Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications, such as smartphones and electric vehicles. This paper proposes an ...innovative approach to devising optimally health-conscious fast-safe charge protocols. A multiobjective optimal control problem is mathematically formulated via a coupled electro-thermal-aging battery model, where electrical and aging submodels depend upon the core temperature captured by a two-state thermal submodel. The Legendre-Gauss-Radau pseudospectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting highly nonlinear six-state optimal control problem. Charge time and health degradation are, therefore, optimally traded off, subject to both electrical and thermal constraints. Minimum-time, minimum-aging, and balanced charge scenarios are examined in detail. Sensitivities to the upper voltage bound, ambient temperature, and cooling convection resistance are investigated as well. Experimental results are provided to compare the tradeoffs between a balanced and traditional charge protocol.
The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and ...computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor information is available for the controller, and the actual future driving profile is completely unknown. Basic principles of exponentially varying, stochastic Markov chain, and neural network-based velocity prediction approaches are described. Their sensitivity to tuning parameters is analyzed, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared.
► Twelve equivalent circuit models for Li-ion batteries are compared.
► Model complexity, accuracy and generalization are comprehensively evaluated.
► The usefulness levels of the models for two ...types of Li-ion cells are studied.
► Multi-swarm particle swarm optimization algorithm is used to parameterize models.
► Battery test system and characterization datasets are depicted.
This paper presents a comparative study of twelve equivalent circuit models for Li-ion batteries. These twelve models were selected from state-of-the-art lumped models reported in the literature. The test data used is obtained from a battery test system with a climate chamber. The test schedule is designed to measure key cell attributes under highly dynamical excitations. The datasets were collected from two types of Li-ion cells under three different temperatures. The multi-swarm particle swarm optimization algorithm is used to identify the optimal model parameters for the two types of Li-ion cells. The usefulness of these models is then studied through a comprehensive evaluation by examining model complexity, model accuracy, and robustness of the model by applying the model to datasets obtained from other cells of the same chemistry type.
Battery health monitoring and management is of extreme importance for the performance and cost of electric vehicles. This paper is concerned with machine-learning-enabled battery state-of-health ...(SOH) indication and prognosis. The sample entropy of short voltage sequence is used as an effective signature of capacity loss. Advanced sparse Bayesian predictive modeling (SBPM) methodology is employed to capture the underlying correspondence between the capacity loss and sample entropy. The SBPM-based SOH monitor is compared with a polynomial model developed in our prior work. The proposed approach allows for an analytical integration of temperature effects such that an explicitly temperature-perspective SOH estimator is established, whose performance and complexity is contrasted to the support vector machine (SVM) scheme. The forecast of remaining useful life is also performed via a combination of SBPM and bootstrap sampling concepts. Large amounts of experimental data from multiple lithium-ion battery cells at three different temperatures are deployed for model construction, verification, and comparison. Such a multi-cell setting is more useful and valuable than only considering a single cell (a common scenario). This is the first known application of combined sample entropy and SBPM to battery health prognosis.
Supercapacitors (SCs) have high power density and exceptional durability. Progress has been made in their materials and chemistries, while extensive research has been carried out to address ...challenges of SC management. The potential engineering applications of SCs are being continually explored. This paper presents a review of SC modeling, state estimation, and industrial applications reported in the literature, with the overarching goal to summarize recent research progress and stimulate innovative thoughts for SC control/management. For SC modeling, the state-of-the-art models for electrical, self-discharge, and thermal behaviors are systematically reviewed, where electrochemical, equivalent circuit, intelligent, and fractional-order models for electrical behavior simulation are highlighted. For SC state estimation, methods for State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are covered, together with an underlying analysis of aging mechanism and its influencing factors. Finally, a wide range of potential SC applications is summarized. Particularly, co-working with high energy-density devices constitutes hybrid energy storage for renewable energy systems and electric vehicles (EVs), sufficiently reaping synergistic benefits of multiple energy-storage units.
This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with ...modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. To determine the optimal control behaviors and power distribution between two energy sources, a novel RL-based energy management strategy is introduced. For comparison purposes, the two velocity prediction processes are examined by RL using the same realistic driving cycle. The look-ahead energy management strategy is contrasted with shortsighted and dynamic programming based counterparts, and further validated by hardware-in-the-loop test. The results demonstrate that the RL-optimized control is able to significantly reduce fuel consumption and computational time.
Holistic energy management of plug-in hybrid electric vehicles (PHEVs) in smart grid environment constitutes an enormous control challenge. This paper responds to this challenge by investigating the ...interactions among three important control tasks, i.e., charging, on-road power management, and battery degradation mitigation, in PHEVs. Three notable original contributions distinguish our work from existing endeavors. First, a new convex programming (CP)-based cost-optimal control framework is constructed to minimize the daily operational expense of a PHEV, which seamlessly integrates costs of the three tasks. Second, a straightforward but useful sensitivity assessment of the optimization outcome is executed with respect to price changes of battery and energy carriers. The potential impact of vehicle-to-grid (V2G) power flow on the PHEV economy is eventually analyzed through a multitude of comparative studies.
•Integrated cost-optimal control scheme is devised to maximize PHEVs economy.•Charging, power management, and battery aging are optimally coordinated.•PHEVs evolve toward EV with future gasoline and battery prices.•Vehicle-to-grid (V2G ) implication to PHEVs economy is studied.•Convex programming powerfully optimizes plug-in hybrid drivelines.
Since a battery pack consists of hundreds of cells in series and parallel, inconsistencies between cells make it difficult to create an explicit model to simulate its behaviors effectively. ...Therefore, the widely used and sophisticated model-based methods (such as Kalman filters) are difficult to apply to SOC (state of charge) estimation of battery packs. In this paper, a data-driven method based on Gaussian process regression (GPR) is proposed to provide a feasible solution. Its superiority includes the ability to approximate nonlinearity accurately, nonparametric modeling, and probabilistic predictions. First, a feature extraction strategy, including data preprocessing, correlation analysis, and principal component analysis, is employed to obtain a compacted input set with a high correlation with SOC. Second, the squared exponential kernel function is used, and the automatic relevance determination is applied to optimize the weights of features. Third, besides the regular GPR model, an autoregressive GPR model is also constructed to further improve estimation accuracy and confidence. The experimental results verify that the autoregressive model has better SOC estimation performance than the regular model, and its estimation error under different dynamic cycles, temperatures, aging conditions, and even extreme conditions is lower than 3.9%, and the confidence interval is also much narrower.
•A feature extraction method is employed to obtain a crucial and compacted data set.•Gaussian process regression is used to predict the state of charge of battery pack.•Automatic relevance determination is used to optimize the weights of features.•An autoregressive model is created to improve estimation accuracy and confidence.•The method is verified in different dynamic cycles, temperatures, and aging states.
Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a ...plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.
This article aims to develop an optimal look-ahead control framework to maximize car-following fuel economy, while fulfilling requirements of intervehicle safety. Three original contributions make ...this work distinctive from the existing relevant literature. First, a model predictive fuel-optimal controller is constructed to optimize the vehicle speed and continuously variable transmission (CVT) gear ratio. The controller leverages state trajectories of the leading vehicle transmitted via Vehicle-to-Vehicle/Vehicle-to-Infrastructure (V2V/V2I) communication. How operating conditions affect the engine efficiency and CVT efficiency is explicitly taken into account. Second, the controller is sufficiently evaluated in a variety of traffic flows, such as cruising, urban, and highway-like driving, and is compared with a short-sighted alternative without V2V/V2I connectivity. Finally, we further demonstrate the advantages of the proposed scheme by a comparison with two existing benchmark controllers.
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