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Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring the SOC of a battery is a very complex and tedious process. This paper presents an online data-driven battery model identification method, where the battery parameters are updated using the Lagrange multiplier method. A battery model with unknown battery parameters was formulated in such a way that the terminal voltage at an instant time step is a linear combination of the voltages and load current. A cost function was defined to determine the optimal values of the unknown parameters with different data points measured experimentally. The constraints were added in the modified cost function using Lagrange multiplier method and the optimal value of update vector was determined using the gradient approach. An adaptive open circuit voltage (OCV) and SOC estimator was designed for the LIB. The experimental results showed that the proposed estimator is quite accurate and robust. The proposed method effectively tracks the time-varying parameters of a battery with high accuracy. During the SOC estimation, the maximum noted error was 1.28%. The convergence speed of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the high accuracy and robustness, the proposed method can be used in the design of a BMS for real time applications.

Abstract:The dual active bridge isolated bidirectional DC-DC converter (DAB-IBDC) is one of the prime converters used in dual active bridge renewable energy storage system (RESS) applications, particularly where a high-power density is required. The digital DSP (Digital Signal Processer) control technique also provides intelligence to applications and achieves a super compact elegant system by reducing the complicated control hardware. All power converters, including the DAB-IBDC converter, often draw an inrush current, which is many times higher than their steady state current. The inrush current is the maximum current drawn by a converter for a very few milliseconds while being freshly energized. Although it appears for only a very few milliseconds, it can cause severe damage to the entire energy storage system, including the sources and loads. To save the RESS system from the starting inrush current and peak overshoot voltages, this paper proposes a five-phase digital soft-start control algorithm for a high-power DAB-IBDC converter that was implemented at a renewable energy storage system aimed at developing an intelligent self-powered energy zone. The proposed five phase digital soft-start algorithm can alone solve the startup transients without the use of any additional hardware. First, it prevents the output current and voltages from transients, such as the inrush current and peak overshoot voltages, by ensuring that the output current does not increase too rapidly while starting up. Second, it also eliminates the large backflow inrush current released by a partially discharged energy storage device at the starting period. Third, it helps achieve a simple super compact size DAB-IBDC converter with a simple elegant design by ensuring the control and soft-start in digital technology.

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