Deep reinforcement learning (DRL) has been widely used in the field of automotive energy management. However, DRL is computationally inefficient and less robust, making it difficult to be applied to practical systems. In this article, a customized energy management strategy based on the deep reinforcement learning-model predictive control (DRL-MPC) self-regulation framework is proposed for fuel cell electric vehicles. The soft actor critic (SAC) algorithm is used to train the energy management strategy offline, which minimizes system comprehensive consumption and lifetime degradation. The trained SAC policy outputs the sequence of fuel cell actions at different states in the prediction horizon as the initial value of the nonlinear MPC solution. Under the MPC framework, iterative computation is carried out for nonlinear optimization problems to optimize action sequences based on SAC policy. In addition, the vehicle's usual operation dataset is collected to customize the update package for further improvement of the energy management effect. The DRL-MPC can optimize the SAC policy action at the state boundary to reduce system lifetime degradation. The proposed strategy also shows better optimization robustness than SAC strategy under different vehicle loads. Moreover, after the update package application, the total cost is reduced by 5.93% compared with SAC strategy, which has better optimization under comprehensive condition with different vehicle loads.

The predictive energy management strategy (PEMS) offers potential advantages in enhancing the driving economy of electrified vehicles using vehicle speed prediction. However, realizing accurate predictions in practical contexts remains a challenge. Departing from conventional PEMS that rely on historical speed or static traffic data, we introduce a real-time traffic-aware PEMS for improved performance. To better understand the interplay between the host vehicle and its surrounding traffic, we use a Transformer network as the predictor that employs the speeds and relative distances of the surrounding six vehicles to forecast future speed sequences for the host vehicle. To augment this data-driven approach, we develop a dual-predictor strategy based on the deep ensemble technique. This strategy measures the Transformer’s output uncertainty to gauge prediction reliability and introduce an automated threshold mechanism. Based on this threshold and real-time uncertainties, the strategy chooses between the Transformer and an exponential predictor to achieve improved prediction outcomes. A reinforcement learning method is integrated as the PEMS optimizer. For validation, we generate training data with traffic information based on the next generation simulation (NGSIM) dataset and create a test scenario in the SUMO simulator. The results confirm that speed predictions based on real-time traffic data surpass traditional PEMS, either directly inputting traffic data or excluding it. The Transformer predictor significantly outperforms the state-of-the-art predictor. Importantly, our dual-predictor design amplifies prediction accuracy by 27.2% against the standard single-network predictor under non-training conditions. Overall, our PEMS enhances driving economy by 11.1% relative to traffic-unaware models and 8.0% over non-Transformer schemes.

Fast charging of lithium-ion batteries (LIBs) is critical for the further popularity of electric vehicles (EVs). However, overlooking physical limits of LIBs may cause quick health degradation or even catastrophic safety issues. Motivated by this, this paper proposes a hybrid multi-physics-constrained charging strategy for LIBs combining an active set method (ASM)-based model predictive control (MPC) and a rule-based method. A general form of the optimal charging problem is constructed as a constrained quadratic program (QP) to balance the charging rapidity, thermal safety, and battery degradation. Enabled by this formulation, the cost-efficient ASM is proposed to solve the optimization problem, which virtually gives a safety-and health-aware fast charging strategy. Comparative results suggest that the proposed strategy outperforms the traditional MPC solutions remarkably in terms of the computational tractability. Long-term cycling experiments validate the superiority of the proposed strategy in the optimal balance among the charging rapidity, thermal safety, and life extension.

This article proposes a multiobjective sizing method of the retired battery integrating with the photovoltaic solar energy used for the electric vehicle charging station (EVCS) against the charging demand uncertainty. The proposed size optimization approach employs non-dominated sorting genetic algorithm II (NSGA-II) to minimize the renewable energy waste, energy purchased from the external grid, as well as the cost characterized by the net present value produced in 20 years. Especially for the remaining life prediction of retired batteries, this article leverages the calendar-life degradation model by integrating the battery cycle-life counting method. Also, in this article, the charging demand uncertainty is built as different charging patterns for various EVCS scenarios with different combinations of fast- and slow-charging demand. Furthermore, the technoeconomic attractions of retired batteries are verified by a comprehensive comparison with the new batteries. Case studies are implemented with real-world data, and the results show that under the proposed sizing method, the EVCS could achieve a 29.4% cost reduction in the long-term operation with the retired batteries.

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today's high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online estimation of maximum power is accomplished by formulating and solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, a scheme based on multistep nonlinear model predictive control is found to be computationally affordable and accurate.

Accurate detection of capacity degradation is critical to the safe and efficient utilization of battery systems. Many data-driven capacity estimators were proposed based on emerging intelligent algorithms, but their accuracy depends on the data of complete charged/discharged process and complex algorithm structures. This article developed a computer vision (CV)-based method, constructing battery multidimensional aging features as the key image to estimate capacity using specific charging data segment. Specifically, the designed image-aging recognition method is used to extract multidimensional aging features from the partial charging current sequence and then establish map inputs for a computer vision model that recognizes the constructed feature maps. Consequently, the mapping relationship between the charging information and capacity degradation can be obtained as the 2-D grayscale images that contain massive extracted features in their small size hence greatly simplify the network structure in CV model so as to improve estimation accuracy and efficiency significantly. More importantly, since the model input is a specific charging current segment rather than the data of complete charging process, the model applicability to the random and incomplete charging process of electric vehicles can be greatly improved. Battery cycling data from different types of Li-ion cells were utilized for performance verification. Compared with the conventional estimation methods proposed previously, the proposed method demonstrates the great superiority in terms of the model applicability, estimation accuracy, and computational efficiency for online capacity estimation in actual battery usage.

This article presents a comprehensive, multidisciplinary overview of the development of powertrain technologies for energy-efficient heavy-duty earthmoving machines. The heavy-duty earthmoving equipment industry has been among the biggest contributors to emissions globally. However, due to high power demand and multidisciplinary powertrain structures, improving the energy efficiency of heavy-duty mobile machines has been a pressing and challenging task in the industry. To cope with this challenge, hydraulics and power electronics (PE) have been the key driving forces. As such, the relative developments in both fields are covered in this article. For hydraulics, developments of efficient hydraulic circuits will be overviewed in detail along with the introduction of hydraulic energy recovery technologies. In addition, developments of PE architectures in hybrid and electrified machines will be introduced. Furthermore, potential medium-voltage dc mining site power distribution and the valves of wide bandgap devices will also be discussed with the hope to open up new research opportunities in PE. Moreover, emerging hybrid electrohydraulic drive technology is introduced. Based on the overview in this article, it is anticipated that electrohydraulic hybridization will be the future trend in the earthmoving machine industry. Deeper collaboration between the two areas is desirable.

This article proposes a novel model-based estimator for distributed electrochemical states of lithium-ion (Li-ion) batteries. Through systematic simplifications of a high-order electrochemical–thermal coupled model consisting of partial differential-algebraic equations, a reduced-order battery model is obtained, which features an equivalent circuit form and captures local state dynamics of interest inside the battery. Based on the physics-based equivalent circuit model, a constrained ensemble Kalman filter (EnKF) is pertinently designed to detect internal variables, such as the local concentrations, overpotential, and molar flux. To address slow convergence issues due to weak observability of the battery model, the Li-ion's mass conservation is judiciously considered as a constraint in the estimation algorithm. The estimation performance is comprehensively examined under a wide operating range. It demonstrates that the proposed EnKF-based nonlinear estimator is able to accurately reproduce the physically meaningful state variables at a low computational cost and is significantly superior to its prevalent benchmarks for online applications.

A precisely parameterized battery model is the prerequisite of the model-based management of lithium-ion battery. However, the unexpected sensing of noises may discount the identification of model parameters in practical applications. This article focuses on the noise effect compensation and online parameter identification for the widely used equivalent circuit model. A novel degree of freedom (DOF) eliminator is proposed and combined with the Frisch scheme in a recursive fashion, for the first time, to coestimate the noise statistics and unbiased model parameters. A computationally tractable numerical solver is further proposed for the DOF eliminator to improve the real-time performance. Simulations and experiments are performed to validate the proposed method from theoretical to practical perspective. Results show that the proposed method can effectively mitigate the noise-induced identification biases and outperform the existing methods in terms of the accuracy and the robustness to noise corruption.

Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.

Internal short circuit (ISC) has been proven to be responsible for the thermal runaway failure of lithium-ion battery (LIB). The accurate detection of the ISC failure at the early stage is critical to improve the safety of electric vehicles. In this paper, a ISC detection method with self-diagnostic feature is proposed according to the onboard measured load current and terminal voltage. The state of charge (SOC) is first estimated based on the extended Kalman filter (EKF). The ISC current of the cell is self-calibrated leveraging the EKF-estimated SOC and the measured load current. The estimated ISC current is further median filtered to reduce the stochastic error caused by the uncertainty of SOC estimation. Finally, the filtered ISC current are used to identify the ISC resistance online with the recursive least squares with variable forgetting factor (RLSVF) algorithm. Results suggest that the proposed method can identify the internal short circuit resistance online accurately with a high robustness to the noise disturbance.

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) attributed to their advantages of high energy density and long cycle life. In this vision, fast charging of the LIB system has been a crucial technology to promote the large-scale penetration of EVs in the existing automotive market. Motivated by this, a thermal-constrained fast charging method is proposed based on the model predictive control (MPC) concept in this paper. A coupled electro-thermal model is established, based on which two model-based observers are devised to estimate the state of charge (SOC) and internal temperature of LIB. On this premise, an MPC-based controller is exploited to trade-off smartly the charging fastness and the physical constraints. Comparative results show that the proposed method can optimize the charging towards high speed while keep the terminal voltage and battery internal temperature both within the safety region, which forms an obvious superiority over the traditionally-used constant-current-constant-voltage (CC-CV) protocol.

Degradation dynamics modeling and health prognosis play extremely important roles in system prognostics and health management. Wiener process-based degradation models and remaining useful life (RUL) prediction methods have the advantage of high flexibility and efficiency, with features such as Brownian motion with drift and scale parameters. They can also quantify prediction uncertainty through inverse Gaussian distribution. However, prior studies use offline-identified model parameters, which can result in difficulties in both model adaptability and health prognosis. To improve the performance of Wiener process models, this article proposes a new data-driven Brownian motion model that utilizes the adaptive extended Kalman filter (AEKF) parameter identification method. The proposed model can update model parameters online and adapt to uncertain degradation operations. This data-driven method has the flexibility and efficiency of Brownian motion models but avoids their shortcomings in model adaptability and health prognosis. The model parameters and drift parameter are online estimated based on AEKF using limited historical system measurements. The effectiveness of the proposed data-driven framework in degradation modeling and RUL prediction is evaluated through simulations and experimental results on lithium-ion battery degradation data. The results show that the proposed approach has significant accuracy and robustness for both model adaptability and RUL prediction.

Nowadays, medium frequency transformers have gained growing attention in modern power system. Higher power density, which is realized by increasing the operating frequency, leads to the reduction of the magnetic components size. Along with it, cooling surface is consequently reduced which results in high thermal stress. Careful attention must be paid on the loss mechanisms and thermal analysis of a medium frequency transformer at the design stage. This paper develops an equivalent thermal circuit of an oil-immersed medium frequency transformer to predict its temperature profile. Thermal resistance, capacitance and the losses generated in the core and winding are carefully estimated. Two oil-immersed shell-type transformer prototypes with specifications as 5kW, 500/5000V, 5kHz, interleaved winding construction have been developed and built for verification purpose. Loss and temperature measurements have been performed to verify the presented framework. The accuracy of the proposed thermal model is benchmarked and corroborated through experimental measurements as well as FEM-CFD study with good agreement.

A medium/high-power conversion system, using power electronic (PE) converter in conjunction with a medium/high-frequency transformer, has many desirable effects suitably oriented for modern power system architecture. Switching at high frequency results in lesser volume of magnetics but induces higher loss density. Thus design and characterization of a medium-frequency (MF) high-power (HP) transformer has significant ramification on its performance and application. Thermal management of a MF HP transformer is one of key aspects for its characterization. In this paper, an equivalent thermal model of a multi-layer concentrated winding is derived. Core and copper losses are carefully estimated. Thermal resistance and capacitance are accurately calculated. Time-domain response of proposed thermal network is obtained using Heun's method (Modified Euler) and validated with PLECS. Effects of temperature change on thermal properties of material and coolant (transformer oil) are also discussed. Furthermore, accuracy of said thermal network is corroborated through FEM-CFD study of a 10kW, 0.5/2.5kV, 1kHz natural oil-cooled transformer. Close agreement between analytical and simulation results is observed which substantiates proposed thermal model in terms of accuracy and efficacy of computation.