mechanical Systems.- 3 Algorithms for Optimal Control Calculations.- Second Order Algorithm for Time Optimal Control of a Linear System.- An SQP-type Solution Method for Constrained Discrete-Time Optimal Control Problems.- Numerical Methods for Solving Differential Games, Prospective Applications to Technical Problems.- Construction of the Optimal Feedback Controller for Constrained Optimal Control Problems with Unknown Disturbances.- Repetitive Optimization for Predictive Control of Dynamic Systems under Uncertainty.- Optimal Control of Multistage Systems Described by High-Index Differential-Algebraic Equations.- A New Class of a High Order Interior Point Method for the Solution of Convex Semiinfinite Optimization Problems.- A Structured Interior Point SQP Method for Nonlinear Optimal Control Problems.- 4 Software for Optimal Control Calculations.- Automated Approach for Optimizing Dynamic Systems.- ANDECS: A Computation Environment for Control Applications of Optimization.- Application of Automatic Differentiation to Optimal Control Problems.- OCCAL: A mixed symbolic-numeric Optimal Control CALculator.- 5 Applications of Optimal Control.- A Robotic Satellite with Simplified Design.- Nonlinear Control under Constraints of a Biological System.- An Object-Oriented Approach to Optimally Describe and Specify a SCADA System Applied to a Power Network.- Near-Optimal Flight Trajectories Generated by Neural Networks.- Performance of a Feedback Method with Respect to Changes in the Air-Density during the Ascent of a Two-Stage-To-Orbit Vehicle.- Linear Optimal Control for Reentry Flight.- Steady-State Modelling of Turbine Engine with Controllers.- Shortest Paths for Satellite Mounted Robot Manipulators.- Optimal Control of the Industrial Robot Manutec r3.

The most important safety equipment for the operation of a vehicle is the brake equipment. This research attempts to develop an all-electric braking system that can be used in an electric vehicle. Since the drum brake requires lower actuation input than the disk brake in order to deliver the same amount of braking force. It seems logical to be used in the electric vehicle as far as minimizing energy usage is concerned. The proposed electric drum brake system conceptually is not new as it has been used as a trailer brake for a tractor-trailer vehicle. Adopting the trailer brake for a passenger vehicle requires proper downsizing and different braking control scheme. This paper presents the modelling and simulation results of the proposed electric drum brake. The main interest is its braking performance which can be investigated by the brake force profile or the brake force characteristic. The results are compared with the braking performance of the conventional hydraulic drum brake system which is using validated data from the literature. The simulation results show that the braking force of the electric drum brake system is two times higher than that is of the hydraulic drum brake system subjected to the same actuation force. The results from this paper will be valuable for the development of the controller in the future.

Optimal motion control for energy-aware electric vehicles

Optimal yaw moment control law for improved vehicle handling

The promotion of electric vehicles (EVs), triggered by environmental concerns, can also increase the flexibility of power systems through ancillary services, such as frequency regulation among others. Nevertheless, EV penetration has increased the concern regarding voltage drops in distribution grids. This concern has motivated researchers to examine EV reactive power provision to mitigate such problems. This work proposes a local market perspective to promote EV reactive power provision, enabling distribution system operators to control the voltage level using cost‐effective solutions. The authors propose the extension of a centralised control framework that schedules EV frequency regulation to optimise the reactive power provided by the same EVs. Additionally, extra power losses in chargers while EVs provide reactive power were investigated, and the associated cost in the economic evaluations were considered. A test‐case with the IEEE 33‐node distribution grid is used to assess the market potential of EV reactive power provision. This new service extends EV penetration in a cost‐efficient way without causing voltage problems. The simulation concludes that it is economically feasible to use EVs for reactive local provision with efficient chargers. These outcomes sustain the potential of promoting such new EV services through a proper market in distribution grids.

Fuzzy optimal tracking control for nonlinear underactuated unmanned surface vehicles

Applying optimization techniques to control systems remains a challenging task. Control system technology is emerging rapidly due to the high demands for commercialization in engineering and industrial fields, but existing optimization techniques are considered weak to cope with increasingly complex control problems. More powerful optimization techniques are urgently needed to catch up with the prerequisites for optimal control. Hence, this research is dedicated to the development of an improved optimization algorithm to solve the optimal thermoregulatory control problem for piping systems in a more efficient manner. As a research outcome, the Memetic Alligator Optimization (MeAgtrO) algorithm is proposed. On top of the mathematical hunting and relocating mechanisms, MeAgtrO adds several evolutionary operators that replicate satiety awareness, mating, generational alternation, and dispersed hunting. Unlike the standard optimizer, which only emphasizes global and local search transitions, these improved variants give the ability to shuffle, swap, replace, and disperse agent information for greater flexibility. Upon application to a simulated piping system to optimally control thermoregulation processes, MeAgtrO statistically outperformed the other compared algorithms, showing 100% accuracy, 99.99% precision, and 99.99% robustness in minimizing the tracking error, response time, and equipment burden of the system. MeAgtrO has been shown to have high processing speed for optimal application control, which corresponds to its superior convergence speed to stabilize at 40% of iterations. While showing satisfactory clustering properties, MeAgtrO also demonstrated the best step response with a rise time of 0.40s, settling time of 0.99s, 0 tracking error, 0% overshoot, and 0% undershoot. ABSTRAK: Penerapan teknik optimasi pada sistem kawalan adalah tugas mencabar. Teknologi sistem kawalan berkembang pesat disebabkan oleh permintaan tinggi bagi komersialisasi dalam bidang kejuruteraan dan industri, tetapi teknik optimasi sedia ada dianggap lemah dalam mengatasi masalah kawalan yang semakin kompleks. Teknik optimasi lebih kuat diperlukan dengan segera untuk memenuhi prasyarat kawalan optimal. Oleh itu, penyelidikan ini bertujuan bagi membangunkan algoritma optimasi yang lebih baik bagi menyelesaikan masalah kawalan termoregulator optimum pada sistem paip dengan lebih cekap. Penyelidikan ini mencadangkan algoritma Memetic Alligator Optimization (MeAgtrO). Selain dari mekanisme pemindahan dan pemburuan matematik, MeAgtrO memiliki beberapa pengendali evolusinari yang menggandakan kesedaran kenyang, mengawan, alternasi generasi, dan pemburuan tersebar. Berbeza dengan pengoptimuman standard, penekanan hanya pada peralihan global dan carian tempatan, ini membaiki varian dengan memberikan keupayaan menyusun semula, menukar, mengganti, dan menyebar maklumat ejen bagi fleksibiliti yang lebih besar. Apabila digunakan pada sistem paip bersimulasi bagi mengawal proses termoregulasi secara optimum, MeAgtrO secara statistik mengatasi algoritma lain, menunjukkan ketepatan 100%, kejituan 99.99%, dan kekuatan 99.99% dalam mengurangkan kesilapan pengesanan, masa tindak balas, dan beban sistem peralatan. MeAgtrO telah terbukti mempunyai kelajuan pemprosesan yang tinggi bagi aplikasi kawalan optimum, sejajar dengan kelajuan konvergensi yang lebih bagus bagi menstabilkan iterasi 40%. Di samping menunjukkan sifat kelompok yang memuaskan, MeAgtrO juga memiliki respons langkah terbaik kenaikan masa 0.40s, masa penyelesaian 0.99s, ralat pengesanan 0%, lebih sasaran 0%, dan kurang sasaran 0%.

<div class="section abstract"><div class="htmlview paragraph">The most used rotor material is gray cast iron (GCI), known for its susceptibility to corrosion. The impact of corrosion on the braking system is paramount, affecting both braking performance and the emission of particulate matter. The issue becomes more severe, especially when the brakes are left stationary or unused for extended durations in humid conditions, as seen with electric vehicles (EVs). Brake disc corrosion amplifies the risk of corrosion adhesion between contacting surfaces, leading to substantial damage, increased quantity and mass of non-exhaust particulate emissions, and decreased braking effectiveness. In addition, brake pads' friction material plays a crucial role in generating the necessary stopping force, creating friction that transforms kinetic energy into heat. However, heightened pressure during braking elevates rotor temperatures, contributing to the degradation of the friction material. This degradation manifests in decreased mechanical strength, heightened pad-to-rotor force, wear, and reduced braking efficiency. To address all these challenges and meet the stringent Euro7 particulate emission standards, we have developed controlled wear-resistant nitrocarburized layers on cast iron surfaces with customizable thicknesses, compositions, and porosity coupled with a new generation of post-oxidized layers referred to as Smart-ONC®. These layers, known for their remarkable "self-healing" capabilities, are formed through a controlled, in-situ post-oxidation process by incorporating additional metals into the oxide layer to enhance corrosion resistance and fortify the surface against damage and potential failures. the dyno tests validated the integrity of the FNC-Smart ONC (Ferritic nitrocarburizing-Smart oxidation of the nitrocarburized layer) layers, affirming their ability to preserve mechanical properties without experiencing wear, delamination, or cracking. Notably, FNC-Smart ONC discs demonstrate pad material accumulation, effectively sustaining braking performance as an additional protective interface between pads and discs.</div></div>

This paper analyzes the impact of model simplification on optimal control for electric and conventional engine-powered vehicles. An optimal control problem is formulated to minimize energy/fuel consumption subject to control input constraints and solved analytically using the Pontryagin's Minimum Principle. We found that simplified solutions without nonlinear aerodynamic drag are sub-optimal, and their loss of optimality increases with average travel speed. The energy/fuel savings of each control approach are evaluated using the intelligent driver model to capture driving behavior of a driver. Simulation case studies are presented for illustration.

Application of an H/sub 2/ optimal adaptive control algorithm as a power system stabilizer is described in this paper. The algorithm deals with disturbance attenuation in the sense of H/sub 2/ norm for nonlinear systems. Results of simulation and experimental studies suggest that the H/sub 2/ optimal control algorithm could be successfully used for the control of nonlinear systems such as synchronous generators.

This paper proposes a new scheme of online accurate estimation of wheel-rail adhesion coefficient and optimal adhesion antiskid control of heavy-haul electric locomotives (HHEL) based on sliding mode and asymmetric barrier Lyapunov function (ABLF) theory. To achieve optimal adhesion control of the HHEL, it is necessary to precisely estimate the wheel-rail adhesion coefficient. However, the adhesion coefficient is difficult to be measured with a conventional physical sensor. The first novelty of this paper is to design a smart adhesion coefficient sensor based on sliding mode observer (SMO). The perception of the adhesion coefficient is transformed into the observation of load torque of the traction motors, and the wheel-rail adhesion coefficient is further calculated by using the load torque observed value. The HHEL achieves maximum traction from operating in the optimal adhesion point. However, wheel skidding is most likely to occur at this point. According to the changing trend of the adhesive coefficient characteristic curve, the operating state of a locomotive can be divided into two regions: the stable and skid regions. The second novelty of this paper is the adaptation of ABLF to guarantee that the HHEL operated at a stable region and the optimal adhesion antiskid control of HHEL is achieved. Finally, the simulation and experimental results verify the feasibility and effectiveness of the proposed method.

With the development of UAV technology, the re-search of leader-follower formation tracking has been extensively studied due to its simple formation structure and communication connection. However, the high maneuverability and dynamics of UAVs make the formation tracking control much more complicated and difficult. In this paper, the optimal formation control algorithms are proposed for both near-equilibrium and general dynamic control cases considering the time-varying leader velocity and communication delays. In near-equilibrium control case, a linear optimal control strategy is proposed, and the function coefficient can be derived by using a backward recursion. Then, the result of the near-equilibrium case is extended to the general dynamic case where the leader moves at an uncertain time-varying velocity. In each sampling period, based on the updated state information of UAVs, the optimal formation control strategy can be obtained by the same algorithm design approach as the near-equilibrium case, and the function coefficient is the convergent value deduced by using a backward recursion manner.

In this article, an optimal steady-state voltage control framework is developed based on a novel linear voltage-power dependence deducted from Gaussian process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and, thus, suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control, which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions, which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of effective voltage control source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition.