# Experimental Study on Heating Performances of Integrated Battery and HVAC System with Serial and Parallel Circuits for Electric Vehicle

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## Abstract

**:**

## 1. Introduction

## 2. Experimental Method

## 3. Artificial Neural Network (ANN)

^{−6}and 1000, respectively. The ANN model is trained for the selected algorithm with various combinations, until the maximum training error and the maximum epochs are reached.

## 4. Data Reduction

^{2}), root mean square error (RMSE) and coefficient of variance (COV), respectively [43].

## 5. Results and Discussion

#### 5.1. Heating Performances of Battery and HVAC for Integrated System with Parallel Circuit

#### 5.1.1. Battery out Temperature and Battery Temperature Rise Rate

#### 5.1.2. Battery and HVAC Heating Capacities

#### 5.2. Heating Performances of Battery and HVAC for Integrated System with Serial Circuit

#### 5.2.1. Battery out Temperature and Battery Temperature Rise Rate

#### 5.2.2. Battery and HVAC Heating Capacities

#### 5.3. Total Heating Capacity of Integrated System with Serial and Parallel Circuits

#### 5.4. ANN Model for Battery and HVAC Heating Performances

^{2}), root mean square error (RMSE) and coefficient of variance (COV). The algorithm with highest value of R

^{2}and lowest values of RMSE and COV are suggested as the optimum algorithm for ANN model.

#### 5.4.1. Integrated System with Parallel Circuit

^{2}with 0.999971, 0.999979 and 0.999979, RMSE with 0.154166, 0.183539 and 0.201043, as well as COV with 0.536230, 0.460489 and 0.472796 for heater powers of 2 kW, 4 kW and 6 kW, respectively. The comparison of battery out temperature of the integrated system with parallel circuit for experiment and ANN model with LM-Tan-20 algorithm at various heater powers is also presented in Figure 8a. Figure 8a shows the prediction capability of suggested algorithm and closeness of predicted results by suggested algorithm with the experimental results.

^{2}of 0.803624, 0.956737 and 0.994077, RMSE of 0.631449, 0.549005 and 0.245819, as well as COV of 44.75612, 21.27336 and 7.909049 for heater powers of 2 kW, 4 kW and 6 kW, respectively. The prediction accuracy of the LM-Tan-20 algorithm is better for the battery out temperature than the HVAC temperature difference, because of linear trends of battery out temperature and zigzag trends of HVAC temperature difference for all heater powers. Figure 8b shows the closeness of LM-Tan-20 algorithm predicted HVAC temperature difference and corresponding actual results for all heater powers. For some points deviation between the predicted and actual results is larger because of zigzag trend of HVAC temperature difference curves at all heater powers, the suggested algorithm is not able to follow all points accurately. However, the minimum prediction accuracy is 0.8, which is an acceptable prediction performance for the suggested ANN model. Mohanraj et al. have suggested the Levenberg-Marquardt training algorithm as the optimum for the accurate performance prediction with maximum R

^{2}of 0.999 and lowest values of RMSE and COV [39].

#### 5.4.2. Integrated System with Serial Circuit

^{2}, RMSE and COV values of 0.999970, 0.205704 and 0.554591, respectively, at a heater power of 2 kW, those values of 0.999980, 0.170831 and 0.479862, respectively, at a heater power of 4 kW, and those values of 0.999982, 0.158077 and 0.431001, respectively, at heater power of 6 kW. The battery out temperatures predicted by the LM-Tan-20 algorithm for various time and heater powers are compared with the corresponding experimental values in Figure 9a. A higher degree of closeness between the actual and predicted results could be observed for the suggested algorithm of ANN model.

^{2}and lowest values of RMSE and COV. ANN model with LM-Tan-20 algorithm shows R

^{2}, RMSE and COV values of 0.987539, 0.128786 and 11.30589, respectively, at 2 kW heater power, those of 0.998338, 0.132884 and 4.164997, respectively, at 4 kW heater power and those of 0.998081, 0.134271 and 4.444122, respectively, at 6 kW heater power. Figure 9b shows the comparison of experimental HVAC temperature difference and LM-Tan-20 algorithm predicted HVAC temperature difference for various heater powers. For all heater powers, the predicted results show closer agreement with corresponding experimental results.

## 6. Conclusions

- (a)
- The effect of heater power on heating performances of the integrated system with serial and parallel circuits and effect of flow ratio on heating performances of the integrated system with parallel circuit are analyzed. As the heater power increases, the heating performances increases for the integrated system with serial and parallel circuits. With an increase in the flow ratio to the battery, battery heating performance enhances, whereas HVAC heating performance decreases.
- (b)
- In the case of integrated system with parallel circuit, battery out temperature reaches 40 °C within 20 min at the rate of 1.22 °C/min. Battery heating capacity is evaluated as 764.99 W and HVAC heating capacity is evaluated as 3869.15 W.
- (c)
- The battery out temperature reaches to 40 °C within 10 min at the rate of 2.17 °C/min for the integrated system with serial circuit. The battery and HVAC heating capacities for the integrated system with serial circuit are evaluated as 1025.16 W and 5726.33 W, respectively.
- (d)
- The integrated system with serial circuit enables faster heating performance than the integrated system with parallel circuit for both battery and HVAC. However, the integrated system with parallel circuit enables the tradeoff heating between battery and HVAC at the desired level with the slower rate.
- (e)
- The battery and HVAC heating performances of the integrated system with serial and parallel circuits are accurately predicted using the developed ANN models with back-propagation training algorithm, Levenberg-Marquardt training variant, Tan-sigmoidal transfer function and 20 hidden neurons.
- (f)
- The effects of various operating conditions on the heating performances of battery and HVAC using the proposed integrated system with serial and parallel circuits could be investigated and optimized, to find the optimum point for tradeoff heating of battery and HVAC under various conditions. The extracted results could be used in practical applications such as under cold weather conditions, the extracted optimum point for tradeoff heating of battery and HVAC could successfully achieve the efficient battery and HVAC heating performances of full size commercial electric vehicles with increased driving range and improved battery performance and life. The integrated system with serial circuit could be used for applications where rapid heating of battery or HVAC is needed, whereas the integrated system with parallel circuit could be used for applications where tradeoff simultaneous heating of battery and HVAC is needed. Thus, the proposed integrated system with serial and parallel circuits has the practical applicability to enable rapid, as well as tradeoff heating for both battery and HVAC in electric vehicles.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) Schematic diagram and (

**b**) picture for experimental set-up of integrated system with serial and parallel circuits.

**Figure 2.**Artificial neural network (ANN) model for integrated system with serial and parallel circuits.

**Figure 3.**Variation of (

**a**) battery out temperature and (

**b**) battery temperature rise rate of parallel circuit for various heater power and flow ratios.

**Figure 4.**Variation in (

**a**) battery heating capacity and (

**b**) HVAC heating capacity of parallel circuit with flow ratios for various heater powers.

**Figure 5.**Variation of (

**a**) Battery out temperature and (

**b**) Battery temperature rise rate of serial circuit for various heater power.

**Figure 6.**Effect of heater power on (

**a**) battery heating capacity and (

**b**) HVAC heating capacity of serial circuit.

**Figure 7.**Comparison of total heating capacities of parallel and serial circuits for various heater powers.

**Figure 8.**Comparison of (

**a**) battery out temperature and (

**b**) HVAC temperature difference of parallel circuit for experimental and ANN model with LM-Tan-20 algorithm at various heater powers.

**Figure 9.**Comparison of (

**a**) battery out temperature and (

**b**) HVAC temperature difference of serial circuit for experimental and ANN model with LM-Tan-20 algorithm at various heater powers.

Component | Specification |
---|---|

Pipe | Material: stainless |

Heater | Type: sheath |

Water pump | Max flow: 25 L/min Max head: 25 m Power voltage: 24 VCD |

Radiator | Applied vehicle: GM Volt Core size: 147 $\times $ 206 $\times $ 28 mm |

HVAC | Applied vehicle: Kona Core size: 152 $\times $ 222 $\times $ 26 mm |

Heater | 510 V, 11.8 A |

Device/Instrument | Accuracy |
---|---|

T-type thermocouple | 0.75% |

DAQ | −200 °C ≤ TS ≤ −100 °C, ± (0.10% of reading) −100 °C ≤ TS ≤ 400 °C, ± (0.10% of reading) |

Flow rate sensor | ±1.50% |

**Table 3.**Prediction accuracy of ANN models for battery heating performance and HVAC heating performance of integrated system with parallel circuit.

Heater Power | Algorithm | Number of Hidden Neurons | R^{2} | RMSE | COV | |
---|---|---|---|---|---|---|

Battery heating performance | 2 kW | LM-Tan | 10 | 0.999964 | 0.172015 | 0.598317 |

15 | 0.999965 | 0.170036 | 0.591433 | |||

20 | 0.999971 | 0.154166 | 0.536230 | |||

LM-Log | 10 | 0.999962 | 0.176631 | 0.614369 | ||

15 | 0.999965 | 0.170120 | 0.591725 | |||

20 | 0.999968 | 0.163048 | 0.567126 | |||

4 kW | LM-Tan | 10 | 0.999976 | 0.197036 | 0.494352 | |

15 | 0.999978 | 0.189969 | 0.476623 | |||

20 | 0.999979 | 0.183539 | 0.460489 | |||

LM-Log | 10 | 0.999976 | 0.198585 | 0.498239 | ||

15 | 0.999976 | 0.196935 | 0.494101 | |||

20 | 0.999978 | 0.187425 | 0.470238 | |||

6 kW | LM-Tan | 10 | 0.999975 | 0.218650 | 0.514204 | |

15 | 0.999976 | 0.211399 | 0.497152 | |||

20 | 0.999979 | 0.201043 | 0.472796 | |||

LM-Log | 10 | 0.999931 | 0.360343 | 0.847426 | ||

15 | 0.999976 | 0.212566 | 0.499896 | |||

20 | 0.999977 | 0.208056 | 0.489290 | |||

HVAC heating performance | 2 kW | LM-Tan | 10 | 0.786929 | 0.657742 | 46.61972 |

15 | 0.798038 | 0.640366 | 45.38813 | |||

20 | 0.803624 | 0.631449 | 44.75612 | |||

LM-Log | 10 | 0.785127 | 0.660519 | 46.81651 | ||

15 | 0.794651 | 0.645714 | 45.76716 | |||

20 | 0.799947 | 0.637332 | 45.17311 | |||

4 kW | LM-Tan | 10 | 0.955619 | 0.556058 | 21.54665 | |

15 | 0.956040 | 0.553408 | 21.44398 | |||

20 | 0.956737 | 0.549005 | 21.27336 | |||

LM-Log | 10 | 0.954056 | 0.565763 | 21.92271 | ||

15 | 0.955687 | 0.555626 | 21.52991 | |||

20 | 0.956573 | 0.550047 | 21.31374 | |||

6 kW | LM-Tan | 10 | 0.993304 | 0.261362 | 8.409131 | |

15 | 0.993737 | 0.252775 | 8.132854 | |||

20 | 0.994077 | 0.245819 | 7.909049 | |||

LM-Log | 10 | 0.991877 | 0.287864 | 9.291802 | ||

15 | 0.993562 | 0.256273 | 8.245384 | |||

20 | 0.993748 | 0.252538 | 8.125237 |

**Table 4.**Prediction accuracy of ANN models for battery heating performance and HVAC heating performance of integrated system with serial circuit.

Heater Power | Algorithm | Number of Hidden Neurons | R^{2} | RMSE | COV | |
---|---|---|---|---|---|---|

Battery heating performance | 2 kW | LM-Tan | 10 | 0.999963 | 0.227245 | 0.612664 |

15 | 0.999966 | 0.220835 | 0.595383 | |||

20 | 0.999970 | 0.205704 | 0.554591 | |||

LM-Log | 10 | 0.999961 | 0.234787 | 0.632999 | ||

15 | 0.999964 | 0.226566 | 0.610835 | |||

20 | 0.999967 | 0.216158 | 0.582773 | |||

4 kW | LM-Tan | 10 | 0.999979 | 0.164997 | 0.463474 | |

15 | 0.999978 | 0.197546 | 0.470634 | |||

20 | 0.999980 | 0.170831 | 0.479862 | |||

LM-Log | 10 | 0.999969 | 0.201567 | 0.566196 | ||

15 | 0.999978 | 0.170591 | 0.479186 | |||

20 | 0.999980 | 0.161314 | 0.453130 | |||

6 kW | LM-Tan | 10 | 0.999972 | 0.198939 | 0.542413 | |

15 | 0.999981 | 0.164124 | 0.447487 | |||

20 | 0.999982 | 0.158077 | 0.431001 | |||

LM-Log | 10 | 0.999967 | 0.214850 | 0.585792 | ||

15 | 0.999975 | 0.188060 | 0.512750 | |||

20 | 0.999981 | 0.161996 | 0.441684 | |||

HVAC heating performance | 2 kW | LM-Tan | 10 | 0.986886 | 0.132118 | 11.59836 |

15 | 0.987196 | 0.130549 | 11.46062 | |||

20 | 0.987539 | 0.128786 | 11.30589 | |||

LM-Log | 10 | 0.986881 | 0.132142 | 11.60054 | ||

15 | 0.987086 | 0.131105 | 11.50950 | |||

20 | 0.987226 | 0.130393 | 11.44697 | |||

4 kW | LM-Tan | 10 | 0.998091 | 0.142413 | 4.463665 | |

15 | 0.998137 | 0.140689 | 4.409637 | |||

20 | 0.998338 | 0.132884 | 4.164997 | |||

LM-Log | 10 | 0.998029 | 0.144687 | 4.534949 | ||

15 | 0.998106 | 0.141871 | 4.446673 | |||

20 | 0.998229 | 0.137182 | 4.299688 | |||

6 kW | LM-Tan | 10 | 0.997789 | 0.144090 | 4.769143 | |

15 | 0.997864 | 0.141669 | 4.688989 | |||

20 | 0.998081 | 0.134271 | 4.444122 | |||

LM-Log | 10 | 0.997779 | 0.144445 | 4.780861 | ||

15 | 0.997815 | 0.143260 | 4.741652 | |||

20 | 0.997894 | 0.140671 | 4.655938 |

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**MDPI and ACS Style**

Lim, T.-K.; Garud, K.S.; Seo, J.-H.; Lee, M.-Y.; Lee, D.-Y.
Experimental Study on Heating Performances of Integrated Battery and HVAC System with Serial and Parallel Circuits for Electric Vehicle. *Symmetry* **2021**, *13*, 93.
https://doi.org/10.3390/sym13010093

**AMA Style**

Lim T-K, Garud KS, Seo J-H, Lee M-Y, Lee D-Y.
Experimental Study on Heating Performances of Integrated Battery and HVAC System with Serial and Parallel Circuits for Electric Vehicle. *Symmetry*. 2021; 13(1):93.
https://doi.org/10.3390/sym13010093

**Chicago/Turabian Style**

Lim, Taek-Kyu, Kunal Sandip Garud, Jae-Hyeong Seo, Moo-Yeon Lee, and Dong-Yeon Lee.
2021. "Experimental Study on Heating Performances of Integrated Battery and HVAC System with Serial and Parallel Circuits for Electric Vehicle" *Symmetry* 13, no. 1: 93.
https://doi.org/10.3390/sym13010093