#### Energy Management System Based on Threshold Control Method and Fuzzy Logic

1. Energy management system based on threshold control method

The control strategy of the energy management system usually adopts the threshold value control method, that is, a threshold value of the remaining energy is set:
If the remaining energy is higher than this threshold, the thermal management system is turned on; if the remaining energy is below this threshold, the thermal management system is turned off. Now introduce an energy management system power distribution coefficient KT-M, which represents the weight of the maximum power allocated to the thermal management system, and its definition domain is [0, 1]. The maximum power allocated to the thermal management system is then shown in Figure 1:

where PTM-MAx(t) is the peak power of the high-voltage components of the thermal management system. The maximum power allocated to the drive system is therefore Fig: 2:

When PT-M(t)=0, it is the way to reduce the energy usage rate: allocate the limited battery power to the drive system to the greatest extent.
In the energy management system threshold control method, KT-w(t) is the variation, and the control strategy is as shown in Figure 3:

Among them, SOCTH is the threshold value.
From the above mathematical description, it can be seen that the threshold value control process of the energy management system is shown in Figure 4:

The driver steps on the accelerator pedal, understands the driver’s driving intention through the accelerator pedal opening θ curve, and obtains the motor demand power PMR; the energy management system can allocate the power Pe, and distribute the power to the thermal management system and the drive system according to the threshold control strategy . When the system completes the work, the energy of the power battery becomes Ee’, the maximum distributable power becomes Pe’, and the temperature of the power battery becomes T’ and the state of charge becomes SOC’.

The optimization purpose of the energy management system is to reduce the energy consumption rate of the whole vehicle when the total rated energy of the power battery remains unchanged, and to minimize the ECR by distributing the power of the energy management system between the drive system and the thermal management system. Generally, the economic performance of pure electric vehicles is evaluated by energy consumption rate.

Among them, the mileage of the whole vehicle is S, the unit is km; E is the battery energy consumed by the whole vehicle when it travels, the unit is J; k is the unit conversion factor.
The optimization of the power battery energy management system is to add an optimization unit to the control strategy of the conventional energy management system, by dynamically adjusting KT-w(t) in the interval [0, 1] to minimize the ECR. In addition, during the driving time t of the vehicle, Pmotor(t) should be as large as possible larger than the required power PMR(t) of the drive motor. From the above analysis, the mathematical model of the optimization problem of the energy management system of pure electric vehicles can be established as shown in Figure 6:

It can be seen that this problem belongs to a typical constrained nonlinear optimization problem.

1. Energy management system based on fuzzy logic

The main parameters of the control strategy design that affect the power distribution of the pure electric vehicle energy management system are:
SOC(t), battery SOC at time t;
dθ/dt, the rate of change of the accelerator pedal opening at time t;
△T, the temperature difference between the battery temperature and the battery optimal temperature working range at time t. Figure 7:

Among them, Topt-max is the upper limit of the optimal operating temperature range of the battery; Topt-min is the lower limit of the optimal operating temperature range of the battery; Tbat is the battery temperature. Due to the inconsistency of the temperature of the power battery body, it is taken in the actual calculation. average value.

Therefore, on the basis of the original control strategy, an optimized control process as shown in Figure 8 is established, and a fuzzy controller for power distribution in the energy management system is added, with SOC(t), dθ/dt and ΔT as the input parameters, and the output control parameter KT-M (t), after the thermal management system and the drive system do work according to the power distribution coefficient of KT-w(t), SOC(t) and ΔT change, and then feedback to the fuzzy controller as the next input to form a closed loop.

The working process of the fuzzy control of the pure electric vehicle energy management system is described as follows:
①When the vehicle starts, if the operating temperature Tbat of the power battery is low, PT-M is given priority.
②When the vehicle starts, if the working temperature of the power battery is normal, Pmotor is given priority.
③ When the car accelerates rapidly or runs on a hill, that is, dθ/dt is greater than a certain set threshold, the Pamr is started first to meet the high power demand of the drive motor, and then the PT-M is started.

④ When the car is running at a normal speed, that is, dθ/dt is less than a certain threshold, the priority is to meet the PT-w to improve the charging and discharging efficiency, and then meet the Pmotor to ensure the ordinary power demand of the drive motor.
⑤ If the battery SOC is low, Pmotor should be given priority.
⑥ If the battery SOC is high, PT-M should be given priority.

According to the above working process, the following basic principles are followed when formulating fuzzy control rules:
(1) When the remaining battery power SOC(t) is low, if the temperature difference ΔT is relatively small, and the accelerator pedal opening change dθ/dt is relatively large, the power allocated to the thermal management system is relatively small, namely KT-M(t) smaller.
(2) When the remaining battery power SOC(t) is high, if the temperature difference ΔT is relatively large, and the accelerator pedal opening change dθ/dt is relatively small, the power allocated to the thermal management system is relatively large, namely KT-M(t) bigger.

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