Supercapacitor Electro-Mathematical And Machine Learning Modelling For Low Power Applications

Low power electronic systems, whenever feasible, use supercapacitors to store energy instead of batteries due to their fast charging capability, low maintenance and low environmental footprint. To decide if supercapacitors are feasible requires characterising their behaviour and performance for the load profiles and conditions of the target. Traditional supercapacitor models are electromechanical, require complex equations and knowledge of the physics and chemical processes involved. Models based on equivalent circuits and mathematical equations are less complex and could provide enough accuracy. The present work uses the latter techniques to characterize supercapacitors. The data required to parametrize the mathematical model is obtained through tests that provide the capacitors charge and discharge profiles under different conditions. The parameters identified are life cycle, voltage, time, temperature, moisture, Equivalent Series Resistance (ESR) and leakage resistance. The accuracy of this electro-mathematical model is improved with a remodelling based on artificial neuronal networks. The experimental data and the results obtained with both models are compared to verify and weigh their accuracy. Results show that the models presented determine the behaviour of supercapacitors with similar accuracy and less complexity than electromechanical ones, thus, helping scaling low power systems for given conditions

New Energy Management Systems for Battery Electric Vehicles with Supercapacitor

Recently, the Battery Electric Vehicle (BEV) has been considered to be a proper candidate to terminate the problems associated with fuel-based vehicles. Therefore, the development and enhancement of the BEVs have lately formed an attractive field of study. One of the significant challenges to commercialize BEVs is to overcome the battery drawbacks that limit the BEV’s performance. One promising solution is to hybridize the BEV with a supercapacitor (SC) so that the battery is the primary source of energy meanwhile the SC handles sudden fluctuations in power demand. Obviously, to exploit the most benefits from this hybrid system, an intelligent Energy Management System (EMS) is required. In this thesis, different EMSs are developed: first, the Nonlinear Model Predictive Controller (NMPC) based on Newton Generalized Minimum Residual (Newton/GMRES) method. The NMPC effectively optimizes the power distribution between the battery and supercapacitor as a result of NMPC ability to handle multi-input, multi-output problems and utilize past information to predict future power demand. However, real-time application of the NMPC is challenging due to its huge computational cost. Therefore, Newton/GMRES, which is a fast real-time optimizer, is implemented in the heart of the NMPC. Simulation results demonstrate that the Newton/GMRES NMPC successfully protects the battery during high power peaks and nadirs. On the other hand, future power demand is inherently probabilistic. Consequently, Stochastic Dynamic Programming (SDP) is employed to maximize the battery lifespan while considering the uncertain nature of power demand. The next power demand is predicted by a Markov chain. The SDP approach determines the optimal policy using the policy iteration algorithm. Implementation of the SDP is quite free-to-launch since it does not require any additional equipment. Furthermore, the SDP is an offline approach, thus, computational cost is not an issue. Simulation results are considerable compared to those of other rival approaches. Recent success stories of applying bio-inspired techniques such as Particle Swarm Optimization (PSO) to control area have motivated the author to investigate the potential of this algorithm to solve the problem at hand. The PSO is a population-based method that effectively seeks the best answer in the solution space with no need to solve complex equations. Simulation results indicate that PSO is successful in terms of optimality, but it shows some difficulties for real-time application

Real-time Energy Management of a Battery Electric Vehicle Hybridized with Supercapacitor

The increased interest in electric vehicles (EVs) in the recent years has intrigued numerous research, on improving efficiency and reducing ownership costs of these vehicles. As the battery in EVs is the sole energy provider, it is exposed to degradation due to high peaks and rapid fluctuations in the power demanded by the driver. Therefore, integrating a supercapacitor (SC) pack into the energy storage system of these vehicles has been proposed as a potential solution; maintaining the battery as the main energy source of the vehicle while using the SC when exposed to high power peaks and power fluctuations. However, just like any other hybrid system, the maximum benefit of this integration can only be exploited when applying a proper energy management controller. Various energy management controllers have been used for these systems through the literature; ranging from simple rule based control strategies to more complex optimal control approaches. In this thesis, nonlinear model predictive control (NMPC) strategies have been designed as energy management controllers for battery-SC hybrid energy storage systems (HESSs) in a Toyota Rav4EV. Although traditionally used in applications dealing with slow dynamics like process control, with the rapid improvement in electric control units (ECUs) in the recent years, NMPCs have received a great deal of attention in areas with systems of faster dynamics, including the automotive sector. However, the question still needs to be addressed whether NMPC can demonstrate performance improvement over other state-of-the-art controllers, while maintaining computational efficiency necessary for automotive real-time applications. This investigation has been conducted through Model-in-the-Loop (MIL) simulating and Hardware-in-the-Loop (HIL) testing on the NMPC energy management strategies designed in this work. The NMPC uses a control-oriented model of the system, some form of the future trip prediction, and an optimization solver to find the optimal power split between the battery and SC at each time step during the trip. The designed NMPC has been compared to other state-of-the-art controllers in the literature. A number of methods for future trip prediction have also been studied through the thesis and the NMPC shows to outperform other controllers even with no prior knowledge of the future trip whatsoever. The results obtained through HIL testing on a dSPACE ECU indicate that upon carefully choosing the prediction and control horizon length, as well as the maximum number of iterations allowed, the execution time for NMPC falls far below the necessary sampling time of 10 milliseconds in vehicle control. The correlation between each of these parameters and turn-around time have been presented; constructing a benchmark for NMPC design