Name
Abstract | ||
|---|---|---|
This paper presented a method of evaluating the health of lithium battery based on the continuous hidden Markov model (CHMM). This paper focuses on how to use CHMM to build the evaluation model. The capacity of battery is chosen as the observation variable. The evaluation process is divided into two phases: leaning phase and evaluation phase. First, learning data is used to estimate the elements of CHMM. Then, the battery state and the next state can be identified and predicted by using this model. At last, the simulation results prove the applicability of CHMM in the application of evaluating the health of lithium battery. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/QRS-C.2017.43 | 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) |
Keywords | Field | DocType |
continuous hidden Markov model,evaluation of the health of lithium battery,recognition | Data modeling,Lithium battery,Markov process,Markov model,Artificial intelligence,Engineering,Battery (electricity),Lithium,Hidden Markov model,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-2073-1 | 1 | 0.35 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Lin | 1 | 96 | 15.05 |
| Mingyu Hu | 2 | 1 | 1.02 |
| Xuhong Yin | 3 | 7 | 0.80 |
| Jian Guo | 4 | 5 | 2.46 |
| Zhaojun Li | 5 | 78 | 8.58 |