Research on Mode Switching and Energy Management of Hybrid Electric Vehicle

Aiming at the optimization of mode switching control and energy management strategy for parallel hybrid electric vehicles, a mode switching control method and A-ECMS steady-state energy management strategy is proposed. Firstly, in order to effectively suppress the impact caused by mode switching, and a fuzzy controller is designed for clutch engagement displacement. Then, on the basis of ensuring the smoothness of mode switching, A-ECMS control strategy based on SOC feedback is established. Finally, Isight and Cruise are used to build a co-simulation platform, and multi-island genetic algorithm is used to study the influence of initial value selection of equivalent factor on the controller. The results show that the proposed mode switching control and energy management control strategy, combined with A-ECMS strategy, can effectively improve the vehicle fuel economy on the basis of ensuring the mode switching smoothness.


1.Introduction
The development of new energy vehicles provides a practical solution for China's transportation and energy transformation.However, mode switching in these vehicles can cause shocks due to sudden changes in engine and motor torque [1].This presents challenges in terms of driving performance during mode conversion, particularly in parallel hybrid configurations [2].
Researchers have proposed approaches to minimize shocks during hybrid mode switching.Su et al. [3] suggested an engine restart technique (ERT) to reduce system shock and vibration.Fu et al. [4] developed a coordinated control strategy considering the dynamic response of the internal combustion engine and electric motor.Koprubasi et al. [5] treated mode switching as a switching control problem and designed controllers to minimize shocks.Song et al. [6] implemented a clutch control strategy to suppress torque fluctuations.Chiang et al. [7] designed a robust fuzzy slip-friction controller for smooth switching in parallel hybrid vehicles.
In terms of energy management, Picchirallo et al. [8] proposed the equivalent consumption minimization strategy (ECMS) theory, aiming to select the minimum fuel consumption point in real-time.focused on control methods based on the ECMS approach for parallel hybrid electric vehicles to minimize fuel consumption.
Addressing shocks during mode switching and optimizing energy management are ongoing challenges.In order to address the effect of mode switching on the system when transitioning from electric mode to parallel drive mode, a fuzzy controller was designed based on the clutch engagement displacement during mode switching.The initial values of the equivalence factors were then optimized using the A-ECMS strategy of the multi-island genetic algorithm.The proposed control strategy ensures smooth mode switching and improves the overall fuel economy.

Hybrid Vehicle Mode Switching
This paper proposes a fuzzy control-based hybrid powertrain to address the issue of torque fluctuations during the transition from electric motor drive to engine starting process.The goal is to achieve smoother transitions and enhance ride comfort for both the driver and passengers.

Dynamic Characterization of Motor and Drivetrain
This section focuses on the analysis of the vehicle's longitudinal motion characteristics during driving, specifically considering the transmission system as a rigid body model.It simplifies the model by ignoring the bending and torsional vibrations of the transmission system and assuming purely rolling wheels without considering tire elastic deformations.Figure 1 illustrates the implemented joint simulation platform.2.2Hybrid power system model Hybrid vehicle drive system mode switching is divided into three types of power switching with the engine start-up and shutdown process as the boundary, and here the direct engine drive, engine drive and charging and hybrid drive are categorized as parallel drive types.Figure 2 shows the classification of hybrid vehicle drive system operation modes.
In order to further analyze the key factors affecting the smoothness of the whole vehicle, the power switching process is analyzed from the perspective of dynamics, and the simplified model of the hybrid system is shown in Figure 3.
Figure 3 Hybrid power system model Where, Jeis the engine moment of inertia,, Jm is the moment of inertia of the motor, Jg is the moment of inertia of the transmission, Jo I is moment of inertia of the main gearbox, Jwis the moment of inertia of the wheel, mis the total vehicle mass, ris the wheel radius, the concentrated effective moment of inertia of the engine side, motor side, tires and body, respectively Ja ,Jb , Jc,ba,bb,bc indicates the damping coefficients of the engine, motor side shafts and drive shafts, respectively, Te indicates the torque output of the engine at the current moment, Tm indicates the output torque of the motor at the current moment, Tc indicates the torque transmitted by the clutch through friction, Tw indicates the load torque of the wheel, Td indicates the torque transmitted by the drive shaft.

Engine
Motor Wheel

Controller design
Figure 4(a)-Figure 4(c) depict the membership functions for the rate of change of the accelerator pedal, the speed difference between the motor and engine, and the change in clutch engagement displacement, respectively.The fuzzy rules governing the controller are presented in Table 1.

Simulation Analysis
In order to verify the feasibility of the mode switching control strategy, a parallel hybrid vehicle model is built for simulation in combination with the Cruise-based platform.
The state machine for switching between different modes is established in Stateflow, and the state machine schematic is shown in Figure 5. has not yet reached idle speed, the engine torque remains at zero.The electric motor compensates by generating additional torque to overcome engine resistance, resulting in a noticeable increase in motor torque.At 25.030s, the engine reaches idle speed and begins contributing torque.However, due to its delayed response, the electric motor continues to assist and gradually transitions its torque contribution as the engine torque increases and the motor torque decreases.As shown in Fig. 7, the peak value of the shock degree during mode switching is 4.492 m/s3, which is less than 5 m/s3, effectively improving the smoothness of power transmission during the first mode switching.

4.1Adaptive ECMS control
ECMS is currently the closest control strategy to the practical application.The block diagram of the built ECMS algorithm is shown in Fig. 10.The adaptive ECMS (A-ECMS) strategy adjusts the size of the equivalent factor value according to the real-time SOC value of the power battery pack.
The comparison results of the effects of different initial values of the equivalence factor (S0) on the controller are shown in Table 2.According to Table 2, as the initial equivalence factor increases from 1.5 to 3.5, the proportion of the electric system circuit in the vehicle's total output power decreases.The vehicle controller compensates for the energy consumed by the electric system circuit by adjusting the power source's torque and charging the power battery pack, ensuring that the power battery's state of charge (SOC) fluctuates within the desired range.

Controller parameter optimization based on multi-island genetic algorithm
In order to solve the defects of manual adjustment of the initial value of the equivalence factor, a joint simulation platform based on Isight and Cruise software is built to establish an optimization model, and a multi-island genetic algorithm is used to determine the optimal initial value of the equivalence factor with the optimization objective of 100 km power consumption.
The operating point distribution of the engine with logical threshold value energy management strategy and the operating point distribution of the engine with A-ECMS energy management strategy are shown in Fig. 9 and Fig. 10.As can be seen from Fig. 9 and Fig. 10, some of the engine torque points of the logic threshold energy management strategy are in the fuel inefficient zone, which makes the engine consume more fuel and the emission is relatively poor; while most of the engine torque points of the A-ECMS energy management strategy are distributed between the external characteristic curve and the optimal operating torque curve, which is in the fuel This zone is the fuel-efficient zone, and the emission is better.
The engine circuit fuel consumption and power pack power variations for the logic threshold energy management strategy and A-ECMS energy management strategy are shown in Fig. 11, Fig. 12, Fig. 13 and Fig. 14, respectively.

Figure11 Logical threshold energy management strategy engine circuit fuel consumption
Figure13 A-ECMS energy management strategy engine circuit fuel consumption Figure12 Logic threshold value energy management strategy SOC variation diagram Figure14 Graph of SOC variation for A-ECMS energy management strategy The fuel economy comparison between the logical threshold energy management strategy and the A-ECMS energy management strategy is shown in Table 3. Table 3 shows that the hybrid vehicle using the A-ECMS energy management strategy achieves a combined fuel consumption of 3.66 L per 100 kilometers, which is 16.6% lower than the 4.39 L fuel consumption under the logical threshold strategy.The A-ECMS strategy ensures smooth mode switching, improves fuel economy, and maintains power balance in the battery pack.

Conclusion
The experimental results show that the peak value of the shock degree of the fuzzy controller in the mode switching process is 4.492 m/s3, which is less than 5 m/s3, and effectively improves the smoothness of the mode switching process.Meanwhile, a joint simulation platform was established based on Isight and Cruise software, and a multi-island genetic algorithm was used to optimize the initial value of the equivalence factor S0. The results show that compared with the logic threshold algorithm, the A-ECMS energy management strategy can improve the vehicle fuel economy by 16.6%.

Figure 1 .
Figure 1.Joint simulation platform Figure 2. Hybrid vehicle drive system operation mode

Figure 4
Figure 4(a) Accelerator pedal rate of change affiliation function Figure 4(b) Affiliation function of speed difference Abs between motor end and engine end

Figure
Figure 5. Switching between different modes

Figure 7 .
Figure 7. Impact degree during mode switching Figure 8. Block diagram of ECMS algorithm

Table . 1
Fuzzy control rules table

Table 2 .
Results corresponding to different S0 values

Table 3
Fuel economy results comparison