The lithium-ion battery plays a crucial role in the power supply of the electric vehicles (EVs). Battery remaining useful life (RUL) is critically vital to ensure the vehicles' safety and reliability. Due to the complicated aging mechanism, predicting RUL for the battery management systems is challenging. In this paper, a novel degradation indicator was constructed using the information extracted from the discharge voltage. The indicator reflected the complete and effective energy information from the voltage signals to reveal battery degradation characteristics. Additionally, an innovative FRGM-UPF framework was developed for RUL prediction in this study. To improve the accuracy and traceability of prediction, the framework adopted a novel fractional grey model (FRGM) to update the state transition equation in UPF. Meanwhile, the UPF was employed to extrapolate trends of the indicator and achieve the RUL prediction. The performances of FRGM-UPF with the degradation indicator were synthetically verified by the data from various types of batteries under different aging tests. The experimental results indicated that the proposed method could achieve precise prediction results and had a wide range of practicability and universality. The developed technologies could be incorporated with the other control algorithms for application in BMS of EVs.

The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin’s minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin’s minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.

To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.

The fuel economy of hybrid electric vehicles is inextricably linked to the energy management strategy (EMS). In this study, a practicality-oriented learning-based EMS for a power-split hybrid electric bus (HEB) is presented, which combines the generative adversarial imitation learning (GAIL) and deep reinforcement learning (DRL). Considering the regular and fixed route of the HEB, optimal control samples that are not affected by the cost function can be obtained by the boundary-line dynamic programing (B-DP) method. On this basis, the samples are dynamically fitted using the GAIL method to inverse derive the reward function that can explain the B-DP control behavior. Concurrently, the proximal policy optimization DRL algorithm will regulate the energy distribution of the vehicle in real time and continuously optimize the energy management capability based on the reward feedback from GAIL. Finally, the feasibility and effectiveness of the proposed EMS is verified by simulation. The results show that the proposed strategy exhibits near-optimal control performance under both the China heavy-duty commercial vehicle cycle-bus and China city bus cycle.

The diesel engine is the core in the field of engineering machine power plants. While both at home and abroad for the cold start of diesel engine, the transient emission characteristics below 0 °C and above 2000m is almost a blank. Therefore, aimed at high altitude and low-temperature environment emission characteristics of cold start, this article has carried on the systematic analysis and research. In this paper, a simulation test system for the cold start of the diesel engine at low temperature at high altitude is established. The cold start experiments of a heavy diesel engine at different ambient temperatures (10°C, 0°C, -10°C and -20°C) and different altitudes (0m, 3000m, and 4000m) is carried out. In this paper, the gas emission of the diesel engine during the speed-up period of cold start is studied. The main conclusions are as follows: (1) With the increase of altitude and the decrease of ambient temperature, HC emission of the diesel engine during cold start speed-up period is obviously increased, and NOx emission increases gradually. HC emission mainly concentrated in the cycle with high cylinder pressure and increased significantly when the rotation speed fluctuated violently during the starting process, The NOx emission mainly occurs after the cycle of high pressure in the cylinder. (2) At 3000m and below -10°C, the total HC emission at cold start is 18~1280 times that at 0m and 10°C. The total NOx emission rises to 15~190 times.

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.