Accurate velocity prediction is crucial for predictive torque distribution in dual-motor electric vehicles (EVs). This paper proposes a surrounding vehicle-aware predictive torque distribution strategy to enhance energy efficiency. A Transformer-based velocity predictor is developed by integrating the historical velocities of ego vehicle and surrounding vehicles with relative distance, achieving 49% MAE and 48% RMSE reductions compared to a single-vehicle prediction baseline. The predicted velocity sequence is embedded into a model predictive control framework to optimize front/rear motor torque distribution, generating 8.2-16.1% more high-efficiency motor working points while maintaining battery state-of-charge (SOC). Simulation results demonstrate 0.35% improvement in end-of-cycle SOC and smoother current profiles under real driving cycles, validating the effectiveness of spatiotemporal interaction modeling for energy-saving torque distribution.

The rise of electric vehicles has created a demand for more advanced battery management systems. Pre-trained large models, such as language, time-series, vision, and multimodal models, offer promising yet underexplored opportunities for enhancing battery management. This review examines their integration through methods like prompt learning and fine-tuning, with applications in state-of-charge/state-of-health estimation, remaining useful life prediction, and anomaly detection. It also discusses key challenges, including data limitations, computational demands, and model interpretability, while outlining future directions. The paper provides insights into how these models can enable safer, more efficient, and longer-lasting electric vehicle batteries.

Gear ratio optimization is essential for improving transmission efficiency and dynamic performance of four-wheel distributed drive electric heavy trucks. This study proposes a gear ratio optimization method that integrates battery health and shifting-induced energy losses and considers shift frequency. The method employs particle swarm optimization to optimize front and rear axle gear ratios under realistic truck operating conditions, followed by dynamic programming to determine the optimal gear-shifting sequence and real-time torque allocation. Through iterative refinement, the proposed method achieves optimal gear ratios of [32.17, 18.16] for the front axle and [35.53, 14.61] for the rear axle. Simulation results demonstrate that the optimized configuration reduces annual operational costs by 0.5%-1.9% compared to other optimization methods, yielding savings of 103,328 RMB per year while mitigating battery degradation and kinetic energy loss during gear shifts.

To address the limitations of traditional energy management strategies in hybrid energy storage systems for electric vehicles, including poor adaptability to dynamic conditions and safety risks in online reinforcement learning, this paper proposes an offline reinforcement learning framework integrating a time-aware variational autoencoder and Decision Transformer. Initially, high-quality expert trajectories are generated by a dynamic programming-based energy management strategy. Subsequently, a bidirectional long short-term memory network extracts temporal features from state sequences, while variational autoencoder synthesizes physics-constrained trajectories to mitigate distribution shift. Finally, Decision Transformer employs a self-attention mechanism to conduct multiscale temporal modeling of historical state-action sequences, establishing implicit policy mapping. Experimental results under the Dallas5 driving cycle demonstrate that the energy management strategy trained with a mixed dataset D1 outperforms the strategy trained with a pure expert dataset D2: battery capacity loss is reduced by 5.5%, and the final state of charge of the supercapacitor is stably maintained at 0.7320. This highlights the critical role of data diversity in enhancing generalization, offering a novel pathway for robust EMS design in real-world vehicular applications.

Deep reinforcement learning (DRL) has emerged as a promising approach for energy management in hybrid electric vehicles. However, the current focus of energy management in DRL primarily centers on energy-saving performance while neglecting safety constraints during the training process. To address this challenge, this paper proposes a rational-safe reinforcement learning energy management strategy for hybrid electric vehicles. First, a safety evaluation mechanism based on eXtreme Gradient Boosting is developed to assess the safety of actions generated by the agent. Subsequently, a physics-informed safety layer is introduced to modify irrational control signals through constrained optimization when the agent’s outputs are evaluated as unsafe. Experimental results demonstrate that the proposed method ensures the safety of output actions while improving fuel economy by 5.64%-6.28% compared to existing reinforcement learning approaches.

This paper investigates the state-of-charge (SOC) estimation problem for lithium-ion batteries in balancing systems. Currently, most research focuses on the SOC estimation of individual lithium-ion cell. However, in practical applications, lithium-ion batteries are often connected to balancing circuits to eliminate imbalances within the battery pack. When the balancing circuit is activated, the dynamic characteristics of the battery system change considerably, and existing estimation methods often fail to effectively capture this dynamic transition. To address this issue, this paper proposes a switched Gaussian process regression(GPR) method. First, we conduct a qualitative analysis of the lithium-ion battery balancing circuit switched system based on a switch resistor balancing circuit. Then, building on this analysis, we develop a switched GPR method that adapts to the on/off state of the balancing circuit. Finally, we construct an experimental platform and validate the proposed switched model through comparative experiments with traditional methods. The results demonstrate that the proposed switched model exhibits significant advantages in terms of SOC estimation accuracy and adaptability, effectively handling the complex dynamic changes in the system after the balancing circuit is activated.

The demand for efficient energy management strategies (EMSs) in electric vehicles (EVs) has become increasingly critical. However, existing EMSs based on predictive reinforcement learning (RL) often exhibit low sample efficiency and limited predictive accuracy due to reliance on simple velocity features and conventional sequential models. This paper proposes a novel prediction-enhanced RL framework that integrates an iTransformer-based velocity predictor with Soft Actor-Critic (SAC). Specifically, the predictor forecasts the vehicle velocity for the next three seconds, and the predicted velocity is then processed by an EV dynamics model to calculate future power demand. This predicted information is incorporated into the SAC state space to enhance decision-making. Based on the augmented state, SAC learns an adaptive EMS that reduces energy consumption and battery aging, extends driving range and the system lifespan, and maintains the supercapacitor state of charge within a desirable range. Experimental validation using real-world data shows that the proposed method achieves a 4.20% reduction in energy consumption costs and a 6.11% decrease in battery aging costs, leading to an overall 4.11% cost reduction compared to the SAC without predictive information.

For distributed drive electric vehicles, energy consumption is affected by the power demand and energy management strategy. In this paper, an adaptive cruise control and energy management strategy cooperative framework for dual-motor electric vehicles is proposed based on the deep deterministic policy gradient algorithm. Firstly, the energy management problem in the car-following scenario is decomposed into two subproblems: the adaptive cruise control governs vehicle acceleration, while the energy management strategy allocates driving torque. Then, based on the cooperative architecture, the speed trajectory and torque allocation strategy are co-optimized to realize cooperation between the adaptive cruise control and the energy management strategy. Finally, the proposed cooperative strategy is compared with the traditional hierarchical strategy under the worldwide harmonized light vehicles test cycle. Results show that the proposed cooperative strategy can reduce the maximum acceleration and maximum deceleration by 10.8% and 10.4%, respectively, and improve energy consumption by 4.3% compared with the traditional hierarchical strategy.

With the high proportion of distributed energy resources with randomness and intermittency penetrating the distribution network, traditional centralized optimization methods face problems such as communication packet loss, frequent failures, and low reliability and are difficult to apply to large-scale DC microgrids with wide-area dispersion effectively. Therefore, distributed optimization methods have attracted widespread attention due to their superior scalability and robustness. This paper investigates a convex relaxation-based distributed control strategy for DC microgrids with constant power loads (CPLs) and MPPT-controlled distributed generations (MPPT-DGs) to achieve global optimization. First, an optimal power flow (OPF) problem model for large-scale DC microgrids under a distributed framework is established, and a convex relaxation method taking exactness into account is proposed to transform the non-convex original problem into a new convex problem with the same optimal solution. Then, the Karush–Kuhn–Tucker (KKT) condition and its equivalent consistency-based condition are derived based on convex relaxation, and a distributed global optimization control method is proposed to achieve global optimal system operation, avoiding the solution of large-scale non-linear optimization problems. Finally, simulations and numerical experiments are presented to verify the correctness of the proposed strategy.

Accurate and effective prediction of battery remaining useful life (RUL) is crucial for the retirement planning of electric buses and the secondary utilization of battery packs. This study utilizes four years of operational data from nine electric buses to achieve precise RUL prediction for power batteries. First, considering the real-world operating characteristics of electric buses, this paper introduces a new RUL definition based on remaining mileage (RML) and remaining days of life (RDL) to characterize the remaining lifespan of battery packs. Subsequently, SOH labeling is conducted using charging data and filtering algorithms, followed by determining the end-of-life point of battery packs from SOH degradation trajectories. Finally, multi-time-scale features—including battery features, historical features, seasonal features, and discharging features are extracted from raw data, and the predictive performance of multiple ensemble learning models is compared. The results indicate that the AdaBoost model achieves the best performance in predicting RML and RDL, with a mean absolute error of 98 days and 16,852 km, respectively.

Lithium-ion batteries are widely used in portable devices and mobile medical equipment due to their high energy density and long cycle life. However, long charging times for lithium-ion batteries can limit their usability. This paper proposes a multi-stage constant current charging protocol.

Guaranteed safety and performance under different cases have significant influence on the development of lateral control of autonomous vehicles. This paper proposes a robust nonlinear controller using barrier Lyapunov function within the constraints. In the case of unknown bounded uncertainty, the barrier Lyapunov function is appropriately arranged into the controller to restrict the state variables of the designed safe region in the process. Thus, the proposed method is composed of the nonlinear controller using barrier Lyapunov function and kalman filter. The kalman filter is designed as an observer to estimate the state variables. At the meantime, the method we proposed meets the constraints of output and the external disturbance. Moreover, the validity of the proposed method is validated in the co-simulation of the MATLAB/Simulink and CarSim.

In electric vehicles equipped with hybrid energy storage systems, it is crucial to achieve fast and precise regulation of the DC bus voltage to match the target reference. Existing techniques often struggle to avoid voltage overshoots while pursuing fast stabilization, which leads to significant fluctuations in the DC bus voltage. To overcome this challenge, this paper presents an advanced energy management strategy using predictive setpoint modulation. We develop an innovative set-point modulation technique that achieves efficient regulation of the DC bus voltage by utilizing a feed-forward compensator in combination with parallel operation of multiple DC-DC converters and supercapacitors (SCs). This configuration not only ensures voltage stability, but also optimizes the charge/discharge cycle management of the supercapacitors, which improves the energy utilization efficiency and overall system performance. Laboratory experimental results show that our strategy significantly reduces DC bus voltage fluctuations and maintains stable system operation under various load conditions, outperforming conventional technologies. In addition, our technology has the advantages of fast response and easy integration into existing systems, providing an efficient and reliable solution for energy management in electric vehicles.

The aging process of lithium-ion batteries is a complex nonlinear process involving multiple electrochemical reactions. During different charge-discharge cycles, the battery exhibits different degradation characteristics. Accurate State of Health (SoH) estimation is a fundamental requirement for battery prediction and health management. Existing SoH estimation methods often rely on manually selected features, which introduce subjective bias, limiting their generalizability and robustness. This paper proposes a feature extraction framework based on a Bayesian optimization algorithm. First, partial incremental capacity (IC) analysis is performed within a specific voltage range, and this framework searches for effective Cycle-Voltage-IC features in the multidimensional feature space based on the self-fluctuation of the features and their correlation with battery capacity. Bayesian optimization is used to search for feature indices and intervals to extract features. Then a stacking ensemble model is developed that combines Bayesian ridge regression and randomized consistent sampling regressor to enhance the robustness of SoH estimation. This method provides interpretable feature extraction and improves the accuracy of SoH estimation. Compared with the feature extraction method based on manual experience, the feature extraction framework and SoH estimation model proposed in this paper can better fit the capacity degradation process of the four battery models, and the absolute errors of their SoH estimation are reduced by 21.88%, 66.67%, 8.92%, and 32.59%, respectively.

Supercapacitors are widely used as energy storage devices due to their high power density, long lifespan, and excellent charge/discharge capabilities. However, when connected in series to meet higher voltage requirements, cell imbalances can occur over time, negatively impacting system performance. To address this, we propose a novel method called Passive Balancing Circuit Voltage Estimation (PBVE), which utilizes existing passive balancing circuits to indirectly estimate cell voltages. This method reduces the reliance on extensive sensor networks, thereby lowering system complexity and cost. The PBVE method was validated using Simulink simulations, and the results demonstrated improved efficiency and reliability in monitoring and managing supercapacitor systems under various operational conditions.

Temperature heavily affects the lifespan and performance of batteries. High temperatures accelerate capacity degradation and can cause thermal runaway, highlighting the importance of battery cooling strategies for electric vehicles. This paper proposes the battery cooling strategy for electric vehicles with LiFePO4batteries using the twin delayed deep deterministic policy gradient (TD3) algorithm. Firstly, the electric-thermal-aging and active battery thermal management system models are introduced, and the thermal management problem is formulated as a continuous Markov decision process. Then, a reward function is designed based on prolonging battery life, reducing refrigeration cost, and maintaining battery temperature. Finally, a TD3 algorithm based on the double-delayed update mechanism is designed to obtain the optimal battery cooling strategy in the continuous state-action space. Simulation results demonstrate that the proposed strategy outperforms the traditional methods and closes to the optimal benchmark, offline dynamic programming, with battery capacity loss of less than 3.32% and an SoC consumption of less than 0.55%, significantly reducing the operational cost and mitigating the battery aging.

Under the background of dual carbon, lithium batteries are widely used in the energy field. However, range anxiety limits the popularity of electric vehicles. Optimization strategies for fast charging of lithium-ion batteries have been extensively studied to solve this problem. The huge parameter space of charging protocols and the complex aging mechanism of batteries limit the application of fast charging methods. The ability of reinforcement Learning to learn from the environment and adapt to the stochastic nature of battery behavior is a significant advantage. In this paper, we propose an innovative method for fast charging lithium-ion batteries using the Two-Delay Deep Deterministic Policy Gradient (TD3) algorithm and the Single Particle Model with Electrolyte model. The trained agent dynamically adjusts the charging current according to the state every 5 seconds to optimize the trade-off between fast charging and safety limits and can charge the state of charge (SOC) of the battery from 0.2 to 0.8 in 410 seconds, while protecting against overvoltage and overheating. Meanwhile, it still works well for different initial SOC.

The low-level control problem of fixed-wing unmanned aerial vehicles(UAVs) is complex and challenging. Although reinforcement learning (RL) algorithms are widely applied in the field of robot control, their convergence speed is typically slow due to the sparsity of rewards. An imitation reinforcement learning(IRL) algorithm based on proximal policy optimization(PPO) algorithm for attitude control of fixed-wing UAVs has been proposed. The strategy of imitation learning enables the avoidance of ineffective and perilous exploration during the initial stages of training. The nonlinear adaptive distribution rejection control (NLADRC) algorithm has been devised as the imitated agent, and a smooth transition strategy has been proposed to effectuate the switch from imitation learning to reinforcement learning. A series of simulation experiments demonstrate that the proposed strategy significantly improves the quality of reinforcement learning training, and even in strong gale wind environments, it still demonstrates superior control effectiveness compared to the imitated agent.

It is a significant challenge to reconcile the competing interests of individual microgrid units when energy is shared in a multi-energy system. Moreover, the majority of these systems solely focus on power sharing, without adequately addressing the associated carbon emissions. This paper puts forward a sharing approach that makes use of multi-agent collaboration to optimise energy sharing in multi-energy systems and achieve low-carbon operation. Firstly, a system model is constructed to describe the interrelationship between each microgrid and the energy-sharing platform. Subsequently, a shared pricing mechanism will be devised to incentivise each microgrid to prioritise its participation in the local sharing market. The shared pricing mechanism is employed to construct the microgrid utility maximisation problem and transform it into a Markov game process. A multi-agent cooperative approach is put forth as a means of optimising the energy-sharing strategy. Ultimately, the simulation results demonstrate that this energy-sharing strategy can satisfy the operational constraints, maintain equilibrium between supply and demand, reduce energy costs, and promote the economic and low-carbon operation of the microgrid.

Accurate modeling of open circuit voltage hysteresis for LiFePO4batteries is crucial for establishing an advanced battery model. However, existing hysteresis modeling methods often yield suboptimal results due to inadequate parameterization. This paper proposes an optimal modeling method for open circuit voltage hysteresis based on the Prandtl-Ishlinskii model and an associated parameterization method. First, an asymmetric operator with cubic envelope functions is designed to enhance the classical Prandtl-Ishlinskii model, which originally features a symmetric and linear operator. This modification enables the proposed model to accurately capture intricate hysteresis. Second, a hierarchical parameterization method is proposed to identify optimal parameters. Specifically, an improved grey wolf optimizer is employed to determine the operator-related parameters. Then, the remaining parameters are calculated using the least squares algorithm, enhancing computational efficiency. Finally, the proposed model is validated on the experimental hysteresis data from three distinct scenarios. The modeling error of the proposed model decreased by 66.57 % and 32.51 % compared with two other benchmark models.

Model predictive control is a real-time energy management method for hybrid energy storage systems, whose performance is closely related to the prediction horizon. However, a longer prediction horizon also means a higher computation burden and more predictive uncertainties. This paper proposed a predictive energy management strategy with an optimized prediction horizon for the hybrid energy storage system of electric vehicles. Firstly, the receding horizon optimization problem is formulated to minimize the battery degradation cost and traction electricity cost for the electric vehicle operation. Then, the optimal control sequence is solved to obtain the power allocation between the battery and the supercapacitor. Furthermore, the effect of different horizons on the optimization results is analyzed under diverse operating conditions, determining the optimal horizon to balance the system costs and computation burden. Compared with the short horizon, the optimal horizon can achieve 5.2%$\sim$8.5% performance improvement with the acceptable computation time approaching 1 s.

Accurate speed prediction plays a critical role in the predictive energy management of electric vehicles. This paper proposes a spatial-temporal data-driven speed prediction method for the predictive energy management of battery/supercapacitor electric vehicles. The proposed speed prediction method is performed using a long short-term memory network and validated on a real-world commuting data set in China. Different from existing prediction methods based only on speed and acceleration, we take spatial information as an additional input to improve speed prediction accuracy. The predicted future speed is then leveraged by a model predictive control-based energy management strategy to minimize the battery degradation cost. Quantitative comparisons illustrate that the proposed speed prediction method can reduce the root mean square error and mean absolute error by 10.01-19.15% compared with no spatial information prediction method. The more accurate prediction can further improve the optimality of the predictive energy management strategy, i.e., reduce the battery capacity loss and yield closer results to model predictive control with completely accurate prediction.

Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.

Traditional lighting systems suffer from the problem of low energy efficiency and low illumination quality due to its disappointing management. To address this issue, in this paper, a novel traffic-flow adaptive scheme of smart lighting systems is proposed on the basis of the cyber-physical cloud system. The cyber-physical cloud system consists of the digital twin and cyber-physical system. The operation of the lighting system is simulated in the counterpart twin system with the digital twin technology. The cyber-physical system realizes data collection, information interaction, analysis, and processing, as well as complex computation and remote control. The traffic adaptive scheme works according to the brightness sequence to improves the energy efficiency of the lighting system and provide higher illumination quality for drivers. Extensive simulation results verify the proposed control scheme could improve the energy efficiency of lighting systems.

The filter-based real-time energy management method has been proved practical and widely utilized in hybrid energy storage systems. However, the determination for the cutoff frequency of the energy-split filter is challenging. In this paper, an optimal filter-based energy management strategy is proposed for a battery/ultracapacitor electric vehicle to minimize the total energy consumption. A cost function of energy consumption for the cutoff frequency is established first. Considering the working condition of ultracapacitors, dynamic programming is adopted to obtain the optimal cutoff frequency series, i.e., the optimal energy distribution between batteries and ultracapacitors. Such an off-line optimization process is carried out under different driving cycles, e.g., urban and highway road conditions. Optimization results are used to determine the optimal cutoff frequency of a real-time filter-based energy management strategy. Simulation results indicate that the proposed strategy can minimize the total energy consumption of the hybrid energy storage system with ultracapacitors state of charge limitations being guaranteed. Compared with the existing real-time energy management strategies, the energy consumption is reduced 23.85% under aggressive acceleration conditions and 7.08% under urban conditions by the proposed strategy.

Different from previous data-driven methods for lithium-ion battery State-of-Charge (SoC) estimation, this paper aims to develop a hierarchical SoC estimation method to address the data dependency issue and measurement noise interferences. In the off-line training layer, aging-aware features are extracted to improve SoC estimation accuracy throughout the entire battery life cycle. Extreme gradient boosting (XGBoost) is introduced to map the relationship between the extracted features and SoC for its strong nonlinear fitting ability. In the on-line estimation layer, Ampere-hour integral method is utilized to provide SoC reference to guarantee the stability of the proposed method. Meanwhile, to suppress the measurement noise, we adopt Kalman filter to correct the SoC value estimated by XGBoost. The superiority of the proposed method is proved under the random walk discharging experiment by comparing with the results of XGBoost, i.e., without Kalman filter. The proposed method improved the accuracy of lithium-ion battery SoC by 4% to 10%.