# The Impact of Hybrid Energy Storage System on the Battery Cycle Life of Replaceable Battery Electric Vehicle

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## Abstract

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_{4}) power battery is taken as the research object, and a vehicle dynamics simulation model is established on the MATLAB/Simulink platform. Parameter matching and control optimization for a hybrid energy storage system (HESS) are conducted. Through a proven semiempirical cycle model of the LiFePO

_{4}power battery, the operating cycle life model is derived and used to estimate the battery cycle life. World Light Vehicle Test Cycle (WLTC) simulation results show that the HESS with 308 ultracapacitors can extend the cycle life of the LiFePO

_{4}power battery by 34.24%, thus significantly reducing the operation cost of the battery replacement station.

## 1. Introduction

_{4}) battery and ultracapacitor to form a hybrid energy storage system, which improves the efficiency of the vehicle energy storage system [5]. Wang et al. used the dynamic programming (DP) algorithm to obtain the optimal energy allocation strategy, reducing the peak current of the battery and improving the energy efficiency of the energy system [6]. Zhang et al. used model predictive control (MPC) to obtain operating conditions data and optimize HESS energy allocation in the prediction domain through partial historical data and prediction models [7]. Alaoui et al. optimized and normalized the known offline energy consumption data features and trained artificial neural networks (ANNs) to obtain allocation results that maximize HESS efficiency [8]. The above control optimization relies on known offline operating conditions data and accurate prediction models, and the algorithm is complex and difficult to use online in real vehicles. Therefore, it is also known as an offline control strategy.

_{4}battery as the research object. Firstly, we establish a vehicle dynamics simulation model on the MATLAB/Simulink platform, using a rule-based controller, and optimize the HESS control strategy parameters while optimizing its matching parameters. In order to calculate the battery cycle life more accurately, a proven semiempirical life model of a LiFePO

_{4}battery was used to derive its driving cycle life model, which was then used to estimate the battery cycle life. In order to make the simulation results closer to the actual situation, the World Light Vehicle Test Cycle (WLTC) driving cycle, which is widely considered to be closer to the actual situation, was used to analyze and study the capacity degradation of LiFePO

_{4}batteries in the single battery energy system and the hybrid energy storage system.

## 2. Vehicle Parameters and Models

_{4}battery pack and two 130 kW drive motors. The New European Driving Cycle (NEDC, Figure 1a) provides a comprehensive driving range of the vehicle of 400 km. However, according to the data from a car enthusiast forum, the actual testing mileage is about 360 km. To make the simulation results closer to reality, the World Light Vehicle Test Cycle (WLTC, Figure 1b) driving cycle is adopted. Table 2 provides the NEDC and WLTC driving cycle test parameters and data. It can be seen that the maximum speed, average speed, and maximum acceleration of the WLTC driving cycle are all higher than those of NEDC in terms of vehicle power demand indicators. The testing content of the NEDC standard includes five operating conditions, four urban cycles, and one suburban cycle. The testing content of the WLTC standard includes four types: low speed, medium speed, high speed, and ultra-high speed [24]. The NEDC testing standard was born in the 1980s and was last modified in 1997, which is relatively outdated. The WLTC driving cycle is the testing standard for global light vehicle testing standards, developed by the United Nations and born in 2017. Under the WLTC driving cycle simulation, the vehicle has a driving range of approximately 366 km, which is basically consistent with the car enthusiast forum.

## 3. Driving Cycle Life Model of LiFePO_{4} Battery

_{4}battery is essential. In this paper, the proven semiempirical constant current charging and discharging battery cycle life model is used to derive the LiFePO

_{4}battery driving cycle life model.

#### 3.1. Cycle Life Model of Constant Current Charge and Discharge for LiFePO_{4} Battery

_{4}battery. Ref. [28] studied the constant current discharge cycle life of a 2.2 A·h cylindrical LiFePO

_{4}battery and obtained the following formula:

_{4}battery with a fixed discharge rate is as follows:

#### 3.2. Cycle Life Model of LiFePO_{4} Battery under Driving Conditions

#### 3.2.1. Equivalent Cumulative Ampere Hours Released at Different Discharge Rates under Equal Lifespan Conditions

#### 3.2.2. Battery Driving Cycle Life Model

## 4. Hybrid Energy Storage System Model

#### 4.1. Parameters and Model of LiFePO_{4} Battery

_{4}battery with a high energy density structure, which has higher battery efficiency and reliability. The rated voltage of the battery is 3.2 V, and the capacity is 135 A·h. The main technical parameters for constant current charging and discharging at room temperature are shown in Table 4.

#### 4.2. Parameters and Model of Ultracapacitor

#### 4.3. Topological Structure of Hybrid Energy Storage System

#### 4.4. Hybrid Energy Storage System Control Strategy and Parameter Matching

_{4}batteries with different numbers of ultracapacitors, as shown in Figure 8. As the number of ultracapacitors increases, the energy consumption of vehicles also increases. This is due to the increase in energy system mass caused the use of ultracapacitors, resulting in an increase in overall vehicle energy consumption. The cycle life of the battery first significantly improved, reaching its optimal value at 308 ultracapacitors, and then the overall energy consumption of the vehicle significantly increased. After reaching its optimal value, the cycle life of the battery began to decline.

## 5. Simulation Discussion

#### 5.1. Battery and Ultracapacitor Power Demand

#### 5.2. Battery Current and Efficiency

#### 5.3. Battery Cycle Life

_{4}battery is approximately 600 yuan/kW·h at present and a company has laid out over 1500 battery replacement stations in China. The latest data show that it took 102 days to switch from 20 million times to 25 million times, with an approximately average of 50,000 times per day. According to a conservative estimate of 50,000 vehicles, adopting hybrid energy storage system technology can save battery costs of over CNY 700 million, as shown in Table 7.

## 6. Conclusions

_{4}battery at a fixed discharge rate was adopted, and on this basis, the battery cycle life model under driving cycle conditions was deduced. The simulation results show that the optimized hybrid energy storage system can extend the cycling life of the original vehicle battery by 34.24% under WLTC driving cycle conditions, greatly saving the operating costs of battery replacement for automotive companies.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 7.**Different ${P}_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}}$ and ${P}_{\mathrm{c}\mathrm{h}}$ with vehicle energy consumption and battery cycle life: (

**a**) Vehicle energy consumption; (

**b**) Battery cycle life.

**Figure 8.**The relationship between the number of ultracapacitors and vehicle energy consumption and battery cycle life.

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

Curb weight/kg | 2290 |

Windward area/$A$/m^{2} | 3.368 |

Wind resistance coefficient/${C}_{\mathrm{d}}$ | 0.26 |

Wheel radius/$r$/m | 0.365 |

Rolling resistance coefficient/$f$ | 0.009 |

Wheelbase/m | 2.9 |

Drive motor power/kW | 130 |

Maximum speed of drive motor/rpm | 12,000 |

Parameters | NEDC | WLTC |
---|---|---|

Time/s | 1184 | 1800 |

Distance/km | 10.93 | 23.26 |

Max speed/km/h | 120 | 131.32 |

Average speed/km/h | 33.21 | 46.49 |

Max acceleration/m/s^{2} | 1.06 | 1.7 |

Discharge Rate | $\mathit{B}$ | $\mathit{E}\mathit{a}$ | $\mathit{z}$ |
---|---|---|---|

0.5 | 30,330 | 31,500 | 0.552 |

2 | 19,300 | 31,000 | 0.554 |

6 | 12,000 | 29,500 | 0.56 |

10 | 11,500 | 28,000 | 0.56 |

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

Mass/kg | 3.04 |

Capacity/A·h | 135 |

Nominal voltage/V | 3.2 |

Charging cut-off voltage/V | 3.65 |

Discharge termination voltage/V | 2.5 |

Internal resistance/m Ω | 0.686 < R_{int} < 0.7080 |

Cycle life/80%DOD 25 $\mathbb{C}$ | >3000 |

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

Mass/kg | 0.36 |

Capacity/F | 2500 |

Nominal voltage/V | 2.7 |

Internal resistance/m Ω | 0.35 |

Cycle life | >500,000 |

Energy Type | Number of Batteries | Number of Ultracapacitors | Energy System Mass/kg |
---|---|---|---|

Battery | 166 | 0 | 505 |

Hybrid energy storage system | 166 | 308 | 505 + 111 (UC) |

Items | Number |
---|---|

Battery pack energy/kW·h | 71.72 |

Unit price 10,000 yuan/kW·h | 0.06 |

Unit price of battery pack/10,000 yuan | 4.3 |

Number of battery exchanges/10,000 | 5 |

Total price of battery pack/10,000 yuan | 215,160 |

Battery cycle life increase/% | 34.24 |

Battery savings/10,000 yuan | 73,668 |

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**MDPI and ACS Style**

Zhang, W.; Yang, J.
The Impact of Hybrid Energy Storage System on the Battery Cycle Life of Replaceable Battery Electric Vehicle. *World Electr. Veh. J.* **2023**, *14*, 248.
https://doi.org/10.3390/wevj14090248

**AMA Style**

Zhang W, Yang J.
The Impact of Hybrid Energy Storage System on the Battery Cycle Life of Replaceable Battery Electric Vehicle. *World Electric Vehicle Journal*. 2023; 14(9):248.
https://doi.org/10.3390/wevj14090248

**Chicago/Turabian Style**

Zhang, Wei, and Jue Yang.
2023. "The Impact of Hybrid Energy Storage System on the Battery Cycle Life of Replaceable Battery Electric Vehicle" *World Electric Vehicle Journal* 14, no. 9: 248.
https://doi.org/10.3390/wevj14090248