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With the increase of electric vehicles and lack of standardization in charging infrastructure, the variance in the charger cable length, battery health, and battery capacity can result in unevenness in the charging of lithium-ion batteries (LIBs), which increases the charging time and can deteriorate the battery’s health. Enabling adaptive charging of LIBs can accelerate the commercial application of electric vehicles (EVs). Charging of LIBs is critical and can be optimized to curtail the effective charging time. In this paper, a multistage adaptive charging strategy is presented for LIB-based EV applications to boost the SOC of the battery system in the shortest time. In the proposed charging strategy, initially, multiple pre-charge CC stages are employed to bring the battery out of the deep discharge state and to simultaneously calculate the resistances of the harness (line resistance), and the battery.

Battery packs used in Electric Vehicles (EVs) pose significant safety risks and can incur additional costs and downtime when facing extreme conditions such as thermal and undervoltage hard cut-off. This article emphasizes the importance of implementing thermal and voltage based derating techniques to ensure the safe operation of battery packs. Thermal derating controls the maximum allowed battery current to prevent thermal runaway along with maintaining the health of the cells. While voltage derating prevents cut-off at low SOC regions by managing the cell voltage operating range through real time calculation of DCIR based voltage drop. By adopting these methods, battery packs can operate more safely and reliably in various environment conditions, which is essential in many applications.

This paper presents a model-based algorithm designed for electric vehicles to estimate, control and optimize their range. By utilizing both short-term and long-term energy consumption data, the algorithm accurately predicts the range based on the current riding pattern. To achieve the desired range, the algorithm incorporates Hamilton-Jacobi-Bellman (HJB) optimization, which optimizes a cost function. The algorithm leverages short-term energy consumption patterns to smoothen the real-time watt-hour consumption for accurate range estimation. Simultaneously, it monitors long-term energy consumption patterns to account for factors such as vehicle aging, wear, terrain dynamics, and initial wh/km calculation. A comprehensive cost function, considering parameters like wh/km, rider demand, and terrain requirements, ensures optimal range without compromising the overall ride experience.