芝士就是菜

1. Introduction#

CD/CS strategy is the simplest EMS control strategy, which is rule-based and the simplest control strategy. It is easy to implement on actual vehicles and is usually used in scientific research to compare with other strategies to demonstrate its advantages.

2. Pros and Cons#

Pros:

• Simple implementation
• Real-time
• Engine can operate in the efficient zone

Cons:

• Poor adaptability
• Battery frequent charging and discharging

3. Flowchart#

4. Principle#

1. CD Mode#

The demand power is provided only by the battery. The state variable SOC (State of Charge) is derived first.

Derivation of SOC (Ampere-hour method)

$SOC_{k+1}=SOC_k-\int_{t_k}^{t_{k+1}}{\frac{\eta I{\rm d}t}{C_e}}$

The equation $U_{OCV}I_{ess}-I_{ess}^2R_{ess}=P_{ess}$ is used to solve for the root.

Where P_ess is the power of the power battery, I_ess is the current of the power battery, C_ess is the additional capacity of the power battery, R_ess is the internal resistance of the power battery, and U_ocv is the open circuit voltage of the power battery.

2. CS Mode#

CS mode is the charge-sustaining mode. The range extender control adopts a fixed-point control strategy, and the engine operates at a fixed working point.

• The demand power is provided by the range extender, and excess power is used to charge the battery.

3. Regenerative Braking#

• 20% energy recovery

5. Code Implementation#

1. Flow Logic#

• Import driving cycle data
• Initialize vehicle parameters
• Calculate power demand for the driving cycle
• Cycle through the driving cycle

2. Notes#

• Efficiency issues are not considered
• 20% energy recovery during regenerative braking
• CD mode accuracy can be compared with the EV mode of the powertrain using NEDC (New European Driving Cycle).

3. Implementation Environment#

1️⃣ Matlab

• Recommended version: 2021b+ for code prompts and completion.

2️⃣ Python

• Version: 3.7
• Libraries: numpy, math, scipy, matplotlib

6. Simulation Results#

1. Matlab#

2. Python#

7. GitHub Link#