In electric vehicles, a battery management system highly relies on the measured current, voltage and temperature to accurately estimate state of charge (SOC) and state of health (SOH). Thus, the normal operation of current, voltage and temperature sensors are of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two non-adjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the pre-defined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.

State of health estimation of lithium ion battery is a key but challengeable technique for the application of electric vehicles. Due to the ambiguous ageing mechanisms and sensitivity to the applied conditions of lithium-ion batteries, the recognition of ageing mechanisms and SOH monitoring of the battery might be difficult. A novel SOH estimation and ageing mechanism identification method is presented in this paper. First, considering the dispersion effect, a fractional order model is constructed and the parameter identification approach is proposed, and a comparison between integer order model and fractional order model has been done from the prospect of predicting accuracy. Then, based on the identified open circuit voltage, battery ageing mechanism can be analyzed by the means of incremental capacity analysis method. Moreover, the normalized Incremental capacity peak is used to estimate the remaining capacity. Finally, the robustness of the SOH estimation method is validated by batteries aged at different conditions based on the idea of cross validation and the estimation error of the remaining capacity can be reduced within 3.1%.

Accurate estimation of state-of-charge (SoC) is vital to safe operation and efficient management of lithium-ion batteries. Currently, the existing SoC estimation methods can accurately estimate the SoC in a certain operation condition, but in uncertain operating environments, such as unforeseen road conditions and aging related effects, they may be unreliable or even divergent. This is due to the fact that the characteristics of lithium-ion batteries vary under different operation conditions and the adoption of constant parameters in battery model, which are identified offline, will affect the SoC estimation accuracy. In this paper, the joint SoC estimation method is proposed, where battery model parameters are estimated online using the H-infinity filter, while the SoC are estimated using the unscented Kalman filter. Then, the proposed method is compared with the SoC estimation methods with constant battery model parameters under different dynamic load profiles and operation temperatures. It shows that the proposed joint SoC estimation method possesses high accuracy, fast convergence, excellent robustness and adaptability.

Accurate state of charge (SOC) estimation of a battery pack is more meaningful than that of a cell in practical applications. The existing methods are difficult to provide an accurate SOC of a battery pack under a wide range of temperature due to cell inconsistency. In this paper, a SOC estimation method for a series-parallel lithium-ion battery pack based on the newly constructed OCV-SOC-temperature relationship was proposed. In the proposed method, firstly the cosine similarity is used to quantify cell inconsistency and the representative cells in the battery pack are determined by the largest cosine similarity value. Secondly, the OCV-SOC curves of the representative cells at different temperatures are used to construct the OCV-SOC-temperature relationship of the battery pack. Finally, a multi-branch fusion method is developed to estimate the SOC of the battery pack through the SOC of each branch by means of a Bayesian probability formula. The effectiveness of the newly constructed OCV-SOC-temperature relationship and the proposed SOC estimation method of the battery pack is verified under various dynamic conditions. The estimated SOC error of the series–parallel battery pack is less than 1.5%, which is approximately 60% lower than the traditional methods.