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 PHEVs with Supercapacitor Using Nonlinear Model Predictive Control

The earth is facing the global warming phenomenon these days, and one of its main contributors is greenhouse gas emission. As transportation produces an enormous portion of greenhouse gasses and also due to the increasing price of oil, automotive companies are now motivated more than ever to manufacture more hybrid vehicles compared to conventional vehicles. Although hybrid vehicles decrease the fuel consumption and greenhouse gas emissions, the cost of ownership and short lifespan of batteries have always been a drawback for them. Comparing the merits and demerits of batteries and Supercapacitors (SC) convinced researchers to use a combination of both to utilize vehicles with, as none of them could replace the other completely. An energy management strategy is crucial for maximizing benefits from utilizing vehicles with a hybrid energy storage system. This study includes modelling an SC module and developing Nonlinear Model Predictive Control (NMPC) strategies for the Toyota Prius Plug-in Hybrid Electric Vehicle (PHEV) with SC. Enhancements in vehicles processing units have absorbed attentions into more complex energy management strategies like MPC. Model-in-the-Loop (MIL) simulations and Hardware-in-the-Loop (HIL) tests investigate the performance of the proposed strategies. HIL tests results suggest the prediction horizon lengths for that the proposed controllers can be real-time implementable. Moreover, the MIL simulations results investigate the performances of fuel consumption and lifespan of the battery. Repeating the MIL simulations for different scenarios guarantees the performance enhancement regardless of driver's behaviour. Using Nonlinear Model Predictive Controller (NMPC) as Energy Management Strategy (EMS) in this study shows improvements in fuel consumption and lifespan of the battery up to 7.4% and 62%, respectively. While hybridizing Energy Storage System (ESS) with Supercapacitor (SC) can achieve up to 47% reduction in the battery load

Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles using Convex Optimization

This paper presents a framework for optimizing the power allocation between a battery and supercapacitor in an electric vehicle energy storage system. A convex optimal control formulation is proposed that minimizes total energy consumption whilst enforcing hard constraints on power output and total energy stored in the battery and supercapacitor. An alternating direction method of multipliers (ADMM) algorithm is proposed, for which the computational and memory requirements scale linearly with the length of the prediction horizon (and can be reduced using parallel processing). The optimal controller is compared with a low-pass filter against an all-battery baseline in numerical simulations, where it is shown to provide significant improvement in battery degradation (inferred through reductions of 71.4% in peak battery power, 21.0% in root-mean-squared battery power, and 13.7% in battery throughput), and a reduction of 5.7% in energy consumption. It is also shown that the ADMM algorithm can solve the optimization problem in a fraction of a second for prediction horizons of more than 15 minutes, and is therefore a promising candidate for online receding-horizon control

Cloud-Based Dynamic Programming for an Electric City Bus Energy Management Considering Real-Time Passenger Load Prediction

Electric city bus gains popularity in recent years for its low greenhouse gas emission, low noise level, etc. Different from a passenger car, the weight of a city bus varies significantly with different amounts of onboard passengers, which is not well studied in existing literature. This study proposes a passenger load prediction model using day-of-week, time-of-day, weather, temperatures, wind levels, and holiday information as inputs. The average model, Regression Tree, Gradient Boost Decision Tree, and Neural Networks models are compared in the passenger load prediction. The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability. Given the predicted passenger load, dynamic programming algorithm determines the optimal power demand for supercapacitor and battery by optimizing the battery aging and energy usage in the cloud. Then rule extraction is conducted on dynamic programming results, and the rule is real-time loaded to onboard controllers of vehicles. The proposed cloud-based dynamic programming and rule extraction framework with the passenger load prediction shows 4% and 11% fewer bus operating costs in off-peak and peak hours, respectively. The operating cost by the proposed framework is less than 1% shy of the dynamic programming with the true passenger load information

Optimization-Based Power Management of Hybrid Power Systems with Applications in Advanced Hybrid Electric Vehicles and Wind Farms with Battery Storage

Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, ``brain\u27 of these ``hybrid\u27 systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with ``else-then-if\u27 logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may result in the need for repeated control system redesign. To address these shortcomings, we formulate the power management problem as a nonlinear and constrained optimal control problem. Solution of this optimal control problem in real-time on chronometric- and memory- constrained automotive microcontrollers is quite challenging; this computational complexity is due to the highly nonlinear dynamics of the powertrain subsystems, mixed-integer switching modes of their operation, and time-varying and nonlinear hard constraints that system variables should satisfy. The main contribution of the first part of the dissertation is that it establishes methods for systematic and step-by step improvements in fuel economy while maintaining the algorithmic computational requirements in a real-time implementable framework. More specifically a linear time-varying model predictive control approach is employed first which uses sequential quadratic programming to find sub-optimal solutions to the power management problem. Next the objective function is further refined and broken into a short and a long horizon segments; the latter approximated as a function of the state using the connection between the Pontryagin minimum principle and Hamilton-Jacobi-Bellman equations. The power management problem is then solved using a nonlinear MPC framework with a dynamic programming solver and the fuel economy is further improved. Typical simplifying academic assumptions are minimal throughout this work, thanks to close collaboration with research scientists at Ford research labs and their stringent requirement that the proposed solutions be tested on high-fidelity production models. Simulation results on a high-fidelity model of a hybrid electric vehicle over multiple standard driving cycles reveal the potential for substantial fuel economy gains. To address the control calibration challenges, we also present a novel and fast calibration technique utilizing parallel computing techniques. The second part of this dissertation presents an optimization-based control strategy for the power management of a wind farm with battery storage. The strategy seeks to minimize the error between the power delivered by the wind farm with battery storage and the power demand from an operator. In addition, the strategy attempts to maximize battery life. The control strategy has two main stages. The first stage produces a family of control solutions that minimize the power error subject to the battery constraints over an optimization horizon. These solutions are parameterized by a given value for the state of charge at the end of the optimization horizon. The second stage screens the family of control solutions to select one attaining an optimal balance between power error and battery life. The battery life model used in this stage is a weighted Amp-hour (Ah) throughput model. The control strategy is modular, allowing for more sophisticated optimization models in the first stage, or more elaborate battery life models in the second stage. The strategy is implemented in real-time in the framework of Model Predictive Control (MPC)

Combined design and control optimization of hybrid vehicles

Hybrid vehicles play an important role in reducing energy consumption and pollutant emissions of ground transportation. The increased mechatronic system complexity, however, results in a heavy challenge for efficient component sizing and power coordination among multiple power sources. This chapter presents a convex programming framework for the combined design and control optimization of hybrid vehicles. An instructive and straightforward case study of design and energy control optimization for a fuel cell/supercapacitor hybrid bus is delineated to demonstrate the effectiveness and the computational advantage of the convex programming methodology. Convex modeling of key components in the fuel cell/supercapactior hybrid powertrain is introduced, while a pseudo code in CVX is also provided to elucidate how to practically implement the convex optimization. The generalization, applicability, and validity of the convex optimization framework are also discussed for various powertrain configurations (i.e., series, parallel, and series-parallel), different energy storage systems (e.g., battery, supercapacitor, and dual buffer), and advanced vehicular design and controller synthesis accounting for the battery thermal and aging conditions. The proposed methodology is an efficient tool that is valuable for researchers and engineers in the area of hybrid vehicles to address realistic optimal control problems

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously