This paper deals with the energy management strategy (EMS) for an on-board semi-active hybrid energy storage system (HESS) composed of a Li-ion battery (LiB) and ultracapacitor (UC). Considering both the nonlinearity of the semi-active structure and driving condition uncertainty, while ensuring HESS operation within constraints, an adaptive model predictive control (AMPC) method is adopted to design the EMS. Within AMPC, LiB Ah-throughput is minimized online to extend its life. The proposed AMPC determines the optimal control action by solving a quadratic programming (QP) problem at each control interval, in which the QP solver receives control-oriented model matrices and current states for calculation. The control-oriented model is constructed by linearizing HESS online to approximate the original nonlinear model. Besides, a time-varying Kalman filter (TVKF) is introduced as the estimator to improve the state estimation accuracy. At the same time, sampling time, prediction horizon and scaling factors of AMPC are determined through simulation. Compared with standard MPC, TVKF reduces the estimation error by 1~3 orders of magnitude, and AMPC reduces LiB Ah-throughput by 4.3% under Urban Dynamometer Driving Schedule (UDDS) driving cycle condition, indicating superior model adaptivity. Furthermore, LiB Ah-throughput of AMPC under various classical driving cycles differs from that of dynamic programming by an average of 6.5% and reduces by an average of 10.6% compared to rule-based strategy of LiB Ah-throughput, showing excellent adaptation to driving condition uncertainty.

To improve the accuracy of insulation monitoring between the battery pack and chassis of electric vehicles, we established a serial battery pack model composed of first-order resistor-capacitor (RC) circuit battery cells. We then designed a low-voltage, low-frequency insulation monitoring model based on this serial battery pack model. An extended Kalman filter (EKF) was designed for this non-linear system to filter the measured results, thus mitigating the influence of noise. Experimental and simulation results show that the proposed monitoring model and extended Kalman filtering algorithm for insulation resistance monitoring present satisfactory estimation accuracy and robustness.

Inconsistencies that are associated with parallel-connected cells used in electric vehicles induce varied states of charge (SOCs) in each cell. Thus, loop current in the battery pack is inevitable, and this reduces overall capacity, energy utilization rate, and pack lifetime. However, no method is available to address loop current. To reduce loop current and the resulting battery inconsistency, a parallel-connected cell pack (PCCP) model that considers thermal effects is established, and a novel Simscape model that is based on PCCP is successfully constructed. Furthermore, the strategy of parallel-connected cell energy management (PCCEM) is proposed to utilize fuzzy logic control (FLC) strategy, which automatically adjusts the number of cells in a circuit in accordance with the load demand, and turns on the first N switches in the corresponding SOC order. The New European Driving Cycle (NEDC) driving cycle simulation shows that the PCCEM strategy considerably reduces loop current and improves the consistency of battery performance and the utilization rate of battery power.

To further enhance economic efficiency and optimize energy conservation and emission reduction performance, an optimized energy management strategy (EMS) tailored for the hybrid power system of rubber-tyred gantry cranes is proposed. Wavelet packet decomposition (WPD) was employed as the signal processing approach, and this method was further integrated with EMS for hybrid power systems. Through a three-layer progressive architecture comprising WPD frequency–domain decoupling, fuzzy logic real-time adjustment, and PSO offline global optimization, a cooperative optimization mechanism has been established in this study between the frequency-domain characteristics of signals, the physical properties of energy storage components, and the real-time and long-term states of the system. Firstly, the modeling and simulation of the power system were conducted. Subsequently, an EMS based on WPD and limit protection was developed: the load power curve was decomposed into different frequency bands, and power allocation was implemented via the WPD algorithm. Meanwhile, the operating states of lithium batteries and supercapacitors were adjusted in combination with state of charge limits. Simulation results show that this strategy can achieved reasonable allocation of load power, effectively suppressed power fluctuations of the auxiliary power unit system, and enhanced the stability and economy of the hybrid power system. Afterward, a fuzzy controller was designed to re-allocate the power of the hybrid energy storage system (HESS), with energy efficiency and battery durability set as optimization indicators. Furthermore, particle swarm optimization algorithms were adopted to optimize the EMS. The simulation results indicate that the optimized EMS enabled more reasonable power allocation of the HESS, accompanied by better economic performance and control effects. The proposed EMS demonstrated unique system-level advantages in enhancing energy efficiency, extending battery lifespan, and reducing the whole-life cycle cost.

Effective energy management techniques are essential for the full utilization of energy in the field of extended-range electric vehicle research, with the goals of lowering energy consumption and exhaust emissions, enhancing driving comfort, and extending battery life. To achieve optimal comprehensive usage costs, this article uses bargaining game theory to design an adaptive energy management strategy (EMS_ad-bg) that focuses on battery life. In the study, a power system model was first built based on AVL/Cruise software and MATLAB/Simulink software. The impact of discount factors on strategy results was analyzed through simulation experiments. The results showed that the discount factor for auxiliary power unit (APU) focused more on energy optimization, while the discount factor for battery focused more on optimizing the degradation of battery life. When the initial state of charge (SoC) is high, the specific value of the discount factor also has a significant impact on the battery SoC value at the end of the trip. To improve the strategy’s adaptability to various initial SoC values, a fuzzy controller was created that can adaptively modify the discount factor based on the battery SoC. The results of the simulation experiment demonstrate that the bargaining game strategy taking SoC into account has more pronounced advantages in terms of overall usage cost when compared to the strategy of the fixed discount factor. The creation of an EMS_ad-bg that takes battery life into account based on a bargaining game can serve as a helpful model for the creation of a clever EMS that lowers the total cost of operating a vehicle.

Regenerative braking in a hybrid energy storage system for a rubber-tyred gantry crane (RTG-HESS) requires a trade-off between energy recovery and battery charging stress. This study develops an energy management strategy using game theory (EMS_GT) in a MATLAB/Simulink–AVL Cruise co-simulation model comprising a diesel range extender, a lithium-ion battery, and a supercapacitor. An auxiliary finite zero-sum matrix evaluates candidate power allocation policies under competing objective emphases. The matrix is solved by linear programming, and the resulting probabilities are reused heuristically as weights in a deterministic constrained allocation cost. This mapping does not establish a Nash-equilibrium guarantee for the physical controller. Under a shared 100 A battery charging limit, EMS_GT achieves a recovery efficiency of 0.914, compared with 0.890 for the rule-based strategy (EMS_RB), corresponding to an absolute increase of 0.024 and a relative increase of 2.70%. Its normalized battery-degradation indicator is 0.641 relative to the constrained EMS_RB reference of 1.000, and its supercapacitor utilization is 0.817. Additional ECMS, parameter sensitivity, Monte Carlo, and ablation results characterize performance within the tested conditions. These co-simulation results support a trade-off between recovery and modeled charging stress; they do not constitute measured battery-lifetime extension or comprehensive braking-safety validation.