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Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several challenges remain to be addressed. Therefore, providing highly accurate and optimized models has become the primary research goal of many studies. This paper analyzes appliance energy consumption through a variety of machine learning-based strategies. Utilizing data recorded from a single-family home, input variables comprised internal temperatures and humidities, lighting consumption, and outdoor conditions including wind speed, visibility, and pressure. Various models were trained and evaluated: (a) multiple linear regression, (b) support vector regression, (c) random forest, (d) gradient boosting, (e) xgboost, and (f) the extra trees regressor. Both feature engineering and hyperparameter tuning methodologies were applied to not only extend existing features but also create new ones that provided improved model performance across all metrics: root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best model (extra trees) was able to explain 99% of the variance in the training set and 66% in the testing set when using all the predictors. The results were compared with those obtained using a similar methodology. The objective of performing these actions was to show a unique perspective in simulating building performance through data-driven models, identifying how to maximize predictive performance through the use of machine learning-based strategies, as well as understanding the potential benefits of utilizing different models. [ABSTRACT FROM AUTHOR]

Heavy-duty electric powertrains provide a potential solution to the high emissions and low fuel economy of trucks, buses, and other heavy-duty vehicles. However, the cost, weight, and lifespan of electric vehicle batteries limit the implementation of such vehicles. This paper proposes supplementing the battery with on-board photovoltaic modules. In this paper, a bus model is created to analyze the impact of on-board photovoltaics on electric bus range and battery lifespan. Photovoltaic systems that cover the bus roof and bus sides are considered. The bus model is simulated on a suburban bus drive cycle on a bus route in Davis, CA, USA for a representative sample of yearly weather conditions. Roof-mounted panels increased vehicle driving range by 4.7% on average annually, while roof and side modules together increased driving range by 8.9%. However, variations in weather conditions meant that this additional range was not reliably available. For constant vehicle range, rooftop photovoltaic modules extended battery cycle life by up to 10% while modules on both the roof and sides extended battery cycle life by up to 19%. Although side-mounted photovoltaics increased cycle life and range, they were less weight- and cost-effective compared to the roof-mounted panels. [ABSTRACT FROM AUTHOR]

When vehicle manufacturers are developing new hybrid and electric vehicles, modeling and simulation are frequently used to predict the performance of the new vehicles from an early stage in the product lifecycle. Typically, models are used to predict the range, performance and energy consumption of their future planned production vehicle; they also allow the designer to optimize a vehicle's configuration. Another use for the models is in performing sensitivity analysis, which helps us understand which parameters have the most influence on model predictions and real-world behaviors. There are various techniques for sensitivity analysis, some are numerical, but the greatest insights are obtained analytically with sensitivity defined in terms of partial derivatives. Existing methods in the literature give us a useful, quantified measure of parameter sensitivity, a first-order effect, but they do not consider second-order effects. Second-order effects could give us additional insights: for example, a first order analysis might tell us that a limiting factor is the efficiency of the vehicle's prime-mover; our new second order analysis will tell us how quickly the efficiency of the powertrain will become of greater significance. In this paper, we develop a method based on formal optimization mathematics for rapid second-order sensitivity analyses and illustrate these through a case study on a C-segment electric vehicle. [ABSTRACT FROM AUTHOR]

With a myriad of alternative vehicle powertrain architectures emerging in the industry, such as electric vehicles and hybrid electric vehicles, it is beneficial that the most appropriate system is chosen for the desired vehicle class and duty cycle, and to minimize a given cost function. This paper investigates this issue, by proposing a novel framework that evaluates different types of powertrain architectures under a unified modular powertrain structure. This framework provides a systematic and objective approach to comparing different types of powertrain architectures simultaneously, and will highlight the benefits that can be achieved from each architecture, thus making it possible to develop the reasoning for manufacturers to implement such systems, and potentially accelerate customer take-up of alternative powertrain technology. The results from this investigation have indicated that such analysis is indeed possible, by way of identifying the "cross-over point" between powertrain architectures, where one powertrain architecture transitions into a different architecture with increments in the required travel range. [ABSTRACT FROM AUTHOR]

Mallon et al. [[10]] explored the modeling and control of a lithium-ion battery and ultra-capacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. 10.3390/en15020634 10 Mallon K., Assadian F. A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems. Utilizing an adaptive equivalent consumption minimization energy management strategy, the proposed combined system shows a fuel consumption reduction of 18.4% over the standard FTP75 cycle. [Extracted from the article]

Since the automotive industry is shifting towards electrification, brake-by-wire technologies are becoming more prevalent. However, there has been little research comparing and optimizing brake-by-wire actuators in terms of their energy expenditure and response time. This paper investigates the comparison of three different smart brake-by-wire actuators, Electro-Hydraulic Brakes (EHB), Electro-Mechanical Brakes (EMB), and Electronic Wedge Brakes (EWB), first by defining an objective metric and then using both linear and nonlinear optimization techniques. Modeling of the actuators is performed using the bond graph method. Then, the controllers are designed using a robust control strategy, Youla parameterization. After designing the controllers, two types of optimization are performed on the actuators. Optimizations are performed in two ways: 1. by linearizing the plants and optimizing using their transfer functions and 2. by nonlinear optimization of the plants in the closed-loop following a specific clamp force target. The objective metrics or the cost functions for these optimizations are chosen to be the energy usage of the plants during the closed-loop operation, maximum power requirement, and their dynamic responsiveness. Using this optimization framework, we can show a significant improvement in the energy usage of the actuators and slight improvements in their responsiveness. In the end, the actuators are compared in terms of their energy usage for sets of initial and optimized physical parameters. [ABSTRACT FROM AUTHOR]

Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy. [ABSTRACT FROM AUTHOR]

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%-9% being realizable. [ABSTRACT FROM AUTHOR]

Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving. [ABSTRACT FROM AUTHOR]

Tire normal forces are difficult to measure, but information on the vehicle normal force can be used in many automotive engineering applications, e.g., rollover detection and vehicle and wheel stability. Previous papers use algebraic equations to estimate the tire normal force. In this article, the estimation of tire normal force is formulated as an input estimation problem. Two observers are proposed to solve this problem by using a quarter-car suspension model. First, the Youla Controller Output Observer framework is presented. It converts the estimation problem into a control problem and produces a Youla parameterized controller as observer. Second, a Kalman filter approach is taken and the input estimation problem is addressed with an Unbiased Minimum Variance Filter. Both methods use accelerometer and suspension deflection sensors to determine the vehicle normal force. The design of the observers is validated in simulation and a sensitivity analysis is performed to evaluate their robustness. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system makes full use of the advantages of independent control of wheel torque of electric vehicle driven by front in-wheel motors to achieve steering assistance and reduce the steering effort of the driver, as the electric power steering (EPS) system does. However, as an indirect steering assist technology that applies steering system assistance via differential drive, its linear control algorithm, like existing proportion integration differentiation (PID) controllers, cannot take the nonlinear characteristics of the tires' dynamics into account which results in poor performance in road feeling and tracking accuracy. This paper introduces an active disturbance rejection control (ADRC) method into the control issue of the DDAS. First, the third-order ADRC controller of the DDAS is designed, and the simulated annealing algorithm is used to optimize the parameters of ADRC controller offline considering that the parameters of ADRC controller are too many and the parameter tuning is complex. Finally, the 11-DOF model of the electric vehicle driven by in-wheel motors is built, and the standard working conditions are selected for simulation and experimental verification. The results show that the ADRC controller designed in this paper can not only obviously reduce the steering wheel effort of the driver like PID controller, but also have better nonlinear control performance in tracking accuracy and smooth road feeling of the driver than the traditional PID controller. [ABSTRACT FROM AUTHOR]