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Lithium plating in lithium-ion batteries for electric vehicles, occurring due to low-temperature or high-rate charging, is a significant factor impacting safety and service life. To address this issue, a novel adaptive charging approach is proposed, combining ultrasound-assisted diagnosis and model predictive control (MPC). In the method, a discrete state-space electrochemical model is used to describe the dynamic characteristics of the battery, and a model predictive controller (MPC) is utilized to optimize the charging current to avoid lithium plating. Considering that factors such as battery performance degradation and variable working temperature affect the battery model's judgment of lithium plating, an ultrasound-assisted diagnosis method is used to determine the critical point of lithium plating. The effectiveness of the method is validated through low-temperature charging and cycle aging experiments. The results indicate that without complex model parameter calibration in different temperatures, the new charging method not only has a higher charging speed than constant current charging, but also can effectively suppress the occurrence of lithium plating on the negative electrode of the battery. The method is expected to be applied in electrochemical energy storage systems to enhance safety and service life. • Simplified battery electrochemical model is designed for charging control. • Model predictive controller is utilized to optimize the charging current. • Ultrasound-assisted diagnosis method is used to diagnose lithium plating. • Model parameters do not need to be recalibrated at different temperatures. [ABSTRACT FROM AUTHOR]

In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model. • Data-driven Koopman MPC for EVs under vehicle-following scenarios is proposed. • A Koopman prediction model is developed considering parameters such as slope. • The proposed method reduces energy consumption by 5.55%. [ABSTRACT FROM AUTHOR]

Recently, battery fast charging strategies have gained increasing interest as range anxiety and long charging time have been the main obstacles to the wider application of electric vehicles. While simply increasing the current can reduce charging time, it might also tend to accelerate the irreversible capacity degradation and power fade. To solve the dilemma between charging speed and battery lifetime, in this work, we proposed a life-extending optimal charging method that considers the charging time and the aging-related effects within the battery. A multi-physics battery model coupled with thermal and electrochemical degradation dynamics is developed and integrated into a model predictive control framework to manipulate the optimal charging current considering the constraints of safe requirements and the rate of internal aging side reactions. The design method was extensively validated by in-situ and ex-situ experiments. Results show that by reducing the rates of side reactions and minimizing detrimental morphological changes in the anode material, the proposed charging method can prolong the battery lifetime by at least 48.6%, compared with the commonly used constant current and constant voltage charging method without obviously sacrificing charging speed. [Display omitted] • An electrochemical-thermal-aging battery model is developed and widely validated. • Battery degradation mechanisms are analyzed at various operating conditions. • Optimal charging profile is designed by considering multiple physical constraints. • The impact of algorithm hyper-parameters is discussed. • Capacity loss is reduced by 48.6% without sacrificing the charging speed. [ABSTRACT FROM AUTHOR]

• Enhanced battery power constraint compliance is realized for an electric vehicle by integrating SoP prediction and driving torque regulation together. • A tailored adaptive model reduction technique is proposed to ensure real-time model predictive control of the integrated SoP prediction and driving torque regulation. • Guaranteed sufficient accuracy of the model reduction is mathematically justified. • Forward dynamic programming is employed to solve the control optimization problem. To comply with battery power constraint in the operation of electric vehicles, traditional methods usually estimate battery's power capability first and then regulate vehicle's driving torque based on the estimation. These methods have fundamental drawbacks due to their separated nature. A better way is to integrate the estimation and regulation together, which increases the model complexity of the control problem though. To tackle this issue, a reduced-order model predictive control (MPC)-based approach is proposed in this paper, where the dimension of the control model is reduced from two to one by exploiting a quasi-linear relationship between the two state variables. Rigorous mathematical justification proves that sufficient accuracy is retained as long as MPC's prediction horizon is determined according to the initial states and battery's polarization dynamics. The superiority of the proposed method is validated by model-in-the-loop tests, which demonstrate that the proposed method reduces the possibility of over-discharge and can make a flexible trade-off between power constraint handling and target vehicle speed tracking. [ABSTRACT FROM AUTHOR]

• A novel tri-stage online dispatch framework for a microgrid is proposed. • The proposed framework coordinates the EV charging behavior at different timescales. • Multiple uncertainties introduced by EV, PV, and load are taken into account. • The real-world data of eight EVs in Beijing is collected to model EV charging behavior. This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced. [ABSTRACT FROM AUTHOR]

• A dynamic J-type battery thermal control system is established. • The mode switching control succeeds to maintain the battery thermal performance. • The energy efficiency is improved by 15.8% by employing model predictive control. Battery thermal control plays an indispensable role in terms of the safety and performance for electric vehicles. For air-based cooling technologies, one of the most pressing challenges is to balance the temperature uniformity and constrain the maximum temperature simultaneously under varying driving conditions. This paper proposes a self-adaptive intelligent neural network-based model predictive control strategy for a J -type air-based battery thermal management system. The J -type structure is first optimized through surrogate-based optimization to improve the temperature uniformity before control. Based on the optimized J -type configuration, an operation mode switching module is developed to mitigate the temperature unbalance. The thermal control approach is tested using an integrated driving cycle, and its evaluations are threefold: (i) the neural network-based control without mode switching fails to meet the thermal requirements; (ii) the control with mode switching succeeds in constraining the maximum temperature and maintaining the temperature uniformity within 1.33 K; (iii) the added model predictive control approach slightly enhances the thermal performance but improves the energy efficiency significantly by 15.8%. The results show that the J -type structure with its appropriate control strategy is a promising solution for light-duty electric vehicles using an air-cooling technology. [ABSTRACT FROM AUTHOR]