## 1 Introduction:
For the later method, an accurate EC model is required [15], and the battery parameters should be updated according to the aging state of the battery, which is a challenging task. Many techniques have been developed and some combined algorithms have been used to estimate SOC (directly or indirectly through the estimation of the open circuit voltage [OCV]), consequently estimating the SOH, such as the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The EKF and UKF are effective techniques for SOH estimation. For instance, in the literature [16], a novel joint SOC and capacity estimator based on EKF has been introduced. The results showed that the proposed technique can capture the variation of the parameters in varying operating conditions and battery aging. Similarly, the authors of [17] proposed a new technique for SOH and SOC estimation, employed Coulomb counting method (CCM) to estimate SOC, taking the benefits of EKF to reduce accumulative errors of CCM, due to the current sensor noises. Moreover, the SOH was estimated based on the relationship between the dis/charge current and estimated SOC. The results demonstrated a reasonable estimation of SOH and SOC. These techniques are called joint estimation, and can estimate the SOH of the battery as accurately as the battery is modeled. This means that the accuracy is highly dependent on how the battery is modeled. Moreover, large matrix operation and inversions are required, leading to a high complexity. Furthermore, the joint estimation method may have poor numerical conditioning and suffer from instability [7]. Nonetheless, for this method, a dual estimation technique has been implemented, meaning that instead of one estimation algorithm, two adaptive filters are used. One of the filters estimates SOC and the other one is employed for the estimation of the model parameters. Sometimes, instead of the second filter used for model parameters identification, evolutionary algorithms are used [18]; a battery model was established and then a genetic algorithm was used to identify the model parameters and then estimate the SOH. In the literature [19], a multi-scale framework EKF was introduced to effectively estimate the state and parameters of the ECM, applied to a Li-ion battery for the capacity and SOC estimation. The results indicated that the proposed technique has a less than 3% error for the SOC estimation. In contrast to the joint estimation, the dual-technique consists of two adaptive filters. This technique demands a lower computational effort and the dimensions of the respective model matrices are lower than the joint estimation technique. In the literature [20], an effective joint SOH and SOC estimation technique was introduced. In this work, KF and UKF were combined to predict the state of the battery. The result regarding the SOC estimation is promising; nevertheless, the error of the SOH indicator is around 20%. In the literature [15], an adaptive sliding mode observer was employed to estimate the SOH and SOC of the Li-ion battery. The ECM consisted of two resistor and capacitor networks; furthermore,the results showed a high performance and robustness on the SOH and SOC estimations. However, similar to the joint technique, an accurate battery model is essential for the SOC and SOH estimations. Indeed, observer techniques, known as a close-loop method, whose adaptability and effectiveness are utterly dependent on the credibility of the EC models and the robustness of the technique [10]. As stated previously, the techniques employed in ECM, suffer from inaccuracy owing to the lack of thorough understanding of the electrochemical dynamics and physics of the battery [21]. This drawback could be lessened via data-driven models, utilizing the information of the measurement ensemble. Consequently, prior knowledge of electro-chemistry is not required as a result of their capability to work with imprecise data and their self-learning ability [22].