# Smart Emergency EV-to-EV Portable Battery Charger

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Power Structure of the Proposed EPBC

## 3. Design of Novel Assorted Nonlinear Controller Based on Deep Reinforcement Learning

#### 3.1. Formulation of Model Free-Integral Backstepping Control (MF-NIBC) Approach

#### 3.2. Deep Deterministic Policy Gradient Methodology

Algorithm 1: Procedure of the DDPG algorithm. |

1: Randomly initialize critic $\mathcal{Q}$ and actor $\mu $ networks with weights ${\theta}^{\mathcal{Q}}$ and ${\theta}^{\mu}$ 2: Initialize target networks ${\mathcal{Q}}^{\prime}$and ${\mu}^{\prime}$ with weights ${\theta}^{{\mathcal{Q}}^{\prime}}\leftarrow {\theta}^{\mathcal{Q}}$, ${\theta}^{{\mu}^{\prime}}\leftarrow {\theta}^{\mu}$ 3: Set up empty replay buffer $R$ 4: for episode = $1$ to $M$ do5: Begin with an Ornstein-Uhelnbeck (OU) noise $\mathcal{N}$ for exploration 6: Receive initial observation state 7: for t = $1$ to $T$ do8: Apply action ${a}_{t}=\mu \left({s}_{t}|{\theta}^{\mu}\right)+\mathcal{N}$to environment 9: Observe next state ${s}_{t+1}$ and reward ${r}_{t}$ 10: Store following transitions $({s}_{t},{a}_{t},{r}_{t},{s}_{t+1})$ into replay buffer $R$ 11: Sample random minibatch of $K$ transitions from $R$ 12: Set ${y}_{i}={r}_{i}+\gamma {\mathcal{Q}}^{\prime}({s}_{i+1},{\mu}^{\prime}({s}_{i+1}|{\theta}^{{\mu}^{\prime}})\text{}|{\theta}^{{\mathcal{Q}}^{\prime}})$ 13: Update critic by the loss: $L=\frac{1}{N}{{\displaystyle \sum}}_{i=1}^{n}{\left({y}_{i}-\mathcal{Q}\left({s}_{i},{a}_{i}|{\theta}^{\mathcal{Q}}\right)\right)}^{2}$ 14: Update the actor policy using the sampled policy gradient:
$${\nabla}_{{\theta}^{\mu}}{J}^{{\theta}^{\mu}}\approx \frac{1}{N}{{\displaystyle \sum}}_{i}{\nabla}_{a}\mathcal{Q}(s,a|{\theta}^{\mathcal{Q}}){|}_{a={\mu}^{\theta}\left(s\right)}{\nabla}_{{\theta}^{\mu}}\mu \left(s|{\theta}^{\mu}\right)$$
15: Update the target networks:
$${\theta}^{{\mathcal{Q}}^{\prime}}\leftarrow \tau {\theta}^{\mathcal{Q}}+\left(1-\tau \right){\theta}^{{\mathcal{Q}}^{\prime}},{\theta}^{{\mu}^{\prime}}\leftarrow \tau {\theta}^{\mu}+\left(1-\tau \right){\theta}^{{\mu}^{\prime}}$$
16: end for17: end for |

#### 3.3. Optimizing the Key Coefficients of MF-NIBC

## 4. Performance Evaluation

**Case1:**In the first scenario, it is assumed that the batteries in both EVs are the same. Therefore, the voltage in the input and output of the DAB dc-dc converter is 400 V and the nominal current is 20 A. In this situation, the output current of the DAB dc-dc converter is controlled to set the proper value and speed of the charging process. In the emergency mode, as shown in Figure 8a, the maximum current of the converter is controlled to be 25 A at 400 V. In this condition, the maximum value of transferred energy is limited to 15% of the capacity of the battery. Moreover, the minimum amount of current that can be transferred from one EV to another EV is limited to 15 A.

**Case2:**In the second scenario, the batteries in the EVs are considered different. It means that the nominal voltage of the EV which injects the power to the other one is 400 V; the nominal voltage of the second EV which receives the energy is assumed to be 300 V. Here, the maximum and minimum values of the injected current in the emergency charging mode are 25 A and 15 A, respectively. In order to prove the superiority of the MF-NIBC controller against other controllers, the transient outcomes of the output voltage of the proposed charger are shown in Figure 10. It can be concluded from Figure 10 that the MF-NIBC controller provides the fastest transient response over other controllers, which means the suggested method can guarantee the voltage stability and provides proper regulation of the output voltage and current simultaneously.

**Case3:**In the last scenario, it is assumed that the nominal voltage of the second EV is 500 V, whereas the voltage of the EV which acts as a source is 400. As mentioned previously, since the EPBC works in emergency mode, it is allowed to transfer up to 15% of the capacity of the battery. Therefore, as depicted in Figure 8a, the injected current is limited between upper and lower predefined values. Figure 11 expresses the transient outcomes of the proposed MF-NIBC controller, MPC controller, and PI controller. With the help of the suggested MF-NIBC controller, Figure 11 indicates that our suggested smart charger has a reliable performance with the lowest overshoot compared to the traditional controller.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 8.**Real-time results for the power changes in the emergency charging mode in the proposed EPBC: (

**a**) output current, (

**b**) output power.

**Figure 9.**Comparison of the voltage transient responses of the proposed MF-NIBC controller, MPC, and the conventional PI controller during a step change in the output power in the first scenario.

**Figure 10.**Comparison of the voltage transient responses of the proposed MF-NIBC controller, MPC, and the conventional PI controller during a step change in the output power in the second scenario.

**Figure 11.**Comparison of the voltage transient responses of the proposed MF-NIBC controller, MPC, and the conventional PI controller during a step change in the output power in the third scenario.

Parameters | Value | Parameters | Value |
---|---|---|---|

${V}_{in}$ | 400 V | ${\mathit{C}}_{\mathit{o}\mathit{u}\mathit{t}}$ | 200 μF |

${V}_{out}$ | 300–500 V | $\mathit{L}$ | 40 μH |

${I}_{rated}$ | 20 A | $\mathit{n}$ | 2.5 |

${P}_{rated}$ | 8000 W | ${\mathit{C}}_{\mathit{i}\mathit{n}}$ | 20 μF |

Parameters | Value |
---|---|

${K}_{p}$ | 0.003 |

${K}_{i}$ | 25 |

Prediction horizon (N) | 6 |

Weighting factor(λ) | 0.5 |

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**MDPI and ACS Style**

Mosayebi, M.; Fathollahi, A.; Gheisarnejad, M.; Farsizadeh, H.; Khooban, M.H.
Smart Emergency EV-to-EV Portable Battery Charger. *Inventions* **2022**, *7*, 45.
https://doi.org/10.3390/inventions7020045

**AMA Style**

Mosayebi M, Fathollahi A, Gheisarnejad M, Farsizadeh H, Khooban MH.
Smart Emergency EV-to-EV Portable Battery Charger. *Inventions*. 2022; 7(2):45.
https://doi.org/10.3390/inventions7020045

**Chicago/Turabian Style**

Mosayebi, Mahdi, Arman Fathollahi, Meysam Gheisarnejad, Hamed Farsizadeh, and Mohammad Hassan Khooban.
2022. "Smart Emergency EV-to-EV Portable Battery Charger" *Inventions* 7, no. 2: 45.
https://doi.org/10.3390/inventions7020045