# Review on the State of Charge Estimation Methods for Electric Vehicle Battery

^{*}

## Abstract

**:**

## 1. Introduction

## 2. The Definition of SOC

_{c}is the residual power of the battery at the moment of calculation, and its unit is A·h; Q is the total capacity of the battery, and its unit is A·h [24]. Q

_{e}is the battery charge.

## 3. SOC Estimation Methods

#### 3.1. Traditional Methods Based on Experiments

#### 3.1.1. Open Circuit Voltage

_{P}, compared with the Thevenin model. The advantage of the PNGV model is that it need not high-performance processor and is easy to implement, so it is suitable for simulation dynamic analysis. The disadvantage of the PNGV model is that it does not consider the charging process, so it is not suitable for long-term stable discharge simulation of the battery.

#### 3.1.2. Ampere-Hour Integral Method

_{0}is the initial charge of the battery, unit: A·h. $\eta $ is the charging efficiency. S is the electric quantity of self-discharge, unit: A·h. i

_{c}is the charging current, unit: A. i

_{d}is the discharge current, unit: A.

_{5}). The experimental results show that it is robust and accurate.

#### 3.1.3. Internal Resistance Method

_{oc}is the open-circuit voltage of the battery. R

_{1}is the ohmic resistance of the battery. R

_{2}is the polarization resistance of the battery, which forms a parallel RC loop with the polarization capacitor C

_{2}. The polarization effect inside the battery can be simulated. C

_{b}is the battery capacitor, which is due to the OCV change blocked together with the current I

_{1}.

#### 3.1.4. Discharge Test Method

#### 3.2. Modern Methods Based on Control Theory

#### 3.2.1. Neural Network Method

#### 3.2.2. Kalman Filter Method

_{k}is the input of the system, generally referring to the variables such as current, temperature, residual electric quantity, and internal resistance. y

_{k}

_{+1}is the output of the system, usually referring to the voltage, x

_{k}is the state quantity of the system, including the estimated value of SOC. Functions f(x

_{k}, u

_{k}) and g(x

_{k}, u

_{k}) refer to the nonlinear equations established on the battery model, but they need to be linearized in the calculation process.

#### 3.2.3. Linear Model Method

#### 3.2.4. Particle Filter Algorithm

#### 3.3. Other Methods Based on the Innovative Ideas

_{0}) is obtained by open-circuit voltage method, and the SOC

_{1}(t)was obtained by Ah integration method and the SOC

_{2}(t) corresponding to load voltage method are averaged.

## 4. Conclusions

## 5. Current and Future Developments

- A rich database should be established to make the SOC estimation more reliable, which depends on a large number of experiments.
- It is important to improve the hardware technology, improve the accuracy of voltage and current parameters, and strive to ensure the accuracy of SOC.
- A more accurate battery with good dynamic characteristics and versatility model should be built to accurately describe the dynamic characteristics of the battery in use.
- We must carry on the effective synthesis of each kind of method, strives for the biggest degree to display respective superiority, promotes the strong point and avoids the weak point.
- Make full use of interdisciplinary advantages and transfer theoretical knowledge from other disciplines to the remaining electricity estimates.
- Establish theoretical methods with better dynamic adaptability and precision, and improve the processing methods and theoretical basis of nonlinear systems.
- Increase efforts to study more stable batteries, such as battery internal resistance and polarization problems.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 4.**Open Circuit Voltage (OCV) curve of lithium iron phosphate and lithium manganese acid battery.

Estimation | Advantages | Disadvantages |
---|---|---|

Traditional methods based on experiments | The simple and stable algorithm is simple and stable Mature technology Easy to implement | High requirements on hardware The effect is usually better in a certain period of battery estimation A large amount of experimental investment is required Obvious cumulative effect of errors |

Modern methods based on control theory | Better eliminate error accumulation effect Correct the noise well High convergence speed and accuracy | Higher requirements for battery model The algorithm is too complex |

other methods | Strong pertinence Interdisciplinary and interdisciplinary applications | The practical applicability needs to be further verified Complex algorithm |

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**MDPI and ACS Style**

Zhang, M.; Fan, X.
Review on the State of Charge Estimation Methods for Electric Vehicle Battery. *World Electr. Veh. J.* **2020**, *11*, 23.
https://doi.org/10.3390/wevj11010023

**AMA Style**

Zhang M, Fan X.
Review on the State of Charge Estimation Methods for Electric Vehicle Battery. *World Electric Vehicle Journal*. 2020; 11(1):23.
https://doi.org/10.3390/wevj11010023

**Chicago/Turabian Style**

Zhang, Mingyue, and Xiaobin Fan.
2020. "Review on the State of Charge Estimation Methods for Electric Vehicle Battery" *World Electric Vehicle Journal* 11, no. 1: 23.
https://doi.org/10.3390/wevj11010023